8 min read By Rick Wong  Updated Jun 13, 2026

For years, many United States-based Amazon sellers have managed campaigns with limited visibility into the full customer journey. You may have invested heavily in competitive marketplaces, adjusted keyword bids, and tracked your Advertising Cost of Sales (ACoS).

Yet much of that reporting has depended on a metric that can miss key parts of the customer journey. That metric is last-touch attribution, a legacy measurement model that can miss earlier ad interactions.

Amazon has expanded access to Multi-Touch Attribution (MTA), which gives advertisers another way to measure how different ads contribute to a purchase. This change can affect how sellers measure campaign value, compare ad formats, and allocate budget in 2026.

The key point: If you rely only on last-touch reporting, you may undervalue awareness and consideration campaigns that help create future sales.

In this guide, we explain how the old attribution model works, how Amazon’s MTA model changes reporting, and how sellers can adjust their Amazon Ads strategy in 2026.

Part 1: The Limits of Last-Touch Attribution

To understand this shift, we first need to look at the limits of the last-touch attribution model.

Under the traditional framework, Amazon assigned 100% of the credit for a sale to the very last advertisement that a shopper clicked or viewed before completing their purchase. This model used a simple rule-based system that missed many earlier customer interactions.

The “Credit Hog” Effect

Consider the typical, multi-stage path to purchase for a high-value consumer item:

1. Monday (Awareness): A customer watches your brand’s video advertisement on Prime Video (Streaming TV).
2. Wednesday (Consideration): They engage with your Sponsored Display retargeting banner while reading a recipe blog.
3. Friday (Conversion): They finally search for your brand name on Amazon and click your Sponsored Products ad to buy the item.

Under the old last-click model, the Sponsored Products campaign received the entirety of the credit for that revenue. The video ad and the display ad—despite doing the heavy lifting of introducing the brand, educating the consumer, and keeping the product top-of-mind—were recorded as having generated zero sales.

This creates a phenomenon that seasoned marketers refer to as the credit hog effect. Lower-funnel search campaigns can receive credit that earlier awareness campaigns helped create.

The Risk of Incomplete Data

When brand managers and agency executives review their weekly performance reports under a last-touch model, the data tells a highly deceptive story. The report suggests that Sponsored Products are wildly profitable, while Amazon Demand-Side Platform (DSP) and Streaming TV campaigns appear to be bleeding money with terrible Returns on Ad Spend (ROAS).

A seller may respond by pausing upper-funnel campaigns and moving more budget into search campaigns. However, this shift can create a growth plateau:

• By cutting the awareness campaigns, they stop feeding the top of the funnel.
• A few weeks later, the search volume for their branded terms begins to dry up.
• Overall sales velocity drops, and their organic ranking slips.

They successfully optimized their last-click metrics while simultaneously starving their brand of new customer acquisition.

Part 2: What is Amazon Multi-Touch Attribution (MTA)?

Amazon introduced Multi-Touch Attribution (MTA) to help advertisers see how different ad touchpoints may influence a shopper before purchase.

Rather than assigning all the glory to the final touchpoint, this advanced model distributes fractional credit across the various ad interactions that influenced the shopper along their unique path to purchase.

Distributing the Fractional Credit

If a conversion involves three distinct advertising touchpoints, the multi-touch engine mathematically determines how much weight each specific interaction carried in driving the final decision. It then divides the revenue credit accordingly.

This distributed credit system changes how advertisers evaluate campaign performance:

• Upper-Funnel Value Becomes Clearer: An upper-funnel video campaign that previously showed low ROAS may now receive partial credit for later purchases.
• Holistic Strategy Validation: By illuminating the interconnected relationship between different ad formats, MTA empowers advertisers to build holistic, full-funnel strategies.
• Justified Brand Building: You can finally justify investing in brand building, knowing that the resulting revenue will be properly tracked and attributed back to the initiating campaigns.

Part 3: The Data Science Behind the Measurement

Amazon uses data science methods to decide how to distribute fractional credit. It is not based on arbitrary rules or simple linear division (e.g., dividing 100% of the credit equally among four clicks).

Instead, the MTA engine relies on a highly sophisticated combination of Machine Learning (ML) models and Randomized Controlled Trials (RCTs).

1. Randomized Controlled Trials (RCTs): The Gold Standard

A randomized controlled trial is a strong method for measuring causal incrementality. It is the exact same rigorous methodology used in medical research to determine if a new pharmaceutical drug actually works.

In the context of Amazon advertising, the platform isolates a big audience and randomly divides them into two distinct groups:

• The Treatment Group: Eligible to be served a specific advertising campaign.
• The Holdout Group: Intentionally shielded from seeing those exact ads.

By meticulously monitoring the subsequent shopping behavior of both groups using Amazon’s rich first-party signals, the data scientists can definitively measure the true, incremental sales lift generated purely by the advertisement. This effectively strips away all the organic sales that would have happened anyway, proving true causation.

2. Advanced Machine Learning Ensembles

While RCTs provide unbiased truth regarding ad effectiveness, they are incredibly resource-intensive to run for every single campaign across millions of advertisers simultaneously. This is where machine learning bridges the gap.

Amazon trains vast ensembles of artificial intelligence models using the massive troves of observational data generated by billions of daily shopper interactions. These models evaluate complex behavioral variables, including:

• The exact timing of the clicks.
• The sequence and order of the ad formats.
• The time elapsed between the first view and the final conversion.
• The historical conversion patterns of similar consumer profiles.

3. The Symbiosis of ML and RCTs

The brilliance of Amazon’s methodology is how it marries these two distinct scientific approaches. The platform can use RCT results to calibrate and improve machine learning models.

If the ML model assumes that a display ad should get 50% of the credit, but the RCT proves that the display ad actually caused a much smaller incremental lift, the system automatically adjusts its algorithmic weighting.

The Result: The MTA model assigns credit based on the estimated role that each ad interaction played in the customer journey. This gives advertisers a more balanced view than last-touch reporting alone.

Part 4: The 2026 View-Through Attribution Update

Complicating the attribution landscape further was Amazon’s silent update to view-through metrics that went live on January 1, 2026.

While Multi-Touch Attribution distributes credit across multiple interactions, Amazon also overhauled how it views single interactions, specifically ad impressions that do not result in a click (view-throughs).

The Problem with the Old 14-Day Window

Historically, Amazon utilized a broad, highly forgiving 14-day view-through attribution window. If a shopper scrolled past your Sponsored Display ad without clicking it, and then purchased your product 13 days later, the system credited that ad with a sale.

This often resulted in highly inflated metrics. The ad view might have had no psychological impact on a shopper who was already planning to buy the item anyway, leading to a false sense of campaign efficiency.

The Shopping-Signal Enhanced Model

To combat this inflation, the early 2026 update introduced a shopping-signal-enhanced last-touch attribution model specifically for viewable impressions. This update fundamentally tightened the quality control on what counts as a valid ad view.

The system now utilizes machine learning to evaluate the shopper’s state of mind at the exact moment the ad was displayed. It looks for moments of genuine brand discovery, such as exploratory, broad-category browsing.

• If the algorithm determines that the shopper was already highly likely to purchase the item before seeing the ad (e.g., they already had the item in their cart), it completely strips the attribution credit away from that view.
• If the algorithm determines the ad served as genuine discovery, the impression enters the MTA pool for fractional credit.

Why Reported Metrics May Change

After this update, some advertisers may see lower reported revenue for DSP, Sponsored Brands, or Sponsored Display campaigns that rely on view attribution.

It is critical to understand that your actual business revenue did not drop; Amazon simply became much stricter about which ads were allowed to claim credit for the sale. This view-through update works in perfect tandem with the rollout of MTA. The view-through update ensures that only highly influential ad impressions enter the attribution pool, and the multi-touch model then decides how to fairly divide the credit among those qualified impressions and the subsequent clicks.

Part 5: Strategic Playbook – Reallocating Your Ad Budget

Understanding this complex, multi-layered measurement ecosystem requires a complete strategic overhaul of how United States sellers allocate their advertising capital.

Under older reporting habits, many sellers placed most of their budget into Sponsored Products and gave less support to upper-funnel campaigns. In the era of Multi-Touch Attribution, that strategy is a recipe for stagnation and eventual market-share loss. Because we can now mathematically measure the fractional impact of awareness, brands must transition to a balanced, multi-stage allocation model.

A Sample Full-Funnel Budget Framework

A larger Amazon brand may use a structure like this as a starting point:

• 40% – Sponsored Products (The Harvesters): Nearly half of the budget remains dedicated to the foundational, high-converting Sponsored Products campaigns. These capture bottom-of-funnel intent. You must be present when the customer makes their final search.
• 30% – Sponsored Brands & Video (The Bridge): Thirty percent is allocated to Sponsored Brands and Sponsored Brands Video. These formats serve as the critical mid-funnel bridge, introducing the brand story and unique value propositions to shoppers searching broad, generic category terms (e.g., “coffee maker” instead of your specific brand name).
• 20% – DSP & Sponsored Display (The Prospectors): Twenty percent is shifted aggressively into the Amazon Demand-Side Platform and Sponsored Display. This capital is used to actively retarget past viewers and aggressively prospect for new-to-brand audiences across the broader internet, utilizing Amazon’s rich first-party data.
• 10% – Experimental Formats (The Vanguard): The final ten percent is reserved purely for strategic experimentation. This allows you to test cutting-edge ad formats like Interactive Video Ads (IVA) on Prime Video or programmatic audio ads on the Podcast Audience Network, ensuring you are always ahead of the adoption curve.

The Synergy Effect

This balanced approach ensures that you are constantly filling the top of the funnel while simultaneously harvesting the demand at the bottom. By tracking the multi-touch metrics, you will begin to see a beautiful synergy emerge.

You will notice that as you increase your investment in top-of-funnel DSP advertising, your bottom-of-funnel Sponsored Products campaigns suddenly become dramatically more efficient. Your CPC (Cost-Per-Click) might remain the same, but your conversion rate on those clicks will surge because the shopper was already pre-sold on your brand story by the display ads they consumed earlier in the week. MTA provides the mathematical proof needed to confidently sustain these upper-funnel investments.

Part 6: Leveraging Amazon Marketing Cloud (AMC) for MTA

Implementing this multi-touch strategy effectively relies heavily on leveraging the Amazon Marketing Cloud (AMC). For brands that have outgrown the standard Seller Central advertising console, AMC represents the pinnacle of e-commerce data analysis.

The Data Clean Room

AMC is a secure, privacy-safe “data clean room” environment. Inside this environment, Amazon provides advanced advertisers with access to pseudonymized, event-level data sets. Instead of relying on the pre-packaged, aggregated, and inflexible reports found in the standard console, the clean room allows data scientists to write Custom Query Language (SQL) to extract the exact, granular path to purchase for your unique customer base.

Mapping the Custom Journey

Within the Amazon Marketing Cloud, multi-touch reporting becomes more useful when advertisers connect it with deeper campaign data. You can isolate the exact sequence of ad exposures that generates the highest possible return on ad spend.

For example, a custom AMC query might reveal that a customer who sees two Streaming Television ads followed by one Sponsored Products ad converts at triple the rate of a customer who only sees the search ad. Armed with this hyper-specific, deterministic insight, you can instruct your programmatic DSP bidding algorithms to aggressively target shoppers who have already been exposed to your video content, knowing with certainty that they represent your most profitable demographic.

The Barrier to Entry

However, extracting this level of insight from a data clean room requires a highly specialized skill set. The AMC environment is dense, deeply technical, and entirely reliant on advanced data science and SQL capabilities. Using AMC without the right data skills can lead to poor reads on performance and budget allocation.

Conclusion: The SellerMetrics Advantage

This is where an Amazon Ads agency like SellerMetrics can help.

At SellerMetrics, we do not simply manage your keyword bids; we architect your entire multi-touch measurement framework. Our team of seasoned data analysts and programmatic strategists practically lives inside the Amazon Marketing Cloud.

• Custom Attribution Modeling: We map out your custom attribution models, identifying the exact combination of upper-funnel and lower-funnel tactics that drive your unique business growth.
• Performance Benchmarking: We help you establish true year-over-year performance benchmarks, ensuring that the transition to the new 2026 attribution standards does not disrupt your reporting or cause panic among your key stakeholders.
• Actionable Execution: We turn multi-touch reporting into clear budget, bidding, and campaign decisions.

When the data reveals that your Sponsored Brands Video campaigns are heavily assisting your search conversions, we proactively scale the video budgets to maximize your total market share. We operate with a full-funnel mindset because we finally possess the technological tools to measure the full-funnel reality.

The move beyond last-touch reporting gives brand builders a clearer way to measure upper-funnel and mid-funnel activity. It aggressively rewards sellers who invest in high-quality creative, who tell compelling brand stories, and who understand that acquiring a customer is a psychological relationship-building process, not a single transactional click.

Brands that rely only on last-touch reporting may miss signals that show how awareness campaigns support future sales. Brands that use Multi-Touch Attribution well may gain a clearer path to scalable, more predictable ad performance. SellerMetrics can help you read multi-touch data, adjust your budget, and build a more balanced Amazon Ads strategy.

FAQ: Amazon Multi-Touch Attribution Guide for Sellers

The post Amazon Multi-Touch Attribution: What Sellers Need to Know in 2026 appeared first on SellerMetrics.

8 min read By Rick Wong  Updated Jun 13, 2026

Sponsored Brands Collections used to be fairly simple to build, even though they required a lot of hands-on maintenance after launch. Sellers would choose a group of strong keywords, select a few proven ASINs, add a lifestyle image, write a headline, and push the campaign live with the hope that the setup would keep performing.

The problem usually showed up after launch, once the campaign had to deal with real stock changes and shopper behavior. A product might sell out, a headline might stop pulling attention, or the creative might lose its effect after shoppers had seen it too often. At that point, the ad could continue spending while performance slipped, unless someone caught the issue and manually rebuilt the collection.

The setup could work for sellers with a small catalog, but it became harder to manage once brands had more products, changing inventory, and several campaigns running at the same time. The more product lines a seller tried to scale, the more manual work started slowing everything down.

Amazon’s late April 2026 update did not just tweak the Collections format. It removed two things advertisers had always controlled: the ability to write their own headlines and upload custom lifestyle images. 

Now the format relies on AI to pull product details from your listings, build the title, assemble the landing page, and decide which items should appear for each shopper. This shift means your Amazon SEO and PPC strategies can no longer sit completely separate from each other. 

What this means practically is that the most important decision happens before the campaign even starts spending. With Automatic Mode, Amazon’s algorithm takes over more of the product mix, using each shopper’s behavioral signals and browsing history to decide which eligible items from your catalog are most likely to convert. 

Manual Mode works differently because you decide which products appear, and that level of control becomes important when you are dealing with tight margins, a new launch, or brand positioning you do not want to leave entirely to an algorithm.

The console makes the choice between Automatic vs. Manual Mode looks simpler than it really is. Once the ad group is live and spending, that choice becomes fixed. Switching modes is not an option after creation, so a misaligned setup can quietly send budget toward the wrong products, weaken how the brand comes across, or build a collection that works against what the campaign was supposed to do.

The Paradigm Shift in Sponsored Brands

To understand why this Sponsored Brands Collections update matters, start with the shopper. Someone searching for a brand term like “kitchen gadgets” might be looking for a cheap tool, a giftable product, a premium appliance, or a bundle. One static ad showing the same three products cannot match all of those different intentions.

Amazon is pushing the format toward a more flexible setup by replacing manual headlines and images with real-time detail page data, so shoppers can see current pricing, star ratings, and live deal badges that mirror the actual shopping experience.

The bigger shift happens with product selection, because Sponsored Brands Collections are no longer just a fixed top-of-search banner with the same items shown to everyone. With AI involved, the ad can act more like a small dynamic storefront, adjusting the product mix based on the shopper’s likely intent.

This shift also changes how sellers think about relevance inside the Amazon A9 algorithm, because the products shown in the ad now need to match shopper intent more closely.

For sellers, this changes the way Sponsored Brands should be planned. The focus is moving away from showing the exact products you want every shopper to see and toward deciding when Amazon’s algorithm should personalize the collection for you. That is the real shift behind Automatic vs. Manual Mode. It also means your Amazon SEO Strategy can no longer sit completely separate from paid visibility. 

Deep Dive: The Mechanics of Automatic Mode

Choosing Automatic Mode means letting Amazon’s AI act as the merchandising layer for the ad group. You still provide the keyword targets and brand name, but Amazon handles most of the product selection once a shopper triggers the ad.

The system works through dynamic curation. Before the page loads, Amazon reviews signals such as the shopper’s search query, recent browsing behavior, past purchase patterns, and how products in your catalog have converted before. From there, it pulls from your eligible products and builds the collection around the items most likely to match that shopper’s intent.

A shopper who has been looking at premium cookware, for example, may see your stainless steel products in the collection. Knowing Amazon conversion rate by category can also help sellers judge whether a product is truly underperforming or simply competing in a tougher niche.

Another shopper using the same keyword, but showing more interest in budget-friendly items, may see lower-priced products or items with active deals instead. That kind of product matching would be almost impossible to manage by hand across every shopper and every search.

Automatic Mode also generates the ad title and builds the landing page dynamically. Clicks on the logo or title send the shopper to the curated collection, while clicks on a product image take them straight to the item’s detail page.

There is still one important control point for advertisers: product exclusions. Amazon lets you block up to one thousand ASIN’s from the automated rotation, which is useful when certain products should not appear in a top-of-search placement. 

That matters for clearance items, out-of-season products, and low-margin ASINs that cannot handle premium click costs. Without those exclusions, the algorithm may favor products that look strong for conversion but still drag down return on ad spend.

Deep Dive: The Mechanics of Manual Mode

Automatic Mode works well when personalization and scale matter, but some campaigns still need tighter control over which products shoppers see. That is where Manual Mode makes more sense. 

With Manual Mode, you do not let Amazon dynamically choose the product lineup. You select between three and ten related products yourself, and the ad only shows those ASINs in the order you arranged them, regardless of the shopper’s browsing history or price preferences. 

The ad still pulls live details from the listings, including pricing, star ratings, and deal badges, but the product strategy stays with you. Manual Mode also gives you more control over where headline and logo clicks go.

Unlike Automatic Mode, which sends traffic to an AI-built landing page, Manual Mode lets you choose between an auto-generated landing page for the selected products or a specific sub-page inside your Amazon Brand Store.

Inventory is the part that can make Manual Mode risky. If the collection falls below three active, in-stock ASINs, that ad can pause, so smaller product lines and variation-heavy catalogs need closer stock planning before this setup makes sense.

Manual Mode fits best when the product lineup needs to be intentional, especially for branded searches, new launches, planned cross-sells, or high-margin products that should stay in front of a specific audience.

The Strategic Imperative: The Permanence of Ad Group Architecture

The most important technical detail in this update is that your collection type is locked once the ad group is created. If you choose Manual Mode or Automatic Mode at launch, you cannot simply switch that same ad group to other setup later. 

Amazon’s structure also allows only one automatic collection request per ad group, which makes planning more important than it used to be. The old habit of launching first and adjusting the collection as you go no longer works the same way.

That matters when performance does not match what you expected. If you build a Manual Mode ad group around a broad keyword and later realize the selected products are not getting enough clicks, you cannot flip a switch and let Amazon’s AI take over. 

You would need to pause that ad group and create a new Automatic Mode version from scratch. That reset can cost you the relevance performance history the original ad group had already started to build. 

Because of that, sellers need to think through the funnel before launching. Broad discovery campaigns, mid-funnel consideration campaigns, and lower-funnel brand defense campaigns may each need a different mode depending on the goal.

Account structure now matters as much as the bids themselves. The mode you choose should match the role of the campaign before the first dollar is spent. That planning matters even more when your campaigns are competing inside the Amazon advertising auction.

Strategic Deployment: When to Leverage Automatic Mode

Automatic Mode is not the right choice for every campaign, but it can be powerful when the goal is broad discovery. It works best for upper-funnel campaigns where the keyword has high search volume but the shopper’s intent is not yet clear. 

A term like “home decor” or “men’s athletic wear” can mean very different things depending on the shopper. One person might want a low-cost wall print, while another may be comparing premium rugs or higher-end apparel. If you use Manual Mode on a keyword that broad, you are betting that your selected products match the needs of a wide mix of shoppers, which can lead to weaker click-through rates and higher click costs. 

Automatic Mode helps in this kind of broad search environment because Amazon can use more shopper context before choosing what to show. Rather than showing the same collection to everyone, it can adjust the product mix based on browsing behavior, price interest, and purchase intent. A more relevant product lineup can improve click-through rate and may also lower the bid needed to compete for top-of-search placement. 

This is also where Automatic Mode becomes useful for brands with larger catalogs. If you sell hundreds of phone case variations, for example, building separate manual ad groups for every model, device type, and keyword combination can quickly become unmanageable. Automatic Mode lets you target broader category terms while Amazon surfaces the product variation most likely to match the shopper’s device history or browsing pattern. 

For sellers using Automatic Mode, the goal is not to give up strategy. It is to use automation where manual product selection would be too slow, too narrow, or too difficult to maintain and scale. 

Strategic Deployment: When to Enforce Manual Mode

Manual Mode makes the most sense when search intent is clear, margins need protection, or the brand story matters more than broad personalization. This is especially true for lower-funnel brand defense, where shoppers searching for your brand name or proprietary product lines are already close to buying.

In that situation, giving the algorithm full control can create risk. Amazon may keep showing a clearance item because it has converted well before, even if that product does not fit the role you want this campaign to play. Manual Mode gives you room to keep the spotlight on flagship items, stronger margin bundles, or newer products that need help getting noticed.

Manual Mode also makes sense for cross-selling and product launches. Automatic Mode usually leans toward products with stronger sales history, so a new variation can struggle to get attention on its own. With Manual Mode, you can place that new item beside two proven best-sellers and use their existing traffic to help the launch get its first discovery clicks. 

This is where Amazon PPC product targeting can support the strategy, especially when you want related products to appear together instead of leaving the lineup to automation.

Manual Mode also gives you more control over where headline and logo clicks go. If your Amazon Brand Store has a page built for education, bundles, or premium product lines, you can send shoppers there instead of using an auto-generated landing page. That gives the brand story more room than a single product page usually can. 

The Impact of the 2026 Policy Landscape

The shift toward AI-powered Collections also needs to be viewed alongside Amazon’s broader 2026 policy changes. These updates affect how much risk sellers take on when they choose between Automatic Mode and Manual Mode. 

Inventory is the first issue. With FBA inventory commingling ending in March 2026, sellers have less room to rely on pooled inventory when their own stock runs low. That matters for Manual Mode because  a Manual Collection needs at least three active, in-stock ASINs to keep running. If stock drops below that point, the ad can pause until the product lineup is fixed. 

Automatic Mode has an advantage when inventory changes often. Since Amazon can pull from eligible products in the catalog, it can replace out-of-stock items with available alternatives, keeping the campaign active. That does not remove the need for inventory planning, but it does give sellers more flexibility when stock levels move quickly.

The second issue is creative control. Since Sponsored Brands Collections no longer rely on custom lifestyle images or manually written headlines, the product detail page has to do more of the work. The ad pulls visible details from the listing, including the main image, price, star rating, and active deal badges. 

For sellers who need help tightening those product page assets, Amazon Listing Optimization Services can support the work before campaigns start scaling.

An Amazon listing audit can catch weak images, unclear titles, or missing deal elements before the ad starts spending. 

That makes listing quality harder to ignore. A shopper only gets a small preview before deciding if the ad is worth a click, so the main image and product page have to carry more weight now. The old lifestyle banner gave sellers some cover, but with that gone, weak product photos or unclear listing details show up much faster in campaign performance.

Navigating Product Exclusions in the AI Era

For sellers using Automatic Mode, product exclusion can make a major difference in whether the campaign supports profit or simply spends through budget. Amazon’s AI is strong at predicting which products may convert, but it does not automatically understand your margins unless you guide it. 

That matters because the system may favor a low-priced product that converts often, even if that item barely makes money after ad costs. Your sales numbers might look healthy on the surface, but return on ad spend can suffer if premium top-of-search clicks are going toward products with weak margins. 

This is also where sellers start asking What is good ACoS on Amazon, because a product can convert well and still leave the campaign unprofitable.

Before launching an Automatic campaign, go through the catalog and decide which products should not be part of the rotation. Loss leaders, heavy discounts, clearance items, and ASINs with weak margins usually need to be filtered out before the campaign starts spending, especially when Amazon advertising cost is already high for top-of-search placements.

Once those products are added to the exclusion list, the AI has less room to waste clicks on the items that do not support the account’s profit goals. The campaign can still chase conversions, but it does so from a cleaner product pool where the sale has a better chance of helping the bottom line, which works alongside Negative keywords Amazon strategies, where the goal is to stop wasted spend before it builds up.

The A/B Testing Framework for Collections

Since the collection type is locked once the ad group is created, testing Automatic Mode against Manual Mode needs to happen in parallel. You cannot run one setup, switch it later, and expect a clean comparison. 

Start with a group of mid-funnel keywords that have enough search volume but still show some purchase intent, such as “stainless steel mixing bowls.” Then build two separate Sponsored Brands campaigns around the same keyword set.

A Reverse keyword search Amazon workflow can help identify those mid-funnel terms by showing which searches already drive visibility for similar products. 

In Campaign A, use Automatic Mode and exclude any ASINs that should not be part of the test, especially low-margin or unprofitable products. For Campaign B, keep the keyword targets the same, but use Manual Mode and choose the three products you would normally want leading that search. 

To make the comparison useful, give both campaigns the same conditions. Use the same daily budget and bidding strategy, then let them run side by side for fourteen to twenty-one days so normal attribution delays are not skewing the result. For larger accounts, Amazon PPC Software can make this easier by helping track budgets, bidding changes, search terms, and performance across both test campaigns. 

After the test has enough data, do not judge the winner by ROAS alone. Check Amazon Click Through Rate to see which format shoppers responded to, and compare cost per click to understand whether Automatic Mode earned cheaper traffic through stronger relevance. 

The search term report is where results usually get more interesting, especially for Amazon Search Term Optimization because it shows which queries actually turned into sales. Manual Mode may give you tighter sales around the three products you choose, while Automatic Mode may uncover demand across more of the catalog or help move slower inventory. The better choice depends on what you needed that campaign to prove.

Why Partnering With SellerMetrics is Your Ultimate Advantage

Amazon advertising has become harder to manage casually. With AI-powered Automatic Collections, stricter ad group rules, and fewer manual creative controls, Sponsored Brands campaigns now need better planning before launch and closer review once performance data starts coming in. 

For many sellers, the difficult part is not choosing Automatic Mode or Manual Mode in theory. It is knowing which products belong in each setup, which ASINs should be excluded, and how each campaign fits into the wider account strategy. 

A wrong decision at the ad group level can send the budget toward products that convert on paper but do not support your margins. That also means looking beyond one campaign, especially when sellers are trying to answer What is a good TACoS on Amazon for the whole account. 

That is where Amazon Seller Agency like SellerMetrics can help. Our team reviews your catalog, margins, inventory risks, and campaign goals before building the structure, and for sellers managing several product lines, Amazon account management services can help connect campaign choices with inventory, margin planning, and account structure. 

We also build the A/B testing framework, manage product exclusions, and review your product detail pages so the new dynamic ad format has stronger listing assets to pull from. This kind of Amazon advertising management helps keep automation tied to profit goals instead of letting the algorithm chase every possible sale. The goal is not let Amazon’s AI make every decision on its own. It is to pair the speed of automation with a strategy that still protects profitability, brand positioning, and long-term growth. 

The goal is not just to spend more, but to understand how to increase sales on Amazon without losing control of profit. 

FAQ: Automatic vs. Manual Mode

The post Sponsored Brands Collections: AI Automatic Mode vs. Manual Mode appeared first on SellerMetrics.

8 min read By Rick Wong  Updated Jun 13, 2026

For the past twenty years, the basic online shopping process has stayed mostly the same. A customer identified a need, navigated to the Amazon homepage, typed a specific string of keywords into the search bar, and scrolled through a vertical list of products, manually filtering, reading reviews, and comparing prices until they made a decision. It was a self-serve, highly manual process. For Amazon sellers, most Amazon optimization work was built around that keyword-based shopping behavior (from backend search terms to Sponsored Products bidding). 

On May 13, 2026, Amazon changed that familiar shopping flow. Amazon officially announced the retirement of its beta AI chatbot, Rufus, and the simultaneous launch of its replacement: “Alexa for Shopping.” This was more than a name change; it was the unification of Amazon’s most powerful generative artificial intelligence models into a single shopping assistant with stronger personalization and automation features. By merging the conversational intelligence of Rufus with the massive, trusted ecosystem of Alexa+, Amazon has changed how shoppers can find and compare products. The search bar is no longer just a query box; it is now the front door to a personalized, “agentic” artificial intelligence that can compare products, answer shopping questions, and support some purchase actions. 

This shift matters for United States-based Amazon sellers. In this new shopping flow, the way your products are priced, described, and positioned dictates whether an AI assistant recommends them to a buyer, rather than just determining if a human shopper clicks on them. Optimizing for an algorithm that matches text strings is entirely different from optimizing for an AI assistant that uses context, reviews, product data, and shopping history. 

In this guide, we explain how Alexa for Shopping works. We will explore why Amazon made this major shift in May 2026, analyze the new features like “Scheduled Actions” and “Buy for Me,” and provide a practical plan for modernizing your product listings, advertising campaigns, and pricing strategies. Conversational commerce is no longer limited to beta testing. It is now part of Amazon’s shopping experience. To defend your market share and scale your brand, you need to understand how Alexa for Shopping selects, compares, and recommends products. 

The Evolution: Why Rufus Graduated into Alexa for Shopping

To understand Alexa for Shopping, we must first look at the growth of Rufus and Amazon’s decision to replace the Rufus name. Launched initially in 2024 and scaled rapidly throughout 2025, Rufus served as Amazon’s proof-of-concept for conversational commerce. It lived as a discrete chat bubble in the corner of the Amazon Shopping app, allowing users to ask open-ended questions like, “What are the best running shoes for flat feet?” or “Compare this blender to a Vitamix.” 

Rufus gave Amazon clear proof that shoppers use conversational product guidance. According to industry reports, by the end of 2025, Rufus had reached over three hundred million customers. More importantly, Amazon’s internal metrics revealed that shoppers who engaged with Rufus were sixty percent more likely to complete a purchase than those who relied on traditional search. The AI assistant was directly credited with driving nearly twelve billion dollars in incremental, annualized sales. During the Black Friday and Cyber Monday weekend of 2025 alone, AI chatbots and agents drove billions of dollars in gross merchandise value across the US e-commerce landscape. Rufus showed that consumers, when given the option, prefer conversational guidance over manual searching. 

So, why did Amazon move away from the Rufus brand? The answer lies in branding, scale, and the concept of “ambient computing.” While Rufus was highly capable, it was a new brand that required consumer education. Shoppers had to actively choose to click the chat bubble to initiate a conversation. In contrast, Alexa is an established household utility. It exists in millions of American homes, integrated into Echo devices, smart TVs, and mobile applications. Many consumers already use Alexa to ask questions. 

By replacing Rufus with Alexa for Shopping, Amazon eliminated the friction of introducing a new interface. They consolidated their artificial intelligence infrastructure around a brand that consumers already inherently trust. Furthermore, this unification created a massive technological advantage: shared memory. Previously, what a customer asked their Echo device in the kitchen was disconnected from what they typed into Rufus on their smartphone. Today, Alexa for Shopping operates as a single, continuous entity. If a customer asks their Echo Show to find a gluten-free protein powder, and then opens the Amazon app on their phone three hours later, Alexa remembers the context, preferences, and parameters of the conversation. This cross-device context can create a more personalized shopping experience. 

The New Real Estate: The Search Bar Becomes the Agent

For Amazon sellers, the most visible change introduced by Alexa for Shopping is its integration into the user interface. The AI is no longer hidden behind a secondary chat icon. Instead, the recognizable cursive “A” of Alexa has been embedded directly into the main Amazon search bar across the entire desktop website and mobile application. This real estate is one of Amazon’s most important shopping entry points. 

By placing the AI directly in the primary search flow, Amazon is signaling that conversational discovery is no longer an alternative shopping method; it is the default standard. When a user begins typing into the search bar, they are no longer just presented with a list of auto-completing keywords. They are met with conversational prompts, generative AI overviews, and direct answers to complex queries. 

For example, if a user types “gifts for a five-year-old interested in space,” the traditional search engine would struggle, returning a messy, disorganized grid of random astronaut toys and star projectors based on loose keyword matches. Under the new Alexa for Shopping interface, the AI intercepts the query. It generates a curated, structured response, organizing recommendations by category (e.g., “Educational Books,” “Building Sets,” “Room Decor”), accompanied by AI-generated explanations of why each specific product is developmentally appropriate and highly rated by other parents. 

This fundamentally alters the concept of “Page One.” In an agentic search environment, there is no traditional grid of sponsored and organic listings for complex queries. The top of the shopping path may be shaped by Alexa’s recommendations. If your product is not selected by the AI as one of the recommended options to the user’s conversational prompt, your product may receive far less visibility in that shopping path. A recommendation from Alexa for Shopping may become as valuable as a top organic ranking, and the rules for achieving that status have entirely changed. 

How “Buy for Me” and “Shop Direct” Affect Sellers: Going Off-Amazon

One major feature introduced in the May 2026 rollout is the expansion of Alexa for Shopping beyond the Amazon marketplace. Amazon has introduced two important capabilities: “Shop Direct” and “Buy for Me.” 

Recognizing that competitors like Google (with their new Universal Cart) and generative AI platforms like Perplexity and ChatGPT were attempting to become the main shopping assistants across the web, Amazon expanded its shopping assistant beyond its own marketplace. Through the “Shop Direct” feature, Alexa for Shopping can now index, evaluate, and recommend products from third-party, off-Amazon retail websites. If a customer is looking for a highly specific, direct-to-consumer artisanal product that is not listed on Amazon, Alexa may surface it through Shop Direct. 

The more important change is the “Buy for Me” feature. If Alexa locates the perfect product on a completely separate retailer’s website, Alexa can help move through that purchase flow, add the item into the external shopping cart, and complete the transaction on the user’s behalf, utilizing the payment details and shipping addresses securely stored in their Amazon account. 

For US-based Amazon sellers, the implications of this feature are profound. Historically, your primary competition consisted of the other sellers actively listing their products on the Amazon marketplace. If you dominated your category within Amazon, your market share was secure. Today, Alexa for Shopping has essentially turned the entire internet into a single, massive marketplace. If you sell premium leather wallets on Amazon for fifty dollars, and an independent Shopify brand sells a comparably rated leather wallet on their own website for forty dollars, Alexa may recommend the off-Amazon product and support the purchase for the Prime member. 

This reduces the old barrier between marketplace sellers and direct-to-consumer brands. Amazon is prioritizing the customer’s ultimate satisfaction over its own immediate marketplace transaction fees, betting that becoming the indispensable, universal shopping agent is the most lucrative long-term strategy. To survive in an ecosystem where Alexa is comparing your Amazon listing against the entire open web, your product’s value proposition, pricing, and external brand presence must be clear, competitive, and consistent. 

The Core Features Sellers Must Understand: Automation and Logic

Beyond external purchasing, Alexa for Shopping introduces several automation features that make repeat shopping easier for the consumer. Understanding these capabilities is essential for sellers, as they influence when shoppers may buy. 

The most significant of these features is “Scheduled Actions,” which allows the AI to autonomously manage a user’s inventory and budget. A customer can issue a complex, conditional command to Alexa, such as: “Automatically buy a thirty-pound bag of this specific dog food brand whenever the price drops below forty-five dollars, but do not buy it more than once a month.” Alternatively, they might say, “Monitor the price of these three different noise-canceling headphones, and automatically purchase whichever one drops to a twenty percent discount first.” 

Once these parameters are set, the customer never has to look at the search results page again. The AI agent operates silently in the background, constantly scraping price histories, evaluating active lightning deals, and monitoring Prime Exclusive Discounts. When the conditions are met, Alexa can notify the shopper, add items to the cart, or complete eligible purchases based on the user’s setup. 

This creates a new pricing challenge and opportunity. Traditional sales velocity is typically driven by human impulse or immediate need. In the era of Scheduled Actions, a group of shoppers may wait for specific prices or restock conditions before buying. A seller who strategically deploys a highly targeted, short-term coupon or deal might suddenly capture more price-triggered purchases during a short promotion as Alexa acts on saved shopping conditions. Managing your pricing matrix is no longer about winning the Buy Box against other sellers; it is about triggering the buying conditions set by shoppers. 

Why Keyword Stuffing No Longer Works: Optimizing for Contextual Relevance

Once sellers understand the basic mechanics, the critical question becomes: how do you optimize a product listing for an AI shopping assistant? The old methodology of search engine optimization (SEO) on Amazon involved identifying high-volume search terms and stuffing them into the title, bullet points, and backend search fields. If a user searched “garlic press,” and your title contained “garlic press,” you had a high probability of matching. 

Alexa for Shopping does not merely match strings of text; it utilizes Large Language Models (LLMs) to perform semantic analysis. It reads product content for meaning, context, and product fit. If a user asks the AI, “I need a kitchen tool to crush garlic that is easy for someone with arthritis to squeeze,” the AI is not looking for the exact keyword phrase “arthritis garlic press.” It is scanning product descriptions for details such as easy grip, low effort, comfort, and simple handling. 

Therefore, your listing copy must transition from keyword-dense technical writing to clear product copy that explains the benefit. You must explicitly state who the product is for, what specific problems it solves, and where and how shoppers would use it. 

Your bullet points should answer the exact types of conversational questions a user might ask. Instead of a bullet point that simply reads “Stainless Steel Construction,” the copy should read, “Constructed from rust-proof 304 stainless steel, ensuring this tool can withstand daily runs through the dishwasher without degrading, making cleanup completely effortless.” By giving Alexa clear context about durability and maintenance, you equip the agent with the exact talking points it needs to recommend your product when a user asks for “a garlic press that is easy to clean.” 

Furthermore, optimizing the backend attributes of your listing has never been more critical. Amazon’s catalog relies heavily on structured data, the specific item attributes like material, weight, dimensions, battery life, and compatibility. When Alexa is comparing three different products side-by-side to determine which one to recommend, it relies on this structured data to formulate its logic. If your competitor has filled out every backend attribute field, and you have left them blank, the AI will naturally favor the competitor because it has a higher degree of mathematical confidence in what that product actually is. In the agentic era, incomplete structured data is a real disadvantage. 

Sentiment Analysis: Your Reviews Are Your New Keywords

In traditional Amazon optimization, the text within customer reviews was largely viewed as a conversion rate factor. If a human shopper read a review and liked it, they bought the product. The search algorithm did not heavily index the actual linguistic content of the reviews for ranking purposes. 

Under Alexa for Shopping, this changes. Generative AI relies heavily on customer reviews and sentiment analysis to form its opinions and generate its recommendations. When a customer asks Alexa, “Are these running shoes true to size, and do they hold up well on wet pavement?”, the AI does not look at your perfectly crafted bullet points for the answer. It knows that seller-written copy is inherently biased. Instead, the AI instantly analyzes thousands of historical customer reviews, extracts the aggregate sentiment regarding sizing and traction, and creates a direct answer for the user based entirely on the voice of the customer. 

Customer reviews now carry more weight in AI-assisted recommendations. If the AI detects a consistent pattern of negative sentiment regarding a specific feature, for example, if ten percent of reviews mention that the zipper on your backpack breaks easily, Alexa may recommend your product less often when a user asks for a “durable backpack for travel.” Conversely, if your reviews consistently praise the “incredible customer service and lifetime warranty,” the AI will use that sentiment to recommend your product to users who express anxiety about product longevity. 

Managing your product sentiment is no longer a passive exercise. Sellers must utilize advanced review analysis tools to identify the exact phrases and concepts customers are using in their positive reviews, and then highlight those exact concepts in their primary listing copy and advertising creative. If a negative sentiment trend begins to emerge, it must be addressed at the manufacturing and supply chain level immediately. You cannot out-market or out-advertise bad sentiment in an AI-driven ecosystem, because Alexa may surface those concerns before the shopper checks out. 

Mastering Pricing Strategy in the Era of Agentic Automation

As touched upon regarding the “Scheduled Actions” feature, pricing on Amazon has evolved from a static display number into a dynamic trigger for shopper actions. Alexa for Shopping can show 30-, 90-, and 365-day price history. It can factor in previous prices, including major sale periods such as Prime Day, it knows your average moving price over the last ninety days, and it provides this historical data directly to the consumer in the form of generative overviews. 

Artificial price inflation—raising your price significantly right before a major event just to offer a “fake” discount—is now easier for shoppers and AI tools to notice. Alexa may flag this behavior to the shopper, stating, “While this item is currently twenty percent off, it is still priced higher than its average cost over the last three months.” This can reduce shopper trust and actively dissuade the AI from recommending the purchase. 

To succeed in this environment, sellers must adopt highly strategic, algorithmic pricing models. The focus must shift toward maximizing absolute profit dollars rather than obsessing over top-line revenue vanity metrics. Because Alexa can execute automated purchases when price thresholds are met, utilizing Amazon’s official promotional levers (such as Prime Exclusive Discounts, Lightning Deals, and clipped Coupons) becomes incredibly powerful. These official promotional badges serve as clear signals to the AI agent that a high-value event is occurring, prompting the agent to notify users who have expressed interest in your category or who have your item sitting in a digital wish list. 

Furthermore, because Alexa for Shopping utilizes the “Shop Direct” feature to compare prices across the open web, maintaining strict pricing parity across all your sales channels is important. If you sell a product on Amazon for sixty dollars, but offer it on your own Shopify store for fifty dollars, Alexa will identify the discrepancy and may advise the Prime member to execute the purchase off-platform using the “Buy for Me” feature. While you still capture the sale, you lose the crucial Amazon sales velocity and conversion data necessary to maintain your organic algorithmic ranking within the marketplace. Unified, omnichannel pricing governance is now important for sellers who use several sales channels. 

The Evolving Role of Amazon Advertising and DSP

If the AI agent is determining what products to recommend based on context and sentiment, what happens to traditional Pay-Per-Click (PPC) advertising? The reality is that advertising does not disappear, but it moves more focus toward upper-funnel activity. 

Relying only on high bids for exact-match Sponsored Products keywords may become less effective. If the user is engaging in a conversational query, and the AI has curated a list of highly relevant, sentiment-approved recommendations, a sponsored product that mathematically matched a keyword but lacks the semantic relevance required by the AI may receive less attention from the shopper. 

Advertising budgets must transition toward building brand authority and establishing behavioral footprints before the user ever initiates a conversation with Alexa. This is where the Amazon Demand-Side Platform (DSP) and Amazon Marketing Cloud (AMC) become the important tools for sellers that need stronger brand awareness. 

By leveraging the programmatic capabilities of Amazon DSP, sellers can target high-intent custom audiences across the broader internet, through Streaming TV, interactive video ads, and the newly integrated Podcast Audience Network. The goal is to aggressively build brand awareness and educate the consumer on your unique value propositions. When that highly educated consumer eventually interacts with Alexa for Shopping, they are no longer asking generic questions like, “What is the best coffee maker?” They are asking highly specific, branded questions like, “Does the [Your Brand] coffee maker have the thermal carafe currently in stock?” 

By driving branded search volume through top-of-funnel DSP campaigns, you bypass the generic, highly competitive AI evaluation process entirely. You give shoppers a clearer reason to ask for your specific product, leveraging the conversational interface to facilitate a simpler purchase path. Furthermore, by utilizing the advanced analytics within Amazon Marketing Cloud, you can definitively track how these upper-funnel advertising exposures influence the recommendations made by Alexa, allowing you to mathematically prove your return on ad spend and continually refine your messaging. 

Preparing Your Brand for the Future with SellerMetrics

The transition from a keyword-driven search engine to an agentic AI assistant is a major change in e-commerce. The strategies that built massive Amazon brands in 2023 may no longer work as well in 2026. Understanding the nuances of Alexa for Shopping, optimizing your catalog for semantic relevance and review sentiment, and executing complex, multi-channel DSP campaigns requires a level of technological sophistication and data analysis that most brands simply do not possess in-house. 

This is why many growing brands on the marketplace partner with an Amazon Ads Agency like SellerMetrics. SellerMetrics supports more than traditional PPC management; we are a full-funnel Amazon advertising team focused on Amazon advertising, analytics, and AI-driven shopping behavior. We use Amazon Ads, AMC, and performance data to guide strategy. 

When you partner with SellerMetrics, we conduct a full audit of your entire digital presence. We overhaul your product listings, transitioning them from outdated keyword repositories into clear, context-rich product pages designed to give Alexa clearer product information to process. We deploy advanced sentiment analysis to ensure your reviews are working as a strength rather than a hidden risk. We manage your complex, cross-channel pricing matrices to ensure you capture the price-triggered demand triggered by Scheduled Actions. 

Most importantly, we architect the sophisticated, programmatic DSP campaigns required to build stronger brand awareness, driving the highly coveted branded search volume that helps Alexa for Shopping understand and recommend your products over the competition. The era of manual, self-serve Amazon shopping is over. AI now plays a larger role in how shoppers compare and choose products. SellerMetrics can help your brand become easier for AI-assisted shopping tools to process and recommend.

FAQ: Why Merge Rufus and Alexa?

The post Amazon Merges Rufus and Alexa: Preparing Your Brand for Agentic AI Search appeared first on SellerMetrics.

14 min read By Rick Wong  Updated Jun 08, 2026

Sponsored Brands Video (SBV) is one of the highest-converting ad formats available to Amazon sellers today. In 2026, it has become even more powerful: the same video creative that wins keyword search placement now surfaces inside Amazon’s AI shopping assistant (Alexa for Shopping, formerly Rufus), giving brands a second discovery channel from a single production investment.

But the format is not plug-and-play. Amazon’s creative review rejects ads that do not meet spec, captions are functionally required since most viewers watch with sound off, the first three seconds determine whether the viewer scrolls past, and bidding has grown 20–40% more competitive as AI-driven inventory expanded.

This guide is the exact playbook we use at SellerMetrics. It covers everything from video specs and campaign setup to 2026-specific strategy around Rufus, the SBV vs. SPV distinction, and optimizing your first 72 hours live.

What Is Amazon Sponsored Brands Video

Amazon Sponsored Brands Video (SBV) is a search-result ad format that auto-plays a 15–30 second horizontal video at or near the top of Amazon search results when a shopper searches for a relevant keyword. When clicked, it sends the shopper directly to a product detail page or Amazon Store.

What’s new in 2026:

In 2026, SBV inventory became eligible for Alexa for Shopping placements (Amazon’s AI shopping assistant, rebranded from Rufus in May 2026). When shoppers ask Alexa for Shopping for product recommendations, SBV creative can now surface alongside organic listings. Sponsored Brand Prompts moved from free beta to billable general availability in the US on March 25, 2026, operating on the same cost-per-click model as standard SBV. This is not a separate campaign you build (your existing targeting and listing quality determine eligibility) but it meaningfully expands the reach of every video you produce.

The practical implication: optimize your product listing copy for natural, intent-aligned language (how shoppers actually describe problems, not just how they keyword-search), because listing quality now determines AI placement eligibility in addition to search ranking.

The Difference between SBV & SPV

Before you start building, it is critical to understand that there are now two distinct Amazon video ad formats, and they serve different purposes:

Sponsored Brands Video (SBV) — this guide

• Requires Amazon Brand Registry enrollment (registered or pending trademark)
• Large banner placement at the top of search results
• Links to a product detail page or your Amazon Store
• Strong for brand awareness and upper-funnel consideration
• Available in 2026 to Alexa for Shopping inventory

Sponsored Products Video (SPV)

• Does NOT require Brand Registry — available to all sellers
• Appears inside the standard search results grid (inline with product tiles)
• Links directly to a single product detail page
• Strong for bottom-of-funnel, high-intent, direct-conversion targeting
• Launched broadly and is growing rapidly as a separate format

If you are not yet Brand Registered, SPV is your entry point into video advertising. If you are Brand Registered, both formats are worth running simultaneously with different creative and targeting strategies. They are complements, not substitutes.

Know Your Shopper’s Search Intent

Before you storyboard a single frame, you must align your creative with where the shopper sits in the buying funnel. Amazon shoppers are not passively browsing like they do on social media, they are actively looking to make a purchase. Generic demographic questions like “What is their age?” matter far less than understanding where they are in the decision process.

• Top-of-funnel (broad search terms):
  If you are targeting terms like “kitchen gadgets,” the shopper does not know exactly what they want yet. Your video needs to visually demonstrate the problem your product solves within the first three seconds to interrupt their scroll. Broad terms warrant a problem-first hook (show the frustration before you show the solution).
• Bottom-of-funnel (exact search terms):
  If you are targeting highly specific terms like “stainless steel garlic press with silicone handle,” the shopper knows exactly what they want. Skip the broad lifestyle branding entirely. Immediately highlight the premium materials, ergonomic design, and anything that justifies your price point over competitors. These viewers are in the virtual aisle comparing options (your video should behave like a product demonstration, not a commercial).

The rule of thumb: match your video’s opening message to the specificity of the keyword triggering it. This is why you should never run broad and exact match keywords in the same campaign — your creative strategy for each is fundamentally different.

Localize Your Video Ads

If you sell in multiple Amazon marketplaces, localization is worth the investment. But do it strategically, not as an afterthought.

Four localization priorities:

1. Translate overlays and script:
   Text overlays and spoken script should be localized, not just subtitled. Direct translations often carry unnatural phrasing that reduces trust. Work with a native speaker for each target market.
2. Check for cultural relevance:
   Colors, gestures, settings, and product-use contexts can carry different meanings across markets. A kitchen product filmed in a setting that feels distinctly American may underperform in Japan or Germany. Audit your visuals for unintended cultural signals.
3. Use local voiceover and subtitles:
   Even if your video contains no dialogue, adding local-language text overlays boosts engagement in non-English markets. Always add closed captions for any spoken content.
4. Apply regional targeting at the campaign level:
   Build separate campaigns for each marketplace. Do not translate one campaign’s keywords directly, keyword behavior and bid competition vary significantly by market, and a keyword that converts well in the US may be low-volume in Canada or Australia.

Before creating ads for a new market, check whether your brand name, tagline, or any product claims carry unintended meanings in the local language. A quick search for your brand name in the target language takes five minutes and can prevent a rejection or, worse, a viral mockery moment.

How to Build and Launch Your Sponsored Brand Video Campaigns

When you launch a new video campaign on Amazon, you are committing real capital. A poorly structured campaign can drain a weekly budget in hours with zero return. Here is the exact, defensive setup process we use at SellerMetrics.

1. Confirm Your Ad Meets Amazon Video Requirements.

Amazon’s review team will reject your ad if it fails to meet any of the following. Double-check before uploading to avoid losing 24–72 hours of review time.

Amazon SBV Technical Specifications (2026):

2026 note on vertical video: Amazon is piloting 9:16 vertical video support for mobile-specific placements. This spec is not yet broadly available in the standard SBV campaign builder, but if you produce your video content in a square or vertical format for other channels, flag this with your video editor, a vertical cut may become a useful asset later in 2026.

Beyond the technical specs, your video must be engineered for the Amazon shopping experience:

• Closed captions are non-negotiable: Videos auto-play on mute in search results. If your value proposition relies on a voiceover, it will be completely lost. Use clear, high-contrast text overlays and closed captions throughout.
• Demonstrate, don’t just show: Shoppers want to see the product in use. A static product sitting on a desk looks like a generic commercial. Show a real person using the product to solve a specific, relatable problem.
• Open with the product, not your logo: The average SBV watch time is approximately 18 seconds. Opening with a logo reveal is the fastest way to lose a viewer. The product and the problem it solves must appear in frame 1.
• End with a clear call-to-action: Close with your branding and a specific directive; “Shop Now” or “Available with Prime.” Do not leave the viewer guessing about their next step.

2. Campaign naming conventions and a Portfolio

Before you upload anything, build your organizational foundation. When you are managing multiple campaigns across products, losing track of what is running is a budget killer.

In your Amazon Advertising Console, create a dedicated Portfolio for your SBV campaigns and set a hard portfolio-level budget cap. This is your financial safety net; it prevents any single video campaign from consuming your entire advertising budget during the learning phase.

Use a standardized naming convention so you know what any campaign does at a glance:

Formula: [Product Name] – [Targeting Type] – [Match Type] – [SBV]

Example: Garlic Press – Competitor ASINs – Product Targeting – SBV

This makes filtering, reporting, and troubleshooting dramatically faster at scale.

3. Create your SBV campaign in Seller Central

From Seller Central, navigate to Advertising → Campaign Manager → Create Campaign and select Sponsored Brands. 

After that, on the format screen, choose the Video option.

Next, locate the product you’re creating an ad for by selecting it from the “New Product List Page.”

Afterward, click “Add” to upload your video file.

Amazon provides an inline preview panel so you can see exactly how the ad will render before submission.

Note: Amazon manually reviews every SBV submission. Do not assume your campaign will go live immediately after upload.

4. Segment your targeting for clean data

This is where most sellers make a costly structural mistake. Do not mix broad keywords, exact match keywords, and ASIN targeting in the same campaign. Mixing targeting types makes it impossible to know which specific input is driving sales or wasting spend.

Build three separate campaigns:

Campaign A (ASIN / Product Targeting):
Utilize Amazon PPC product targeting to place your video ad directly on competitor product detail pages. This is a high-intent placement, shoppers are already looking at the category.

Campaign B (Exact Match Keywords):
Use 5–10 exact match keywords with a proven conversion track record from your existing Sponsored Products campaigns. Exact match gives you clean data. When a sale comes in, you know precisely which search term triggered it.

Campaign C (Broad or Phrase Match (optional, with a tighter budget)):
Use this for discovery only. If you run broad match, monitor the search term report aggressively, broad match in SBV is the fastest route to irrelevant traffic and wasted spend, because video clicks carry a premium cost per click.

By isolating targeting types, you can scale what works and kill what does not with confidence.

5. Set conservative daily budgets and starting bids

Video ads typically generate higher click-through rates than standard Sponsored Products, which means your budget can be consumed significantly faster. Start conservatively:

• Daily budget: $20–$50 to gather initial data.
  Note: In highly competitive categories (supplements, electronics, kitchen) you may need $50–$100+ to generate statistically meaningful data within a week.

Opening bids: Start 10–15% below Amazon’s suggested bid range, not at or above it. Incrementally raise bids every 48 hours until you begin winning impressions. Bidding high from day one means you are paying a premium CPC while your video’s conversion rate is still unknown.

And the final step is to submit your ad for review from Amazon. Amazon will approve or deny your video in ten minutes to a few hours.

If they support your ad, congratulations! However, if they reject your video ad, you’ll need to move on to the next section.

2026 bid context: SBV bidding has become 20–40% more competitive over the past 12 months as Alexa for Shopping expanded inventory demand. Budget floors that felt generous in 2024 may generate very limited impression share in 2026 in competitive categories. Watch your impression share in the first 72 hours. If it is near zero, your bid, not your creative, is the likely bottleneck.

6. Submit for review and monitor the first 72 hours

Once you click submit, Amazon’s moderation team begins review. Current approval timeline: 24–72 hours for first submissions. Resubmissions after a rejection can take 3–5 business days, as the team verifies the prior rejection reason has been addressed. Plan for at least one rejection cycle in your launch timeline. First submissions are approved on the first attempt roughly half the time.

Once your campaign goes live, follow this discipline:

• Do not touch bids for the first 72 hours. The algorithm needs this window to learn the relevance of your video against the search terms you are targeting. Premature bid changes reset this learning process.
• After 72 hours: Pull your search term report. Identify any irrelevant queries that triggered your ad. Immediately add these as Negative keywords to protect your budget from unqualified clicks. This is the single most impactful optimization action in the first week.

After 7 days: Evaluate CTR and view-through rate by targeting type. Scale the campaign generating the strongest ROAS; pause the weakest performers.

Resolve Amazon Brand Ad Rejections

Amazon rejects SBV ads more frequently than standard Sponsored Products campaigns because the review process includes additional policy checks around claims, functionality references, and branding presentation. Knowing the common rejection reasons lets you pre-flight your creative before it ever enters the review queue.

Does It Educate, Inform, And Demonstrate To Its Audience?

Your video should do all three. Lead with what the product does (demonstrate), explain why it matters (inform), and give the viewer a reason to trust your brand (educate). A video that purely pitches pricing or promotions will be rejected. Keep your brand as the focal point and highlight the product’s most compelling features with context, not just visuals.

Does it tell a story — without feeling like a sales pitch?

Your customers want to feel like they’re getting an interesting, valuable story in their sponsored brand videos.

If your video feels commercial or like a sales pitch, Amazon may not approve it. Ensure that all product demonstrations are helpful and easy to follow.

Finally, make sure that you’re not trying to sell or promote products in a way that Amazon disapproves of.

Creating an Amazon Sponsored Brand Video campaign can seem daunting at first, but you can do it! Follow this Amazon Sponsored Brands Video campaign tutorial to make the process simple.

Does it follow Sponsored Brands Video content policies?

When you violate any of Amazon’s advertising policies, it can result in ad rejection.

Make the necessary changes and upload the corrected version for approval if this happens. Amazon is usually quick to approve ads that follow their guidelines.

Common rejection reasons that can be avoided with a pre-submit checklist:

• Blurry or visibly low-resolution video (below 720p).
• Customer reviews, star ratings, or “best seller” claims included in the video.
• Promotional discount language (“Save 30% today,” “Limited time offer”).
• Text overlays that are illegible (too small, poor contrast, or on-screen too briefly).
• Poor audio quality or out-of-sync audio.
• High-pressure language or urgency tactics.
• Amazon trademarks, logos, or the Amazon “smile” used in your creative.
• Fake UI elements (play buttons, cursor animations, social media watermarks, or anything that could be mistaken for a clickable interface element).
• Black or blank frames at the start or end of the video.
• Video content that does not match the product on the listing (wrong color, size, or configuration shown).
• Distracting background sounds or music that overwhelms the message.

But don’t worry if you can’t figure out exactly what’s wrong with your video.

Amazon will explain precisely why they’re not approving your ad so that you can correct it quickly! And if you want to guarantee your videos meet quality standards, here are a few tips that’ll help!

Best Practices: Add Your Branding & A Call-To-Action

In Amazon Video Ads, you can overlay text and branding elements throughout the video. Introduce your brand logo or name in the first 3 seconds alongside the product, not as a standalone opening frame. Keep brand elements consistent with your Amazon Store and product imagery for visual continuity.

End every video with a clear, specific CTA. Strong options:

• “Shop Now on Amazon”
• “Available with Prime (Free Same-Day Delivery)”
• “See All Colors and Sizes”

Avoid vague CTAs like “Learn More” — Amazon shoppers are in purchase mode, not research mode.

Best Practices: Include Easy-To-Read Closed Captions

Because SBV auto-plays on mute, closed captions are not a courtesy, they are a core part of your ad’s communication strategy. Industry data shows 60–70% of Amazon shoppers view video ads with sound off, the majority on mobile devices. Follow these captioning standards:

• Use high-contrast colors (white text with a dark outline/shadow, or dark text on a light bar)
• Keep on-screen text to one short phrase at a time (do not wall the screen with words).
• Match caption timing precisely to any spoken audio (mismatched captions are a rejection risk)
• If your video has no dialogue, use text headings to guide the viewer through each visual beat, supported by background music

Conclusion

Scaling your brand on Amazon in 2026 means capturing shopper attention faster and more effectively than your competitors at the search result level and increasingly at the AI assistant level. Sponsored Brand Video gives you exactly that advantage, provided you build it on a solid foundation.

The sellers who win with SBV in 2026 share three habits:

1. They align their creative precisely with shopper intent
2. They isolate their targeting to generate clean ROAS data
3. They treat the first 72 hours after launch as a learning phase, not a results phase. 

They also stay ahead of the format’s evolution. The Alexa for Shopping integration is early-stage; the brands building strong SBV creative libraries today are positioning themselves for an AI-driven discovery channel that will become significantly more competitive over the next 12 months.

Managing granular video campaigns, monitoring bids, and staying current with Amazon’s evolving ad policies is a significant operational commitment. At SellerMetrics, our Amazon PPC software helps sellers, brands, and agencies manage this complexity through advanced bid automation, bulk bid changes, and campaign-level analytics — so you can focus on creative strategy while we handle the operational layer.

FAQ: All About Sponsored Brand Video Campaigns

The post Master Amazon Sponsored Brand Video (Expert Guide 2026) appeared first on SellerMetrics.

15 min read By Rick Wong  Updated Jun 07, 2026

You launch a new Amazon PPC campaign, hoping to dominate your category and scale your sales. But weeks later, you are staring at a rising ACoS, stagnant order volume, and a blended Cost-Per-Click (CPC) that seems to climb higher every day. Before you can even determine what is a good ACoS for your specific category, you have to find out where the bleed is coming from.

If you are tired of watching your ad spend evaporate with nothing to show for it, we got you covered. In this guide, we will unpack exactly why this internal overlap happens, how to diagnose it using your Search Term Reports, and the step-by-step governance framework you need to plug the leak and reclaim your lost profits. 

What Is Search Query Cannibalization in Amazon Ads?

Search query cannibalization occurs when more than one of your campaigns or ad groups competes to serve an ad for the same shopper search term. Instead of presenting a unified front in the auction, your account splits its strength and pushes multiple entities to jostle for the same impression. The typical symptom is deceptively simple: you pay more while learning less, because performance signals are scattered across duplicated touchpoints. The result is an inefficient spend pattern in which your data becomes noisy, your bidding decisions become less confident, and your visibility becomes inconsistent across placements and times of day. 

In practice, advertisers often observe performance volatility when multiple campaigns target the same query. Amazon must determine which eligible ad is most appropriate to serve for that shopper search.  When it sees two or three of your own campaigns signaling interest in the same term, it must make a choice on your behalf, and that choice often changes from hour to hour as budgets fluctuate and bids shift. Over a week or a month, this volatility creates a fog over your metrics. The Auto campaign may win mornings, a Broad ad group may pop up in the afternoon, and your carefully tuned Exact campaign might only capture a subset of prime impressions. None of those entities receives the full weight of signal it needs to stabilize top-of-search performance and to accumulate the data that drives smarter bids. Meanwhile, your competitors benefit from the inconsistency and can win more auctions at a lower clearing price. 

Short periods of overlapping coverage can be useful during testing or transitions, such as when you are migrating a promising query from Auto discovery to Exact ownership and need a few days to validate conversion consistency. That transitional overlap should be time-boxed and followed by decisive negative keyword actions. Cannibalization, by contrast, is unmanaged and persistent. It happens when the same query continues to trigger multiple campaigns without a clear owner and without the negative keyword map that prevents internal poaching. The difference between these two scenarios (temporary redundancy versus chronic cannibalization) will determine whether your ad system becomes sharper over time or slowly erodes its own efficiency. 

The Three Horizons of Amazon Cannibalization

To successfully stop profit erosion within your Amazon catalog, you must realize that keyword cannibalization does not live in a single silo. It operates across three distinct horizons within your account architecture: PPC-to-PPC, PPC-to-Organic, and ASIN-to-ASIN. Failing to isolate these vectors leads to a common issue: optimizing your match types in one campaign while your listings quietly drain profit from each other in another. 

1. PPC vs. PPC (Internal Bidding Chaos)

This occurs when multiple ad entities inside your Advertising Console bid on the same search term. Whether you are relying on Manual PPC or Automated PPC, your Auto campaign targets the phrase, your Broad ad group catches it as a variant, and your Exact campaign pursues it aggressively. Instead of scaling your reach, your ad spend gets split across multiple targets. This dilutes performance signals, distorts conversion histories, and artificially inflates your clearing cost-per-click (CPC).

2. PPC vs. Organic (The Profit-Draining Illusion)

This dynamic appears when a sponsored ad placement directly displaces your own organic position. Understanding the interplay between Amazon SEO and PPC is critical; if your product holds the top organic rank for a high-intent term, but you run an aggressive Sponsored Product campaign for that same query, you end up paying for a click you likely would have won for free. While your ad console reports strong conversions, your Total Advertising Cost of Sales (TACoS) remains flat because you are shifting free organic traffic into a paid attribution channel.

For mature brands, occupying both sponsored and organic placements can increase total category share. The key question is not whether overlap exists, but whether the incremental sales generated justify the additional ad spend.

3. ASIN vs. ASIN (The Catalog-Level Conflict)

This challenge affects brands managing complex variation listings (child ASINs), multi-packs, or similar product lines. When different listings within your catalog lack unique keyword mapping, they target the same consumer search query. Amazon’s search algorithm struggles to identify the most relevant offer, which splits your conversion signaling and leaves your listings trading page-one visibility back and forth.

Why Search Query Cannibalization Happens in Amazon PPC

Cannibalization rarely springs from a single dramatic misstep. It accumulates through a series of reasonable decisions that, taken together, blur the lines of query ownership. 

1. One of the most common pathways involves the natural evolution of a growing account. 
   An advertiser launches a discovery campaign to explore new territory, finds several winning queries, and adds those queries into a manual exact campaign. Because results look good in both places, the team leaves them active, rationalizing that more coverage is better. Weeks later, the Auto is still bidding on those same terms, the Broad ad group continues to capture close variants, and the Exact is pushing hard to hold top of search, yet no one has applied the negatives that would assign ownership. The line between exploration and exploitation never becomes a gate; it remains a revolving door. 
2. A second pathway emerges from cloning. 
   Marketers duplicate an existing campaign to isolate budget or to test a different bid philosophy. The duplicate inherits keyword lists and match types, and in the rush of execution, the team forgets to deduplicate the target set or to add negatives back into the source. The moment both go live, two entities with similar keywords inhabit the same auction space. Because each campaign starts with slightly different bids and budgets. Different campaigns may win impressions at different times due to differences in bids, budgets, relevance, and auction dynamics. That alternation weakens the signal density for each campaign and turns what should have been a clean experiment into an ongoing tug-of-war. 
3. A third pathway involves product targeting colliding with keyword targeting. 
   It is common to build a detail-page strategy that targets relevant ASINs or categories to win product page placements. However, when the products being targeted rank for the same queries your keyword campaigns already pursue, you can pay twice to reach the very same shopper journey. The shopper types a query, clicks into a detail page, and sees your product-targeting ad competing against your keyword ad for the next impression. Without careful placement modifiers and clear intent separation, product and keyword strategies can become twin streams that flood the same valley. 
4. The final pathway involves the nuances of match types and close variants. 
   Broad and phrase can easily capture the exact query that an exact match keyword is supposed to own, especially when stems, plurals, and common misspellings are involved. If you never create the negative exact barrier that tells discovery tiers to stand down once a query is promoted, you allow the Beta tier to poach the Alpha’s territory indefinitely. 

The logic behind match-type tiering is simple: broad discovers, phrase narrows, and exact owns. The mistake is treating this as an organizational suggestion rather than a control system. Without negatives, the tiers blur and your account’s internal borders dissolve.

Overlapping Keywords Across Campaigns and Ad Groups 

Overlaps most often present as a web of small collisions rather than a single obvious crash. A broad ad group comes within reach of your champion query, a phrase ad group catches a promising long-tail variant that shares the root, and your exact ad group is tuned to compete aggressively for the precise term. All three are technically doing their jobs in isolation. Yet together, they turn one query into three partial data streams.

For example: A scenario where “wireless tennis earbuds” is the revenue engine in your category. Your exact ad group holds the canonical “wireless tennis earbuds” keyword, your phrase ad group includes “tennis earbuds,” and your broad ad group explores “wireless earbuds for sports”. The shopper’s literal query may match all three pathways depending on Amazon’s interpretation. On Monday, the broad ad serves the impression and absorbs the click; on Tuesday, phrase gets the nod; on Wednesday, exact carries the day. Each entity collects a slice of the truth, but none receives enough signal to anchor stable bids, placements, or budgets. 

Cloning magnifies this effect. When you copy a campaign to stage a seasonal push or to isolate budget for a subset of SKUs, the cloned campaign often carries forward the same high-value keywords. If you do not immediately assign ownership by adding negatives to the source or the clone, the two campaigns begin skirmishing for the same search terms. In the reporting view, this looks like fluctuating clicks and conversions that refuse to consolidate. The story is enticing because each campaign shows flashes of success, yet the blended performance is poorer than if one had been allowed to fully own the query. The fix is not to abandon cloning but to combine it with deduplication and an explicit negative map at the moment of launch. 

Product targeting can overlap with keyword targeting in less obvious ways. Consider a popular ASIN that ranks for “best tennis elbow brace.” Your keyword campaign is tuned to secure top-of-search on that phrase, but your product targeting campaign, hungry for product page share, aggressively targets competing ASINs in the same subcategory. The shopper moves from the search results into a product page where your product-targeting ad appears, while your keyword ad fights to stay visible at the top of results for the next related search. Those two streams are now competing for the same buyer’s attention within the same session. Without careful placement controls and explicit intent rules that prioritize where each campaign should win, you spend twice to chase the same intent.

Poor Match-Type Segmentation and Missing Negatives 

The Alpha/Beta method, sometimes called Exact/Discovery, is widely discussed for good reason. When it is used as a principle rather than a template, it provides the discipline your account needs to prevent internal collisions. The idea is straightforward. Discovery tiers (Auto and Broad) are there to cast a net. They excel at uncovering new angles, longer tails, and adjacent terms. When a query consistently converts above your thresholds, that query graduates into a precision tier where you invest with confidence. The critical move happens at the moment of promotion. The instant a query becomes an exact keyword, it should be walled off in the discovery tiers through negative exact in Auto and Broad, and when necessary, negative phrase for families of overlap. The discovery tiers stop poaching the champion’s territory, and the exact tier becomes the single source of truth for that query’s performance, bids, and placements. 

Negatives must live at the layer that reflects your intent. Campaign-level negatives are the blunt instrument that protects a campaign’s mission. When an Exact campaign exists solely to own promoted queries, it deserves campaign-level protection from duplicate matches elsewhere. Ad-group-level negatives provide nuance. When a single campaign contains multiple ad groups for legitimately different roles, you can partition responsibilities inside the campaign without cutting off useful exploration in parallel ad groups. What matters is the clarity of the ownership map. Every high-value query needs a home and a record of who is not allowed to chase it. Without that ledger, accounts drift back toward chaos as new products and promotions come online. 

How Search Query Cannibalization Affects Your Ad Performance

The damage caused by cannibalization is multidimensional.

It starts with spending efficiency. When two of your campaigns participate in the same auction pattern, even if they do not directly bid against one another in a single instant, their combined presence can contribute to higher effective CPCs over time by fragmenting performance signals and reducing bidding efficiency. The marketplace learns that you are willing to pay more for those impressions, and your blended cost-per-click rises accordingly. Each click then carries a greater burden to convert profitably, which means your allowable bid landscape shrinks, which in turn can restrict volume. The spiral is subtle and slow, but it is persistent. 

The second dimension is data quality. Marketers depend on clean signals to make good decisions. If fifty conversions that belong to one query are split across Auto, Broad, and Exact, your view of true performance is diluted. The Auto campaign may appear to be more productive than it really is, because it is harvesting conversions that should have been credited to Exact. The Broad campaign may present a middling return that understates the power of a few long-tail variants now lost in the average. Meanwhile, the Exact campaign may look less robust than it deserves, because it is only receiving a fraction of the total demand. Decision makers see three moderate outcomes instead of one outstanding one, and budgets follow the wrong narrative. 

The third dimension affects machine learning and auction behavior. Algorithms rely on consistency. When one entity consistently proves it can convert a query profitably, the system allows that entity to maintain better positions with less volatility. By splitting the signal, you reduce the consistency the system needs to reward you. Top-of-search share becomes erratic, product page presence expands into areas you did not intend, and the rhythm of impressions becomes choppy. That choppiness can reverberate into organic ranking because sales velocity on priority terms becomes less predictable. A brand that could have climbed steadily finds itself treading water.

Higher ACoS and Reduced ROAS 

Rising ACoS and shrinking ROAS are the first financial indicators that internal overlap is eroding your account. Imagine the blended CPC on a core query increases from one dollar and twenty cents to one dollar and forty-five cents after you add a new exploratory campaign that accidentally contains the same target. Nothing about your product page or offer changed. The only new factor is the internal competition that persuades the marketplace that you must pay more to be present. Even if conversion rate remains stable, the cost per acquisition rises, and the math works against you. Some teams respond to this by raising bids in the Exact campaign to retain prime placement, which further escalates the clearing price and widens the gap between spend and revenue. The healthiest response is to remove the internal friction so the Exact campaign can win at the lowest possible rate consistent with your goals. 

Lost Impressions and Ranking Confusion 

The second visible symptom is scattered share of voice. When ownership is murky, you may find that one campaign captures early-day impressions while another captures evenings or weekends. You might notice that product page placements swell while top-of-search withers, or vice versa, without a corresponding change in strategy. What looks like the market shifting is often the algorithm juggling your entities in an effort to make sense of your mixed signals. Shoppers see an inconsistent brand presence, and your position relative to competitors oscillates without a clear external catalyst. The cure is not merely to push more budget. The cure is to concentrate your signal by assigning ownership and eliminating duplicate participation at the query level so Amazon can learn a reliable preference for your best-suited asset. 

Advanced Catalog Governance Strategy

To run a highly efficient Amazon account, you need a systematic method for allocating your keywords. You can resolve catalog friction by assigning single keyword clusters to individual products based on consumer search intent.

A major part of catalog governance is managing PPC vs. Organic Cannibalization, which occurs when your paid ads steal clicks from your own organic rankings. Knowing when to bid on your own brand terms versus when to let your organic ranking do the heavy lifting is crucial to protecting your profit margins.

When building this framework, map keywords out by intent rather than features. Enforce it by applying strict Negative keywords Amazon protocols across matching discovery campaigns to keep your traffic sources clean. If you sell an insulated stainless steel water bottle line, do not let your 32oz sports bottle and 16oz coffee tumbler bid on the generic query “insulated flask.” Instead, assign “sports gym bottle” to the 32oz listing, and “travel coffee mug” to the 16oz option.

Enforce these boundaries by applying Negative Exact filters across matching discovery campaigns to keep your traffic sources clean. This channels your ad spend into the listings best suited to convert each query, protecting your margins and keeping your account structured for scalable growth.

Continuous Monitoring and Optimization Practices

Preventing cannibalization is not a one-time cleanup. Accounts breathe. New ASINs enter the catalog, seasonal bundles debut, and promotional flights reorder priorities. Each change is an opportunity for overlap to creep back in. To help secure your Amazon advertising sessions while managing these changes, experts recommend using a trusted VPN service like Surfshark – current coupon codes can reduce subscription costs while keeping your workflows secure. The antidote is a deliberate weekly rhythm that treats query ownership as an operational discipline rather than an occasional project. 

The heart of that rhythm is the search term report. A weekly export across a sensible date window provides the raw material for a simple but powerful audit. By grouping data by customer search term and inspecting which campaigns and ad groups fired that term, you can identify duplicates immediately. The duplicates represent your list of conflicts to resolve. Resolution follows a consistent pattern. 

1. First, nominate an owner for each duplicated query. In most cases, the exact campaign should own a champion term, because it is the entity designed for precision bidding and budget allocation. 
2. Second, apply negative exact in every non-owner entity that matched the query. In certain cases, a negative phrase is appropriate when the family of variants causes frequent collisions, such as brand phrases leaking into non-brand exploration. 
3. Third, confirm that the owner has sufficient budget and appropriate placement modifiers to fully capture the opportunity you have just protected. If the owner frequently exhausts its budget midday, the system will lean back toward discovery tiers simply because they remain able to spend, and your careful negative work will not save you from budget starvation. 

Documentation makes this sustainable. A lightweight ownership sheet that records the nominated owner for each important query, along with the entities where negatives have been applied, creates institutional memory. A simple negatives log that records date, term, match type, location, and reason allows your team to revisit decisions and prevents accidental reintroduction when new campaigns are spun up. These two artifacts turn knowledge into process, so that new hires and rotating contributors uphold the same standards without relying on tribal memory. Examples bring the process to life. Consider a mid-sized brand that sells a niche tennis recovery accessory. For months, the account balanced three steady performers: an Auto campaign for discovery, a Broad campaign for expansion, and an Exact campaign for champions. Performance looked acceptable on the surface, yet TACoS refused to improve despite solid product reviews and competitive pricing. A weekly audit revealed that the phrase “tennis elbow brace” appeared in all three campaigns repeatedly. After the brand nominated Exact as the owner and applied negative exact in Auto and Broad, the blended CPC fell within two weeks, ACoS tightened, and top-of-search share stabilized. With cleaner signals flowing into the exact ad group, the algorithm rewarded the brand with more consistent premium placement, which improved CTR, which further reduced the effective CPC. None of that required a complex restructuring of the account. It required a disciplined assignment and a few targeted negatives. 

A second example centers on product targeting. A brand pursuing aggressive product page presence noticed strong clicks but soft conversion on placements adjacent to a dominant competitor. Investigation showed that many of those sessions originated from the same search term the brand already owned in its exact campaign. In effect, the brand was paying for a top-of-search click through the exact ad, then paying again to appear in the next step of the journey on the competitor’s page. By moderating product page emphasis for that specific ASIN cluster and reinforcing top-of-search for the owned query, the brand reduced cannibalistic overlap and rebalanced the funnel for healthier economics. 

Sustained prevention is a habit of attention. Each week, the audit identifies duplicates; each duplicate receives an owner; each non-owner is blocked with the precise negative necessary; and each owner is resourced to win. Over time, that habit compounds into cleaner data, calmer bidding behavior, and a more predictable growth curve.

Conclusion: Cannibalization Defense Strategy.

Stopping cannibalization does not require dramatic account restructures; it relies on enforcing simple rules with unwavering consistency. To stop internal bidding wars, align your account with these four pillars of control:

• Separate Campaign Roles: Adopt an Alpha/Beta mindset where every match type has a specific job. Discovery tiers (Auto and Broad) exist solely to scout for new terms, while your precision tiers (Exact) exist to own proven winners. Treat discovery as a phase, not a final destination.
• Enforce Query Ownership: This is your primary defense line. When a query proves profitable, promote it to your Exact campaign and immediately trigger a reflex: apply negative exacts across your discovery tiers. For broader protection, use negative phrases to prevent your high-converting brand terms from leaking into non-brand exploration.
• Support with Settings: Use placement modifiers to aggressively secure top-of-search for your exact campaigns while discouraging unnecessary product-page skirmishes. Crucially, allocate enough daily budget to your “owner” campaigns so they do not pause at midday—which would simply invite your discovery tiers back into the auction. Finally, use strict naming conventions so your team always knows which campaign owns which intent.
• Commit to Clarity: Enforce a strict “One Query = One Owner” rule. If two campaigns are splitting traffic on the same term, pick one winner and consolidate. A single campaign accumulating all the conversion data teaches Amazon’s algorithm exactly which ad to trust. This is how you naturally lower your blended CPC, stabilize your top-of-search visibility, and support your organic rankings.

The thread running through this entire approach is clarity. By maintaining strict query ownership and a weekly audit cadence, you build a system that stops paying to fight itself and starts paying to win the market.

Managing search query cannibalization requires rigorous weekly auditing. Stop manually untangling overlapping bids and let an expert Amazon advertising management agency instantly protect your profit margins.

FAQ: Prevent Search Query Cannibalization

The post Fix Amazon Keyword Cannibalization: PPC Optimization Guide appeared first on SellerMetrics.

20 min read By Rick Wong  Updated Jun 06, 2026

Pricing on Amazon feels exactly like walking a tightrope in a windstorm. Make the wrong move and you risk losing the Buy Box entirely, wiping out your profit margin. But when you set up your Amazon product pricing strategy perfectly. You unlock greater visibility, higher click through rates, and actual profits.

This guide is getting right to the core of what actually works when pricing products on Amazon. We are the premier Amazon Seller Agency to help you scale. You will find proven, effective strategies you can implement right now to turn your pricing model into your competitive advantage.

Optimize Your Amazon Pricing Strategy to Stay Competitive

Pricing on Amazon is kind of like walking a tightrope in a windstorm. If you make the wrong move, you’re either losing the Buy Box, losing your margin, or (worse) both. But when you get it right, it can be a total game-changer for your store. More visibility, better click-through rates, and yes, actual profits, not just the illusion of sales.

The catch is it’s never as simple as “just lower the price.” You’re up against real-time algorithm updates, hyperactive competitors who change prices like it’s a sport, and customers who expect two-day shipping and a bargain. Oh, and let’s not forget seasonal spikes and razor-thin margins that make you question your life choices.

There’s a whole menu of pricing moves you can try, dynamic repricing tools, coupons, undercutting (handle with care), value-based pricing, and more. But not every strategy fits every seller or every product. What works for your top seller might completely tank a new listing if you’re not keeping a close eye on costs and conversion.

That’s where this guide comes in. We’re cutting through the noise and getting straight to what actually works when it comes to pricing on Amazon. No fluff. Just real strategies you can actually use.

Inside, you’ll get:

• Practical pricing strategies that make sense and make money
• A closer look at how Amazon’s algorithm treats pricing
• Tools that help you automate without turning your listing into a clearance bin
• Ways to stay competitive and profitable (yep, it’s possible)
• Common pricing traps to avoid like your profit depends on it, because it does

Let’s turn your pricing strategy into one of your biggest strengths, and not the thing that keeps you up at night refreshing your sales dashboard.

How the Amazon Pricing Algorithm Works

Amazon’s algorithm isn’t just some mysterious black box pulling numbers out of thin air. It’s constantly sizing you up based on a bunch of factors that go way beyond just having the lowest price.

The Buy Box Formula
Amazon won’t tell us exactly how it works, but from what we’ve seen, it’s a mix of things: 

• Your price
• How fast you ship
• How well you’re rated
• Whether you’re using FBA
• Whether your product is actually in stock. 

FBA sellers who keep their prices competitive and their metrics clean usually have the upper hand.

Pricing Activity Matters
Regular price reviews help sellers stay competitive in changing markets. If your prices just sit there collecting dust, that can work against you. Amazon will reward competitiveness, availability and customer experience aspects, it signals to Amazon that you’re actively competing, which can give your visibility a nice boost.

Price Parity Warnings
If Amazon’s bots catch your product listed for a lower price on Walmart, Target, or even your own Shopify store, they rarely slap you with a formal account health violation anymore, but the penalty is swift. The algorithm will replace your prime “Add to Cart” button with a generic “Available from these sellers” link. Significant price discrepancies between Amazon and external channels may reduce Featured Offer eligibility under Amazon’s Fair Pricing systems.

The “Lowest Price Wins” Myth
A lot of sellers still think that the Buy Box always goes to the cheapest option. Not true. Price is a big factor, sure but so are trust and reliability. A seller with slightly higher pricing but solid ratings and fast shipping can beat out a rock-bottom listing from someone less reliable.

Amazon’s whole ecosystem is built to reward sellers who adapt. Price is one of the clearest signals you can send that you’re in the game and paying attention, so it pays to treat it like a moving target, not a one-time decision.

Why Pricing Matters on Amazon

Buy Box Eligibility
If your pricing isn’t sharp, you’re probably not going to see that little white box anytime soon. Amazon gives a ton of weight to competitive pricing, especially if you’re using FBA. Even with great metrics, a price that’s even a few bucks too high can kick you right out of the Buy Box rotation.

Sales Velocity
Sure, lower prices can boost your sales volume and rankings. But it’s a slippery slope. Sell too cheap for too long, and suddenly you’re moving units like crazy but barely making a dime. You’ve got to know where your margins are and set guardrails.

Product Visibility
Amazon loves listings that convert. If your price point isn’t pulling in clicks or sales, the algorithm will quietly show you the door, often negatively impacting your Amazon conversion rate by category. And once your listing drops in search, climbing back up takes serious effort or serious ad spend.

Customer Trust and Perceived Value
People read more into pricing than you’d think. If it’s too low, they start wondering what’s wrong with the product. If it’s too high, they’ll scroll right past. Amazon shoppers aren’t clueless; they know what good value looks like, and they’re quick to move on if something feels off.

Amazon vs Other Marketplaces
This isn’t like running your own Shopify store or listing on Walmart. Pricing on Amazon is fast-moving, driven by real-time competition, seller performance, and even your reviews. It’s not a place for set-it-and-forget-it pricing. You’ve got to stay flexible and keep adjusting.

Types of Amazon Pricing Strategies

Different stages of your product’s life and different market conditions call for different moves. Here are the main pricing strategies sellers lean on:

Competitive Pricing
Probably the most common one. You either match the lowest price or go a few cents under to stay in the game. It’s great for grabbing the Buy Box, but if you don’t have a margin limit in place, you could be racing straight into unprofitable territory.

Value-Based Pricing
This one’s less about being the cheapest and more about what your product is worth in the eyes of your customer. Got great branding, clean packaging, and strong reviews? That extra polish can justify a higher price. It’s a smarter play if you’re building a brand, not just moving boxes.

Cost-Plus Pricing
Pretty straightforward – you calculate your total cost (including product, shipping, FBA fees, Amazon fees, all of it), then add your ideal markup. It guarantees you don’t sell at a loss, but on a platform like Amazon, it won’t always keep you competitive unless you stay flexible.

Dynamic Pricing
This is where automation steps in. You set rules, and a repricer updates your price automatically based on what’s happening in the market. Super useful if you’re juggling a lot of SKUs or want to stay competitive without constantly checking your listings.

Promotional Pricing
Coupons, Lightning Deals, or timed discounts – these can spike your traffic and boost conversions, especially around big shopping events or product launches. Just be strategic so you’re not giving away margin without a return.

Mastering Your Amazon Pricing Strategy

Start with a Little (Smart) Spying

Before setting your price, peek at what the competition’s doing. Tools like Keepa, Helium 10, or Jungle Scout can show you how prices have moved over time, who’s running out of stock, and how wild (or stable) the market is.

Also, watch out for MAP policies (Minimum Advertised Price). Some brands don’t like it when you price below a certain number, and Amazon doesn’t like drama.

Know Your Numbers (And The Hidden Profit Killers)

Before you slap a price tag on that ASIN, you need to calculate your fully landed cost and subtract the inevitable operational drain. If you’re just guessing, you’re basically playing with fire. To find your true break-even price, here is exactly what you must factor in:

• Landed Costs: How much you paid to manufacture the product and ship it to the fulfillment center (including ocean or air freight, customs, and administrative duties).
• Amazon Fees: The standard referral fee (usually 8–15%), alongside variable FBA fulfillment fees based on the exact weight and dimensions of your product.
• Ad Spend & Launches: Your projected advertising costs, especially during the heavy-spend launch phase where TACoS (Total Advertising Cost of Sales) eats into margins.
• The Hidden Killers (Returns & Storage): This is where most sellers bleed out. You must factor in your historical return rate and long-term storage fees. If you ignore the cost of processing returns or clearing out dead weight, your projected profits will vanish the moment inventory sits too long or customers send items back.
• Software Overheads: The subscription costs of third-party tools. If your unit margins don’t cover these, you’re paying out of pocket.

Calculating Your True Break Even Point and The Rule of Thirds

Understanding your absolute bottom line is the critical step in your entire Amazon product pricing strategy. Many sellers mistakenly calculate their break even point by simply adding their manufacturing cost to their shipping fees. This oversight quietly destroys profitability. To find your true break even price, you must calculate your fully landed cost. This includes the unit price, ocean or air freight charges, customs duties, insurance premiums, and administrative overhead.

Once you have your landed cost, you must factor in the Amazon referral fee, which typically hovers around 15 percent, alongside variable Fulfillment by Amazon fees based on the exact weight and dimensions of your product. Furthermore, you must account for the inevitable cost of returns and lost inventory. If you ignore your average return rate, your projected profits will vanish the moment customers start sending items back.

Many experienced private-label sellers use a simplified “Rule of Thirds” benchmark during product research. The retail price of your product should be divided into three equal parts. 

• One third of the revenue covers your fully landed costs. 
• The second third covers all Amazon fees including fulfillment and referrals. 
• The final third is your actual gross profit margin. 

If your planned retail price cannot accommodate this three way split, you either need to negotiate better sourcing terms or rethink the product entirely.

Strict Compliance with the Amazon Marketplace Fair Pricing Policy 

Amazon prioritizes customer trust above all else. To enforce this, they deploy the Amazon Marketplace Fair Pricing Policy. Understanding this policy is not optional. If the algorithm detects pricing practices that harm customer trust, Amazon may remove Featured Offer eligibility, suppress listings, or take enforcement action.

To remain compliant, you must never set a misleading reference price or charge excessive shipping fees on merchant fulfilled orders. More importantly, you cannot price your product significantly higher than recent average prices for that exact same item. Amazon constantly monitors external marketplaces. If they discover you are selling your product for a lower price on another website, they will instantly remove your Buy Box eligibility on Amazon. You must maintain price parity across all your sales channels to protect your account health.

Capturing B2B Sales with Amazon Business Pricing 

If you are only pricing your products for individual consumers, you are leaving massive amounts of revenue on the table. Amazon Business caters to commercial buyers, corporate procurement teams, and educational institutions looking to buy in bulk. By creating specific business prices, you can tap into this highly lucrative audience.

You can set up tiered volume discounts within Seller Central that only registered business buyers can see. For example, you might offer a five percent discount when a business purchases ten units, and a twelve percent discount when they purchase fifty units. Commercial buyers are far less sensitive to minor price fluctuations and care deeply about streamlined procurement and tax exempt purchasing. Offering a compelling business price encourages massive order volumes and significantly boosts your overall sales velocity.

Pricing Just Above the Free Shipping Threshold 

Buyer psychology plays a massive role in conversion rates. While Prime members enjoy free shipping by default, millions of non-Prime shoppers must hit a specific minimum order value to qualify for free delivery. You can use this fluctuating threshold to your immediate advantage.  

Identify Amazon’s current free shipping minimum for your target marketplace. If the current threshold in your marketplace is $35 and you currently price a product at $34.99, a non-Prime buyer will be forced to pay a shipping fee or find another item to add to their cart. This creates unwanted friction and often leads to cart abandonment. By simply raising your price by a few cents to cross that exact threshold, the buyer instantly qualifies for free shipping. They perceive the slightly higher price as a massive win because it saves them money on delivery fees, while you effortlessly increase your average order value.  

Leverage Strategic Bundling to Maximize Average Order Value (AOV)

If you are only selling single units, you are letting fulfillment fees quietly eat away at your bottom line. Bundling complementary products or offering multi-packs is one of the most effective strategies to increase your Average Order Value (AOV) while drastically reducing the impact of shipping costs per unit.

Think about the customer journey: if someone is buying a beard trimmer, they are likely going to need beard oil and replacement blades soon. By bundling these items together, you create an offer that delivers massive convenience. You can afford to pass a slight discount onto the buyer because bundling can improve fee efficiency and increase average order value when products are packaged strategically.

Amazon even encourages sellers to study their sales data to identify which products are frequently purchased together, allowing you to create high-converting bundles with slight, enticing discounts. This not only protects your profit margins but makes your listing much harder for competitors to hijack or undercut.

Use Tiered Pricing to Drive Bigger Orders

Want to squeeze more out of your ad spend? Encourage shoppers to buy more. Try small discounts on multi-unit purchases, bundles priced just right, or coupons that kick in on bigger carts. It’s a smart way to lift your AOV without killing your margins.

Give Each ASIN Its Own Job

Not all your products serve the same purpose, so they shouldn’t all be priced the same way. Assign each ASIN a “role” and price accordingly.

1. Hero Products (aka the Moneymakers)
   These are your all-stars, your top-selling, revenue-driving products.
   
   Your Goal: Make money, but don’t price yourself out of the Buy Box.
   Pricing Tip: No need to be the cheapest, just be consistent, keep reviews strong, and support it with smart ads and great content.
   
2. Launch-Phase Products (The Newbies)
   These are new listings that need love (and reviews).
   
   Your Goal: Build momentum, even if that means lower margins at first.
   Pricing Tip: Go a little lower than average, offer coupons, use “.97” pricing, and lower margins or controlled promotional spending to build rank.
   
3. Clearance Products (Dead Weight)
   These are items you need gone – fast.
   
   Your Goal: Sell through before fees start biting.
   Pricing Tip: Go heavy with the discounts, Lightning Deals, or steep coupons. Pair that with aggressive ads to clear the shelves.
   
4. Margin-Makers (Slow but Profitable)
   These don’t sell like hotcakes, but when they do, they pay the bills.
   
   Your Goal: Protect your margins and make your brand look premium.
   Pricing Tip: Avoid auto-repricers unless you’re using value-based rules. Focus on making the product look like it’s worth every penny.

Aligning Your Brand Positioning with Pricing Psychology

Your price tag tells your customers exactly who you are, so your brand vibe, your margin goals, and buyer psychology must work in unison.

First, determine your positioning. If you want to be viewed as a premium brand, you can command higher margins (aiming for 35–50%), but your product photography, copywriting, and review profile must flawlessly back up that premium price. If you are targeting the budget-conscious shopper, your margins will be tighter, meaning you need to rely on perceived value through discounts or bundles.

Once your positioning is set, leverage human psychology to maximize conversions:

• The Power of “.99”: “Charm pricing” works. Pricing an item at $19.99 instead of $20.00 subconsciously feels significantly cheaper to buyers and is an easy win for everyday listings.
• Price Anchoring: Displaying a discounted price alongside a higher reference price can increase perceived value and encourage conversions. However, any reference price or strike-through price should accurately reflect a genuine discount and comply with Amazon’s promotional pricing requirements.
• The Goldilocks Effect (Tiered Options): If you offer variations, create a “good, better, best” pricing tier (e.g., $39, $69, $99). Most shoppers will inherently gravitate toward the middle option, seamlessly lifting your average order value.

How to Optimize Your Amazon Pricing Over Time

The market changes, your goals shift, and customer behavior evolves. To stay profitable (and keep winning the Buy Box), you need to stay flexible and always be fine-tuning your strategy.

Think Seasonally (Because Your Buyers Do)

Certain times of the year = bigger sales opportunities. If you know when those spikes are coming, you can tweak your pricing to match.

Big sale moments to watch:

• Holidays
• Back-to-school
• Prime Day
• Black Friday
• The entire Q4 (hello, Christmas rush)
  

Here’s how smart sellers play it:

• Raise prices slightly when demand peaks, this boosts your profit without killing conversions
• Offer bundles or discounts in slow seasons to keep sales moving
• Use tools like Amazon Business Reports, Sellerboard, or DataHawk to compare performance year-over-year

Test Small, Win Big: A/B Testing Your Prices

One of the biggest pricing mistakes sellers make is changing prices based on gut feeling instead of data. Rather than making large adjustments, test small price changes and measure the impact on profitability, conversion rate, and sales velocity over time.

A practical approach is to compare performance across controlled periods. For example, you might increase your price by 5% for two weeks, then compare key metrics against the previous period while accounting for major variables such as advertising spend, seasonality, and inventory availability.

When evaluating a pricing test, monitor:

• Conversion Rate (CVR)
• Click-Through Rate (CTR)
• Sales Velocity
• Buy Box Percentage
• Total Revenue
• Profit Margin
• ACoS and TACoS

You can also use Amazon Business Reports, Seller Central analytics, and third-party tools to identify trends and compare performance across similar products or time periods.

The goal is not to find the lowest possible price. The goal is to identify the price point that maximizes long-term profitability while keeping your product competitive in the marketplace.

Remember: successful pricing optimization is an ongoing process. Test, measure, learn, and refine your strategy as market conditions change.

Dynamic Adjustments Based on Inventory Velocity

Your pricing strategy should never exist in a vacuum; it must be directly tethered to your inventory levels. Amazon’s algorithm severely penalizes listings that stock out, often requiring weeks of aggressive advertising to regain lost organic rank. If your inventory is running dangerously low and a restock shipment is delayed, your immediate move should be to raise your price. This intentionally slows down your sales velocity, preserving your stock and your organic ranking until the new inventory arrives.  

On the flip side, if your warehouse is overflowing or your product is moving at a sluggish pace, it is time to experiment with discounted pricing. Sitting on stagnant inventory doesn’t just tie up your capital; it actively drains your profits through Amazon’s monthly and long-term storage fees. Combining limited-time deals and discounts with an optimized PPC software can clear out dead weight, injecting your business with the cash flow needed to reinvest in hero products. The golden rule here is adaptability: your price must rise and fall in harmony with your supply chain.  

Mastering Amazon’s Native Pricing Tools

While third-party software is incredibly powerful, you should not ignore the native pricing tools built directly into Seller Central. Here is the breakdown on each tools:

Common Pricing Mistakes That Cost You Sales (and Profit)

Even top sellers slip up sometimes, and when it comes to pricing, the smallest mistake can kill your momentum. Here are some of the most common Amazon FBA mistakes that quietly eat into your profit and performance:

Chasing the Lowest Price Without Running the Numbers

Sure, undercutting competitors might win you the Buy Box, for a while. But if you’re not factoring in Amazon fees, ad spend, and shipping costs, you’re just selling yourself short. Know your true minimum price before slashing numbers.

“Set It and Forget It” Pricing

Amazon doesn’t stand still, and neither should your pricing. Market shifts, new competitors, and algorithm updates can change the game overnight. If you’re not checking in and adjusting regularly, you’re leaving money (and visibility) on the table.

Forgetting About Your Ad Costs

That “profitable” price point might look good on paper… until you add your ACoS and TACoS. Advertising eats into your margins fast. Make sure you’re pricing with your actual ad spend in mind, especially during launches and promo pushes. Not sure what to aim for? Check out What is a good TACoS on Amazon so you can price strategically with real ad costs factored in.

Not Switching Gears After Launch

Early on, it makes sense to price low and build momentum. But once you’ve got reviews and rank, it’s time to start thinking about profit. Too many sellers forget to shift gears and end up stuck in “launch mode” way too long.

Sync Your Pricing with Your Advertising Strategy

Your price and your ads don’t work in silos, they need to move together. If they’re out of sync, you’re either overspending or leaving profit on the table.

How Price Impacts ACoS and TACoS

A low price means your ads need to convert like crazy just to break even. A higher price gives you more breathing room and can actually improve your ACoS. Even a 10% bump in price can make your ad spend a lot more efficient. Learn What is a good ACoS on Amazon here to better understand how your pricing strategy ties directly into your ad performance metrics.

Price Floors Keep Your Ads Profitable

If your product relies on paid traffic, set a price floor. That way, you’re not overspending in the Amazon advertising auction or wasting budget on Negative keywords Amazon PPC. Tools with custom rules can help automate this and protect your margin.

Conclusion: Take Back Control of Your Pricing Strategy

Your Amazon price isn’t just a number on a product page. It’s one of the most powerful levers you can use to influence visibility, conversions, profitability, and long-term growth.

The most successful sellers don’t rely on guesswork or race competitors to the lowest price. They understand their costs, monitor market conditions, test pricing decisions with real data, and adjust their strategy as inventory levels, advertising performance, and customer demand change over time.

Here’s what to remember:

• Pricing plays a major role in Featured Offer eligibility and overall competitiveness.
• Different products may require different pricing strategies depending on their goals and lifecycle stage.
• Understanding your true costs helps protect profit margins and prevent unprofitable sales.
• Strategic tools such as business pricing, bundles, volume discounts, and promotional offers can increase revenue without relying solely on lower prices.
• Regular monitoring and controlled testing help you identify the price point that maximizes both profitability and sales performance.

Amazon’s marketplace is constantly evolving, and pricing is never a set-it-and-forget-it decision. Sellers who continuously evaluate and refine their pricing strategy are often the ones who build sustainable growth while protecting their margins.

If you’re looking for additional ways to improve marketplace performance, check out our guide on increasing sales on Amazon.

Need help building a data-driven pricing strategy that supports profitable growth? Contact us today for Amazon account management services to take control of your pricing strategy and unlock new levels of profitability.

FAQ: All About Amazon Product Pricing

The post Amazon Product Pricing Strategy: Maximize Profit in 2026 appeared first on SellerMetrics.

8 min read By Rick Wong  Updated May 24, 2026

The Podcast Audience Network integration with Amazon DSP lets advertisers run targeted audio ads across premium podcast content using Amazon’s first-party shopper data, all inside the same platform used to manage display and video campaigns. 

Amazon launched this integration in the United States in January 2026, powered by Art19’s dynamic ad insertion technology. Brands can now reach podcast listeners based on real Amazon shopping behavior and track whether those listeners go on to buy on Amazon.

Amazon advertising in 2026 is more expensive and more competitive than ever. Sponsored Products alone can no longer carry your growth, and podcast advertising used to be impossible to measure. That changed in January 2026 when Amazon integrated the Podcast Audience Network into Amazon DSP. 

This guide from SellerMetrics covers how the integration works, how to target and measure it, what it costs, and how to build it into your 2026 advertising strategy.

What Is the Amazon Podcast Audience Network?

The Amazon Podcast Audience Network is Amazon’s curated collection of premium podcast inventory available to advertisers through Amazon DSP. It lets brands place audio ads inside podcast episodes at scale, using Amazon’s first-party shopper data to decide which listeners hear those ads.

The network is powered by Art19, an enterprise podcast hosting and audience intelligence platform that Amazon acquired in 2021. Art19 supports dynamic ad insertion, meaning your ad is stitched into a podcast episode at the exact moment a listener downloads or streams it. 

This is fundamentally different from baked-in ads that every listener hears, regardless of who they are or what they have been shopping for.

Here is what sets audience-level targeting apart from traditional podcast buying:

• Traditional show-level targeting: You pay for ad space on a specific podcast and hope its general audience contains your ideal customer.
• Amazon DSP audience-level targeting: You build an audience using Amazon’s shopper data, and the system finds those specific people across thousands of podcasts, regardless of which show they happen to be listening to.

This shift from broad show buys to precise audience targeting is what makes the Podcast Audience Network so meaningful for Amazon sellers who want measurable results from audio advertising.

How the Podcast Audience Network Integration with Amazon DSP Works

Before this integration, running podcast ads and running Amazon DSP campaigns meant working across separate platforms with no shared data and no unified reporting. The Podcast Audience Network integration with Amazon DSP fixes that by bringing podcast buying into the same platform used for display, video, and streaming TV.

Amazon DSP is Amazon’s programmatic media buying platform. It draws on first-party data from hundreds of millions of Amazon shoppers, including what they search for, browse, add to their cart, and ultimately buy. 

It is important to understand the difference between Amazon ads vs Amazon DSP: standard Sponsored Ads target keywords and appear inside Amazon search results, while DSP targets audiences and places ads across a far wider range of surfaces, including websites, apps, streaming TV, and now podcast audio.

Here is how the integration works in practice:

1. You log into the Amazon DSP console and select the Podcast Audience Network as an inventory source.
2. You build an audience using Amazon’s first-party shopper data, such as in-market signals, purchase history, or lifestyle segments.
3. When a listener who matches your audience plays a podcast episode, Art19’s dynamic ad insertion technology places your audio ad into that specific user’s stream.
4. If the next person downloading the same episode does not match your criteria, they hear a completely different ad.
5. The impression is logged and linked to a pseudonymized Amazon user ID, which feeds into your DSP reporting dashboard and Amazon Marketing Cloud.

This means you pay to reach the exact people you want, across thousands of podcast episodes, instead of paying for an entire show’s audience and hoping some of them are your ideal customer.

Why This Integration Matters for Amazon Sellers in 2026

Podcast listeners are one of the most valuable advertising audiences available to you right now. According to Edison Research’s Infinite Dial report, podcast listeners in the US tend to have higher household incomes, higher levels of education, and they shop online more frequently than the general population. 

They also listen actively, often through headphones during commutes or workouts, which means there is no banner to scroll past and no skip button within easy reach.

For Amazon sellers, here are the four reasons this integration changes what is possible:

1. Rising CPC costs are squeezing your margins. 

Amazon CPC online advertising costs have increased sharply across most product categories. Podcast audio ads are priced on a CPM (cost per thousand impressions) basis and currently offer a more cost-effective route to reaching a qualified audience before they reach the search bar.

2. Attribution is now accurate and trackable. 

Traditional podcast buying relied on promo codes and vanity URLs to measure results. The problem is that most listeners who hear an ad and decide to buy will search directly on Amazon rather than type in a code. 

With Amazon DSP, every audio impression is tied to an Amazon user ID. You can see whether that listener later visited your product page and made a purchase.

3. You reach buyers before they reach the search bar. 

When someone hears about your brand through a podcast and later searches for it on Amazon, that branded search costs less to convert, strengthens your organic keyword ranking, and makes your listing harder for competitors to displace. This downstream effect is known as the halo effect.

4. You can build a connected, multi-format strategy in one platform. 

Pairing podcast audio ads with Amazon sponsored product video ads, display banners, and streaming TV ads inside the same DSP campaign lets you reach buyers at multiple points in their day using a single audience data set and one reporting view.

Amazon DSP Podcast Audience Targeting Capabilities

One of the biggest strengths of the Podcast Audience Network on Amazon DSP is how deep the targeting goes. You are not selecting genres or guessing which shows your customers prefer. You are building audiences using real Amazon shopping behavior, grounded in what people actually do.

The main audience types available to you include:

• In-market audiences: Shoppers actively browsing or researching products in your category on Amazon right now.
• Lifestyle audiences: Groups defined by consistent purchasing patterns, such as regular buyers of premium fitness supplements or frequent shoppers of outdoor equipment.
• Retargeting audiences: Users who viewed your product detail page, added your product to their cart, or previously purchased from your brand.
• Lookalike audiences: People who share behavioral signals with your existing customer base.

Beyond audience type, you can also control these campaign parameters:

• Geographic targeting: Available in the United States only as of January 2026.
• Device targeting: Reach listeners on mobile, Alexa-enabled smart speakers, and connected devices where companion display banners can also appear.
• Frequency capping: Limit how many times the same listener hears your ad within a set period to avoid listener fatigue.
• Dayparting: Schedule your ads for specific times of day, such as weekday mornings when commuter listening peaks.

According to the Interactive Advertising Bureau (IAB), US podcast advertising revenue has grown consistently year over year, and advertiser interest is accelerating as attribution tools continue to improve. Amazon’s integration positions you at the front of that trend.

Audio Ad Creative Requirements and Best Practices

Precise targeting gets your ad to the right person. Strong creativity determines whether they pay attention.

The technical specifications for audio ads on the Podcast Audience Network are:

• Format: MP3 (recommended) or OGG
• Ad length: 15 seconds, 30 seconds, or 60 seconds (30 seconds is the industry standard)
• Bitrate: 128 kbps minimum
• Companion banner size: 300×250 pixels is the most common, appearing in podcast apps that support visual units alongside the audio

Because your ad is pre-recorded rather than host-read, how you write and produce it makes a significant difference. Use these best practices to improve performance:

• Open with a clear, relevant statement in the first three to five seconds. Generic openings lose listeners before the message lands.
• Focus on one specific problem your product solves. Trying to cover multiple features in 30 seconds usually results in nothing landing clearly.
• End with a simple, Amazon-specific call to action. Tell the listener exactly what to type, such as “Search for [Brand Name] on Amazon today.”
• Match the tone to the podcast format. Conversational, calm, creative outperforms aggressive, loud ad reads in podcast environments.
• Use a consistent voice, music bed, or audio element across campaigns to build audio brand recognition over time.

Companion banners give listeners a way to click through to your Amazon product listing without typing anything, so make sure each banner includes your logo, a product image, and a visible call to action.

Costs, Budgets, and the Economics of Programmatic Podcast Advertising

Understanding Amazon advertising cost structures for DSP campaigns helps you set a realistic budget from the start.

Podcast ads on Amazon DSP use a CPM pricing model. Typical CPMs for podcast inventory range from around $15 to $30 or more, depending on how specific your targeting is and what inventory is available. Narrower, more specific audiences tend to cost more per impression but deliver better-qualified listeners.

Here is what you need to know about budget expectations:

• Managed service campaigns through an Amazon Ads partner agency typically require a minimum of around $10,000 per campaign.
• Self-service via ADSP gives you more flexibility, but DSP campaigns are better suited to brands with a meaningful advertising budget already in place.
• DSP podcast ads should complement your Sponsored Products strategy, not replace it. Use audio to fill the top of the funnel while Sponsored Ads close sales at the bottom.

A practical budget approach for new DSP podcast advertisers:

1. Start with a test budget of $5,000 to $10,000 running over four to six weeks.
2. Use this period to gather audience data and identify which targeting segments generate downstream purchase activity.
3. Scale budget on segments showing detail page views, branded search lift, or new-to-brand purchases.
4. Aim to allocate DSP podcast spend at roughly 15 to 25 percent of your total Amazon advertising budget when running brand awareness campaigns.

Measuring Your Results with Amazon DSP and Amazon Marketing Cloud

Measurement has always been the biggest barrier to podcast advertising for e-commerce brands. The Podcast Audience Network integration with Amazon DSP removes that barrier in a meaningful way.

When you run podcast campaigns through Amazon DSP, every audio impression is tracked and linked to a pseudonymized Amazon user ID. That data feeds into your DSP reporting dashboard and into Amazon Marketing Cloud, giving you access to attribution insights that traditional podcast buys could never deliver.

The key metrics to monitor include:

• Reach: Total unique listeners exposed to your ad during the campaign.
• Frequency: Average number of times each listener heard your ad.
• Detail Page Views (DPV): Listeners who visited your Amazon product page after the ad.
• New-to-brand (NTB) purchases: Buyers who had not purchased from your brand before the campaign.
• Branded search lift: Increase in direct brand keyword searches on Amazon during or after your campaign.

Since podcast ads operate at the top of the funnel, do not evaluate them using the same direct-conversion benchmarks as Sponsored Ads. 

Knowing what a good ACoS on Amazon is matters for your Sponsored Ads management, but for full-funnel campaigns that include podcast DSP spend, what is a good TACoS on Amazon is the right metric because it accounts for organic sales growth driven by brand awareness activity. Your Amazon click-through rate on companion banners also indicates how well your creative and targeting are working together.

For deeper cross-channel analysis, Amazon Marketing Cloud (AMC) is the most powerful tool available. Inside AMC, you can map the full customer journey from first audio impression to final purchase, compare conversion rates between listeners and non-listeners, and calculate the true incremental revenue your podcast campaign generated. 

This level of attribution turns audio from a brand awareness exercise into an accountable performance channel.

How to Set Up a Podcast Audience Network Campaign in Amazon DSP

You can access the Podcast Audience Network through the Amazon DSP console as a self-service advertiser or through an approved Amazon Ads partner agency. Here is a step-by-step overview of the setup process:

1. Log in to the Amazon DSP console. If you do not have ADSP access, work through an approved Amazon Ads agency.
2. Create a new Order. This is the campaign-level container. Set your campaign name, flight dates, and overall budget.
3. Add a Line Item. This is where you set your audience, supply source, bid, and creative.
4. Select Podcast Audience Network as your supply source. This restricts your delivery to podcast inventory only.
5. Build your audience. Choose your targeting type and layer behavioral signals to tighten your audience.
6. Set your CPM bid and frequency cap. Decide how much you will pay per 1,000 impressions and how often each listener hears your ad per week.
7. Upload your audio creative and companion banner. Confirm both meet the technical specs before uploading.
8. Configure attribution settings. A 14-day view-through attribution window is the standard for awareness campaigns.
9. Launch and allow the campaign time to deliver. Wait at least seven to fourteen days before reviewing results. Audio campaigns need frequency to build before behavioral shifts appear in your data.

Common Mistakes to Avoid in Amazon DSP Podcast Campaigns

Even with the right setup, certain decisions can reduce your results significantly. These are the most common mistakes and how to avoid them:

• Reviewing performance too early. Podcast advertising shifts behavior over time. Evaluate results after a minimum of eight weeks, not after the first week of delivery.
• Targeting too broadly. A large audience does not mean a better campaign. Use Amazon’s data to keep your targeting relevant to your specific product and customer.
• Ignoring frequency capping. Without a cap, the same listener may hear your ad several times a day, creating irritation rather than brand recall.
• Using weak creative. Flat, generic audio will not perform regardless of how precise your targeting is. Write a focused, conversational script that leads with a clear benefit.
• Skipping Amazon Marketing Cloud. Without AMC, you only see a portion of the picture. The real value of podcast advertising often shows up in cross-channel data, where audio exposure connects to downstream search and purchase conversions.

Conclusion: Build Your Brand Before the Search Bar

The Podcast Audience Network integration with Amazon DSP is one of the most significant advertising developments for US Amazon sellers in recent years. It connects podcast audio’s engagement and reach with Amazon’s first-party shopper data and full attribution capability, giving you a way to reach buyers before they ever open the Amazon app and track whether they go on to buy.

CPMs for podcast inventory are currently more affordable than search and video placements. Attribution that once made audio advertising impossible to justify is now fully trackable. And the halo effect of brand awareness campaigns on branded search and organic rankings means the value extends far beyond direct clicks.

This is not a replacement for Sponsored Products. It fills the top of the funnel, warms up your audience, and makes your lower-funnel ads more efficient over time. If you want expert help building and running a full-funnel strategy that includes DSP podcast advertising, explore Amazon Advertising Management with SellerMetrics and start growing beyond the search results page.

FAQ: Podcast Audience Network integration

The post Podcast Audience Network Integration with Amazon DSP: What Amazon Sellers Need to Know in 2026 appeared first on SellerMetrics.

8 min read By Rick Wong  Updated May 24, 2026

Amazon Creative Agent is a free, AI-powered tool built inside Amazon’s Creative Studio. It works through a simple chat interface and guides you through the full ad creation process, from researching your brand and product to delivering finished video and display ads.  

The tool draws on Amazon’s first-party retail data and requires no design skills to use. It is available to advertisers in the US, UK, Canada, Germany, France, Italy, Spain, and China via the Amazon Ads console. 

For most small and mid-sized brands, running professional Amazon ads has always meant spending tens of thousands of dollars on agencies, video shoots, and lifestyle photography. Amazon Creative Agent removes that barrier entirely by putting a full creative workflow inside your Amazon Ads console at no extra cost.  

This guide covers what the tool does, how to use it step by step, what results you can expect, and how it fits into a complete advertising strategy, with help from the team at SellerMetrics. 

What Is Amazon Creative Agent?

Amazon Creative Agent is an agentic AI tool embedded inside Amazon Creative Studio, which you access directly through your Amazon Ads console. Unlike a standard image generator or template builder, it acts as a full creative partner.  

You give it your product information and campaign goals, and it handles research, concept development, scriptwriting, image generation, and final ad production, all inside a single chat interface. 

What makes this different from Amazon’s earlier tools is the depth of the workflow it manages. Tools like Image Generator produce one output when you provide one input.  

Creative Agent runs the entire creative process. It reviews your product detail page, analyzes your brand, considers audience data, and then presents you with a range of ad concepts, complete with taglines and creative rationale, before you choose a direction. 

Amazon built Creative Agent on AWS technology using a combination of Amazon Nova foundation models and Anthropic Claude. These models work together to give the tool strong research and reasoning capabilities alongside high-quality visual and text generation.  

Because the tool connects to Amazon’s own first-party retail data, its creative decisions are grounded in real shopper behavior, not generic patterns from a public training set. 

As confirmed by the Amazon Ads official announcement, Creative Agent is available at no additional cost to advertisers with an active Amazon Ads account. 

Creative Agent vs. the Broader Amazon Creative Studio Ecosystem

Before diving deeper into Creative Agent specifically, it helps to understand where it sits within Amazon’s wider suite of AI creative tools. Here is how each tool breaks down: 

The key distinction is that Image Generator, Video Generator, and Audio Generator are standalone asset tools. Creative Agent is the agentic layer on top of all of them. It can call on any of those tools during the creative process, orchestrate the full workflow, and deliver a complete campaign from a single conversation.  

Amazon launched Creative Studio in October 2024 as the centralized home for all of these capabilities, and Creative Agent became the most advanced feature within that system. 

How Amazon Creative Agent Works: A Step-by-Step Breakdown

Using Creative Agent follows a clear and repeatable process. Here is how each stage works from start to finish: 

Step 1: Open a Chat in Creative Studio 

Log in to your Amazon Ads console, navigate to Creative Studio, and click the “Chat” button. The conversational interface opens immediately. You do not need any design training or technical skills to get started. 

Step 2: Share Your Brand and Product Information 

Creative Agent asks you to provide your product detail page URLs, brand guidelines, target audiences, and any existing brand materials such as previous ads or logo files. It also pulls information directly from your Amazon listings, Brand Store, and website on its own. 

Step 3: Research and Concept Development 

Using Amazon’s retail and shopper signals, the tool researches your product’s key features and competitive landscape. It then presents several ad concepts, each with a proposed tagline and an explanation of the creative logic behind it. You can select the one that fits your goals, request adjustments to a concept, or ask the tool to start fresh with entirely new options. 

Step 4: Storyboard Creation and Refinement 

Once you choose a concept, Creative Agent builds a scene-by-scene storyboard with scripts and visuals. You can give specific feedback at any point through the chat. If you want a different setting, a shorter tagline, or a tone adjustment, you tell the tool, and it revises immediately. 

Step 5: Final Ad Production 

Creative Agent generates your finished assets. Depending on your campaign goals, these can include multi-scene video, display ads, animations, voiceovers, and music. Every asset requires your explicit approval before it goes anywhere near a live campaign. 

One important feature that sets Creative Agent apart is that it explains its reasoning at each stage. It shows you why it chose a particular creative direction, which tools it used, and how it plans to execute each step. This transparency makes it far easier to give focused feedback and stay in control throughout the process. 

What Ad Formats Does Amazon Creative Agent Support?

Creative Agent produces ready-to-deploy assets across a wide range of Amazon advertising placements. Here is the full list of formats it supports: 

• Sponsored Brands: video and banner creative for Amazon search results pages 

• Sponsored Brands Video: in-feed video ads displayed on Amazon search pages 

• Sponsored Display: display ads that appear across Amazon and on third-party websites 

• Amazon DSP: programmatic display and online video for both Amazon-owned and external placements 

• Streaming TV (STV): full-screen video ads on Prime Video and Fire TV 

• Brand Stores: creative assets to refresh and update your storefront pages 

This range of formats means you can use Creative Agent to build a full-funnel campaign, from awareness-stage streaming TV ads through to lower-funnel retargeting on Sponsored Display.  

According to Amazon’s unBoxed 2025 update, streaming TV support was added to Creative Agent as part of the tool’s expansion, making it one of the most complete AI creative tools available on any advertising platform.  

If you have been wanting to explore Amazon sponsored product video ads but found the production cost too high, Creative Agent makes it a realistic option for brands of almost any size. 

Amazon Creative Agent Performance: What the Numbers Show

The performance case for using AI-powered creative on Amazon is backed by solid data from Amazon’s own research. Here are the key figures: 

• Advertisers using AI-generated images on Sponsored Brands campaigns see an average +10.3% improvement in ROAS compared to campaigns without AI-generated images, according to Amazon’s internal US data from April to June 2025. 

• Brands using Amazon’s AI creative tools advertised five times more products and used twice as many images per advertised product on average, compared to brands that did not use these tools. 

• Advertisers who adopted the Sponsored Brands Image Generator saw an average 10% increase in sales per advertiser within one month of adoption, per Amazon Ads data. 

• As reported by AdExchanger at the Creative Agent launch, Amazon Ads VP Jay Richman stated that advertisers using AI creative tools see an average 12% increase in sales. 

Your Amazon Click Through Rate is one of the most direct performance signals Amazon’s algorithm uses to measure ad relevance. When CTR improves, Amazon rewards your ads with better placements and, over time, this can also lower your cost per click because Amazon’s ad auction favors high-performing ads.  

The downstream impact on your Amazon conversion rate by category is just as significant. Lifestyle imagery and video creative do not just attract more clicks; they also help shoppers feel confident about what they are buying, which translates directly into better conversion. 

Amazon Creative Agent vs. Competing AI Ad Tools

Amazon is not the only platform offering AI-powered creative tools. Meta and Google both have their own versions. Here is how they compare for sellers focused on the Amazon marketplace: 

As Marketing Dive reported at the time of the Creative Agent launch, Amazon positioned the tool specifically as a democratizing solution for small and mid-market brands that could not previously afford professional ad production.  

Creative Agent’s standout advantage over Meta Advantage+ and Google Performance Max is its access to Amazon’s proprietary purchase data from over 300 million active customers. No external tool can replicate this.  

While Meta and Google optimize creative for their own ecosystems, Creative Agent is built from the ground up to drive performance within the Amazon marketplace. 

Who Should Use Amazon Creative Agent?

Creative Agent is designed to be accessible to all types of sellers, but some will benefit from it more immediately than others. Here are the groups that stand to gain the most: 

• Small and mid-sized brands without the budget for professional creative agencies or dedicated video production 

• Sellers scaling into new ad formats such as Sponsored Brands Video, DSP, or Streaming TV for the first time 

• Brands managing ad fatigue that need to rotate creative regularly to keep CTR strong 

• Sellers launching new products who need a fast, polished campaign without a long production timeline 

• Agencies and brand managers who want to prototype and test multiple campaign concepts quickly 

• Vendors and KDP authors looking to expand their presence across Amazon’s media network 

If you are already working on your Amazon Listing Optimization Services, Creative Agent pairs directly with that investment. The tool pulls content from your product detail page, so a well-written and fully optimized listing gives it stronger material to work from.  

Connecting strong creatives to solid Amazon SEO Services also creates a compounding effect: better ads drive more engagement signals, which can reinforce your organic ranking over time. 

How to Get the Best Results from Amazon Creative Agent

Creative Agent produces better output when you give it a strong foundation to work from. Here are the most effective practices to follow: 

1. Optimize Your Listing Before You Start 

Creative Agent draws from your product detail page. A listing with clear, keyword-rich copy, strong images, and A+ Content gives the tool much stronger source material. Investing in Amazon Listing Optimization Services before you use Creative Agent directly improves the quality of what it generates. 

2. Upload Your Brand Kit 

Use the Brand Assets section inside Creative Studio to upload your logo, hex color codes, and brand fonts. The tool will prioritize these elements when building your assets, which keeps your visual identity consistent across campaigns. 

3. Write Specific, Detailed Prompts 

Vague input produces generic output. Instead of saying “make it look premium,” try “use a warm kitchen setting with natural morning light, product centered on a marble countertop, and a calm, confident tone.” The more specific your direction, the more on-brand the result will be. 

4. Generate Multiple Concepts and A/B Test Them 

Creative Agent offers several concept options per session. Use Amazon’s Manage Your Experiments tool to run A/B tests across two or three concepts. Run tests for at least four to six weeks to collect results that are statistically reliable. 

5. Refresh Creative Every Four to Six Weeks 

Ad fatigue causes performance to decline on even the strongest campaigns. Creative Agent’s speed gives you a real advantage here. Use it to rotate fresh creative regularly on your highest-spend campaigns. 

6. Review Every Asset Before Launch 

Creative Agent produces strong output, but you should manually check every asset for accuracy, brand consistency, and compliance with Amazon’s advertising policies before any ad goes live. You remain the advertiser of record regardless of how the creative was produced. 

What an Amazon Creative Agent Cannot Do

Understanding the limitations of Creative Agent helps you use it with the right expectations. Here is what it does not handle: 

• Bid and budget management. Creative Agent is a creative production tool only. Campaign structure, keyword targeting, and bid strategy all still require human decision-making or a dedicated optimization tool. 

• Complete global availability is yet to come. While Creative Agent is now live in eight markets, including the US, UK, Canada, Germany, France, Italy, Spain, and China, it is not yet available in all Amazon advertising markets worldwide. 

• Guaranteed brand accuracy without review. The AI can occasionally misinterpret subtle brand guidelines, particularly around tone, specific visual style, or niche product details. Human review before launch remains essential. 

• Campaign strategy and keyword research. Creative Agent cannot evaluate your keyword mix, assess your competitive positioning, or advise on budget allocation across your funnel. That still requires specialist knowledge. 

• Error-free output every time. As Marketing Dive noted, AI-generated visuals can occasionally include inaccuracies or inconsistencies. Amazon has committed to improving the tool continuously, but your review step is non-negotiable. 

How Amazon Creative Agent Fits into Your Broader Amazon Strategy

Creative Agent works best as one part of a complete advertising system, not as a standalone fix. Great creative improves your CTR and conversion signals, but those gains only compound when the rest of your advertising strategy is sound. 

You need strong keyword targeting to make sure the right shoppers see your ads. You need well-structured campaigns to allocate budget efficiently. And you need a listing that converts the traffic your ads send.  

If you are using ChatGPT Amazon listing optimization to sharpen your product copy or building visibility through Amazon Rufus optimization strategies to align your listings with AI-driven search behavior, Creative Agent connects naturally to those efforts as the creative production layer. 

Effective Amazon Advertising Management is what links great creative to consistent sales growth. Think of Creative Agent as solving the production problem. Bid management, search term strategy, negative keyword management, and performance analysis form the strategic layer that determines whether your creative actually converts into revenue. 

As Digiday noted in its analysis of Amazon’s AI-driven ad system, agencies and specialist partners are not being displaced by these tools. They are shifting focus from production execution to strategy and counsel. The sellers getting the strongest results are those who combine AI-speed creative with expert-level campaign management behind it. 

Conclusion: Pair Creative Agent with a Smarter Amazon Strategy

Amazon Creative Agent removes one of the most persistent barriers that smaller brands have faced on Amazon: the cost and complexity of professional ad creative. It gives you the ability to produce video, display, and audio ads that once required an agency or a production studio, at no additional cost to you. 

But when every seller gains access to the same tools, the advantage goes to those who use them most strategically. That means optimizing your listings first, testing multiple creative concepts, reviewing every asset before it goes live, and building the right campaign architecture to support what the creative does. 

If you want to combine the speed of Amazon’s AI creative tools with expert campaign management that drives real growth, SellerMetrics is here to help. Our team works with Amazon brands to build advertising strategies that turn strong creative into profitable sales.  

Book a free strategy session today and discover how a data-driven approach to Amazon advertising can move the needle for your business.

FAQ: Amazon Creative Agent for SMB Ad Creative

The post Amazon Creative Agent: The Complete Guide for Amazon Advertisers appeared first on SellerMetrics.

8 min read By Rick Wong  Updated May 26, 2026

Amazon PPC advertising has come a long way over the years. It was at one point only available on Amazon.com, and there was only Sponsored Product available. Ahh, the good ol’ days!

Fast forward today, we have 14 different marketplaces where Amazon PPC is supported and three different Amazon PPC Ad types (Sponsored Product/Sponsored Brands/Sponsored Display) plus Amazon DSP. With much slew of new features and marketplace using Amazon PPC, sellers are always looking for Amazon PPC software that can save them both time and money. Therefore, it is important that Amazon sellers find the right software for them to scale their business without scaling costs.

What is the right Amazon PPC software for one situation might not be right for the other, so to examine the best Amazon PPC software we will get into the details and evaluate each software based on the following criteria and features:

Reviewed Features

Reporting and Analytics

Report on the performance of your Amazon PPC advertising by Campaign, Adgroup, and Targets.

Bid Automation

Able to automatically adjust bids for keywords and PAT based on a user define ACoS or RoAS.

Search Term and Negative Search Term Harvesting

Can trigger a list of search terms or ASINs based on user-defined rules. To be added as a new target or a negative to the Amazon advertising account campaign/ad group either automatically or pushed by the user via the software.

Manual Bulk Optimization

Able to make manual bulk changes to either the bid or status in an efficient way.

Dayparting

A function that can turn off and on a campaign based on user-define time of day and day of the week.

Supported Marketplaces

Able to support the numerous marketplaces that are available on the Amazon advertising platform.

Supported Ad Types (SP/SBV/SB/SD)

Does the automation or manual optimization functions support all of the Amazon ad types and their corresponding target types?

Ease of Use

Initial UI/UX experience as well potential learning curve of onboarding the Amazon PPC Software.

Price

What is the pricing structure? Does it charge by % of ad spend or on a per account/marketplace basis? Is the structure transparent?

Hero Feature

Features and functions that are unique and innovative in the Amazon PPC software market.

A note on AI Powered Amazon PPC Software

Before we begin the reviews, I do want to address the use of AI/ML in the Amazon PPC space.

I do have a bit of a background in data analytics. This experience does give me some insight into the AI/Machine Learning field. I probably don’t have the ability to build a model from scratch, though most use AWS Sagewaver and Google Tensorflow anyways, I do know enough about the limitations of these models.

I want to write this section because it seems like AI/Machine Learning feature is all the rage. With some Amazon PPC software marketing this heavily as their key features. The basic premise of all AI/ML models is garbage in and garbage out, this phrase basically means that the predictive ability of the AI/ML models is hugely dependent on the data is coming in.

It is also not only the quality of the data but also the quantity of the data. AI/ML models require large amounts of data to find patterns that humans cannot find. Thus, low quantity of data is a fundamental issue as to why AI-Powered Amazon PPC software is not as effective as they appear if you are a new client with any Amazon PPC software, the vendor is limited to see only 60 days of Amazon advertising data from your account, and that is not even nearly enough data.

So how much data is enough?

So how much data is enough? Well, there is never enough data, but if I were to say at what point AI/ML models can start making an accurate suggestion for Amazon PPC I would say it would be a year worth of data. This would give the model a full set of seasonal patterns for your product. Basically, you would have to be a client of this software for 12 months before being confident in their Amazon PPC software’s bid suggestions.

I do hear the counterargument that these AI/ML Amazon PPC software companies can use loads of data from their existing clients. This is a flawed argument because of the lack of conformal data, as each client is selling products that have a different price range and patterns. So using existing data as a predictive reference to a new client with a different product is extremely low. This is why AI/ML applied well to situations with data conformity and large data sets, an example I could think of is fraud detection.

Also need to mention, that I have found some Amazon PPC software tools that have marketed themselves as AI/ML learning. But upon closer inspection, I am quite certain they are not. Not going to name them, but buyer beware.

How to Choose the Right Amazon PPC Software for Your Business Stage

There are so many Amazon PPC tools out there and different ones will be ideal given the size of your FBA business, the number of products you sell, your niche and so in. Not every Amazon seller needs an enterprise-level solution and a basic tool may be insufficient for large 7-figure sellers.

The biggest mistake we see sellers make is overpaying for complex features they won’t use, or conversely, trying to manage a million-dollar ad spend with a basic tool. Here is how to match the software to your business stage:

Amazon PPC Tools for “Solopreneur” / Businesses in the Launch Phase (Under $5k/mo Ad Spend)

At this stage, your data is limited, and aggressive AI automation can actually hurt your campaigns by making decisions based on statistical noise.

• What you need: Simple rule-based automation (e.g., “If ACOS > 30%, lower bid by 10%”), accurate profit analytics, and a clear dashboard to replace messy spreadsheets.
• What to avoid: Expensive AI “black box” tools that require thousands of data points to function correctly.

Amazon PPC Tools for Businesses in the Growth Stage ($5k – $50k/mo Ad Spend)

You have established best sellers and reliable conversion data. Manual optimization is now a bottleneck preventing you from launching new products.

• What you need: Bulk optimization capabilities, dayparting (hourly bidding), and “Harvesting” features that automatically move converting search terms from Auto to Manual campaigns.
• Focus: Look for tools that offer “Dayparting” and “Keyword Harvesting” automation to scale efficiency without scaling your workload.

Enterprise-Level Amazon PPC Tools for Sizable Sellers and Brands ($50k+/mo Ad Spend)

You are likely running Sponsored Brand Video, Sponsored Display, and DSP. You need to look beyond simple ACOS and focus on Total ACOS (TACOS) and new-to-brand metrics.

• What you need: Amazon Marketing Cloud (AMC) integration, cross-channel attribution (measuring external traffic), and predictive inventory management.
• Focus: Tools that support “Share of Voice” tracking and programmatic ad types.

Adbrew

Adbrew is an award-winning Amazon and Walmart advertising platform built for brands, agencies, sellers, and aggregators looking to scale profitably through automation, analytics, AI-driven optimization, and retail media intelligence.

Trusted by more than 1,000 global brands and agencies managing over $100M in ad spend, Adbrew combines advanced reporting, campaign automation, Amazon Marketing Cloud, DSP management, Share of Voice tracking, and AI-powered workflows into a single platform.

Reporting, Analytics, and Adbrew Intelligence

Adbrew offers highly customizable reporting dashboards designed to give advertisers complete visibility into both advertising and organic performance.

Beyond standard KPIs, the platform provides advanced analytics including branded vs. non-branded sales splits, campaign type performance, targeting-type analysis, placement-level insights, keyword trends, product-level reporting, and performance movement tracking. This helps teams quickly identify growth opportunities, inefficiencies, and account-level changes.

The platform also includes Adbrew Intelligence, an AI-powered analysis layer that helps advertisers deeply analyze account performance, identify root causes behind performance fluctuations, surface optimization opportunities, and take relevant actions directly within the platform.

Dashboards can be fully customized based on reporting requirements and shared internally or externally using white-label reporting capabilities, including custom logos and brand colors.

The dashboard is fully customizable, allowing you to create widgets based on your specific needs. Additionally, it includes white-labeling features, enabling you to add your logo and brand colors, and share the dashboard with your team and clients for quick reporting.

Automation

Automation and AI Optimization

Adbrew combines AI-driven optimization with one of the industry’s most advanced rules-based automation engines.

Recognizing that different campaigns require different optimization approaches, the platform gives advertisers the flexibility to either create granular automation rules or leverage AI-driven optimization workflows.

Brands and agencies can automate a wide range of operational tasks including:

• Bid optimization
• Search term harvesting
• Negative keyword management
• Placement optimization
• Budget allocation
• Bidding strategy adjustments
• Campaign scaling workflows
• Performance alerts and monitoring

This significantly reduces manual optimization effort while still maintaining advertiser control and transparency.

Dayparting and Amazon Marketing Stream

Adbrew provides a detailed view of your hourly ad performance, helping you identify when shoppers are most likely to buy from your brand, when conversion rates are highest, and which hours have high ACoS.

Using this data, Adbrew intelligence  can automatically generate dayparting strategies based on brand goals, enabling adjustments to bids, budgets, and placements based on the time of day and day of the week.

Amazon DSP and Amazon Marketing Cloud Support

Adbrew is one of the few platforms that offer Amazon Marketing Cloud support for all Sponsored and DSP advertisers, helping them generate actionable reports without writing a single line of code. It enables users to create custom audiences using pre-made audience templates and leverage those audiences in Sponsored and DSP ad campaigns to refine targeting.

Adbrew also enables brands and agencies to manage Amazon DSP campaigns with its state-of-the-art campaign launcher, helping them engage shoppers at different stages of the funnel: awareness, consideration, and conversion.

Additionally, advertisers can use automation and bulk management features to streamline DSP campaigns, including managing line items, flights, budgets, and creatives.

Share of Voice and Ranking Based Automation

Most brands optimize their campaigns in isolation, without understanding how they compare to competitors in their category. Adbrew’s Share of Voice tool helps track your brand’s share and rank for different keywords with hourly granularity.

You can see where your products appear on Amazon at various times and the percentage of share your competitors are capturing.

Using this data, you can set up Share of Voice automation to optimize your campaigns based on search rank. For example:

• Get Top of Search Visibility: Increase bids until your products appear at the top of search results for a keyword.
• Stop Organic Sales Cannabilization: If your ads already rank at the top of search and your organic ranking is also high, you can automatically reduce bids until your ads appear below organic results to prevent organic sales cannibalization.

Bulk Campaign Management and Campaign Launching

Adbrew includes dedicated management interfaces for campaigns, targets, search terms, products, placements, bidding strategies, and negative targeting.

These interfaces allow advertisers to quickly filter, analyze, and apply bulk changes across large account structures.

One of Adbrew’s standout capabilities is its advanced campaign launcher, which enables advertisers to create large-scale campaign structures within minutes using customizable templates and automation workflows.

The launcher also leverages AI capabilities to automatically research and add relevant keywords during campaign creation.

Supported Marketplaces

Adbrew supports over 15 marketplaces, including the US, CA, UK, DE, FR, ES, IT, NL, BR, AE, SG, AU, JP, IN, SA, and TR.

Price

Adbrew’s subscription plan starts at $799/month or 2% of ad spend, whichever is higher. Given that it exceeds industry-standard features by supporting Amazon Marketing Cloud, Amazon DSP advertising, and multiple marketplaces, its pricing is well-justified for mid-to-high-tier brands and agencies.

SellerMetrics Review

Reporting and Analytics

SellerMetrics have a high-level dashboard and analytics/reporting features that allow you to view your performance on both a high and the most granular level.

In the analytics function, you can select your reporting by:

• Campaign
• Ad Group
• Keywords
• Product (PAT)
• Search Terms
• ASINs

There is also an ASIN reporting, that allows you to see your metrics by unique ASINs. Basically, the same data in the advertised product report you see in your Amazon ads report. But what is unique about this is that you can see all the related campaigns that are advertising for this ASIN. This is the perfect report if you want to avoid duplicating campaigns that is targeting the same ASIN

It is also really worth noting that the reporting unifies all your metrics and data across accounts.

For example, if you are an Amazon seller that offers your product in multiple marketplaces, their Keyword analytics report can show all the keyword and their corresponding metrics on all related marketplace in your Amazon login under one screen.

This unified account structure in SellerMetrics can really save time, as you can do all your optimization workflow in one single session.

Another big reporting capability with SellerMetrics is Period over Period reporting. This reporting allows the user to analyze the variance of metrics from one period to another such as:

• Impression
• Clicks
• Order
• Spend
• Sales
• CVR%
• ACoS%
• CTR%

The user can analyze the variance on the Campaign/Adgroup/Keywords/Search Term levels as well.

This report allows me to filter for a list of keywords that attributed to fewer orders from one period to the next, allowing me to take action on them before any drastic declines in order.

In the above example, I am looking for keywords that attributed to a decline of 5 or more orders from the current (May) period vs previous (April) period.

Bid Automation

The bid automation feature allows the seller to set the ACoS goal, click threshold, and strategy on a campaign level. Bid automation is very easy to set up on their “Automation Settings” page.

On each of the campaigns, you can also set 3 different bidding strategies, they are Optimize, Moderate, and Growth. The bid automation engine uses the last 57 days’ lookback data to make their bidding decision since there could be 3 days of lagging attribution for sales and order.

Overall the bid automation is very easy to set up and the ability to set your bidding strategy according to your Amazon PPC campaign life cycle is a huge plus as well.

Search Term and Negative Search Term Harvesting

SellerMetrics allows the user to set up Search Term harvesting and Negative Keyword rules. Once these rules are set up, and if these rules are triggered you can move them into an existing ad group or its negative keyword list.

To set up a Search Term rule you will click on “+” sign under the Search Term rules column.

In the dialog, the source campaign is where the search term will be gathered from, while the destination campaign is where the search term that has triggered will be added to. The search term rule also allows you to add the corresponding search term as a negative (exact or phrase) into the source campaign

The “Automate” toggle allows the user a choice to decide if they want the software to move the search term automatically, or the user wants to take the action themselves.

After setting up the rule, you can see all the non-automatic search term that is triggered in their “Converted Search Terms” screen.

To set up a Negative Keyword rule you will click on “+” sign under the Negative Rules column.

The source campaign is where the search term will be gathered from, while the destination campaign is where the search term that has triggered the rule will be added to the negative list.

Basically, the same concept applies to the negative keyword creation,

The search term and negative rule feature is a very strong feature of SellerMetrics that allows the user to easily set up an automation that can negate poor-performing keywords and add strong-performing ones.

Manual Bulk Optimization

You can bulk update the bids on all keywords and PAT using their Manual Optimization Keywords screen. Again the beauty of this software is that you can make changes and optimize bids across all your Amazon PPC account in a single session.

Bids on keywords can be changed by the following:

• Increase/Decrease % from CPC
• Increase/Decrease % from Current Bid
• Increase/Decrease amount from CPC
• Increase/Decrease amount from Current Bid
• Change to an exact amount

Although you can change the bid pretty comprehensively, the downside to this software is probably their lack of ability to bulk change other attributes such as campaign state, adding keywords into an existing campaign or bulk create a new campaign with into the system.

Dayparting

SellerMetrics has an easy-to-use dayparting feature that allows you to set when campaigns are enabled and paused based on the specific date/time you set. This feature works for all Amazon Ad Types (SB/SP/SD/SBV).

Supported Marketplaces

SellerMetrics can support all countries where Amazon had enabled their advertising platform. This list of marketplaces includes:

Supported Ad Types (SP/SBV/SB/SD)

Ease of Use

SellerMetrics have a very clean UI, and quite initiative to onboarding to the software pretty quickly

Price

SellerMetrics is tier-based pricing based on monthly advertising spend.

Each pricing tier supports at least 3 Amazon Accounts. SellerMetrics calculate the subscription based on the aggregate ad spent between all your ad accounts, so if you are currently selling on the US marketplace and want to sell in Canada, there will be no extra cost for SellerMetrics to support your Amazon Canada PPC account.

This pricing model makes SellerMetrics very cost-effective especially if you want to start selling to other Amazon marketplaces

Also comes with a 14 days trial. Can see more details on the pricing here.

Yeah, so ok I have reviewed my own software, might be a bit biased. But I stand by it. If you have read this review, I’ll be happy to give the reader a lifetime discount, just reach out to me on the widget on the button right and mention this article

Hero Features

Automate your Ranking using Amazon PPC

In 2021 Amazon has officially closed the grey area of using Search Find Buy (SFB) and rebates to rank your product on the A9 search results. As such the only game in town to legitimately rank on Amazon is by using Amazon PPC Advertising.

Yeah, I know it sucks to even put more dollars into advertising, but certainly better than risking your Amazon account to rank using off TOS methods.

Enter the new feature from SellerMetrics called the Sponsored Ranker, what this new feature will do is 1) scrape the sponsored keyword results on the Amazon marketplace >> 2) find your ASIN’s Sponsored Product ranking >>3) then proceed to make bid changes until you get your ASINs desired sponsored rank.

Check out how it works in the video below

In other words, this feature will rank keywords for you automatically via Amazon PPC. It is the ultimate Amazon PPC growth feature.

Sponsored Ranker works for CA/US/UK/EU/JP/AUS/IN/BR marketplace

By using our SP Ranker ranking campaign you can ensure you are constantly on the top of search results and thus will indirectly rank organically for your targeted keywords.

We also keep track of your SP Rank in relation to your organic rank in the SP Ranker campaign over time. How cool is that!

One-Click Campaign Setup

Another hero features of SellerMetrics. Create a set of structured Sponsored Product campaigns with only one click of a button. This new feature allows you to set a few high levels settings such as ACoS target, budget, keywords, and product to advertise. The system will then create the following set of campaigns

• SP Automatic Campaign
• SP Manual Keywords Campaigns
• SP Manual Keywords Campaigns – Exact match
• SP Manual PAT Campaigns

Additionally, the bid automation and search term harvesting rules are automatically set up between them so you do not need to spend the extra time to create the rules for the campaigns created with this feature.

Sellozo Review

Reporting and Analytics

Dashboard with all the campaign/account metrics. I do feel the UI for the dashboard could be better, some fonts are just hard to read. I would have to say it is really detailed, you can connect with your MWS and see the FBA fee incurred on an account level so you see your net profit.

Bid Automation

In each of the campaigns under the PPC Manager tab, you can set the bid automation. There is the bid strategy and target ACoS. According to their sales rep and website, their bid engine uses AI. They said that their AI makes the bidding algorithm smarter.

Search Term and Negative Search Term Harvesting

This feature is where Sellozo shines, using their Campaign Studio you can create linkage between campaigns and their ad group in a visual way via dragging and dropping.

Manual Bulk Optimization

In Sellozo you can make bulk adjustments in their PPC Manager tabs. On the targeting tab, you make bulk adjustments in two ways, first, you can bulk change a static then press “push” to copy over your bid to other keywords and targets.

The second method is to make bulk relative bid changes. Overall, Sellozo is very strong in terms of manual bulk bid optimization.

Dayparting

Another nice touch is the dayparting function, where you can turn off and on campaigns between periods of time. Beneficial if you know the optimal time that gives you the most sales.

Supported Marketplaces

Sellozo only seem to support NA and EU Amazon accounts the marketplaces includes:

Supported Ad Types (SP/SBV/SB/SD)

Ease of Use

Not the most intuitive UI experience, takes a while to figure things out. There do have a lot of data on the screen though, the placement and the whole user journey could be better.

Price

They have a different pricing model compare to the market, the product is priced by the # of SKU on your Amazon Seller Central account. So Amazon MWS connection is a requirement.

The per SKU price plan might present a good value for a mid-size Amazon seller that spends more than $10,000 a month on Amazon ads.

If Amazon PPC software uses a % of ad spend pricing model and takes about 2% of ad spend, so if you spend $10,000 then you are looking at $200/month at least.

The 2nd Sellozo pricing plan of $299 a month for 11-1000 SKUs allows for Amazon sellers a lot of room for growth.

The pricing plan is pretty scalable, although it’s only for 1 account, so if you want to use Sellozo on your other marketplaces you would have to pay double (subscription x # of marketplace)

Hero Feature

The campaign studio is a very innovative approach to the Search Term and Negative harvest. I’ve seen a lot of software struggle to come up with an intuitive workflow for the Search Term harvesting function, but I feel the campaign studio is feature is a winner.

PPC Entourage Review

Reporting and Analytics

Their initial high-level analytics and dashboard look alright, above average in terms of information and UI. I do find it too high-level though and cannot easily see my metrics on an ad group and keyword level that easily.

Bid Automation

I really do think that PPC Entourage is made for the seller in mind, it’s all in the details, I think this is especially the case in their bid automation feature. You can go into their ‘Auto-Pilot Manager’ tab and set specific bid automation (Target ACOS) to be either applied either on the campaign or the ad group level.

Search Term and Negative Search Term Harvesting

To continue on the last screenshot, you can set up your Search Term and Negative Search Term Harvesting on the ‘Auto Pilot Manager’ tab as well. The search term harvesting can be set by via Sales, CTR, Unit sold and ACoS, like bid automation it can be set either on the campaign or ad group level.

While the negative harvesting can be set either by setting trigger via clicks or impressions. Like the previous functions, you can set this rule on both the campaign and ad group levels.

Manual Bulk Optimization

You perform the bulk bid automation, via the tab “Bulk Optimization”. On this tab, you can select diff levels of data Keyword/Search Term/SKU. Then you can further filter by the corresponding metrics. Once you get the result you can adjust the keyword bid by:

• To Max CPC (Cost per Click)
• Adjust Current Bid by value and %

Dayparting

PPC Entourage has a very comprehensive Day Parting function where you can, not only select the time and date, but also turn the campaign off during a specific date range of the year. This function would be great if you are not completely in stock for a certain SKU or you feel like certain seasons/holidays are not a good time for your product.

Supported Marketplaces

PPC Entourage support NA and EU Amazon accounts the marketplaces includes:

Supported Ad Types (SP/SBV/SB/SD)

Ease of Use

This is a tough one, PPC Entourage is not poorly designed by a long shot. Since it is so feature-rich, it can be a bit complicated for not as experienced users to navigate and to be onboarded. Overall, the ease of use is not because of design but it is just so packed that the learning curve is relatively higher.

Price

PPC Entourage is tier-based pricing based on monthly advertising spend and per-account basis. If you hit each tier($1000 and $10,000) it would double the subscription fee. Per-account base pricing means if you sell on Amazon US and Canada you will have to pay 2x on what is on the price chart below.

Hero Feature

The one feature in PPC Entourage that I was amazed about is the ‘Community Data’ feature. This report shows the data of all other Amazon advertisers that are using the PPC Entourage tool. It lets you know how you stack up against the average .

Sellerlab (Ignite) Review

Reporting and Analytics

Overall it has an ok high-level dashboard that shows your performance on an account level. You can go to the ‘performance’ tab to see things on Keyword and Search term level. But I don’t how you can view data by ad group, so that is the drawback as far as I can tell.

Bid Automation

First, you need to set up the target ACoS in the “Campaigns” tab, then under the ‘Suggestions’ tab, you can set the bid automation on a campaign level. Another thing of note is that the bid automation currently does not support Sponsored Brands video.

Search Term and Negative Search Term Harvesting

It seems that Ignite only can automate the creation of negative keywords. But cannot perform the function of search term harvesting, which I feel is a very important part of the Amazon PPC optimization workflow.

Manual Bulk Optimization

I feel manual bulk changes and optimization is probably what Sellerlabs Ignite can do best. They have an intuitive screen for bulk bid changes for keywords

Dayparting

Sellerlab Ignite does not have dayparting functionality

Supported Marketplaces

SellerLabs support NA, EU, and Fareast Amazon accounts the marketplaces includes:

Supported Ad Types (SP/SBV/SB/SD)

Ease of Use

Overall ease of use is good, but it seems to like the opposite of PPC Entourage, ease of use is due to the lack of functionality.

Price

SellerLab is tier-based pricing based on monthly sales on a per-account basis. If you hit each tier($250K and >$1M) it would double the subscription fee. Per-account base pricing means if you sell on Amazon US and Canada you will have to pay 2x on what is on the price chart below.

BUT this pricing is not only for their Amazon PPC Software, Sellerlabs offer an entire suite of software solutions for the Amazon seller. It an all-in-one price.

Hero Feature

No hero feature to speak for Sellerlabs. Keep in mind I have only reviewed their Ignite suite.

Viral Launch (Kinetic) Review

Reporting and Analytics

Below is the dashboard that shows data on an account level. I can see my campaigns on this screen, and if I click on the campaign, I can drill down to see the ad group and then the keyword level. Overall this reporting and analytics setup seems alright, though I would like to see the ad group and keyword reporting on its own without having to click on the campaign first.

Also need to note, that this screen does not show my Sponsored Brands video and Sponsored Display campaigns. Viral Launch (Kinetic) does not support these ad types in reporting and bid automation

Bid Automation

You can set different types of rules in their ‘Rules’ tab, for the bid automation you will select the Keyword Targeting and Product Targeting rule option. From there will can file out specific criteria to trigger the rule. As mentioned, Sponsored Brands video and Sponsored Display campaigns are not supported.

Search Term and Negative Search Term Harvesting

In their ‘Rules’ tab you can trigger a list of search terms or ASINs based on user-defined rules. This allows the triggered keywords to be added as a new target or a negative to the Amazon advertising account campaign/ad group either automatically or pushed by the user via the software

Manual Bulk Optimization

It does not allow you to make a bulk campaign or bid changes in any meaningful way. You are allowed to make changes on individual bids, on a row by row basis, but not in bulk.

Dayparting

Viral Lunch Kinetic does not have dayparting functionality

Supported Marketplaces

Ok so this is a huge issue, Viral Launch Kinetic only supports the Amazon USA marketplace.

Supported Ad Types (SP/SBV/SB/SD)

Ease of Use

The UI/UX seems initiative, even though there was not too much onboarding help such as in screen video tutorials, I was able to figure out the function pretty quickly.

Price

The price for the software suite with Kinetic (Viral Launch’s PPC module) is $199. Reminder, Kinetic only support Amazon USA, so if you do scale to other marketplaces Kinetic would not be a good option.

Hero Feature

Viral Launch (Kinetic) has a hidden ace in the hole with the ‘Sponsored Ad Rank Position’ column on its keyword reporting. This allows you to see where your keyword ranks as both organic and sponsored, as most sellers will know being ranks first in sponsored ads means a higher conversion rate and higher sales. It also calls to attention your bid if your Sponsored ranking drops.

Scale Insights Review

Reporting and Analytics

The reporting and analytics show organic and PPC sales, as well as the P&L of the products. There are multiple tabs that are displayed by overall sales trend, units, PPC analytics

There is also an Amazon PPC specific dashboard that shows a comprehensive view of all your campaign levels. This reporting is very detailed, A+ on details.

Bid Automation

I do feel that bid automation is where Scale Insight really shines for some people. Why some people? Let me explain, Scale Insight allows you such a free range of strategies and customization of their bid automation that it can confuse a lot of users.

It is great for the mad scientist that wants really to spend a lot of time testing a wide varies of strategies and algorithms.

In Scale Insight there are rules and strategies. You will create a rule, for example, if click > x then bid this, another example of a rule is only run a campaign during this specific time. There is a large variety of rules to experiment with.

On the other hand, strategy is consists of multiple rules to be applied to a certain campaign or targeting type/situations.

The different automation that can be select is pretty comprehensive. These rules and strategies are set up on the ad group level, so applying the automation will be a pretty long process.

Search Term and Negative Search Term Harvesting

For their search term harvest is what they call an “Import Rule”, you can create your custom rules for both search terms and negatives and apply them to an ad group. It seems it is all automated, there is no way where you can just trigger the search term and negatives to be added into the ad console yourself manually.

Manual Bulk Optimization

Scale Insights doesn’t allow you to change bids, add search terms and negatives in bulk manually

Dayparting

Scale Insights does have dayparting functionality

Supported Marketplaces

Supported Ad Types (SP/SBV/SB/SD)

Ease of Use

I would say this would be one thing it can improve on is the UX, but because it is such feature-rich in its bid automation settings that it will take time to learn and adjust regardless. The UI can also be improved upon as well

Price

The pricing plan is based on the number of ASINs that you set. Within the ASINs you can add many SKU/variation types.

Hero Feature

Their mass campaign creation. The system can create a combination of campaign/auto groups that allows you to test and scale your Amazon PPC. A very nice feature to have.

IntentWise Review

Reporting and Analytics

The data shows a nice and clean reporting first on across all campaigns upon login, then you can go to the left nav bar to show data on the ad group/keyword/product/search term levels. It also shows you more granular details on the campaign deltas, ASINs, and Keywords.

Overall pretty intuitive and easy for the eyes.

Bid Automation

IntentWise bid automation setup is also very intuitive. You set up the bid automation on the campaign level, this is done on the campaign screen. You select the campaign>> Set Target ACoS>>Click on Auto Apply Bid selection”

Search Term and Negative Search Term Harvesting

The search term and negative keywords can be both added manually and automatically.

To add search terms or negative manually you go to the search term screen. Select the search term, you can then add the search either as a new keyword or as a new negative.

Manual Bulk Optimization

IntentWise via its keyword level screens allows you to manually edit keyword bids and status in bulk.

Dayparting

IntentWise does not have a dayparting functionality

Supported Marketplaces

I cannot easily find all the marketplaces that IntentWise supports, it supports the North American marketplace at the very least I assume.

Supported Ad Types (SP/SBV/SB/SD)

Ease of Use

The UX with IntentWise is quite good. The structure of all the screens is very much intuitive. I can easily drill down and up between campaign, ad group, and keyword level quite easily. In terms of UX and UI, it is top tier

Price

The pricing started at $500, I think it is very pricey to start. It is based on a tier, but it doesn’t give any further details than that. I think if you are a small or mid-tier seller, it might be beyond your price range.

Hero Feature

The labeling feature is quite good, it allows you to set custom labels and apply them to a certain keyword.

Pacvue Review

Reporting and Analytics

This is the Amazon PPC Software that I was really looking forward to review, and honestly it didn’t disappoint. The reporting details were pretty amazing, probably the most comprehensive I’ve seen. I definitely saw what the fuse was all about.

Lets start with the initial dashboard, it shows every thing on a high level and low level. As you can see the tiles with the metrics on the top, and on the low level the most common keyword occurrence.

You can also customize what specific reporting tiles you want to see on the dashboard on the top right.

Then in their reporting section there are even more reporting/analytics options

Bid Automation

You know what I said earlier about AI? I think Pacvue understood the limitations. Thus, Pacvue not only offers AI, but also allows you to create custom rules based automations on the bids.

This way you can leverage combinations on both AI and Custom Rule in your bid optimization tool box. For example, campaigns that have a lot of previous data, let the AI take over, while most recent campaigns can be optimize using rule based.

I also like the fact that their sales team did not over sale their AI capabilities.

You can set rules using so many different triggers, as well you can customize:

• The lookback period
• Frequency of the bid changes
• +/- Bid from CPC, Bid and CPA (Current Bid / ACOS )

Then you can also have the option to set the AI optimization on the campaigns of your choosing

Search Term and Negative Search Term Harvesting

In Pacvue you can set up the harvesting rules for both search terms and negative keywords in the same rule creation screen as bids. The awesome thing about the harvesting rules is that you can select different trigger combinations such as CTR/ROAS/ACOS/Orders/CVR etc,.

Then you can select the source and destination of the harvesting. You can pick the source and destination of your keywords on both campaign and ad group level.

Manual Bulk Optimization

Pacvue has a very robust manual bulk change function. You can bulk change bids, add keywords, move keywords and add negative keywords.

Dayparting

Pacvue not only allows you to day part (on and off campaigns), but the day parting feature also allows you to have a bid multiplier (positive and negative) on a particular parts of the day.

For example if you want to bid up during high sales period of week, you can tell the system to bid 2x what you normally bid for this period.

Supported Marketplaces

Pacvue support NA, EU, and Far East Amazon accounts the marketplaces includes:

Supported Ad Types (SP/SBV/SB/SD)

Ease of Use

Pacvue is just so packed with options and features that it will take a lot of time to learn and be completely onboarded on the platform.

You probably would need a pretty strong knowledge on Amazon PPC to really understand all the functions and features. It is amazing for the experts, but for the newer users to Amazon PPC will struggle (not Pacvue target audience anyways).

Price

The old saying goes you pay for what you get. Pacvue rakes 4% of your ad spend, this amount is aggregated across all the marketplace and doesn’t have specific additional fee to sell another marketplaces.

It is probably costliest amongst all the Amazon PPC Software that have reviewed, but at the same time it is amongst the best.

Hero Feature

There are a lot of good features, but if I have to pick one, it would be the Category Intelligence features. This features allow you pick out trends and potential new ASINs/KWs to bid in your category.

Bid X Tool Review

Reporting and Analytics

Very clear reporting. The initial dashboard allows you to see everything on a high level very clearly.

You can also customize the dashboard by creating a new tile where you can customize by the types of graphics, metrics, date range, and KPIs. You can create Line Chart, Bar Charts, Doughnut charts, Table, Score Card, and a customizable Text Box. Overall this very neat feature.

Bid Automation

In Bidx there is the bid automation feature, a feature I feel is must-have in all Amazon PPC software nowadays. The thing that I feel that makes the bid automation in Bidx unique is that it seems the target ACoS is set at the SKU level, where most of the Amazon PPC software I have reviewed seems to have target ACoS set at the Campaign level.

It could be a good thing to have really precise ACoS targeting, but at the same time if you have 50+ SKUs and even more between different marketplaces it could be a headache to set up at first and going forward as you would have to constantly adjust your landing cost.

Under their ‘Management’ tab, you can set up the product cost, then choose your bid automation strategy. There are 4 strategies that you can select:

1. Break-Even ACoS
2. Profitable ACoS
3. Promote ACoS
4. Individual ACoS

Search Term and Negative Search Term Harvesting

Bidx allows you to, first, set the structure of where the search term/negative keyword destination should go to.

Then you would have to set the rule where you want the target harvesting to be triggered.

If you toggle the “Keyword & Negative Keyword Management” the harvesting and negative keyword function will perform harvesting actions automatically.

Manual Bulk Optimization

In BidX under the “Discover” section you can change the bid and status manually, but only 100 rows at a time. There is a sub-tab for Marketplace, Portfolios, Campaigns, Ad Groups, Targets, Product Ads, and Search Terms

Dayparting

BidX does not have dayparting functionality

Supported Marketplaces

BidX Support 10 marketplaces in all, they include:

Supported Ad Types (SP/SBV/SB/SD)

Ease of Use

The user interface is clean, the onboarding feature such as video and tutorial is comprehensive. Will take approx. 2-3 hours to completely set up depending on the number of marketplaces and SKU count.

Price

BidX is tier-based pricing based on monthly advertising spend. The per month price is one Amazon account only, so you would have to 2x subscription price if you want to use Bidx on multiple marketplaces.

Hero Feature

In my opinion, the hero feature has to be the customizable dashboard. It is a very valuable feature that is pretty unique amongst the Amazon PPC Software reviewed so far.

The AI-Based vs. Rule-Based Amazon PPC Automation Debate

In your search for the best Amazon PPC software, you will encounter two distinct methodologies. Understanding the difference is critical to your campaign stability.

Rule-Based Automation (The “Glass Box” Approach) This relies on “If/Then” logic that you set up.

• Example: “If a keyword has 10 clicks and 0 sales, reduce bid by 20%.”
• Pros: Transparent, predictable, and gives you total control. You know exactly why a bid was changed. Perfect for sellers who want to strictly control their margins.
• Cons: Reactive rather than proactive. It requires you to set the parameters correctly; if your rules are bad, your results will be bad.

AI/Machine Learning Automation (The “Black Box” Approach) These algorithms analyze millions of data points across the software’s entire user base to predict future performance.

• Example: The AI predicts that “running shoes” converts higher on Tuesday evenings for your category and raises the bid before the sales happen.
• Pros: Proactive and saves time. It can identify complex patterns (seasonality, time of day, conversion delays) that a human would miss.
• Cons: Requires significant data volume to be accurate. It can be difficult to trust because you often cannot see the logic behind a specific bid change.

The debate as to which approach is better has gained a lot of traction. Since AI tools PPC optimization tools have started proliferating in 2025, more and more tools have become available to sellers. It can be really hard to determine which ones actually provide an added values and which ones are essentially just a ChatGPT / Gemini “wrapper”.

In our experience different approaches can be beneficial in different scenarios. LLMs and AI based tools differently have an edge when it comes to ad copywriting, but they may lack the data to make informed and optimal decisions that go beyond AI hallucinations and guesstimate when it comes to optimizing bids.

Amazon PPC Software vs. Hiring an Agency: Which Do You Need

A common dilemma for growing brands is deciding between buying a tool or hiring a team. While software is powerful, it is not a strategy in itself it is a vehicle for your strategy. Working with a professional Amazon Seller Agency can give you an extra edge: A professional agency will use sophisticated tools and leverage learnings on any single one of the accounts they manage across their entire client portfolio.

When to Choose Software:

• You have in-house expertise: You or your team understand bid management, keyword research, and campaign structure, but you need to execute tasks faster.
• Budget constraints: You want to keep costs low and are willing to trade your time for money.
• Strict control: You want to approve every bid change and fear that an external agency might “burn” your budget.

When to Hire an Agency:

• You are stuck: Your sales have plateaued, and software simply maintaining your current ACOS isn’t helping you grow.
• Strategy deficit: You know how to change a bid, but you don’t know why your competitor is outranking you or how to attack a new category.
• Creative bandwidth: Amazon advertising in 2026 is creative-heavy. Agencies often provide video editing, image design, and A+ content optimization that software cannot do.
• The “Hybrid” Approach: The most efficient sellers often use a hybrid model—hiring an agency for high-level strategy and creative direction while using software for the daily grunt work of bid adjustments.

FAQ: Amazon PPC Software Comparison

The post 25 Best Amazon PPC Software Tools (2026 Review & Buyer’s Guide) appeared first on SellerMetrics.