Key takeaways

  • The FTC wants the public to believe this case is about higher prices for consumers. It is not.
  • Amazon's approach to pricing contradicts any suggestion of consumer harm. We provide customers the lowest prices every day across the widest selection of products, and work to ensure our retail and grocery prices meet or beat those offered by other retailers.
  • On top of everyday low prices at Amazon, customers saved over $230 a year on average last year on essentials and other products through deals, coupons, and our Subscribe & Save reorder program.
  • The FTC’s own complaint cites no evidence of consumer price increases, and consumers are only mentioned a handful of times in over 150 pages.
  • There is no advertiser harm either. From 2019 through 2024, the average cost-per-click for Amazon’s Sponsored Products search ads remained flat adjusted for inflation, while conversion rates grew 24% from 2021 to 2025. Advertisers paid the same and got more as we meaningfully improved ad relevancy and therefore performance.
  • The FTC’s claim fundamentally misunderstands how advertisers operate. Advertisers adjust bids based on real-world performance, not descriptions of auction mechanics. Even accepting the FTC’s flawed premise that advertisers do not adjust bids, we estimate they saved over $8 billion from 2021 to 2025 as a result of Amazon prioritizing ad relevancy over selecting ads on bid price alone.
  • Average winning bids fell 50% from 2019 to 2025 on Sponsored Products search ads, and roughly 92% of placed ads are not given to the highest bid.
  • In 2026, we estimate that advertisers will deliver at least 58% higher sales with this model—rather than being ranked by highest bid amount—and at least 46% better return on ad spend because of our focus on relevancy. Additionally, shoppers are 58% more likely to see ads for products they would consider clicking on or purchasing because we prioritize ad relevancy rather than simply the highest bid.
  • The case centers on generalized second price auction dynamics, which the complaint itself concedes have been “the industry standard for decades.” In no scenario does an advertiser pay more than their bid.
  • After reviewing approximately 1.5 million pages spanning six years, the FTC leans on a handful of simplified communications to allege a companywide effort to deceive. That is patently false.
In the early years, Amazon.com only provided basic search results. We, of course, wanted to show customers the best products, and our shopping results favored items with attributes such as many customer reviews, positive reviews, high click rates, and high purchase rates. Over time, we found that existing items in our product catalog had many of these favorable attributes and became so favored in our systems that they inhibited new, sometimes more helpful products from surfacing in our search results. We also heard from brands that they wanted tools to enhance the discoverability of their products, particularly new ones.
One of the ways we worked to address this challenge was to introduce advertising so that shoppers could more easily discover new products and brands that better served their needs or more affordable alternatives they may not have known about, giving brands a new way to reach customers. For example, customers searching for USB power cables for their phones saw best-selling, positively reviewed power cables that had been around for years. Advertising surfaced a new innovation—portable charging banks. These were not yet showing near the top of search results but solved a significant customer need, demonstrated by their rise in popularity once they were advertised.
Our early advertising was helpful for customers because it surfaced new products, but was powered by fairly rudimentary systems. We used simple rules-based software systems to help ensure ads were relevant to shoppers, but these were imprecise, restrictive, and limiting for advertisers. Given the early stage of the advertising program at the time (and online advertising in general), it worked well enough across a limited range of categories and products. However, over the following years, as the selection in our Store grew to hundreds of millions of products across 35+ categories (from books to electronics to toys to fashion to everyday essentials), the simple rules-based advertising software system did not effectively scale to enable advertisers to place relevant ads across our wide selection, and we started to invent with machine learning and AI to benefit both shoppers and advertisers.
We knew there were two things we had to prioritize: ad relevancy for shoppers and high-performing advertising for brands. Our philosophy was simple: customers discover new brands and engage with ads they are interested in, and advertisers get value and results when our customers are engaging with these ads.
In 2014, we started testing advanced machine learning-based relevance models in an attempt to predict how likely a shopper is to find an ad useful. We intended to test this over a long period of time, and in fact did, before we rolled out broadly. By 2019, they were in use across all our Store advertising to bring greater benefits to shoppers and advertisers. Even more advanced forms of these models, which we have vastly improved and continue to use today, consider a variety of factors including the content surrounding the ad placement, the shopper’s search query and the likelihood a shopper will engage with the advertised product or service. Because these models are far more sophisticated and streamline placement of relevant ads, we’ve been able to develop new ad formats and placements that help shoppers find better or more affordable products, and help brands reach those shoppers more cost-effectively because they aren’t spending advertising dollars on sub-optimal placements.
This approach has been well received by customers and advertisers. Today, consumers worldwide consistently rate Amazon's advertising as the most useful and relevant, according to Kantar, an independent research firm.
At the heart of this innovation was our work to make purchasing ads simple and easy for brands of all sizes. Sponsored Products ads take just minutes to create, and offer flexible targeting, bidding, and budgeting options that advertisers can manage directly in the Ad Console Campaign Builder. Sponsored Products campaigns have no monthly or upfront fees. Advertisers set the maximum amount that they’re willing to pay when a shopper clicks an ad for their products and their daily budgets.
Since we first introduced ads in our Store in 2006, we have priced clicks using a form of generalized “second-price” auction, which was and continues to be the industry standard and means that advertisers may pay less than their bid. Our early approach was simple: advertisers enter a bid, and if they win, they would pay just enough to beat the next highest ranked ad. Our auctions took relevancy into account to a degree, but they were much more weighted toward the highest bid amount.
While we could have decided to continue to favor the higher bids, we instead chose to focus on more relevant bids to ensure the best possible shopper and advertiser experience. As our advanced machine learning-based relevance models more heavily weighted relevance versus highest bid, we saw winning bids drop significantly. That was good for advertisers and shoppers but meant premium placements in our Store were being undervalued.
Like any retail advertising space, whether shelves or end caps, there is a value to specific placements. With relevant ads increasingly winning at prices below market value, we began to test a concept called “soft reserve prices,” a real-time minimum value that seeks to better reflect what each placement is actually worth. We also introduced what we call a “hard reserve,” which is the minimum a bid must surpass to enter an auction. The hard reserve helps cover our costs whereas soft reserves represent what we estimate to be the true market value of the ad placement. Reserves like these are common across the industry.
Our auction looks at a combination of which ad is most relevant to the customer and the price an advertiser is willing to pay. Here’s how it works: Advertisers bid a maximum price for a placement. When the winning advertiser’s bid exceeds both the hard and soft reserve, they pay the soft reserve, which is less than they were willing to pay. When the winning advertiser’s bid exceeds the hard reserve but doesn't meet the soft reserve, we still grant the placement to that advertiser and they pay their bid. In no scenario does an advertiser pay more than their bid.
On any given day, we will review billions of bids to place ads on our site across many different placements and ad formats. No two auctions are entirely alike. For any single customer search, there could potentially be hundreds of thousands of ads competing to be selected to show the shopper. We reduce that down to less than a thousand candidates by removing less relevant ads. We then apply a ranking score to each eligible ad based on relevance and bid. Over time, our ranking formula has increasingly given greater weight to relevancy over bid amount. The highest ranked ad is the one that best balances relevancy and bid value.
With this approach, in 2024, approximately 92% of selected Sponsored Products ads were not the highest bid, often by a wide margin. The mean winning advertiser’s bid is typically about the 128th bid by amount. This means the winning advertisers' cost is almost always lower than if we had selected ads on bid alone.
The FTC claims advertisers were harmed because they didn’t understand how our auction worked and therefore overpaid. Not only do we properly describe our pricing and auctions to advertisers, but this claim fundamentally misunderstands how advertisers behave. Advertisers adjust bids based on real-world outcomes, not descriptions of auction mechanics. However, even if you accept the FTC’s flawed premise that advertisers keep their bids constant regardless of ad performance, we estimate that advertisers have saved over $8 billion from 2021 to 2025 as a result of Amazon incorporating ad relevancy into our auction versus selecting ads on bid alone. The reality is that from 2019 to 2024, the average winning bid for Sponsored Products search ads fell 50%, demonstrating that highly relevant ads were increasingly winning placements with lower bids.
There is also no harm to consumers. The FTC’s own complaint cites no evidence of consumer price increases. In over 150 pages, consumer harm is mentioned only a handful of times and is never substantiated with data. Their damages model assumes no pass-through to consumers. Their proposed redress goes to advertisers, not shoppers. This is because there is no consumer harm: for Sponsored Products search ads, from 2019 through 2024, the average cost-per-click to advertisers remained flat when adjusted for inflation. Advertisers paid the same or less for advertising that delivered increasingly better results. If advertising costs were rising and being passed to consumers, the data would show it. It does not.
In 2026, we estimate that advertisers will deliver at least 58% higher sales with this model—rather than being ranked by highest bid amount—and at least 46% better return on ad spend because of our focus on relevancy. Additionally, shoppers are 58% more likely to see ads for products they would consider clicking on or purchasing because we prioritize ad relevancy rather than simply the highest bid. Conversion rates (the rate at which a customer buys the advertised product after seeing the ad) have increased over 24% for individual Sponsored Products advertisers from 2021 through 2025. From 2019 through 2024, the average cost-per-click to advertisers remained flat when adjusted for inflation, meaning advertisers were paying the same cost for a product that delivered increasingly greater value.
The FTC has questioned whether Amazon's communications properly described how our auction works after we introduced more advanced relevance models and reserve prices, and whether we should have improved our communication with advertisers.
In 2018, we were confident we would fully launch the advanced machine learning-based relevance models, so we updated Sponsored Ads content to clearly explain our focus on relevancy and that advertisers could be charged up to their bid. The Ad Console Campaign Builder, where advertisers build campaigns, set bids, and manage spend, has clearly stated since 2018 that a bid represents the maximum an advertiser could be charged. We did that to reflect how our models work in delivering advertising that is increasingly relevant for customers and drives greater value for advertisers.
The FTC cherry-picked a small number of materials, such as a few online educational videos and training content that contained older or simplified examples about how our auctions are run, which were missed when we audited our materials as part of that initial update. These were generally low-reach, low-engagement materials that were never part of the advertiser campaign management console. When we discovered them, we either removed or updated them.
To put the reach of these materials in perspective: three training courses the FTC has pointed to referenced older auction language had a combined 1,849 enrollments and 779 completions over their entire lifetimes. One course had only 23 enrollments and 14 completions in total. The median enrollment across the three courses was more than 100 times lower than the median of Amazon's top training courses. Even the most used of the three drew less than 1% of the enrollments for the top training course. One of the educational videos cited in the complaint was viewed by only 928 viewers in a 2.5-year period, representing approximately 0.09% of U.S. Sponsored Ads advertisers.
We carefully monitor inbound advertiser contacts across all topics, and throughout all these changes, contacts related to how our auctions work have always been very low. The customer contacts overwhelmingly reflect practical campaign-management concerns: bids, budgets, cost-per-click, spend, campaign settings, performance questions, and optimization. Following broad U.S. and international media coverage about our auctions, there were very few advertiser contacts, and we did not see changes to advertiser spending as a result.
When the FTC raised concerns about our auction descriptions, we took them seriously. We conducted a review to ensure all communications were current and we updated our Amazon Ads Help content to explicitly explain the use of reserve prices. In addition, we now conduct regular cross-team audits and sales trainings to ensure clear and consistent communication about changes to our auction.
In addition, after reviewing more than 1.5 million pages of emails and documents over a span of several years, the FTC is relying on a small number of instances where we used simplified explanations about our auctions to allege that there was a concerted company-wide effort to deceive advertisers. This is patently false. With a writing culture like we have, people at times use email as a brainstorming and suggestive medium, expressing ideas, testing hypotheses, but sometimes expressing thoughts that are either ill-formed or that they change later with the benefit of conversation and other views. Most every decision of any import is decided in a meeting and through multiple meetings. A stray email is not indicative of a team's intent or even collective viewpoint. Also, as a customer-centric advertising company, our advertising team, including senior leaders, communicates with advertisers on a regular basis and it’s not unusual to simplify a description in these instances.
Back to how advertisers really form their bids, ad buyers today use highly sophisticated, automated, programmatic advertising platforms that have a deep understanding of how auctions work across different providers, enable bid experimentation and are used to maximize results and return on investment.
Reserve prices are common in the industry and our use of them is consistent amongst industry leaders. The presence of reserve prices actually doesn't change how advertisers bid in practice. Advertisers optimize their campaigns based on actual auction outcomes—what they pay, what they win, and the performance they see. They don't bid based on simple descriptions of auction format. Even if an advertiser wanted to factor reserve prices into their strategy, they couldn't easily do this because reserves are determined in real time and aren't predictable in advance by anyone, including Amazon or the advertiser.
Advertisers of all sizes actively manage their bids using a range of tools and data. Many use automated bidding tools from Amazon or third-party services to manage campaigns based on real-time performance data. These tools monitor clicks, purchases, cost per click, and return on ad spend, and automatically adjust bids to get the best results. We invest heavily in making these tools accessible to advertisers at every level.
On active campaigns, from 2019 to 2024, eighty percent of bid changes on clicked Sponsored Products search ads occurred within one day of a prior change. Advertisers, and the automated tools they use, respond to competitive conditions and actual auction outcomes. As an example, in one study, when one advertiser stopped bidding on specific keywords, average bids for those keywords from competitors dropped about 83%, from $15.96 to $2.74. Advertisers optimize based on what is actually happening, in real time.
Amazon invests heavily in giving advertisers the tools and data they need to drive results. Advertisers get real-time reporting on actual cost-per-click, return on ad spend, click volume, purchase data, and sales attribution. One such tool, Amazon Marketing Stream, goes further: it pushes hourly performance data at the keyword and placement level directly into advertisers’ own systems, so bids, budgets, and campaign settings can be updated automatically in response to what is working, without requiring anyone to manually pull a report or review a dashboard.
We have shared all of this with the FTC on multiple occasions. The data, the industry context, the evidence of how advertisers actually behave, and the information showing our auction works as intended. They have shown little interest in engaging with the facts and appear more focused on trying to secure a substantial monetary victory for themselves and states they can lure with this possibility.
The evolution of Sponsored Ads has been a journey toward better outcomes for everyone: providing sellers and vendors the ability to promote their products in the Store, highly relevant advertising for shoppers, and stronger sales and performance for advertisers. We’ve provided advertisers with guidance about our auctions and pricing in the main tools they use to manage their campaigns, and we continue to update that guidance. We look forward to making our case in court.