Image depicting the hidden manipulation of Amazon search results.

Amazon's Algorithm Under Scrutiny: Are You Really Seeing the Best Deals?

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Reese Marlowe in Business & Economy June 2025 • 4 min read.

"A deep dive into new research reveals whether Amazon's recommendations are truly unbiased or if self-preferencing is skewing your shopping experience."


Digital platforms like Amazon have revolutionized the way we shop, offering a vast selection of products and convenient purchasing options. But behind the scenes, complex algorithms are constantly at work, shaping our search results and influencing our buying decisions. One of the biggest concerns surrounding these algorithms is the potential for 'self-preferencing,' where platforms prioritize their own products or services over those of third-party sellers.

Legislators and consumer advocates have long been worried about this, questioning the fairness of online marketplaces. Are consumers truly seeing the best deals, or are they subtly guided toward Amazon's own offerings? This question is at the heart of a new research paper that aims to measure self-preferencing on digital platforms. The findings could change how we think about online shopping and the regulations that govern it.

The study dives into Amazon's search engine, examining whether the platform gives its own products an unfair advantage in search results. By analyzing millions of data points and surveying consumer perceptions, the researchers offer a compelling look at the inner workings of the world's largest online marketplace. Get ready to have your assumptions challenged about what you see when you hit that 'search' button on Amazon.

Decoding Amazon's Algorithm: Is Self-Preferencing Real?

Image depicting the hidden manipulation of Amazon search results.

The core issue is simple: when a platform like Amazon also sells its own products, there's a potential conflict of interest. Does Amazon's search algorithm remain neutral, or does it subtly boost its own items? To tackle this, researchers conceptualized 'recommendation' as the level of search engine visibility across an entire platform, moving beyond individual search queries. This broader view allowed them to develop two tests for self-preferencing.

These tests were then applied to data from three major Amazon marketplaces: Germany, France, and the United Kingdom. The study looked at two key scenarios: identical products sold by different sellers (including Amazon), and private-label products (Amazon Basics vs. competitors). Millions of daily product-level observations were analyzed to see if Amazon-sold products received more visibility than their third-party counterparts, even after accounting for other factors.

  • Study A: The 'Buy Box' Battle - In this scenario, researchers examined if Amazon was more likely to win the Buy Box over time than a third-party seller. Amazon prioritizes offers that the seller controls. The analysis of the buy box was only done for new products.
  • Study B: Amazon Basics vs. The World - They looked at Amazon's private label brand, Amazon Basics, and whether those products were shown more frequently than other private label products. The team focused on 3 different samples of product, the analysis found almost no evidence of self-preferencing.
What they found was surprising, and perhaps reassuring: the analysis found almost no evidence of self-preferencing. In fact, in some cases, Amazon Basics products were significantly less visible than comparable items from other brands. The study suggests that Amazon's algorithm might be more neutral than many consumers believe.

What Does This Mean for You (and Amazon)?

While the study's findings are encouraging, they don't give Amazon a free pass. Consumers overwhelmingly expect Amazon to favor its own products, and this erodes trust in the platform. Even if the algorithm is currently neutral, that perception can damage Amazon's relationship with its customers. Moving forward, more transparency is needed. Platforms could benefit from better informing consumers regarding the validity of their recommendation systems, e.g., by letting independent third parties audit their recommendations for self-preferencing, using methodologies such as the one developed herein.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

Everything You Need To Know

1

What is self-preferencing and why is it a concern for online shoppers?

Self-preferencing refers to a platform prioritizing its own products or services over those of third-party sellers. This practice is concerning because it raises questions about fairness and whether consumers are truly seeing the best deals. If the algorithm favors a platform's own offerings, consumers might miss out on better products or prices from other sellers, potentially leading to less competitive markets and higher costs for consumers. The study addresses this by evaluating Amazon's search algorithm and the potential for self-preferencing.

2

How did the research investigate self-preferencing on Amazon's platform?

The research investigated self-preferencing by examining Amazon's search algorithm and the visibility of products. The researchers developed two tests to measure if Amazon-sold products received more visibility than their third-party counterparts, even after accounting for other factors. The study used data from Amazon marketplaces in Germany, France, and the United Kingdom. One test, 'Buy Box' Battle, examined if Amazon was more likely to win the Buy Box over time than a third-party seller. The second test, 'Amazon Basics vs. The World', focused on Amazon's private label brand, Amazon Basics, and whether those products were shown more frequently than other private label products.

3

What were the key findings of the study regarding Amazon's algorithm?

The study's findings were surprising. The analysis found almost no evidence of self-preferencing. In fact, in some cases, Amazon Basics products were significantly *less* visible than comparable items from other brands. This suggests that Amazon's algorithm might be more neutral than many consumers believe.

4

What is the 'Buy Box' and how was it used in the study?

The 'Buy Box' is the prominent area on an Amazon product page where a customer can directly add an item to their cart. The study examined the 'Buy Box' Battle to see if Amazon-sold products were prioritized to win the 'Buy Box' over time compared to third-party sellers. The analysis of the buy box was only done for new products.

5

Even if Amazon's algorithm is neutral, why is transparency still important?

Even if the algorithm is neutral, the perception of self-preferencing can damage Amazon's relationship with its customers. Consumers overwhelmingly *expect* Amazon to favor its own products. Moving forward, more transparency is needed. Platforms could benefit from better informing consumers regarding the validity of their recommendation systems, for example, by letting independent third parties audit their recommendations for self-preferencing, using methodologies such as the one developed in this research. This helps build trust and assures consumers that they are receiving unbiased recommendations.

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