The Cold Reality of the Honeymoon Period and External Traffic

Authors: Danny McMillan & Oana Padurariu

The Honeymoon Period: Unveiling the Data-Driven Realities with Insights from A9 Patents (2022 & 2023) and Science Papers Until Present Day.

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How to Get the Most Out of This Article

This article mainly focuses on the Cold Start Patents and breaks down the difference between the 2022 and 2023 updates.

We explain the Bayesian Update and its frequency. See this as the ranking updates that all the levers plug into, including AI Assistants and behavioral data.

We have used a series of scientific papers to chart the evolution from manual to priors right up to the Bayesian Update.

If you understand this as the initial best guess, real data, and then a new estimate, you will understand ranking far better, and your observations mixed with science will help you make better decisions.

Example: You can use this to extrapolate stockouts, deleting and reinstating ASINs, alongside why giveaways tank in most cases when they are not artificially boosted.

Understand that search engines evolve; it is not a zero-sum game. They update using new advancements. For example, lexical matching is not replaced by semantic, BERT, or other match types; it’s a refinement to fill the gap between the inadequacies of each match type.

Some scientific papers like Cosmo are sexy and accepted, but underneath, there is a plethora of complexity that gets dismissed or is too cumbersome to digest.

We are in the AI age; it’s fast and sexy and plugged into decades of carefully crafted algorithms to aid the end-user experience. We cover this towards the end of the article.

Go into this article with an open mind, as you can use the science, observations, and proven strategies (proven only needs to be proven once; science is repeatable, which is why it is important to test, test, test).

Reporting data from Amazon is incomplete at best; the APIs can be pretty poor, and some of the metrics do not line up, whereas others are pure junk metrics.

Soon, we will be dropping a series of 10-minute sessions (on the pod) from the whiteboard to make this more digestible.

To use this as a music analogy, see this as the album, and we will be dropping singles, reedits, and remixes. This has been a labour of love for me and Oana.

We hope you enjoy it and are able to use this to make your Amazon business more successful. We want to see you win.

Much love,

Danny.

No Honeymoon Period… Today

There is no scientific evidence to support this concept today, and even the person who originally coined the term back in 2015, Anthony Lee, has dismissed it. Watch the recent podcast The Man Behind The Honeymoon as he discusses the evolution over the last nine years since he coined the term.

What Is the Cold Start and What Is Not?

The Cold Start refers to the moment when a product is added to an e-commerce catalogue and indexed for searches, entering a ranking system without any prior interaction data. This lack of historical data, such as clicks or purchases, makes it difficult for the ranking algorithm to accurately assess the product’s relevance, and this is known as the cold start problem.

Manual Override from 2022 Patent

“However, an administrator can set a prediction value for the new item to a number, such as the numerical value thirteen or fourteen, to manually boost the item’s visibility in the search results. Another example of manual curation can include an administrator estimating the number of acquisitions for a new item. However, manual curation can be time-consuming, impractical for a large number of items, error-prone, arbitrary, and/or inefficient.”

Sales Velocity Seeding?

“Sales Velocity Seeding is an ASIN curation feature that copies the unit sales from an existing child ASIN and applies it to a new child ASIN.” Seeding new ASINs is used to tackle the Cold Start problem by boosting the unit sales ranking score of a given ASIN within both search and browse.”

Note: Although Sales Velocity Seeding and Manual Override are technically possible, the odds of your product benefiting from them are about as likely as being struck by lightning twice in one day. With thousands of new products launching on Amazon daily, these tools are reserved for Amazon’s own brands or major superstar brands, not the average seller.

From Its Origins to Modern Solutions: The Evolution of the Cold Start Problem

This section traces the key milestones engineers and researchers have achieved in tackling the cold-start challenge over the past several years. While the issue has existed long before 2016, we will focus on the developments documented in scientific papers from then until the present day.

Now let’s dive deep into the evolution of the cold start. In this section we’ll cover:

• The origins of clearly defining the Cold Start Problem
• Early attempts at manual mitigation
• Using predicted priors to provide initial signals
• Spearfishing to kickstart engagement
• Optimizing the approach for balanced improvements
• Ongoing innovations to further improve discoverability

Before diving into the details, please take a moment to review the images below. These visuals are designed to help you better grasp the concepts, making it easier to understand the dynamics of the algorithm and the mechanism behind a product launch. The images offer a straightforward representation of priors and posteriors, making the key ideas accessible and clear, even if you prefer not to explore the technical details. The below figures are intended to simplify the idea and break the two concepts into two different images, but keep in mind these two are connected as part of a loop process within the algorithm update.

PRIOR PREDICTIONS – Visual Representation

POSTERIOR PREDICTIONS – Visual Representation

The Core Cold Start Challenge

In 2016, Dr. Daria Sorokina of Amazon Search gave a presentation titled “The Joy of Ranking Products,” where she clearly defined the cold start problem for product search systems. She used examples such as new Harry Potter book releases and hot new gadgets to illustrate the issue: these highly relevant new products would initially rank low in Amazon’s search results, even for very related keyword searches. The rankings would gradually improve as more behavioral data accumulated from users engaging with the products. However, new products required tedious, ongoing manual tuning by Amazon’s elite A9 search team to accelerate the pace. This approach was not scalable.

The core of the issue was the lack of user engagement data, which prevented the search algorithms from accurately judging the relevance of new products, causing them to rank much lower than appropriate, even for very relevant queries. The available solutions at the time were limited and inefficient, primarily relying on:

• Waiting for behavioral data to slowly accumulate over weeks and months
• Manually tweaking the rankings of high-priority new items

Neither path was sustainable at Amazon’s massive e-commerce scale, highlighting the need for more automated, scalable solutions to address the cold start problem.

Early Solutions: Priors in Product Search (2019)

By 2019, Amazon researchers had made significant progress in developing more programmatic solutions. Their key innovation was a method to generate “prior” estimates for the missing behavioral signals that stymied new products, such as click-through rates. This approach was detailed in the paper “Treating Cold Start in Product Search by Priors” (2020). The paper proposed using priors to mitigate the lack of historical data for new products by leveraging non-behavioral attributes like brand and author to make initial predictions of a product’s engagement potential. These predictive priors provided enough of an initial behavioral signal boost to improve the rankings of new products.

Advancements in Semantic Search: Leveraging Large-Scale Models (2019)

In 2019, the focus shifted towards enhancing semantic understanding in product search. Research presented in “Semantic Product Search” (2019) highlighted the use of large-scale language models to improve search accuracy. These models, trained on vast datasets, enabled a deeper understanding of product descriptions and user queries, significantly mitigating the cold start problem by providing contextually relevant results even when historical data was sparse. This development marked a critical evolution in addressing cold start issues, allowing for more nuanced and accurate search results from the moment a product was listed.

Further Development: Treating Cold Start in Product Search by Priors (2020)

In 2020, the approach of using priors to mitigate the cold start problem was expanded and refined. As detailed in the paper “Treating Cold Start in Product Search by Priors” (2020), this method utilized predictive priors to leverage the non-behavioral attributes of products, like brand or author, to predict initial product engagement. Bayesian updating models then refined these estimates as real user engagement data began to accumulate. In A/B testing, this method showed promising results, increasing new product impressions by 97% and clicks by 58% compared to a control baseline without priors. However, it also led to lower overall purchase rates because the exploration process sometimes overexposed low-quality new items, degrading the overall user experience.

Spearfishing to Accelerate Behavioral Signals

Beyond algorithmic approaches, marketplace designers employed various tactics to help new products gather the critical initial engagement data needed to break through the cold start barrier. One proven technique was “spearfishing,” which involved meticulously targeting specific, niche queries where a new product was highly likely to rank well and get clicked on. For example, someone searching for “Led Zeppelin 2022 remastered box set” has a clear intent to find that new release. Focusing on tailoring such laser-focused, high-intent queries allowed new products to start accumulating real behavioral signals. However, this process was often slow and unreliable, depending on product owners correctly guessing these long-tail keywords and hoping enough volume existed in those searches.

ColdGuess and Graph-Based Solutions (2022)

By 2022, the use of graph convolutional networks (GCNs) emerged as a cutting-edge solution to the cold start problem, exemplified by the ColdGuess model. According to the paper “ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases” (2022), this approach constructs and analyzes relational graphs that capture interactions between products, categories, and user behaviors. These graphs excel at identifying hidden patterns and connections within the data, even when traditional data points like clicks or reviews are unavailable. ColdGuess leverages these relational insights to make highly accurate recommendations from the outset, representing a significant advancement in cold start solutions.

Iterative Improvements and Real-World Application (2022)

Also, in 2022, Amazon’s research team iterated to significantly improve the real-world results of their cold start systems in production. Their key upgrades focused on balancing the twin goals of discoverability and revenue:

• Aggregated behavior data: Expanded the training data for predictive priors models to include aggregated product-level engagement metrics, providing a stronger signal for generally popular new products while limiting overexposure.
• Rapid Bayesian updating: Implemented real-time indexing of user feedback to accelerate the Bayesian updating loops from 24 hours down to 2 hours, preventing suboptimal priors from being exposed for too long.
• Early stopping: Added business logic to stop showing clearly low-quality explorations after sufficient data had been gathered, further optimizing the user experience.

These enhancements led to much stronger real-world results in A/B testing, including a 14% increase in new product impressions, an 11% increase in new product purchases, and no degradation in overall revenue metrics. Researchers reiterated that empirical Bayes techniques were an effective framework for tackling cold start through smart, selective feature exploration, though further work was needed to turn these directions into fully generalized production systems.

Early Attempts at Manual Mitigation (2022 US11269898B1)

Manual curation is a legacy approach used to adjust the rank of new items that lack user interaction data. In this process, an administrator can manually assign a prediction value to a new product, artificially boosting its visibility in search results. However, as outlined in the 2022 Amazon patent (US11269898B1), manual curation is increasingly seen as inefficient—particularly for marketplaces with a large volume of new items—because it is time-consuming and prone to errors. Modern machine learning models now automate this process, replacing manual adjustments with predictive algorithms that leverage historical data from similar items. These models generate “prior prediction values” to provide a more scalable and accurate ranking system, thus making manual curation obsolete for most scenarios.

Integrating Seasonality: Contextual Similarity in Product Search (2023)

By 2023, more contextually aware methods were being used to tackle the cold start problem. One notable contribution was the introduction of season-aware query-product semantic similarity, discussed in the paper “Improving Product Search with Season-Aware Query-Product Semantic Similarity” (2023). This approach considered not only the semantic relevance of products but also seasonal trends and contextual relevance. This method dynamically adjusted the importance of different attributes based on the time of year and emerging trends, refining product search results, particularly in cold start situations.

Incorporating Common Sense Knowledge: COSMO (2024)

As e-commerce grew more complex, the limitations of simple prior-based methods became apparent. In 2024, researchers at Amazon introduced COSMO, a large-scale e-commerce common-sense knowledge generation system, designed to enhance the understanding of product relations and attributes. The paper “COSMO: A Large-Scale E-Commerce Common-Sense Knowledge Generation and Serving System at Amazon” detailed how COSMO incorporated common sense knowledge graphs to infer relationships between products and their attributes more effectively. This innovation improved the handling of cold start scenarios by filling in gaps left by limited data through common sense reasoning.

In conclusion, the cold start problem has evolved from basic prior-based methods to sophisticated models that integrate semantic understanding, contextual relevance, and relational graphs. These advancements represent significant strides in addressing the cold start issue, with Amazon researchers continuously refining and expanding their approaches to improve product search and recommendation systems.

This summary reflects the evolution of cold start solutions as documented in the provided papers, offering a chronological overview of the key milestones and developments in this field.

Amazon’s Solution to the Cold Start Problem – the A9 patent 2022 US011269898B1

Amazon’s approach to solving the cold start problem in its search and recommendation system is outlined in U.S. Patent No. 11,269,898, issued on March 8, 2022. This patent describes the method that helps new items gain visibility and relevance, even when they lack historical user interaction data, which is typically crucial for accurate ranking in search results.

When a new item is introduced into Amazon’s catalogue, it doesn’t yet have the user engagement data—such as clicks and purchases (no user behavior)—that existing items have accumulated over time. To address this, the patented system first identifies similar existing items in the catalogue that share key attributes with the new item. The system then uses this historical data—how frequently users clicked on, purchased, or otherwise engaged with these similar items—to generate what is called a “prior prediction value” for the new item.

This prior prediction value is essentially an estimate of how likely users are to engage with the new item based on the success of similar items. If these similar items have done well, the new item is given a higher prediction value. This value helps the new item rank more favourably in search results right from the start, even though it hasn’t yet built up its engagement data.

As the new item starts to attract real user interactions—whether through views, clicks, or purchases—Amazon’s system updates the ranking model. It gradually shifts from relying on the prior prediction value to using actual user data (referred to as “posterior prediction values”), ensuring that the item’s ranking becomes more accurate over time.

Overview of Figure 3:

Figure 3 presents a step-by-step diagram showing how the system processes new items that lack user interaction data (which is crucial for accurate ranking) and how it ranks these items when a user submits a search query.

Steps Explained:

Step 1: Training the Machine Learning Model 

The process begins with training a machine learning model using existing data (Step 1). This training data includes attributes from existing items (like brand, author, and genre) and historical user interaction data, such as clicks and purchases. The goal is to understand how these attributes relate to user behavior.

Step 2: Applying the Model to a New Item

Once trained, the machine learning model is applied to a new item that has been added to the system (Step 2). Since this new item has no user interaction data, the model generates a “prior prediction value.” This value estimates the likelihood of user engagement with the new item based on the performance of similar existing items.

Step 3: Receiving a User Query 

The system then waits for a user query (Step 3). This query could be a keyword search, where a user is looking for a specific type of item or using search terms related to the item.

Step 4: Identifying Search Results 

When the user submits a query, the query service identifies a set of relevant search results from the item database (Step 4). This set includes both new and existing items.

Step 5: Ranking the Search Results 

The identified search results are then ranked by the system (Step 5). The ranking model takes into account the prior prediction values for the new items and compares them with the existing items that have historical interaction data. If a new item has a high prior prediction value, it might be ranked higher than other items with less favourable data.

Step 6: Displaying Ranked Search Results 

Finally, the ranked search results are displayed to the user (Step 6). The ranking model ensures that the new item appears in a position that reflects its predicted relevance, allowing it to gain visibility and start accumulating actual user interaction data.

Advancements in Addressing the Cold Start Problem: Insights from the 2023 Patent US20230367818A1

Building on the foundational approach outlined in the 2022 patent (U.S. Patent No. 11,269,898), Amazon has continued to innovate in addressing the cold start problem. This newer patent introduces significant enhancements that not only refine the initial methods but also leverage advanced machine learning techniques to further improve the accuracy and efficiency of ranking new items in Amazon’s catalogue.

While the 2022 patent focused on generating “prior prediction values” based on historical data from similar items to provide new products with an initial ranking estimate, the 2023 patent significantly enhances this approach by directly incorporating query-item pairs into the ranking process. This newer system better understands how specific queries relate to items, improving the accuracy of matching user searches to relevant products. By dynamically processing real-time data, the 2023 patent allows the system to continuously adjust item rankings based on evolving user interactions with specific query-item combinations. This shift reduces reliance on static historical data and improves system responsiveness to real-time user engagement.

A major advancement in the 2023 patent is its ability to transition from prior prediction values to posterior prediction values more seamlessly, as new behavioral data (such as query-item interaction data) becomes available. This makes the ranking process more efficient, particularly in how it responds to shifts in user preferences or trends. By leveraging advanced machine learning techniques, including Bayesian methods, the 2023 system is more adaptive and automated, ensuring a smoother integration of new items into Amazon’s search.

PRIOR PREDICTIONS AND QUERY-ITEM PAIRS INTEGRATION – Visual Representation –

The 2023 patent incorporates almost real-time feedback loops and continuous learning processes that allow for more accurate and faster ranking adjustments. By integrating a Bayesian context, the system continuously updates its predictions as new data becomes available, allowing it to more effectively manage uncertainty and improve the visibility of new items. This Bayesian approach enables the system to rapidly transition from relying on initial priors (based on similar items) to incorporating actual user interactions, which are considered in real-time. As a result, the ranking of new items is not only more accurate but also more closely aligned with current user preferences, ensuring that new products gain traction more effectively from the moment they are introduced into the search index.

POSTERIOR PREDICTIONS USING THE BAYESIAN FORMULA – Visual Representation

The Bayesian framework introduced in this patent allows Amazon’s system to quickly adapt to these changes, ensuring that the rankings of new items are continuously optimized based on the latest available data. This adaptive capability is crucial for maintaining the relevance and accuracy of search results in dynamic markets.

By integrating these advanced Bayesian techniques with real-time data processing and machine learning, the 2023 patent enhances Amazon’s ability to address the complexities of the cold start problem. This results in a more effective and efficient solution that not only improves the initial visibility of new items but also ensures their rankings remain competitive as more interaction data is collected. For sellers, this means a greater likelihood of success when launching new products (the right way), as the system is better equipped to respond to the nuances of consumer behavior. For consumers, it translates into a more personalized and relevant shopping experience, where new products that align with their preferences are surfaced more quickly and accurately.

Overview of Figure 3:

Figure 3 from U.S. Patent No. 20230367818A1 provides a step-by-step diagram illustrating the process of ranking new items within Amazon’s system, particularly when these items have no user interaction data. This figure outlines the system’s method of handling new items from the moment they are introduced into the search index until they start accumulating real data to refine their rankings based on actual user interactions.

The key innovation here is the use of query-item pairs, which allows the system to dynamically assess and rank new items based on the relevance of their attributes to specific user queries.

Steps Explained:

Step 1: Training the Machine Learning Model

The process begins by training a machine learning model using extensive data from existing items. This training data includes a wide range of attributes, such as item categories, brands, and other relevant product features. Additionally, historical user interaction data—such as clicks, purchases, and ratings—is used to train the model. Crucially, the training process involves understanding how these attributes correlate with user behavior, known as behavioral features, which helps the system predict how new items might perform when they enter the catalogue. The training also prepares the model to handle query-item pairs, where it learns to evaluate the relevance of each item in the context of specific user queries.

Step 2: Receiving a User Query

The system then waits for a user query. This query could be a simple keyword search or a more complex phrase that a user enters when looking for a product. The query is crucial as it triggers the process of identifying and ranking relevant items, including the new product. When the query is received, it is paired with items in the database to form query-item pairs. These pairs are the inputs the machine learning model uses to assess relevance and predict user interaction.

Step 3: Identifying Search Results

When a user submits a query, the query service identifies a set of relevant search results from the item database. This set includes both new and existing items. The system leverages the query-item pairs formed earlier to assess which items are most likely to meet the user’s needs.

Step 4: Applying the Model to the Item and Query

Once the relevant search results have been identified, the machine learning model is applied to evaluate the new item in relation to the specific user query. The model uses the previously formed query-item pairs and generates a “prior prediction value” for the new item, predicting how it is expected to perform based on similar historical data.

Step 5: Ranking the Search Results

The identified search results are then ranked by the system. During this phase, the machine learning model compares the prior prediction values of the new items with the historical data of existing items. The model considers behavioral features—such as how similar items have performed in response to similar queries in the past—to rank the items. New items with high prior prediction values might be ranked higher than older items with less favorable data, despite having been on Amazon for a longer time.

Step 6: Displaying Ranked Search Results

Finally, the ranked search results are displayed to the user. This is where the new item gets its first opportunity to be seen by potential buyers. The initial ranking is crucial because it influences the amount of user interaction data the item will begin to accumulate. As the product begins to collect its own interaction data—like clicks and purchases—the system gradually transitions from relying on prior prediction values to using actual user interaction data, known as ‘posterior prediction values.’ This shift ensures that the product’s ranking is continuously refined, moving from an initial estimate based on similar products to a ranking grounded in its own performance.

Cold Start Fig 3

00:00 Introduction to the Amazon Honeymoon Period

00:29 Debunking the Honeymoon Period Myth

02:07 Understanding the Cold Start Problem

03:19 Amazon’s Approach to the Cold Start Problem

04:06 Step-by-Step Breakdown of Amazon’s Ranking Process

08:05 The Role of Behavioral Features in Ranking

Overview of Figure 4:

Figure 4 from U.S. Patent No. 20230367818A1 provides a detailed flowchart illustrating the method for updating the ranking of items in response to new user interaction data, using a Bayesian approach to refine the predictions for item rankings.

Steps Explained:

Step 1: Receive User Interaction Associated with a New Item and Query

The process starts when the system receives user interaction data associated with a new item and a specific query. This data can include clicks, purchases, or any other form of engagement that provides insight into how users interact with the new item when it appears in response to a search query.

Step 2: Determine the Historical Signal

Next, the system determines the historical signal associated with similar items and queries. This step involves analyzing past data to understand how items with similar attributes or in similar categories have performed in response to similar queries. This historical signal serves as a basis for comparison with the new item.

Step 3: Output a Prior 

Using the historical signal, the system generates a “prior” prediction value. This prior is an initial estimate of the new item’s relevance and expected success based on historical data before considering the specific user interaction data for the new item.

Step 4: Output a Posterior for the Item and Query Using the Bayesian Formula 

The system then applies the Bayesian formula to combine the prior with the newly received user interaction data, generating a “posterior” prediction value. This posterior value provides a refined estimate of the item’s relevance, taking into account both the historical data and the new specific user interactions.

Step 5: Rank Search Results 

Finally, the system uses the posterior prediction value to re-rank the item in the search results. This re-ranking process ensures that the item is positioned accurately based on its most up-to-date relevance, as determined by the Bayesian model. The goal is to display the most relevant and successful items to users in the search results.

Figures 3 and 4 together depict a sophisticated ranking system that balances predictive modelling with real-time data feedback. Initially, the system predicts the performance of new items based on historical data (Figure 3). As these items begin to interact with users, the system collects actual performance data, which it uses to update and refine their rankings (Figure 4). This dual-phase approach ensures that all items, whether new or established, are ranked as accurately as possible, providing users with the most relevant search results.

Myth Crusher on the Honeymoon Period

While the concept of the “honeymoon period” has captivated Amazon sellers for years, there is a critical distinction between popular belief and the actual mechanics of Amazon’s ranking algorithm. Contrary to the widespread notion that every new product on Amazon receives a guaranteed boost in visibility for the first 30 days, the reality is more nuanced and sophisticated.

No Guaranteed Boost:

• 2022 Patent (US11269898B1): This patent introduces the concept of “prior prediction values,” which are generated based on the historical performance of similar items. These values help determine the initial ranking of a new product. However, the patent does not suggest a guaranteed boost based purely on newness. Instead, the visibility is tied to how well the new product aligns with the successful attributes of existing items. If the product does not match these attributes well or is poorly indexed, it will not automatically receive a high ranking.
• 2023 Patent (US20230367818A1): The 2023 patent builds upon and refines the approach outlined in the 2022 patent by integrating advanced machine learning models and Bayesian methods, significantly enhancing how new products are ranked on Amazon.

When a new product is introduced, it still relies on prior prediction values to establish an initial ranking (Fig. 3 explained above) which gets calculated based on past performance of similar items that do have sales history. These values are derived from a detailed analysis of the product’s attributes- such as its type, brand, category, and other metadata—cross-referenced with the historical performance data of similar items. The system makes an informed estimate of the product’s potential success based on how similar products have performed in the past. However, this process is now more sophisticated, with the system considering a broader range of attributes.

One of the most significant advancements in the 2023 patent is the incorporation of Bayesian methods into this process. The Bayesian approach allows the system to dynamically update these prior prediction values as new data becomes available. This means that as soon as the new product starts accumulating actual user interactions, the system refines its predictions in real-time. The Bayesian model continuously adjusts the product’s ranking based on this real-time data, ensuring that the product’s visibility is closely aligned with its actual performance in the marketplace.

Importantly, the 2023 patent emphasizes that there is no guaranteed boost in visibility simply because a product is new. The system does not automatically favour new items; instead, it relies on data-driven predictions and real-time user interactions to determine a product’s ranking. If the initial prediction values suggest that the product is likely to perform well, it may receive favourable visibility. However, this visibility is contingent on the product’s ongoing performance. The system’s continuous learning process means that rankings are fluid, with products that meet or exceed their predicted performance being adjusted upward, while those that fail to attract engagement are adjusted downward.

This dynamic adjustment process, powered by Bayesian methods, ensures that the ranking system is highly responsive to real-user engagement, reducing the likelihood of inaccurate or unfair boosts in visibility. The result is a more accurate and fair ranking system that benefits both sellers, who can rely on the system to reward truly successful products, and consumers, who are more likely to see the most relevant and popular items in their search results.

No Fixed 30-Day Window

The belief in a fixed 30-day period of guaranteed visibility is a myth. Amazon’s algorithm does not operate on a set timeframe. Instead, the system dynamically adjusts based on real-time data. Initially, when there’s little or no user interaction data available for a new product, Amazon relies on prior prediction values. But as the product starts accumulating its own interaction data—referred to as posterior prediction values—the system, now enhanced by more sophisticated real-time learning models, gradually shifts its focus from predictions to actual performance metrics.

If a product quickly garners user interest through clicks, purchases, and positive feedback, its visibility may continue to rise. On the other hand, if it fails to attract engagement, it will not sustain its position, regardless of the duration it has been available.

2022 Patent (US11269898B1):

The 2022 patent introduces a method for ranking new items using prior prediction values. These values are generated when a product is first introduced to Amazon’s catalogue, based on the historical performance of similar items. In the initial stage, when there is little or no user interaction data available for the new product, Amazon relies on these prior prediction values to estimate the product’s potential success. This estimate helps position the product within search results, but it does not guarantee a fixed period of high visibility.

As the product starts to gather its own interaction data—such as clicks and purchases—the system gradually transitions from relying solely on prior prediction values to incorporating this new, actual user data. This shift leads to the generation of posterior prediction values, which more accurately reflect the product’s real-world performance. Notably, the patent specifies that this transition can happen as quickly as three days after the item is added to the database, depending on how much interaction data the product has accumulated (US11269898). This dynamic adjustment means that a product’s ranking and visibility are fluid and continuously evolving as more data is collected. Therefore, if a product does not attract significant user engagement, its ranking will not be artificially maintained at a higher level, regardless of how long it has been listed.

2023 Patent (US20230367818A1):

The 2023 patent further enhances this dynamic adjustment process by integrating more sophisticated real-time data processing and machine learning models. This updated approach still begins with the use of prior prediction values but places a greater emphasis on real-time adjustments as soon as user interaction data becomes available. The Bayesian methods incorporated in the 2023 patent allow the system to continuously refine and update the product’s ranking as it garners more user engagement. This ensures that the system quickly and accurately shifts from initial predictions to real performance metrics, providing a more responsive and adaptive ranking process.

As with the 2022 patent, the 2023 system does not adhere to a fixed timeframe during which a new product enjoys guaranteed visibility. Instead, the product’s ranking is constantly evolving based on how well it performs in the marketplace. If a product quickly captures user interest, it may see a rise in visibility. Conversely, if it fails to engage users, its ranking will drop, reflecting its actual relevance rather than relying on any arbitrary time-based boost. The system also recognizes a ninety-day claim window, during which the product’s performance is continually assessed; however, if it is considered that the item does have sufficient user interaction data, the item will become ineligible for the cold start mechanism and therefore will be passed to a behavioral machine learning model.

Strategies for Launching New Products on Amazon

Successfully launching a product on Amazon requires a deep understanding of the platform’s ranking mechanisms, coupled with tried-and-true strategies that have been refined over the years. In this section, we will break down the key elements that contribute to a successful product launch, drawing on both the technical insights from Amazon’s patented processes and the practical wisdom gained from years of experience.

A Deep Dive into Amazon’s Mechanism for Launch Patent 2022 US11269898B1

In this subsection, we’ll use Figure 5 from U.S. Patent No. 11,269,898 to explain the technical process that governs how new products are ranked on Amazon. By quoting directly from the patent, we will clarify how the system transitions from relying on prior prediction values to leveraging actual user interaction data, ensuring that your product launch is not just a shot in the dark but a calculated entry into the marketplace.

The prior prediction values help position the new product in search results, giving it a fair chance to be discovered by potential buyers. The patent describes this process, stating that the cold start service “can train machine learning models based on historical data associated with existing items and attributes from the items.” The output of the machine learning models can be used as input to the ranking service.

As the product begins to collect its own interaction data—like clicks and purchases—the system gradually transitions to using this actual data, known as “posterior prediction values.” This shift ensures that the product’s ranking is continuously refined, moving from an initial estimate based on similar products to a ranking grounded in its own performance. This dynamic adjustment helps ensure that a product’s visibility on Amazon is both strategic and responsive to real-world engagement.

As illustrated in Figure 5, the transition from prior prediction values to posterior prediction values is a crucial aspect of Amazon’s ranking process. Initially, the system relies on prior prediction values to estimate a new product’s potential success, but it is the actual user interaction data—what the patent terms posterior prediction values—that ultimately determines the product’s rank over time. This makes the relevance of the traffic coming to your product page incredibly important. When your product attracts relevant traffic—users who are likely to engage with the product by clicking and purchasing—it accelerates the collection of posterior prediction data. This real user data directly influences the ranking model, allowing the system to refine and improve the product’s visibility in search results.

Thus, ensuring that your initial advertising efforts drive the right kind of traffic to your product page is essential. The quicker and more accurately Amazon can gather meaningful interaction data, the faster the system can adjust the product’s ranking, moving it up in search results based on actual performance. This feedback loop, depicted in Figure 5, underscores the importance of not just any traffic but relevant and engaged traffic in successfully navigating the Cold Start process and securing a strong position in Amazon’s marketplace.

Patent 2023 US20230367818A1

In this subsection, we’ll delve into Figure 5 from U.S. Patent No. 20230367818A1 to elucidate the technical process that underpins how new products are ranked on Amazon’s platform. By directly referencing the patent, we will shed light on how the system transitions from initial data-driven predictions to continuously refined rankings based on real-time user interactions. This ensures that your product launch is strategic and data-driven, rather than leaving it to chance.

Fig-5-How Semantic, BERT, Cosmo Works and The Halo Effect

00:00 Introduction to Beard Oil Search Process

00:19 Collecting and Processing Training Data

00:54 Training the Machine Learning Model

01:34 Understanding Different Algorithms

02:50 Handling User Queries and Search Results

09:45 Ranking and Displaying Search Results

15:38 Monitoring and Updating Interaction Data

21:41 Applying Bayesian Updates

25:18 Integrating Semantic Matching, BERT, and Cosmo

33:00 Introduction to the Honeymoon Period

33:12 Understanding ASIN Deletion and Reintroduction

34:52 Resetting the Honeymoon Period: Why It Fails

37:26 The Myth of the Honeymoon Grace Period

41:10 Guaranteed and Bad Honeymoon Periods

44:49 The Impact of Giveaways on Rankings

47:34 External Traffic: The Halo Effect

51:40 Consequences of Poor Quality Traffic

55:10 Handling External Traffic Without Search Queries

Initially, the system leverages prior prediction values to position new products in search results. These values are derived from advanced machine learning models trained on historical data from similar products, as well as specific attributes associated with the new item. As described in the patent, “the machine learning model generates prior prediction values based on the attributes and historical performance data of similar items.” This allows new products to gain early visibility, even before they have accumulated their own interaction data.

As the product starts to gather actual user interaction data—such as clicks, purchases, and views—the system dynamically shifts to utilizing this data, referred to as posterior prediction values. This transition ensures that the product’s ranking is continuously updated and becomes more reflective of its actual market performance rather than just predictive estimates. The patent outlines this adaptive process, emphasizing the system’s ability to “adjust rankings in real-time as new interaction data is incorporated.”

Figure 5 illustrates the critical transition from prior prediction values to posterior prediction values, a key element of Amazon’s evolving ranking methodology. Initially, the system relies on prior predictions to estimate the product’s potential success, but as real-world data accumulates, these predictions are fine-tuned based on actual user engagement. This mechanism makes the relevance of the incoming traffic to your product page crucial. When your product attracts relevant and engaged traffic—users who are likely to interact positively by clicking and purchasing—the system accelerates the collection and application of posterior prediction data. This real-time data directly influences the ranking algorithm, allowing the system to more accurately position your product within search results.

Therefore, ensuring that your initial marketing efforts drive the right kind of traffic to your product page is essential. The faster Amazon’s system can gather meaningful interaction data, the quicker it can adjust your product’s ranking, enhancing its visibility in search results based on actual performance. The feedback loop depicted in Figure 5 underscores the importance of attracting relevant and engaged traffic during the launch phase. Successfully navigating this process can secure a strong position for your product in Amazon’s marketplace, ensuring sustained visibility and competitiveness.

Optimizing Amazon Advertising: Strategies for Launch and Beyond

Launching a product on Amazon requires more than just listing it; it demands a strategic, well-coordinated advertising approach to ensure visibility and long-term success. By understanding the nuances of Amazon’s algorithm and leveraging insights from the latest patents, you can craft an advertising strategy that not only drives immediate traffic but also builds sustained momentum. In this section, we’ll explore key strategies to optimize your Amazon advertising efforts, from content creation and keyword selection to advanced campaign structures and continuous optimization, all aimed at maximizing your product’s potential during its launch and beyond.

High-Quality Content is The Foundation of Early Success

Before your product goes live, investing in high-quality content is crucial. Both patents stress that user interactions are vital for ranking, and your product listing needs to be fully optimized to generate these interactions. This includes clear, engaging images, detailed product descriptions, and compelling text that highlights your product’s key benefits.

• 2022 Patent: Initial visibility is driven by prior prediction values, influenced by how well your product attributes align with successful items. High-quality visuals and descriptions increase the likelihood of early positive interactions, which are crucial for transitioning to posterior prediction values.
• 2023 Patent: The system dynamically adjusts rankings in real-time based on user interactions. Professional photography, high-resolution images, and engaging content help capture user attention, generating valuable interaction data.

Pro Tip: Never drive traffic to an unoptimized or market-unready page. High-quality content alone won’t drive sales; your strategy must align with your audience, with creative assets clearly communicating your product’s benefits and problem-solving features.

Accurate Product Attributes and Category Selection

Choosing the right product attributes and categories ensures your product is properly indexed and positioned for visibility. Accurate and strategic selection is essential for aligning your product with relevant search queries and competitive products.

• 2022 Patent: Prior prediction values depend on how well your product matches successful existing items. Strategically choosing the correct product attributes and category selections ensures favourable initial visibility.
• 2023 Patent: The 2023 patent enhances the ranking system by integrating advanced machine learning models that continuously refine product rankings as new data is collected. This dynamic adjustment means that accurately chosen product attributes and categories play an even more crucial role in how your product is initially positioned and how it evolves in ranking over time.

Pro Tip: Download the category report and complete all product attributes that apply to your item. Ensure that you accurately fill in all mandatory fields and review the optional columns to identify those relevant to your product. This approach ensures that you include only the most pertinent information.

Amplifying Initial Engagement Through Early Advertising Strategies

Early advertising efforts are essential for generating the initial user interactions that Amazon’s algorithm uses to refine your product’s ranking. Immediate advertising accelerates the transition from prior to posterior prediction values, ensuring your product’s ranking reflects real performance metrics.

• 2022 Patent: The patent emphasizes the need for sufficient user interaction data to transition from prior to posterior prediction values. Immediate advertising can help generate this data, potentially accelerating the process and solidifying your product’s ranking.
• 2023 Patent: The use of Bayesian methods allows the system to continuously update rankings as new data is collected. Early advertising is critical to establishing a strong presence in search results from the start.

Pro Tip: Ensure your advertising campaigns are ready to go live as soon as your product listing becomes active. An aggressive bidding strategy for ranking purposes can help maximize visibility and build crucial user interaction data from day one.

Continuous Monitoring and Adaptation

Amazon’s ranking system is dynamic, continuously adjusting based on new data. Both patents underscore the importance of monitoring performance metrics and making timely adjustments to optimize your listing.

• 2022 Patent: As your product accumulates interaction data, the system shifts from prior to posterior prediction values. Monitoring click-through rates (CTR), conversion rates (CVR), and sales data allows for timely optimizations.
• 2023 Patent: The 2023 patent highlights Amazon’s use of real-time data to continuously adjust product rankings. By leveraging Bayesian models, the system updates product relevance in response to the latest user interactions, ensuring that rankings reflect current market dynamics. For sellers, this means closely monitoring metrics like sales data, conversion rates (CVR), and click-through rates (CTR) is essential for staying competitive, as these metrics directly influence real-time ranking adjustments.

Pro Tip: To stay competitive, maintain a clean and well-organized campaign structure with precise highly relevant targeting. This makes it easier to manage and adjust your strategy as needed. Regularly monitor key performance indicators like your conversion rate (CVR) and compare them against market benchmarks. Additionally, keep a close eye on your market share to identify trends and opportunities.

Leveraging External Traffic and On-Site Advertising

Driving external traffic to your Amazon product listing can significantly impact your launch, but it’s vital to ensure that this traffic aligns with your target audience. According to the patents, training data is stored in the Item Data Storage before or independent of a query search, making it crucial that your external traffic mirrors your on-Amazon efforts. Both on-site and off-site advertising strategies must be synchronized for maximum impact.

Pro Tip: Use Amazon Attribution to measure the effectiveness of your external campaigns and monitor which channels are driving the best results. Start external traffic campaigns simultaneously with on-site ads to ensure that all incoming traffic is relevant and beneficial. Continuously track the performance of these campaigns and make necessary adjustments to optimize targeting and maximize their impact on your product’s ranking.

Final Tips for Optimizing Your Amazon Advertising Strategy

To effectively optimize your Amazon advertising strategy, it’s crucial to align your efforts with the insights from the 2023 patent, particularly how Amazon’s algorithm evaluates and ranks products. Incorporating the knowledge about the COSMO framework into your strategy ensures that every aspect of your product listing and advertising campaigns is optimized for maximum impact. Below are the final tips for fine-tuning your ads.

Disclaimer: Keep in mind that the launch campaign structure should reflect the goals and the available budget. Based on the category, product type, target audience and allocated funds for initial advertising, craft a campaign structure using the suggestions below. However, remember that each launch strategy will differ and require customization.

Prioritize Relevancy in Keyword Selection Rather than Search Volume

When selecting keywords, focus on relevancy rather than just search volume. High-relevancy keywords align more closely with your product’s attributes and customer search intent and behavior, increasing the likelihood of conversions. This alignment is crucial for Amazon’s ranking algorithm, which uses lexical, semantic, BERT, and common sense to measure the relevance of your product to a user query. (For more insights about the common sense matching, check out this article: Mastering the Cosmo Framework: Optimize Your Listings for Maximum Impact)

• Patent Insight: As the 2023 patent explains, “In a learning-to-rank framework, values for behavioral features along with lexical and/or semantic features can be used to measure relevance (r) between query (q) and item (i) as P(r|q, i).” By choosing highly relevant keywords, you ensure that your product is accurately positioned in search results, particularly when it lacks substantial user interaction data, and has a high score for ranking once it gathers positive user interaction.

Optimize Product Exposure with a Strategic Campaign Structure:

When launching a product on Amazon, it’s essential to recognize that the cold start mechanism applies not only to organic rankings but also to sponsored ad positions. Just like in organic rankings, Amazon uses prior data from similar products to estimate your product’s likelihood of conversion. The key difference with sponsored ads is that your bidding strategy and how much you’re willing to pay for specific placements become crucial factors in gaining visibility.

During the launch, focus on building campaigns that target highly relevant, high-intent queries. This approach helps create strong data for query-item pairs, which Amazon’s algorithm uses to evaluate how well a product aligns with specific search terms. The system builds these pairs by analyzing user interaction with both the product and the search queries. The stronger the data tied to specific, high-converting queries, the more accurately the algorithm can refine your product’s relevance over time. This is critical for building both initial visibility and long-term ranking strength.

• To optimize the cold start period, prioritize lower-funnel, conversion-driven audiences by targeting keywords that are highly relevant to your product and tied to strong purchasing intent. This approach will not only increase conversions but will also help solidify your product’s association with high-value search terms. The data gathered from these targeted campaigns helps feed Amazon’s machine-learning models, improving the accuracy of query-item pairs and enhancing your product’s future placement in both organic and sponsored positions.
• As the system continues to gather data, the real user interactions from your campaigns are transformed into posteriors, refining the algorithm’s understanding of your product’s relevance. This means your future rankings and ad placements are continuously updated based on actual performance, ensuring that the system establishes the best possible position for your product in future searches, based on concrete user behavior and engagement.

Implement Structured Ranking Campaigns:

• Begin by implementing a Sponsored Products Exact match ranking campaign targeting the keyword you want to rank for. Choose this keyword carefully to ensure it accurately reflects the term that best defines your product, maximizing its relevance and potential to drive conversions. Employ an aggressive bidding strategy to capitalize on early traffic and establish your product’s visibility. The choice between the two can be made based on the available budget for the ranking strategy and the aggressiveness around the visibility purpose. While taking Amazon’s suggested bids into account, tailor your bidding approach based on your available budget. As your campaign gathers data, closely monitor its performance and make strategic adjustments. Focus on optimizing campaign placements, particularly by adjusting the top-of-search placement percentage, to enhance your product’s visibility for the high-engagement position.
• Budget Management: Ensure your budget is balanced and sufficient to sustain the campaign, avoiding the risk of spreading your resources too thin. This allows you to maintain consistent visibility without compromising the quality of your campaign structure.

Choose Competitors Strategically:

• Utilize Product Targeting Campaigns (both Sponsored Products and Sponsored Display) that target your top competitors’ pages. By aligning your product with a similar audience, you’ll capitalize on their purchasing intent. However, closely monitor the keywords generated through product targeting (Sponsored Products), and block any irrelevant ones to avoid wasting your ad spend on low-converting traffic. Alternatively, if your goal is to grow brand awareness, you can expand your targeting to products that are broadly related to yours and reach a different, less specific audience. Be prepared, though, for a lower conversion rate in this case, as the broader audience may be less likely to immediately purchase your product.
• When a cold start is triggered, Amazon’s system pulls data from products it considers similar to yours to estimate initial performance. You should mirror this approach with your targeting strategy, focusing on competitors who offer products with similar features, benefits, and price points. This ensures your ads are shown to a high-intent audience already interested in products like yours, increasing the likelihood of conversions.

Maintain a Clean and Effective Campaign Structure:

• A well-organized campaign structure not only simplifies management but also enhances your ability to scale successful campaigns. This approach allows you to micromanage effectively, making data-driven adjustments without getting overwhelmed by complexity.
• Based on our understanding of the algorithm, we know that Amazon considers both lexical and semantic matching when aligning items with search queries. Additionally, it’s important to remember that the COSMO framework, which incorporates common-sense knowledge, also plays a role in matching items to queries by leveraging past user interactions. With this in mind, although we have predefined match types in advertising that guide our expectations, it’s crucial to recognize that in Sponsored Products automatic campaigns, Sponsored Products product targeting campaigns, and even Sponsored Products broad match types, the algorithm will utilize the above-mentioned frameworks, alongside to the “new” match types for Sponsored Brands. This means your product will be shown to search terms based on a combination of relevance, lexical and semantic matching, and common-sense reasoning. To maximize the effectiveness of these campaigns, it is essential to analyze the data, eliminate irrelevant elements, and optimize listings for stronger product recognition, but most importantly have a clean campaign structure as this will play a pivotal role in ensuring the success of this approach.

For Established Brands:

• Keep your campaign structure organized and straightforward by grouping campaigns based on clear objectives, such as ranking, discovery, defence, product targeting, or brand awareness. A clean campaign structure facilitates easier monitoring and more effective optimization, ensuring that each campaign performs at its best and contributes meaningfully to your overall advertising strategy. 
• If you already have established products in your catalogue, use virtual bundles and brand experiments to gain visibility for your new launch. These tools allow you to interact with loyal customers who already trust your brand, helping to generate awareness and interest in your newly launched product by associating it with well-known items in your lineup. Additionally, leverage Sponsored Brands (SB) campaigns to drive traffic not only to the product detail page of the new item but also to a dedicated page in your Amazon Store where the new product is prominently featured. This strategy boosts visibility for the new product while capitalizing on the popularity of your existing products to attract and convert customers.
• Brand Leverage: Use your existing brand recognition to drive traffic to a dedicated page in your Amazon Store or a well-optimized product detail page (PDP) where the new product is prominently featured. This strategy not only increases the visibility of the newly launched product but also leverages the strength of your established products to enhance customer trust and engagement across your entire brand.

Don’t Forget to Monitor and Optimize Continuously:

Regularly review the performance of your campaigns, both on Amazon and off Amazon. Use Amazon Attribution to measure the effectiveness of external traffic and adjust your strategies based on what’s driving the best results. This ongoing optimization ensures that your advertising efforts remain aligned with your business objectives.

• By implementing these final tips and applying the insights from Amazon’s 2023 patent, you can develop a robust advertising strategy that supports a successful product launch. This approach will not only maximize your product’s visibility during the critical launch phase but also help ensure sustained success on the Amazon platform. However, remember that there is no one-size-fits-all strategy; each new product is unique and will require a customized launch strategy.

Pro Tip: How Amazon’s Evolving Ads Impact Your Strategy

Amazon’s system has become more sophisticated, focusing on delivering a personalized shopping experience. With the 2023 patent update, shopper behavior and continuous real-time updates now play a much larger role in influencing ad visibility. Big changes have already been rolled out – for example, Sponsored Brand Match types no longer behave as they used to. Exact match no longer guarantees exact query matches, thanks to the COSMO framework that everyone has been excited about this year, which applies commonsense reasoning to advertising. However, Sponsored Products match types, like Exact and Phrase, still remain unaffected and reliable for now.

In addition, Sponsored Products product targeting and Sponsored Brands product targeting now generate both keywords and products, expanding the scope of targeting. Some placements, like “Frequently Bought Together,” are also outside your direct control, as the system focuses on showing products that are most relevant to individual shoppers, based on their unique shopping behavior and preferences.

For advertisers, understanding how this sophisticated targeting and query matching works is crucial. This insight helps you build strategies and optimizations that ensure your ads are seen by the right audience, driving higher engagement and conversions. 

Danny’s Final Take-aways

• Most hacks don’t work, or they are short-term because algorithms are far more sophisticated than at any point in time. Every day it becomes harder and harder.
• Attributes, attributes, attributes—these need to have a similar level of effort that you put into keywords and indexing. It says right in the scientific literature. From Cosmo, BERT, and Semantic all the way back to “The Joy of Search” in 2016. As mentioned early, Today, they use pre-prior calculations to match attributes prior to posterior data on top-performing products before launch. That, with the understanding that the shopping experience is more personalized as the system uses the query-item pairs as we saw in the videos, which is just the technical term that says word-to-word does not work anymore. The system takes behavior of every shopper into consideration and makes sophisticated predictions by putting everything together.
• Most sellers are not successful because of the knowledge they have on ranking; it’s because they are really good at product development. At the end of the day, you can’t polish a turd. They may get a short-run propping product up, but they don’t last in the long term.
• External traffic that is extremely targeted will help trigger the cold start system; however, if you get this wrong and you have little or no conversions, this will impact you today almost in real time. If you don’t have targeted traffic, for example, you are running Google ads and your product has like 30 searches a month, don’t try to find traffic with lots of search volume just to force external traffic to your listings. Remember, it’s about triggering real behavior, not just getting activity going on your page to trigger the cold start mechanism – not all behavior is equal. If you have little volume, then focus on the platform, preferably sponsored product ads.
• None of this knowledge matters if you have not addressed your customer objections. No amount of ranking knowledge outpaces a product with customer objections. And if you have the ability to fix them, then the product will perform much better. You can afford to make mistakes with marketing as long as there is some viability, as you can correct the path. But if you have a product that has customer objections, you’re just going to be in an endless loop chasing observations, proven strategies, and burning through your dopamine receptors on secret hacks and magical strategies that don’t exist for everyone, as it works only on the products it was tested on. Every time you see a screengrab of a 40-100% lift in conversion rate, there is a graveyard of failed tests that did not make the cut.

Make good products, address your customers’ objections, embrace a world beyond lexical matching and indexing, and mix your observations with science. Remember why you are an entrepreneur, because you think differently from 97% (ish) of the population. That includes all the nonsense; be selective and don’t follow the herd, be the shepherd. Use your critical thinking and question everything, then apply what matters and dump the rest.

Oana’s Final Take – aways

1. No Magic PPC Campaign: There isn’t a single perfect PPC campaign that will solve all your problems. Successful advertising takes constant adjustment, iteration, and strategy. Don’t fall for the idea that there’s one magic formula.
2. Understand Your Shoppers: Knowing who your customers are is crucial. Without understanding your audience, you’re just shouting into the void. Craft your messaging and strategies around who they are, what they need, and what they value.
3. Have a Great Product & Seek Customer Feedback: No campaign or ranking strategy can make up for a subpar product. A great product forms the foundation, and customer feedback helps you continuously improve it. Listening to your customers and iterating based on their experiences is key.
4. Optimization Goes Beyond the Basics: Optimization isn’t just about your title, bullet points, or backend search terms any more. Amazon’s personalization, especially with the title personalization feature announced at Accelerate 2024, means your listings need to connect with shoppers on a deeper level. Relevancy, attributes, and personalization are what differentiate you from the competition.
5. Patience and Process: Things take time. Remember the concepts of priors and posteriors—initial estimations (priors) and refined learnings (posteriors) both matter. Building sales velocity and relevance is a journey, not an instant result. Stay strategic, stay focused, and remember that consistent relevance always wins.

Connecting the Layers: From COSMO to Rufus and Beyond

Amazon’s evolution in search and recommendation systems is like building a multilayered foundation, constantly expanding to provide better experiences for both sellers and customers. The COSMO framework laid the groundwork for advanced matching, moving beyond basic keyword relevance to include deeper semantic understanding and even commonsense reasoning. It focused on making sure the context of a shopper’s query was aligned with the attributes of a product, thereby optimizing discoverability.

Rufus took this a step further by integrating real-time adaptability, using behavioral data to refine predictions at a much more granular level. This added a dynamic element to Amazon’s ranking system – no longer was it just about aligning attributes; it became about understanding and reacting to shopper behavior as it happened. This shift made the recommendation process smarter, more fluid, and capable of keeping up with evolving customer needs.

Now, with MERLIN, Amazon is adding another powerful layer. MERLIN uses Graph Neural Networks to deeply understand product relationships through multi-modal and multi-lingual embeddings. This helps in connecting products across different categories and even different languages, addressing the cold-start problem and improving product recommendations on a massive scale. The goal is to enhance the visibility of products – even new or less popular ones – by leveraging sophisticated embeddings and co-purchase relationships.

Together, COSMO, Rufus, and MERLIN represent Amazon’s layered approach to recommendations. Each innovation builds upon the previous one, creating a robust system that isn’t just about relevance but about personalization, adaptability, and interconnectedness. This layered evolution means that sellers must also adapt, recognizing that optimizing for Amazon today requires understanding these multiple layers and how they work together to serve both sellers and customers more effectively.

The journey from COSMO to Rufus to MERLIN illustrates how Amazon continues to add complexity and sophistication to its systems. Sellers who want to stay ahead must embrace this evolution-there’s no longer a simple hack or shortcut. Understanding how these layers interact, from leveraging personalization to employing advanced embeddings, is key to thriving in an increasingly competitive marketplace. It’s about staying dynamic, being data-driven, and constantly seeking to align with Amazon’s drive for smarter and more connected recommendations.

Buckle up – The game is changing and your critical thinking could be the difference between winning and losing on Amazon.