Authors: Danny McMillan & Oana Padurariu
Amazon is rolling out major updates to its product listing guidelines, with a focus on refining the clarity and impact of bullet points. These updates aim to streamline how product information is presented, making it easier for customers to make informed purchasing decisions while empowering sellers to create more compelling listings. This article examines the details of these changes, highlighting how Amazon’s new guidelines and advanced AI technology will shape the future of selling on the platform.
Additionally, we will provide key insights into how these changes will be implemented, delve into the scientific research behind the updates, and offer a step-by-step guide on how to optimize your listings for maximum effectiveness and a free bot to assist.
Bullet Points Policy Change
Starting August 15th 2024, Amazon is rolling out new rules for product bullet points, aiming to make listings clearer and more straightforward. By refining how information is presented, Amazon is not only helping customers make quicker, more informed decisions but also supporting sellers in creating more compelling and effective product listings.
What is changing?
Amazon is aiming to significantly improve the overall shopping experience and improve the effectiveness of product listings. The changes in the policy are focused on streamlining the content and helping sellers present their offerings in the best way possible. Main changes announced:
1. Restriction of Special Characters and Emojis
Amazon will no longer allow the use of special characters ( ™, ®, €, …, †, ‡, o, ¢, £, ¥, ©, ±, ~, â ) or emojis in product bullet points. While these elements might add some visual flair, they can also clutter listings and distract from the key information.
2. Limitation on Certain Phrases
Phrases related to refunds or guarantees, among others, are being restricted. These types of phrases can often be redundant or unnecessary and by removing these, the focus can remain on the most relevant product features and benefits.
3. Guidance on Crafting High-Quality Bullet Points
Amazon is providing more structured guidance to help sellers create bullet points that are both clear and concise. The emphasis is on delivering key product information in a way that is easy for customers to digest quickly. This guidance is intended to help sellers make their product’s most important features stand out.
Amazon Ensuring Quality
To ensure product listings are up to standard, Amazon is introducing a new approach where non-compliant content will be checked and, if needed, replaced with AI-generated content.
Using advanced AI tools, Amazon will automatically identify and remove content that doesn’t meet the updated guidelines. The AI will then generate new bullet points that are clear, concise, and fully compliant with Amazon’s rules.
For sellers, this means their product listings will be cleaner and more effective, with key features and benefits highlighted in a way that helps attract customers. The AI-driven system not only ensures compliance but also enhances the overall quality of the listings, making them more helpful and easier to understand.
The Science Behind AI-Enhanced Catalog Data
The recent policy changes for product bullet points are deeply connected to the insights presented in an Amazon Science article from 30 April 2024, titled “Evaluating the Helpfulness of AI-Enhanced Catalogue Data.” This research provides a scientific foundation for why Amazon is moving towards more streamlined, AI-optimized content in product listings.
TL;DR
- AI-driven Catalog Enhancements: Amazon uses generative AI models to synthesize product data from various sources, making product information clearer and more accurate.
- Causal Random Forests: A scalable machine-learning model helps Amazon run fewer A/B tests by predicting the impact of catalog improvements on different product categories.
- Bayesian Structural Time Series: This observational model allows Amazon to estimate the impact of catalog enhancements on sales without relying solely on A/B testing.
- Improved Customer Decision-Making: Enriched catalog data leads to more informed and confident customer purchasing decisions, directly impacting sales.
- Strategic Implications for Sellers: Understanding these advancements can help Amazon sellers optimize their product listings to align with AI-driven improvements.
Causal Random Forests for Efficient Testing
Running extensive A/B tests to validate the effectiveness of catalog improvements can be resource-intensive and delay the rollout of beneficial updates. To address this, Amazon has developed a causal random forest model, which is an advanced machine-learning algorithm that extrapolates the results of previous experiments to predict outcomes in new contexts.
This model creates an ensemble of decision trees, each splitting the product data into smaller groups based on feature similarity. By aggregating predictions from these trees, Amazon can predict the likely effects of catalog enhancements without conducting new experiments for every change. This not only speeds up the implementation of improvements but also allows for more targeted updates that benefit specific product categories.
Bayesian Structural Time Series for Sales Impact Analysis
In situations where A/B testing is not feasible, Amazon turns to Bayesian structural time series modeling. This approach combines elements of time-series analysis, synthetic control methods, and Bayesian statistics to estimate the impact of catalog enhancements on sales.
The model works by creating a synthetic “twin” for a group of products, which mirrors their sales performance before any changes are made. After implementing catalog enhancements, the model compares the actual sales performance of the enhanced products with that of the synthetic twin. Any significant differences in performance can be attributed to the catalog improvements.
Improved Customer Decision-Making
One of the most significant benefits of AI-enhanced catalog data is the improvement in customer decision-making. With more accurate and comprehensive product information, customers are better equipped to make informed choices. This leads to higher customer satisfaction, fewer returns, and increased brand loyalty—all of which are advantageous for sellers.
Amazon is leveraging AI to enhance product listings, aiming to provide customers with more accurate and comprehensive information to make better purchasing decisions. The integration of machine learning models, such as causal random forests and Bayesian structural time series, allows Amazon to refine product data by synthesizing information from various sources, including seller listings, manufacturer websites, and customer reviews. This approach not only improves product discoverability but also enhances the quality and clarity of product descriptions, aligning with Amazon’s preference for concise bullet points.
Lexical Matching – One Foot in the Past
While Amazon is advancing with AI-enhanced catalog data, many sellers continue to rely on traditional methods of listing products, focusing on lexical matching and keyword density to improve search visibility. This strategy involves indexing numerous keywords and using lengthy bullet points, which can be less effective in the context of semantic search advancements. Models like BERT (Bidirectional Encoder Representations from Transformers) enable Amazon to understand the context and semantics of words, matching queries with relevant content even if exact keywords are absent. BERT or Semantic does not require the listing to be fully indexed, as these algorithms are much smarter and can scan the full listing. Read this article, The Evolution of Matching Search Queries, and our article on COSMO. Between these two, you will gain great insights into how far search technology has come from its humble beginnings of lexical matching (keywords that index) to today’s contextual matching superiority.
By using AI to refine and enhance catalog data, Amazon is essentially making product information more accessible and relevant. For example, instead of simply listing technical specifications, the AI ensures that the most important product benefits are clearly communicated.
The Role of the Cosmo Framework in Product Discoverability
With the new bullet point guidelines, the COSMO framework’s role becomes even more crucial. As sellers need to update their listings to meet the new standards, COSMO ensures that the optimized content is highly relevant for customers’ searches, helping reduce the time for the purchase decision. This means that well-crafted, AI-optimized bullet points don’t just meet the new rules—they also improve a product’s chances of being discovered by the right customers.
How to optimize your bullet points effectively
There are two ways to optimize your listings, either through bulk files or manually from each listing.
In order for you to optimize your listings using bulk files, simply go to Add Products via Upload section in your Seller Central account ➝ Grab the right template for your product by searching for the product’s category and product type ➝Fill the information in ➝ Submit and track the status as changes get processed.
To manually optimize your listing go to Inventory➝ Manage Inventory ➝ choose a listing and hit the Edit button. Once in the back end of the listing, go to Product Details and scroll down to the ➝Bullet Points section.
Amazon’s bullet points section has evolved significantly. Previously limited to 5 sections of 500 characters each, sellers often use this space to stuff keywords.
Now, Amazon is implementing a policy change focusing on relevant and engaging product information rather than a word-to-word focus. This new approach aims to bring value to shoppers and ensure readability, moving away from keyword-heavy descriptions to more useful, customer-centric content.
If you want to quickly check your current bullet points and ensure they are compliant with the new policy changes, use the below free bot and copy-paste your bullet points by choosing the How do the current bullet points align with the guidelines?conversation starter.
It will help you identify current policy issues and help generate new bullet points for you.
Free bot: https://chatgpt.com/g/g-gGhJntX9V-seo-title-and-bullet-points-optimization
The Future of Product Listings
Amazon’s new approach to product bullet points, backed by cutting-edge AI technology, represents a significant step forward in how product information is managed and presented. The combination of strict guidelines and AI-driven content generation ensures that customers will enjoy a smoother, more efficient shopping experience, with search results displaying products that better match their queries.
For sellers, this shift suggests a need to focus on creating concise and informative product descriptions that convey product benefits and features clearly. Although keywords remain important, the emphasis should be on quality content that aligns with customer search intent. This change not only enhances the user experience but also supports Amazon’s efforts to improve catalog data through AI-enhanced techniques. Again, the problem often arises when Amazon strips a listing of its keywords, and the brand is highly unimpressed with the results and panics because there is a chance that the listing will tank. So right now, we are at a crossroads: do we follow the literature and take on Amazon’s AI suggestions? or ignore play it safe and optimize based on decade-old search practices?
