How Amazon’s Recommendation Engine Finds What You Need

Amazon’s recommendation engine is one of the biggest reasons for its eCommerce success. After all, it’s highly known for delivering personalised shopping experiences to customers that seem almost intuitive. However, this sophisticated recommendation system uses powerful algorithms to understand customer preferences at a deep level. 

Behind the scenes, Amazon employs several techniques to gather and analyse vast amounts of data from user interactions, like browsing history, purchase habits, and even time spent on product pages. This results in highly tailored product recommendations that not only improve a customer’s shopping experience but also drive sales and loyalty. It’s no surprise that Amazon’s recommendation engine, which continuously learns and evolves, sets Amazon apart as a leader in digital personalisation in eCommerce.

Let’s find out how it works.

Amazon’s Recommendation Engine: What Does It Mean?

Amazon’s Recommendation Engine, or any recommendation system, works on sophisticated AI/ML algorithms. It is designed to improve a customer’s shopping experience by providing personalised product recommendations after analysing user data. It uses a variety of data sources, including the following:

• User behaviour and demographics
• Product attributes 
• Products a user has saved and listed
• Products a user has purchased and rates
• Trending products
• Location
• User reviews, etc. 

Amazon’s recommendation system works across its eCommerce and streaming platforms. It can be easily found on its home page, product pages, and checkout page. It can help customers discover new products and take advantage of better product deals and promotional offers. For eCommerce businesses, it helps increase average order value and revenue. 

The Mechanics Behind Amazon’s Recommendation System

Amazon’s recommendation system is renowned for its ability to offer customers personalised and relevant product recommendations. It uses data analytics and other advanced technologies to deliver tailored suggestions. 

Let’s find out how it works.

• Collaborative Filtering

Amazon’s recommendation system primarily works on collaborative filtering techniques. Amazon analyses a large amount of data, including user behaviour and preferences, to make product recommendations. It also analyses a customer’s purchase history and their browsing behaviour and ratings. Here, the goal is to identify patterns and similarities among different users. When Amazon finds users with similar tastes and preferences, it can recommend the products one customer has liked to others who have similar interests. 

• Content-Based Filtering

Amazon uses content-based filtering to analyse the characteristics of a product, including their titles, categories, descriptions, specifications, etc. Amazon then recommends similar products based on the features and characteristics of each product by understanding the content of each product. 

• Machine Learning and Deep Learning

Amazon pulls insights from product descriptions, customer reviews, and other textual data by using NLP techniques. For example, if a customer has left a positive review for a product, Amazon’s recommendation engine can identify keywords to recommend similar products to the customer. 

• Real-Time Data Processing

Amazon’s recommendation engine operates in real time. It adapts to changing user behaviour and preferences. For example, if a customer’s browsing behaviour suddenly shows a new interest, Amazon can adjust the product recommendations to reflect the change in the user’s behaviour. 

• A/B Testing and Experimentation

Amazon regularly performs A/B testing to evaluate how effective its different recommendation strategies are. It also compares different approaches and algorithms to determine which one offers the best results in terms of user engagement, conversion rates, and customer satisfaction. Amazon continuously optimises its algorithms through analysis of user feedback and experimentation to make sure its recommendation system is working properly.

• Hybrid Approaches

In a hybrid approach, Amazon combines the strength of collaborative and content-based filtering to provide more diverse and accurate product recommendations. Leveraging multiple techniques enables Amazon to improve its overall recommendation strategy. It also helps Amazon overcome drawbacks that are associated with individual recommendation techniques. 

• Contextual Factors

Amazon’s recommendation engine uses contextual factors to make sure its recommendations are on point. These factors include the time of day, user’s location, device type, browsing history, and more. Taking these factors into account helps Amazon to provide recommendations tailored to each user’s behaviour and preferences. 

• Feedback Loop

Amazon uses a feedback loop mechanism. It regularly collects and analyses user feedback, including reviews, ratings, and purchase history. It helps Amazon refine its recommendation techniques, improving the accuracy and relevance of recommendations in the future. Based on feedback, it can adapt to user preferences and ensure dynamic and personalised recommendations. 

• A9/A10

Related products are proprietary A9 and A10 search algorithms of Amazon. It uses a combination of advanced techniques to personalise search results for customers. However, Amazon has not publicly disclosed how the A9 and A10 algorithm works. 

Strategy Used by Amazon Recommendations

Here are some strategies used by Amazon’s recommendation engine.

• Amazon offers recommendations, highlighting the time-sensitive nature of a product promotion. It creates a collection of product recommendations from a particular day’s deals to entice users. It even lists the previous price of the product, telling customers they are saving a huge amount of money if they purchase the product while it is temporarily discounted.
• Amazon nudges users to log in to their Amazon account for better product recommendations. Amazon’s recommendation system can tailor recommendations on a more granular level when a user is logged in. However, if a user doesn’t log in, it recommends products based on the items they have viewed in that particular session. 

On-Site Recommendations

Here’s how Amazon uses on-site recommendations.

• Recommended for you

When users click on the ‘Your Recommendations’ link, it’ll lead them to a page with product recommendations just for them. Amazon can recommend a range of products from different categories they’ve been browsing. It aims to put the right product in front of the user that they’re likely to click on and buy.

• Frequently bought together

The main goal of this recommendation is to increase the average order value. With ‘frequently bought together’ recommendations, Amazon aims to cross-sell and up-sell products based on the items in their shopping carts or the ones they’re currently browsing. 

• Recently viewed items and featured recommendations based on the user’s browsing history

Amazon analyses the products users have been browsing and recommends very similar products of different brands, shapes, sizes, colours, etc. It helps customers find a product similar to the one they have already shown interest in. 

• Browsing history

Amazon knows if a user has searched for a particular product and showed interest in it. Amazon shows users their browsing history in case they want to go back and buy the product they have already shown an interest in. 

• Products related to items a user has viewed

Amazon displays products related to items a user has already viewed in the past. Once again, the goal is to help customers understand what they might be interested in buying based on their user behaviour and preferences. 

• Customers who bought this item also bought

This is similar to the ‘frequently bought together’ recommendations, meaning the goal is to increase the average order value by up-selling and cross-selling products. Amazon displays products that users have purchased together in the past. 

• There is a new version of this item available

This recommendation technique mostly appeals to customers who want to upgrade their electronic gadgets to the latest models and versions. Amazon recommends the latest models of these items to customers who have already purchased an older version in the past.

• Recommended product based on a previous purchase 

This recommendation technique can be understood with an example. For example, a user purchased a mobile from Amazon. It’ll recommend phone covers for the exact model the user has purchased in an attempt to encourage them to purchase it by cross-selling a highly relevant product. 

• Best-selling product in a particular ‘category’

Amazon recommends top products from a category to users who are looking to buy new and latest products. The term ‘best-selling’ acts as social proof and builds customer trust. Best sellers from a particular category can help users find products from a new category they may have never purchased from before. It opens up great opportunities to up-sell and cross-sell products. 

Off-Site Recommendations Through Email

Now, let’s see how Amazon recommends products off-site via email.

• It sends emails with the best-selling products of the week. This recommendation can be based on their browsing history, recently viewed items, or products of a particular brand in their shopping carts.
• Amazon sends emails with recommendations of products often purchased together. For instance, if a user has purchased a camera, Amazon may try to cross-sell its accessories via email.
• Emails with best-selling products across the entire product category and not from any specific brand are another way Amazon recommends products off-site. This email contains top-selling items from across the entire product category the user has already browsed and the ones that most people end up buying. However, there is no focus on any specific brand. These items have especially positive reviews and high conversion rates. They will likely turn an interested browser into a customer.

The Benefits of Amazon’s Editorial Recommendations

Now, let’s look at the main benefits of Amazon’s editorial recommendations.

• Amazon’s editorial recommendations usually appear for relevant keywords on the first page of Amazon SERPs. It means your products gain more visibility and exposure to buyers who are searching for similar products and are highly likely to buy them. 
• Since editorial recommendations appear on the first page of the search results, it automatically increases organic sales. It reduces the chances of relying on paid ads, improving the overall ROI of your business. 
• According to Amazon, if your products feature in editorial recommendations, your sales can increase by 10% or more. Editorial recommendations drive more traffic to your products and increase the chances of users converting into buyers. Editorial recommendations act as social proof for users, signalling them to make the purchase. 
• Editorial recommendations mean your products are endorsed by reputable publishers. It can significantly enhance your brand image and reputation. Customers are more likely to trust sellers and purchase from ones who are endorsed by established publishers in their fields. It also helps you brand loyalty and retain more customers over time. 
• Amazon editorial recommendations save customers time and effort in researching, comparing, and buying products. With expert and unbiased opinions, these recommendations help customers make better purchasing decisions. 
• It helps you stand out in a competitive market, showcase your products and their unique selling points, and differentiate your business from other sellers who sell similar products. 
• Amazon’s editorial recommendations help you overcome fake and negative overviews. It provides authentic and positive product feedback from well-known sources as compared to reviews from unknown and unverified users. 
• Amazon uses editorial recommendations as one of the factors to decide how and where to rank a product on Amazon search results, boosting overall SEO signals. 

Criteria for Amazon’s Editorial Picks

Your products must meet the following criteria to feature in Amazon’s editorial recommendations.

• It must have at least 100 reviews with a rating of 4 stars or more
• It should not make any medical claims
• It should be among the top 20% of best-selling products in its category on Amazon.
• It has a monthly sales of around $30,000 
• It shouldn’t be related to any religion, drug or sex
• It should perform exceptionally well on relevant keywords
• Lastly, a seller should maintain high levels of product inventory to meet customer demand

If your product meets these criteria, the chances of being featured in Amazon’s editorial recommendations significantly increase. 

How to Increase Your Chances of Being Featured in Amazon Recommendations?

Amazon lists several factors that can help you rank your products higher in search results and increase sales. 

Let’s see how you can increase your product’s chances of featuring in Amazon recommendations.

• Sales rank or the Amazon best sellers rank is one of the most important factors for ranking products. More sales mean higher rankings, with higher rankings bringing in more sales.
• To increase sales, you can offer discounts and attract more customers. You can also adjust your product price based on your costs and competitor research to drive more sales. 
• The number of reviews your product receives and whether they are positive or negative reviews will determine how well it ranks. Product reviews can help you build trust with your customers, answer questions, and encourage more purchases.
• Optimise your product listings, including product titles, descriptions, etc., to rank higher in search results. Conduct keyword research and use the ones most relevant to your products. Product titles should be clear, concise, and high-performing, while descriptions should be informative. 
• Maintain adequate levels of inventory to meet customer demand. If you sell across multiple channels, you can sync inventory to avoid getting out of stock. 
• Display high-quality product images, with shots taken from a variety of angles. The product should fill more than 85% of the frame. 

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Conclusion

Amazon’s recommendation engine does more than just suggest products to customers. It creates an interconnected ecosystem of users influencing one another’s shopping journeys. By leveraging advanced data analytics, dynamic user profiling, and adaptive machine learning, Amazon ensures that its recommendations remain relevant, timely, and personalised. This cutting-edge recommendation system plays an important role in boosting both customer satisfaction and your business’s bottom line. Amazon’s recommendation engine will likely stay one step ahead of the curve, constantly improving how customers shop online.