amazon personalization for better AOV

Amazon Personalization: A Better Way To Increase AVG

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“We need to double our revenue at the end of this year”
“We need to increase our sales by 20% in Q4”

If your CEO keeps asking these questions, then what will be the smartest move? As a marketer, you have to bring in new customers, and taking care of your Average Order Value at the same time is a must to achieve these goals.

Whether you are bringing new customers or taking care of your existing customers, providing a better user experience is the key to retaining them.

Have you ever heard of Amazon Personalization Engine? Amazon is a leader in E-commerce and they invested in personalization dates back to the early 2000s when it designed a plan to create a robust product recommendation engine.

They identified the importance of building humanized digital experiences for their consumers, they are now generating over 35% of their revenue through recommendations.

In this article, we are going to have a look at Amazon’s Personalization Recommendation system and how you can use it in your e-commerce business to improve your average order value through personalization.

If you want to impress your CEO, this will be the smartest implementation you can do.

What is Amazon Personalization Engine?

Amazon Personalization is a fully managed machine learning service that can generate product recommendations for your customers by using their buying journey and data.

It also can generate user segments using customer data which is a huge advantage for marketing and personalization techniques.

Amazon’s recommendation system uses advanced technologies and data analysis to leverage customer behavior, preferences, and item characteristics to deliver personalized suggestions.

How Amazon Personalization Recommendation Engine Works: On-Page and Off-Page

Amazon Recommendations can be used on both on-page and off-page marketing techniques. On-page recommendations are made within the Amazon platform itself and off-page methods can be made on other platforms like emails.

Let’s have a look at where these can be implemented.

On-Page Recommendation Techniques

User-specific product listing

This feature can provide product recommendations based on your customer’s past purchases, and past interactions (products that your customers have visited but not purchased, search history, etc.)

On Amazon’s website, they provide a separate page in the menu bar that contains all the recommended products at once. However, this requires customer action since they have to visit the page to see recommendations.

Recommendations based on purchases made by similar users

Once a customer visits a product page, this method shows similar recommendations after the product information. This is a good method for implementing upselling strategies.

In this case, the system uses the features of the product and user characteristics as the filters to find other users similar to the visitor who also purchased the product they viewing.

Cross-selling based on categories/product relationships

In this method, the system uses the product itself to make recommendations. If a person visits a product information page for a gaming laptop, the system will recommend gaming mouse, headsets, and backpacks by using “gaming” as the core feature.

Amazon’s value proposition here is to help the user find products that accompany the current product being viewed, by filtering items based on product features and categories.

“Frequently bought together” recommendation

This is similar to cross-selling recommendations, but instead of providing the recommended products on the same page, they used a popup to display whenever you select a recommended product.

Recommendations based on browsing history

Whenever a customer logs in to the platform, users are generally presented with recommendations based on products they showed interest in but did not purchase.

In Amazon, they use the two sections “Keep shopping for” and “Pick up where you left off” are recommendations based on your browsing history of products your customers viewed but didn’t purchase. Another segment they will often see is suggestions of items similar to those in their browsing history.

Narrowing based on interests and offers

This recommendation method takes an extra step forward to provide recommendations further narrowing them based on available offers.

Here, the system recommends products based on the customer’s browsing and purchase history, but only those products that have an active discount or offer which increases the chances of capturing interest and making a conversion.

Up-sell recommendations

If a customer visits the product page of an older version of a product, they will see the better (and more expensive) version of the product they are viewing.

The purpose of this recommendation is to upsell – to sell a better version of the product which will also bring in more revenue. The filtering system looks at product features to identify the right recommendation.

These are what marketers can test with the options that Amazon recommendations are providing as on-page options. This will help you to make an instant impact in real-time while your customers are on their buying journey.

Now let’s have a look at how the recommendation system works on off-page channels.

Off-Page Recommendation Techniques

Personalized emails

Here, the system uses on-page data like purchases and browsing history to make off-page recommendations and the first way is through personalized emails.

Once a visitor checks for a particular product, the system sends a follow-up email with a product recommendation. They will get recommendations for the products they viewed as well as for the products similar to what was viewed previously.

Display ads

The system enables the showing of display ads on partner platforms. They are not random recommendations but are personalized to each user based on their past purchases and browsing history.

Now you have a clear idea of how you can cover a 360 view of product recommendations for your customers on both the platform and outside sources at the same time.

Amazon’s Quote About Their Recommendation Engine

This is what Amazon says about its recommendation system.

“We make recommendations based on your interests.

We examine the items you’ve purchased, items you’ve told us you own, and items you’ve rated. We compare your activity on our site with that of other customers, and using this comparison, recommend other items that may interest you in Your Amazon.

Your recommendations change regularly, based on several factors, including when you purchase or rate a new item, and changes in the interests of other customers like you.”

Filtering Methods Used by Amazon Recommendation Engine

Item-to-item collaborative filtering

For the system to work, the engine needs to collect tons of user and product data and create relationships between them. It creates three relationships User-item, Item-item, and User-user.

User-item: A user-specific matrix is created that contains the data of all products they have purchased and interacted with.

Item-item: The item-item matrix contains a mapping of product feature similarities. A gaming laptop and a gaming mouse have the relationship of being an electronic item, a computing item, a gaming product, and so on.

User-user: This matrix contains a mapping of the similarities in user characteristics. Two users who purchased the same product and then gave it a rating of 4, for example, are mapped together.

Content-based filtering

The content-based filtering system is another option used for recommending products. It utilizes the user-item and item-item matrices to achieve this.

Once a customer interacts with a product, the system looks for other products similar to this based on feature relationships in this case and then presents them to the customer.

For example, if a customer views an Asus gaming laptop, they will be recommended other gaming laptops based on similarity in features – CPU cores, processor type, RAM, storage capacity, and so on.

How to Use AI in Your E-commerce Store: Get Inspired from Amazon’s Recommendation Engine

In-store (web and mobile) recommendations

Amazon’s personalized recommendation system is used in their stores to provide a personalized shopping experience for all customers. Products shown on the homepage, suggested items on the product page, and so on, are all personalized to the individual user using a recommendation engine.

Amazon Alexa

Amazon’s voice assistant Alexa also uses AI to collect data points and deliver personalized recommendations. For example, based on what music a user tends to listen to on Alexa, the voice assistant creates personalized playlists and suggestions.

Amazon GO

Amazon GO is Amazon’s unmanned physical store, where consumers can walk in, pick a product, and leave.

These stores use cameras and AI for computer vision to track users and products being picked, scan the barcode, and then add products to the user’s cart on their Amazon GO app.

Users can either pay later or the money is deducted through a preselected payment method. This purchase data is attributed to the user who then gets personalized recommendations on other Amazon platforms, like Alexa and

What You can Recommend for Your Leaders on Achieving Amazon-like personalization for your e-commerce store as a Marketer

As a marketer, the above reading might be enough to get inspired about Amazon’s recommendation engine and propose the decision makers to have such a system in your business store.

Implementing a personalization system is a step-by-step process. You should have a clear understanding of the process to pitch the idea to the leaders and make them happy to spend on a system like this for better results.

Let’s have a look at the steps to implement a Personalized Recommendation system like Amazon in your e-commerce store.

Create a relevant user-journey

Having an idea about your customer’s buying journey should be the first step you should take.

Amazon’s homepage makes it easy for the user to find the product they want, the product page has all the information laid out, and then recommendations are made to upsell or cross-sell, and then users are walked through an easy-to-use checkout process.

Providing better interfaces (UI), experience (UX), and access on multiple devices is the key to bringing more customers and keeping them within the business.

Implement a personalized recommendation engine like Amazon’s

Throughout the entire article, we mentioned what benefits a business can achieve by having a personalized engine. But implementing your own one might not be easy to build, train, and enhance.

But now, there are so many no-code integrations and solutions a business can use to plug and play these engines in your business.

If your leaders are approved to have a personalized engine like Amazon, then we have the experience of developing such solutions. Contact us to have more information.

Follow a simple checkout process

Having a simple checkout process is also important for a better customer experience. Amazon’s approach to making the checkout process simpler is inspiring. They use smart tactics to simplify their checkout process.

One is the ‘Buy Now’ button which lets you immediately purchase a product without having to go to the cart page.
Two is that saving information like your address and the preferred payment method reduces the number of steps in the checkout process.

Use a transparent return policy

A return policy gives consumers a feeling of security. It also helps users get over last-minute resistance that often crops up when buying a product online.

On the flip side, not having a return policy makes consumers suspicious of the brand especially since major e-commerce stores like Amazon offer an easy way to return or exchange products.

Make users feel valued

Customer loyalty is a big driver of sales, and loyalty is nurtured by creating value. Amazon offers its Prime customers a lot of discounts and benefits that make them feel valued and special. Implement strategies that are personal to the user and make them feel valued by your brand.

These are the things that you have to consider if your business is looking to provide a better customer experience. It’s crucial to know these things as a marketer because you are the ones who are dealing with the customers the most and the ones who are responsible for generating more sales.

How Marketers Can Leverage a System like Amazon Personalization Engine

Product Recommendations

Marketers can use Amazon Personalize Engine to generate personalized product recommendations for their customers based on their browsing history, purchase behavior, and preferences.

By integrating personalized product recommendations into their website, email campaigns, or mobile apps, marketers can increase engagement and conversion rates.

Providing this kind of personalized product recommendations will help you to increase the Average Order Value by providing more options for both new and existing customers.

Improve upselling and cross-selling efforts

As we all know, upselling and cross-selling are the best ways to increase the average order value. By using these recommendation methods, marketers can provide more options for the customers and achieve more sales.

Read more about how AI can revolutionize your upselling and cross-selling efforts.

Audience Segmentation

Marketers can segment their audience according to their data and do retargeting campaigns according to their buying behavior. This will also help marketers to provide better customer experience in a more personalized way.

Email Marketing

Marketers can integrate Amazon Personalize Engine with their email marketing platforms to deliver personalized email campaigns.

By analyzing customer data and behavior, marketers can segment their email lists and send targeted, personalized emails with product recommendations, special offers, or content tailored to each recipient’s preferences and interests.

Optimized User Experience

Marketers can use Amazon Personalize Engine to optimize the user experience on their website or mobile app.

By personalizing the user interface, navigation, and content based on individual user preferences and behavior, marketers can create a more engaging and personalized experience for their customers, leading to higher satisfaction and retention rates.

Increase the effectiveness of Display Ads

Marketers can use partner platforms to display personalized product recommendations for their buyers. It will open more opportunities for the customers will see those advertisements and increase their chances of buying.

Have Your Own Recommendation Engine like Amazon Personalization

Are you looking to implement a custom recommendation engine on your e-commerce store? Contact us a message for a demo of our product and we’ll help you launch a fully-functional recommendation engine.

have your own amazon personalization like recommendation system

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