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How to choose the right product recommendation engine?

When an eCommerce business has a vast range of products listed on its website, it can be challenging for customers to navigate through the catalog and find products that match their preferences. A product recommendation engine displaying cross-sell, similar items, and up-sell widgets can solve this problem by analyzing customer behavior, purchase history, and customer-specific preferences to provide personalized product recommendations.

9 must-have features of a product recommendation engine

Product recommendation engines benefit both customers and businesses. For customers, they can save time and effort by suggesting products that match their interests, making the shopping experience more enjoyable and efficient. For businesses, they help to increase the average order value, improve conversion rates and retention, and overall increase the net revenue.

Choosing the right recommendation engine can be a complicated task, especially with the numerous options available on the market. That is why we put together a list of the most important features you should consider when choosing an AI recommendation system for your online shop.

1. Recommendations relevance

The primary function of a product recommendation engine is to provide personalized product recommendations to each customer based on their browsing history, purchase behavior, and preferences. Therefore, the engine should have robust personalization capabilities, using machine learning algorithms, that can analyze customer behavior and suggest products in alignment with their interests.

For instance, Netflix's recommendation engine uses machine learning algorithms to track user preferences and suggests TV shows and movies based on their viewing history. You might have noticed that when watching Netflix, you see a score in the corner of the TV screen, displaying the percentage for which the movie is a perfect match with what you previously watched.

 Golden standard: Truly personal product recommendations, frequently updated.
  Inferior standard: Best-sellers disguised as personal recommendations.

Is your website ready to use an AI-driven product recommendation engine? Calculate your personalization readiness score →


2. Easy integration

It's essential to select a recommendation engine that can seamlessly integrate with your eCommerce platform. Ensure that the engine has plug-ins or APIs that can be easily and quickly integrated with your platform. A plug-in is a piece of software that is installed directly into your platform, while an API is an interface that enables communication between two different systems.

These integrations allow the recommendation engine to access and analyze customer data, such as browsing history, search queries, and purchase behavior, and use it to provide personalized recommendations. Thus, a smooth integration process will save you time and resources, allowing you to focus on other aspects of your business.

Golden standard: <30 mins, a few clicks.
  Inferior standard: 4 weeks with a complicated API.


3. Cross-channel distribution

Check if the recommendation engine of your choice can easily integrate with other third-party tools that you use, such as email marketing (MailChimp, Klaviyo) and customer relationship management tools. This could allow for more extensive use of product recommendations, such as delivering them in the format of personalized newsletters with products that have the highest conversion probability for each recipient.

Golden standard: Be able to integrate with third-party tools and send recommendations via the website, email, and app.
  Inferior standard: No third-party tools integration available.


4. Customization

Adding business rules to influence certain product recommendations can be a desired feature for a recommendation engine. Businesses may want to push specific brands to website visitors, as they may have exclusive partnerships with those companies. Other reasons include high inventory levels of certain products or high-margin products. In such cases, the recommendation engine should have the ability to prioritize those brands and suggest them to the customers, by accommodating specific business rules.

Golden standard: Allows for business rules implementation.
  Inferior standard: Standard solution, lacks flexibility in customization.


5. Scalability

Your business needs will change with time, and you should choose an engine that can scale up or down based on your requirements. A scalable engine will allow you to manage more products, customers, and data as your business grows without any performance degradation. Most of the time a successful online shop will scale up to millions of SKUs (stock-keeping units) which is why it’s important to check whether the chosen engine is able to handle such large volumes of data.

Golden standard: Handles “Black Friday” without sweating too much.
  Inferior standard: Becomes slow or unresponsive during periods of high traffic.


6. Measurability

Attribution is one of the greatest challenges of online businesses as technology and ad providers will often claim the same purchase. That makes it harder to understand what is working, and what is not. A good recommendation engine should provide data on the website sessions and users influenced, as well as how that translates into actual revenue.

The key metrics to look for are the conversion rate and average order value (AOV) linked to users interacting with the product recommendations. In case you want to take it to the next level, the most accurate measurement can be provided by an A/B test, and your eCommerce personalization provider should support your efforts to perform one.

Golden standard: Measures influenced sessions and their conversion rate and AOV.
  Inferior standard: Provides little to no measurement or claims attribution for sessions that were not influenced.


7. Load time

Another important aspect is the load time of the up-sell, cross-sell, and similar items widgets displayed by the product recommendation system. While it might seem less important, a lot of users become frustrated and leave the site when the load time is higher than usual.

According to Google Benchmarking, in 2019 the 'Average Time on Page' for eCommerce websites was 38 seconds. If the duration of loading for the widgets is high this can result in a higher bounce rate, which can in turn harm the site's search engine rankings and overall user engagement.

Golden standard: <200ms load time.
  Inferior standard:  > 2s load time.


8. Design

While there are a lot of product recommendation engines that allow the online shop to customize recommendation carrousels to fit the native design, there are also a lot of AI solutions that allow only for standard designs. Even though this does not directly affect the quality of the recommendations, it can still have an impact on the website visitor, who might find them out of the picture. After all, 94% of website visitors’ first impressions are related to design, according to Kinesis Inc.

 Golden standard: Blends in perfectly with the website design.
  Inferior standard: Uses other fonts, buttons, missing “add to cart”, etc.


9. Return on investment

The cost of the recommendation engine is an important consideration. Choose an engine that provides value for money and aligns with your budget. Some engines charge a monthly subscription fee that has various tiers (based on product orders), while others charge a variable price based on sales. Depending on how many product orders you receive every month you should check which pricing model works best for your business.

Golden standard: >30x ROI (net revenue increase/cost).
  Inferior standard:  <5x ROI (net revenue increase/cost).



Choosing the right product recommendation engine is crucial for the success of eCommerces. It is important for the chosen tool to align with your business goals, as with the right product recommendation engine both the customer and business owner’s outcomes improve.

Is your website ready to use an AI-driven product recommendation engine? Calculate your personalization readiness score →


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