A product recommendation engine is an AI-driven system that analyzes user behavior, browsing history, and purchase patterns to suggest relevant products on eCommerce platforms. These recommendations can appear on-site, in personalized emails, or through targeted advertising, enhancing the shopping experience and driving higher engagement.
Advanced recommendation engines leverage machine learning algorithms to refine predictions, ensuring that suggestions are highly relevant and personalized. By delivering tailored product recommendations, businesses can significantly boost conversion rates, increase average order value (AOV), and enhance customer retention, making this technology a powerful tool for eCommerce growth.
Product Recommendation Engine Algorithms
There are different types of reco engine algorithms, each having its own particularities.
Collaborative filtering algorithms are based on collecting and interpreting large volumes of user behavior data. They also compare similar actions by different users and try to predict what a specific user might be interested in.
They are not based on machine learning but purely on correlating big amounts of data, thus they don’t need to ‘understand’ what the actual interests of the user are. Some of the most famous collaborative recommendation algorithms are used by Amazon, Facebook or LinkedIn.
Content-based filtering algorithms are based on a user profile and on the description of the items that the user interacts with. This type of algorithm is heavily based on keywords and on storing the actions of users in dedicated profiles, based on which product recommendations will be made.
One key feature these algorithms implement is asking the users to vote (up or down) for various types of content, thus with each vote fine-tuning the user profile according to their preferences.
AI-based reco engines are a big part of the future of e-commerce in general. Currently, there are no real AI reco engines but they are being developed by various companies in the field and it won’t be long until they will become publically available. They will be the most complex algorithms since they require the software to constantly learn and update its self, but they will most likely be the most efficient types of reco engines for e-commerce.
Product Recommendation Engine Benefits
Increased content personalization – the users will see more products that they are interested in, thus making it more likely to stay on the page
Increased conversion rates – due to high product relevance, an e-commerce site with a product recommendation engine will convert more compared to a similar site with no reco engine
Increased targeted traffic – recommending relevant products on email or on advertising banners, will increase the amount of highly targeted traffic
Decreased bounce rate and cart abandonment rate – both these KPIs are vital to any e-commerce business and improving them by reducing the bounce rate and the number of abandoned carts is a common effect of a good reco engine
Increased AOV and number of products/cart – reco engines have been proven to improve the average order value and the number of products per transaction
Increased time on site and no of pages/visits – due to higher content relevancy, users tend to stay more time and visit more pages per session
How to Choose a Product Recommendation Software
Product recommendation software can be cumbersome to implement, but once active they work by themselves. This is why, before buying a reco engine tool or service, you should find out what’s involved in the installation process.
Some tools charge a large payment upfront for setting up the software, while others send instructions for your dev team to do the implementation in-house. Either way, expect to make an initial investment of time and/or money. Try to find a software solution that offers the best time/money ratio possible.
Another thing to take into consideration before deciding on a specific reco engine tool is the average lift in conversion rate that you can expect. In order to find that out, look for case studies of that software or ask their support team. Other aspects you should pay attention to are the amount of effort necessary post-installation and advanced reporting of the campaigns with a clear track of results.
FAQs
How Does a Product Recommendation Engine Work?
A product recommendation engine works by following these general steps: Data collection, Data preprocessing, User profiling, Recommendation algorithm, Recommendation generation, Recommendation presentation, and Feedback loop: The recommendation engine collects feedback on user interactions with the recommended items to continuously improve the accuracy and relevance of future recommendations.
What Are the Benefits of Using a Product Recommendation Engine?
There are several benefits of using a product recommendation engine:
Personalized user experience: A recommendation engine provides personalized product suggestions, enhancing the user experience and increasing engagement.
Increased conversions and sales: By suggesting relevant products based on user preferences, a recommendation engine can boost conversion rates and drive additional sales.
Cross-selling and upselling opportunities: Recommendation engines can identify opportunities for cross-selling or upselling by suggesting complementary or higher-priced items to users.
Improved customer retention: Personalized recommendations create a positive user experience, increasing customer satisfaction and fostering long-term customer loyalty.
Time and cost savings: Automation of product recommendations reduces the need for manual merchandising efforts, saving time and resources for businesses.
How Can I Evaluate the Performance of a Product Recommendation Engine?
The performance of a product recommendation engine can be evaluated using various metrics:
Click-through rate (CTR), Conversion rate, Average order value (AOV), Precision and recall,
User feedback.
