How a Product Recommendation Engine Works
Displaying personalized product recommendations real time, suggesting related, relevant and complementary products has become as affordable for eCommerce stores as the prise of a few dinners per months.
Implementing Personalized Recommendations serving every single customer in real time would be very expensive for offline retail units.
Online shops with over 20–25,000 page views per month can not avoid deploying product recommendation engines any more. After adjusted and configured these recommendation engine algorithms helped by artificial intelligence will significantly boost CTR, average order value, revenue, conversion and other important metrics even in the first few weeks.
Recommendation engines like Yuspify offer clusters of product recommendation algorithms that analyze the events made by of every unique visitor. To serve the most up-to-date recommendation package to the store, the tracking system works in real time, refreshing the reco containers, if any change in customer behaviour detected.
The average revenue through recommendations in an eCommerce store is around 10% when using Yuspify. Results can range from different factors like the number of recommendations deployed or factors such as general page design, location of reco widgets strictly determined by the client, or extraordinary traffic in campaign periods.
Shopping user experience also can see a positive effect which ends up in better retention ratio and higher customer satisfaction. Needless to say that the customer whose outspoken, hidden or subliminal demands are fulfilled, will be more willing to make a completed purchase.
The published figures about Netflix recommendation engines shed the spotlight to the importance and the added value of the recommender systems: the recommendation engine belonging to the video giant produces a billion dollar plus revenue per year. More than 250 million subscribers are sorted to almost 2000 taste groups attributed by tags like: “Likes corrupted cops in a bible-belt environment”
According to a VentureBeat-published study 77% of the digital natives can not image their online existence without being surrounded by personalized creations should those be experiences, products or social contacts.
Amazon’s often-cited statistic says 35% of their total revenues arrive through those products that their customers found via recommendations.
Product Recommendation Engine metrics
The revenue generated through recommendations is said to be the most crucial metric when evaluating a recommender system. Industry standard measurement counts revenue through a recommendation when the recommended product is clicked and bought within 24 hours. A recommendation engine dashboard should handle several important metrics in a dedicated importance:
% of Revenue Through Recommendations — A cornerstone metrics. The quotient of the revenue generated through recommendations / the total revenue.
CRR ( Conversion Rate from Recommendations) — The Conversion Rate of the customers who clicked on recommendations — juxtapose this to the general conversion rate and to the CRnR ( Conversion Rate from non-Recommendations)
GMV/1000 Recommendations — the average revenue sent by 1000 recommendations. Arriving through those customers bought products via recommender containers.
Number of Recommendation Clicks — The number of products clicked by those visitors who actively used recommendations while they were roaming in the store. This can be calculated as a total, but can be subdivided to categories like number of products viewed on XYZ category page or on ABC product page etc.
CTR — the commonly used ratio: number of users clicking on the recommendation widgets compared to the total number of customers viewing the page where the same widget shows up.
The Difference Between Recommendations and Personalization
The words of “recommendation” and “personalization” are often used interchangeably. Personalization is a broad category dwelling in the website optimization and applied in the field of recommendations too. Recommendations can be personalized (by utilizing the user profile) or non-personalized (using the data mass of item attributes and other purchases). Using personalization in recommendations is not the obvious choice every time, in certain cases item-based recommendations entail more conversion: well-streamlined recommendation engines launch fallback scenario: does the customer have enough history here to get personalized offers that might imply higher probability for conversion or shall we apply item-only recommendations without personalized to the user?
Summary
An affordable and efficient recommendation engine solution helps e-Commerce sites to boost sales and to blow up conversion rates. Personalized recommendations spread across multiple pages and channels to support online sellers. It helps e-commerce stakeholders to understand their customers’ prevailing intentions and preferences to whom it shows the most appropriate products in real time. It’s important to highlight that a decent recommender system flows seamlessly into the store design, integrating the recommendation widgets to look like an organic part of the online store.
Recommendation engines often provide a free trial period ranging from 7–30 days. This trial period is usually enough to win over the cold start problem and to start the accumulation of recommendation-driven revenue even after a few weeks as this case study shows. Before contracting a solution provider you are eligible to get a cent-perfect breakdown of your costs in the proportion of served recommendations — or in case of Yuspify you can pay a small % based on the revenue generated by recommendations.
