How do Product Recommendations Work?

Product Recommendations

Product recommendations are an essential part of an e-commerce strategy today. These recommendations reach users on different channels like a Storefront webpage, app, or email based on the information collated from various sources. This information lives within customer-specific attributes, Buying or Browsing behavior, or real-time events. The end objective is to devise a system to predict and display the items a user wants to purchase. So that you can provide a personalized shopping experience. This system is referred to as the Recommendation engine.

Recommendation engine capabilities exist within SAP’s entire catalogue of solutions. Commerce Cloud offers the ability to create experiential recommendations on the fly. While Customer Data Cloud gives you a clear picture of your users and the type of recommendations they’re going to want.

Benefits of Product Recommendations

  • Allow customers to find the products they need and are relevant to them quickly and easily.
  • Product Recommendations are essential for businesses to meet their commercial goals and drive profitability.
  • They provide the opportunity to convert a casual shopping session into an actual purchase.
  • Recommendations made to potential customers can drive traffic to the storefront or app.
  • Increase in average order values due to the personalized options shoppers are suggested and resulting in a purchase.

So how do Product Recommendations work?

Recommendation engines are everywhere around us these days. They’re constantly helping Organizations with up-selling and cross-selling different products and services. A recommendation engine uses algorithms to recommend relevant products and services to a customer based on the event/action performed by the customer, like browsing a storefront, viewing app advertisements, etc. 

The basic idea is to collect data from different sources related to the customer’s past buying behavior, previous orders, likes or dislikes, Opt-in & Opt-out, etc. Then devise a pattern to suggest the required products and services.

Examples of a few recommendation strategies could be:

  • Product Bundles
  • New arrivals
  • Bestsellers and top-rated recommendations
  • Previously viewed
  • Similar products
  • What people like you bought
  • Recommended for you
  • Frequently bought together
  • Post-purchase promotion
  • Add-on recommendations
  • People also viewed
  • Trending products

There are different types of recommendation engines that can be used to provide recommendations to potential customers: 

Collaborative filtering – Mostly used to provide recommendations in e-commerce by referencing customer’s past buying behavior, previous orders, etc.

Content-based filtering – Used to provide recommendations by referencing keywords, tags, likes or dislikes, Opt-ins & Opt-outs, etc.

Demographic – Used to provide recommendations for a specific set of Customers based on an attribute.

Hybrid – Used to provide recommendations using both Collaborative & Content-based filtering.

A recommendation engine works using a combination of data and AI-based techniques. Data is the food for such systems, so the effectiveness is dependent on the amount and quality of data provided based on which patterns are derived. Accordingly, recommendation engine processes the data through the below four phases:

Collection

The first phase for creating a recommendation engine is to gather data. This phase works on Explicit data fed by users such as likes/dislikes, ratings, and reviews for products and Implicit data such as page views, order history, clickthrough, and cart events.

Storage

The type of data used to create product recommendations governs the decision for the kind of storage the business should operate and answers questions like scalability, growth, and adaptability to different types/formats of data. 

Analysis

The recommendation engine analyzes and searches the products that fit the recommendation criteria using different analysis methods such as batched analysis, real-time analysis, or near-real-time system analysis.

Filtering

This phase involves filtering the data to provide relevant recommendations to the users by utilizing an algorithm suitable for the recommendation engine. The output of this filtering is to provide recommendations to potential customers. 

In summation, product recommendation is a crucial piece of the e-commerce puzzle. A child of the upselling technique, these engines will remain part of the sales and e-commerce strategy indefinitely. We at #teamASAR hope that this article offered you a deeper understanding of how product recommendation is entwined with e-commerce. Reach out to us if you have questions!

Share Article

Share on facebook
Share on twitter
Share on google
Share on linkedin

Recent Topics