The Power of Recommendation Engines

In a pilot study, Lynchpin’s UK engine achieved a 32% hike in conversion rate and a 23% uplift in revenue.

Those working in the eCommerce industry would readily understand why the results that Lynchpin achieved are exemplary because in an industry marred by ad fraud and useless traffic, gaining a real uplift in conversion rate and revenue is nothing short of finding a unicorn.

Now, you might wonder how you can replicate and even surpass these results for your eCommerce venture. The answer is: Recommendation Engines.

What is a Product Recommendation Engine?

A product recommendation system is an information filtering system that analyzes consumer data and makes product recommendations to consumers using an AI (deep learning) model. Since the engine makes recommendations using AI models, the accuracy and efficiency rate is usually quite high.

Data for Recommendation Engines

An AI model is only as good as the data it has been trained on. Hence, the integrity of data is crucial for developing an efficient recommendation engine. Most machine learning solution providers combine the client’s in-house data with data acquired through open sources or third-parties of the industry to better train the AI model and avoid subjecting it to any bias.

There are two types of data that a recommendation engine is usually trained on:

• Explicit Data i.e. the data an organization gathers from customer feedback on its products. This data highlights which products are the most popular ones among the catalog and which products have weak demand usually in the form of ratings.
• Implicit Data i.e. the data that an organization has about its users. This data is usually indicative of which segment of users (based on various factors like lifestyle, age, area of residence, and so on) prefers which products.

Why Should You Have a Product Recommendation Engine?

To realize your real potential as an eCommerce platform. Simple as that.

Let’s break it down for you how a product recommendation system will help your eCommerce platform.

• Better Sales and Higher Average Order Value

While customized recommendations bring a user to the shopping platform; recommendations about products that other users have bought with the product the user is interested in and a list of similar products, makes a customer spend more than usual.

• A Consistent Brand Experience

Your recommendations must be uniform across all channels a consumer uses to interact with you (like messages, emails, in-app communication). Seamless recommendations not only drive better conversion rates but also boost customer loyalty to the brand.

• Visible Reduction in Consumer Grievances

A recommendation engine filters out products which may no longer be available in stock or are not exactly to the consumer’s liking.

• Meeting CX expectations: customized catalogs and personalized consumer interactions provide the consumer with a good experience. A consumer who has had a good experience with an eCommerce platform is very likely to come back for more.

Best Practices for Using a Recommendation Engine

Much like any other AI business solution, Recommendation Engines also have a set of tried and tested tactics that you can follow:

• Work Towards a User-Friendly Navigation and Usage Experience.
• Update Catalogues and Recommendations Periodically (to reflect upgradations and weed out outdated products and services).
• Customize Recommendations (using both explicit and implicit data, from all sources, whether internal or third-party).

Takeaway

Consumers today are also very particular about which services and products they avail. The process a recommendation engine follows helps it in personalizing the product catalog according to consumers’ preferences and inclinations.

By customizing the products offered according to what a user usually prefers, a recommendation engine significantly boosts chances of customer conversion and consequently increases the revenue.

If you are an eCommerce venture owner and still do not have a recommendation engine, well, what are you waiting for?