Product Recommendation, in simple terms, is the process of determining the products that users are likely to purchase or be interested in by analyzing their buying behavior like purchase history, frequently bought products, most viewed items, and so on.

Product Recommendation drives sales on eCommerce websites. The system or the mechanism that does the work is called a product recommendation engine.

Even if the term sounds new to you, the chances are that you’ve already seen it in action. These days, almost all eCommerce sites include a section that recommends other products that may be of interest.

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Product recommendation engines also drive social media ads presenting the “same, similar or complementary products”, as well as emails or texts promoting the purchase of relevant items, “perhaps with a personalized promotion code”.

How do product recommendation engines work?

A product recommendation engine uses AI/ML (artificial intelligence and machine learning) to analyze data and generate highly personalized purchase suggestions and promotions. As individual shoppers conduct searches, both on the online store itself and on the rest of the internet, they generate data related to their search behavior and product preferences.

Product recommendation engines analyze this data, in addition to a list of products in cart or wishlist, recently viewed or purchased, to create individual customer profiles. The algorithm matches product to profile, and is able to generate content that is likely to appeal to the customer based on this analysis.

The engine serves up contextual offers (such as discounts on products that are likely to appeal) and product recommendations (such as scarf options to pair with a dress that has already been purchased) to drive sales. This is not just on-site – the same offers and recommendations are also pushed via emails and text post-purchase or in order to encourage purchase. These insights can also be mined for marketing communication priorities – for instance, the banner images on the website may vary based on customer profile.

Three Types of Recommendation Engine

There are three types of product recommendation engine:

  • Collaborative filtering systems
  • Content-based filtering systems
  • Hybrid recommendation systems

These vary based on “the specific kind of information they collect, and how they use it to determine the products they suggest to a customer”.

Collaborative filtering: Harnesses “the wisdom of the crowd” to predict the right product to be recommended. Amazon uses collaborative filtering extensively, as the primary requirement is that the brand should have access to large amounts of data. Complementary recommendations are often pushed, such as bedsheets suggested to those who have just bought a mattress.

Content-based filtering: Creates a “unique preference profile” to recommend products that are likely to suit the user’s individual preferences. These profiles and recommendations are highly personalized and assume that if the shopper has liked a particular size-3 dress in red, for example, they may like other size-3 dresses in red.

Hybrid recommendation: A combination of the above, using data from “groups of similar users” as well as individual past preferences. The individual shopper who bought a red dress, for instance, may have searched for size-6 shoes, and other shoppers for that red dress may have also purchased stilettos. In that case, size-6 stilettos in shades to match the dress may be recommended to the shoppers.

What impact does it have on eCommerce?

As they say, data is the new oil, and product recommendation tech is the best way to use client data to improve the eCommerce shopping experience, customer service and of course, sales. 24% of all orders come from “engagement with AI-powered product recommendation”, and through the right email and text communication, product recommendation engines can increase brand or product awareness.

Barilliance conducted comprehensive research into the benefits of intelligent personalized product recommendations, and found that personalization of product recommendations results in twice the click-through rate and quadruple the conversion rate (for those who engage with the recommendation). Salesforce found that “AI-powered product recommendations” resulted in average order value that is 26% higher.

What does this mean? Arguably, it means that AI-powered personalization of product recommendations (that is, leveraging the power of the product recommendation engine) is the single most powerful tool in the eCommerce arsenal. As Salesforce says, “Intelligent product recommendations allow for natural, logical opportunities to upsell and cross-sell.” A good salesperson at a retail store can read the shopper and understand what they want and what products to suggest next, when to show them a new product and when to leave them with the product currently on the table. AI-powered recommendation engines, to a large extent, serve the same purpose for online retail.

Online stores can sort search results based on likelihood that the product will interest the shopper, thus increasing engagement and sales. Since the experience is tailored to the customer, they feel heard, and thus are more engaged by the brand, more likely to purchase, and become long-term brand evangelists.

Integrating a recommendation engine into your online store

A number of SaaS-based product recommendation engines are available in the market, including Salesforce’s Einstein Recommendation Builder and Product Recommendations for Adobe Commerce (powered by Adobe Sensei, a powerful AI tool).

Choosing the right product recommendation engine depends to a large extent on the eCommerce platform selected. The method to integrate into the eCommerce website is different for every product recommendation engine. An experienced development partner such as Ziffity can offer you the right advice and integration support.

The Adobe Sensei-powered Product Recommendations tool has a five-step process using which the admin can design the right algorithm to deliver strong product recommendations. Other engines, similarly, have user-friendly processes through which recommendation algorithms can be crafted. Using machine learning, the AI-powered engines continuously improve the algorithms to maintain recommendation quality.

A smart product recommendation strategy can elevate the user experience of your online store, and increase sales revenue as well. Choosing the right tool and customizing it to deliver the right strategy is key. At Ziffity, we offer tool integration and work with multiple engines and platforms based on your store’s unique requirements.

Contact us today for a consultation.