6 Types of Product Recommendation Engines Your Retail Store Needs

The retail product recommendation engine is becoming a key aspect in driving e-commerce sites and influencing user experiences. Thanks to them, the online stores are always open and they seem to understand what buyers might be looking for and often offer interesting choices. Not surprisingly, product recommendation engine is a vital aspect in the journey to intelligent automation in the retail industry.

What are Product Recommendation Engines?

Product recommendation engines use advanced algorithms that are deployed as a part of digital transformation. They first understand and build a unique profile for every customer. Then these engines filter through their product catalog and based on complex algorithms, recommend products that could likely be of interest to the visitor. It takes into account customer data including purchase history, preferences, and feedback. The gathered customer data is then automatically personalized into accurate recommendations. Since these algorithms are powered by AI and ML, it is evident that  implementing artificial intelligence can save time, assist e-retailers to influence sales and improve customer satisfaction.

6 Types of Retail Product Recommendation Engines

  1. Posters

With the help of posters, retailers can advertise the most frequently bought items and influence customers. Poster based Product recommendation engines track buying patterns and assist the retailer with this information. With this data, retailers can curate interesting posters and email to new customers, or celebrate  personal milestones such as birthday or anniversary.

In this age of digital ubiquity in retail posters can be built around a host of relevant events or milestones using data collected across multiple channels.

  1. Rating-based Recommendations

The popularity of a product is a key influencer in a customer’s purchase decision. User ratings are used to build a perspective of popularity. Ratings can be both explicitly tracked or based on implicit customer behavior. Recommendation engines use a combination of natural language processors and advanced algorithms to review the data and create popularity ranks of similar products.

User ratings, product reviews, and social comments are types that can be explicitly tracked.

The retail product recommendation engine that is based on user-ratings additionally tracks customer behavior on the website like the number of clicks, how long did the mouse hover over a product, what sections of the product description did the user visit. Such browsing behaviors are implicitly tracked and used to make suggestions.

  1. Personalized Recommendations

Recommending targeted content reflects in higher transaction rates than highlighting best sellers or new products. By analyzing the browsing history and earlier purchase patterns, recommendation engines can suggest personalized products for the buyers. The personalized recommendation is helpful in “long tail” product sales and triggers the journey to intelligent automation.

However, personalized product recommendation engine requires a large amount of data about shoppers. It can thus be difficult to provide the service to new visitors. In instances where there is not enough data available on customers, the engine looks for all-purpose and larger category-filtered searches. In such cases, the engine uses metadata in place of customer-based data.

  1. Similar Products

Category-based filtering is another intelligent automation that helps in similar product suggestions. Along with meta-data-based similarity, similar product recommendations enhance the performance of the store. To be effective, a similar product recommendation engine needs clear product details. It needs attributes and categories of each product categorized properly to actually be able to work. This widget can be used for campaigns that address browser abandonment and basket abandonment.

  1. Collaborative Filtering Engine

The collaborative retail product recommendation engine works for both similarity and personalized recommendations as it revolves around collecting preference information from various users. It takes into account the frequency in the purchase history or browsing history of a couple of items by multiple users. It is also called “Customer Who Bought / Viewed This …”.

The collaborative filtering engine that run on ML algorithms are another way of accelerating digital transformation in retail. It recommends other items the customers might like based on what products the site visitors have been engaging with. The widget specifically requires data relating to the purchasing habits of other customers and is often used for welcome campaigns.

  1. Frequently Bought Together Engine

Frequently bought together engine can be a productive recommender if displayed on the cart page. At the time of checking out, if the cart page displays real recommendations to help customers with inter-related products that can be purchased together, it makes the shopping experience much more fruitful. The widget is a great way to generate more sales via cross-sell techniques.

The engine works on the belief that consumers behavior in digital retails can be mapped to other users who have similar tastes and same spending habits. This recommendation engine can be used in post-purchase emails such as the order confirmation emails. The buyer might not purchase the recommended product immediately, but chances are they will eventually buy it to get the most out of their latest purchase.

“Top Products”, “Latest Products”, “Recently Viewed” are some other favorable product recommendation engine. “Top products” is a good option to display the brand’s popular products. when there is limited or outdated customer data. “Latest products” widget helps promote the brand’s latest range and showcase what’s new. “Recently viewed” engine can be used in browsing abandonment emails and retargeting campaigns. It sends an email reminder to a customer who might have been distracted while shopping as no purchase was made.

Different product recommendation engines are suited for different campaigns. They play a crucial role  in today’s retail digital transformation market. Each one of them help e- retailers to take their business to the next level. Some technology solution providers have developed plug and play APIs to fill the gaps in the digital transformation journey.  As a retailer, all you need to do is find a trusted technology partner and get started.

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