What is a product recommendation engine?

This is a system that helps to offer every individual customer a personalized experience by mining data and filtering product listings per their preferences. It applies science and not trial and error.

The goals of building recommendation engine

There are goals you should consider when coming up with a recommendation engine software, they include:

• Relevance

The recommendations should be relevant to the user and invoke their interests.

• Novelty

It will add more value to the business if the products suggested to the users are ones they have not seen or used before but are similar to what they have bought before.

• Serendipity

Value will also be created if the customers are recommended products they have not seen or used before but are also different from what they have bought before.

• Diversity

Recommendations should not always relate to previous purchases. New products that a customer has not purchased before should be recommended.

Did you know? According to reports the Recommendation engine market size is anticipated to grow from USD 5,172.41 Mn in 2023 to USD 21,574 Mn by 2028.

How a recommendation engine works

Below are three approaches that product recommendation engine use:

• Collaborative filtering

Using user similarity collaborative filtering clusters groups with similar preferences, buying habits, and search histories into a single set and analyses their behavior.

If a person with a similar profile to you buys a product, then you are also likely to buy it so they recommend it to you.

• Content-based Filtering

Here an individual’s preferences and likes are mapped with product features to come up with recommendations.

Two things are essential the customer profile and the product tag which includes a name, description, and several keywords about the product.

The user profile and product are then mapped and a recommendation is made on what the user would like.

• Hybrid Recommendation

The hybrid recommendation combines both collaborative filtering and content-based filtering. It is the best-suited product for recommendation engine for eCommerce platforms.

With collaborative filtering concentrating on the profile of users who are interested in and buy similar products and content-based filtering matching product tags with user profiles, the hybrid recommendation leaves no loopholes when applied in a product recommendation engine.

Benefits of recommendation engine

Building a product recommendation engine will be costly at the beginning but overall the benefits will outdo the costs of implementation.

Below are the benefits of product recommendation engine:

• Increased User Engagement

When products are recommended to users based on their likes and preferences, the users feel more connected to the service. Along with user engagement recommendations also increase order value through up-selling and cross-selling the products.

• Reduced Churn and personalized content

When buying things in the physical world recommendations from friends may play a role in influencing one to purchase a particular product. Personalized recommendations on the other hand based on your likes and preferences will most likely influence one to purchase on an eCommerce platform.

Previous reviews from previous purchasers will also be a game changer in influencing one to purchase a product.

• Increased sales and Revenue

Recommendations facilitate the discovery of formerly undiscovered available and useful products. Even though at times users may not make purchases immediately, the product will be on their minds and on a need basis may purchase at a later time. This will lead to a sales increase in products that initially were maybe undiscovered by users.

• Drive traffic

Through targeted personalized email messages a recommendation engine can increase traffic to an eCommerce platform. Creating an opportunity where more data can be got on a user to update their profile.

• Deliver relevant content

By using the user profile the recommendation engine suggests relevant products that may interest the user.

• Convert shoppers to customers

Personalized interactions bring the feeling of being valued to the customer and will lead to increased conversion from shoppers to customers.

• Increase in items per order and average order value

The order value increases as recommendations that are appealing and meet the user’s interests are shown. Unplanned purchases on products that were not initially on the user’s plan may also happen.

• Reduce workload and overhead

Creating a personal shopping experience manually for each customer would be almost impossible. The recommendation engine automates the process making it easier.

How product recommendation engine increase sales

• Driving traffic

By driving traffic through recommendations the resultant effect is an increase in sales. With high traffic comes opportunities for upselling and cross-selling.

• Sending personalized emails

Personalized emails with product recommendations, promotional emails showing products with discounts, thank you emails, cart abandonment emails, and order confirmation emails all work towards customer satisfaction which translates to an increase in sales.

• Personalized recommendations

With product recommendations being personalized, the user is most likely to see a recommended product as something they are interested in and will either make a purchase then or later. Follow-up can be done through re-targeting.

• Customer/User profiles and product tags

Matching user profiles with products is also a game changer in increasing sales. This is because it’s science that is applied and not just trial and error.

• Conversion rates of shoppers to customers

This can also be attributed to a user profile being mapped and preferences in products known and hence recommended.

Best practice tactics for Product recommendation engine

• Include a ‘Recommended for you’ call to action to make several relevant suggestions to the customer. You can add a customer name to make it more personal.
• ‘Frequently bought together’ recommendations are also good and help in boosting the average order value.
• ‘Similar products’ is also an effective strategy because it shows a full range of a product line and customers can now choose according to their preference.
• Customize emails with product recommendations.
• Display best-selling items on the homepage or the popular pages.
• Enable shoppers to have a view of their purchasing history.

Conclusion

Configuring the product recommendation systems may take some time. But when they are up and running, they are good to go and results can start being seen. When properly configured personalized recommendations will lead to impulse and unplanned purchases because users will find the items relevant to their interests. Analyzing the conversion rates is also recommended to make changes to the algorithms according to the analysis.

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