The use of recommendation engines is ubiquitous nowadays. Many companies are constantly improving their algorithms to make accurate individual recommender systems. Facilitating the user’s journey by offering a personalized offer also builds customer loyalty by anticipating their needs. For the user, recommendation systems save time as they no longer have to search for the right product in the entire catalog. Let’s check how you can build a recommendation engine, reach similar users, and enhance user experience.
Generally speaking, personalization generates a sense of uniqueness; it makes each user feel important and understood. As a result, companies that actively use recommendation engines and build customer loyalty observe an increase in the conversion rate and average order values (AOV). However, before we get into the details of the topic, it’s worth defining a recommendation engine.
What is a recommendation engine?
A recommendation engine is an algorithm based on artificial intelligence or a machine learning system that generates product recommendations in real-time on the seller’s website. On large e-commerce sites, several recommendation engines or algorithms can coexist. In such a setup, each algorithm plays a specific role, for example, up-selling, cross-selling, or monitoring the behavior of other buyers. In other words, as the name suggests, the tool “recommends” diverse products to Internet users visiting your online store.
The recommendation engine can also personalize offers in newsletters or suggestions contained in retargeting campaigns. You can use many criteria to filter these recommendations, either individually or in combination. Like other AI systems, product recommendation engines generally gain performance over time through learning.
How the recommendation engine works
The recommendation is usually based on three basic steps: Gathering information, classifying data, and making recommendations.
STEP 1: GATHERING INFORMATION
At this stage, it is necessary to define the user’s preferences. The user is always at the center of the whole recommendation engine. For this purpose, the recommender system collects various information about (and from) the users, including product reviews, time spent on a given product tab, purchasing history, visited categories, etc.
STEP 2: DATA CLASSIFICATION
The classification correlates the collected information about the user with the products available in the online store. That allows you to build a data model.
STEP 3: RECOMMENDATION EXTRACTION
The recommendation system can extract a list of products from the previously built data model to recommend specific products to the user.
Types of recommendation systems
Recommender systems are always based on user data. Here, depending on the nature of data in your store, different types of systems can be implemented:
CONTENT-BASED FILTERING
Content-based recommendation systems suggest similar elements or content for users that have previously searched for, viewed, bought, or rated positively something similar. For this, the system must determine the similarities between the objects. Accordingly, content analysis is performed. In the case of streaming services (for music), the recommendation engine, for example, evaluates the songs according to their structure.
COLLABORATIVE RECOMMENDATION SYSTEMS
This type of recommendation system is based on observing users with similar behaviors in a suitable method. If a group of users has been very interested in a particular object in the past, the system will continue to recommend it.
HYBRID RECOMMENDATION SYSTEMS
The hybrid approach combines the two forms of recommender systems that we discussed above. Hybrid recommendation systems use a variety of machine learning methods. In most cases, model-based or memory-based methods are used. The memory-based process uses all stored classification data and identifies similarities between users or objects. The result serves as a forecast basis for joining objects that have not yet been extracted. Model-driven recommendation services, on the other hand, follow machine learning principles. Based on that data, the system should create a mathematical model that can be used to predict users’ interest in a given product. Amazon uses this form of recommendation system extensively.
It’s time for your steps
The personalization of your offers is becoming increasingly crucial in digital marketing. Typical ads are very quickly identified by users and, therefore, often are overlooked. However, by implementing a recommendation system, you can attract customers’ attention with appropriate information and a personalized approach. Thus, your chances of conversion will be much higher.
To implement a recommendation engine in your e-business, you can use our help. At RecoAI, we offer an artificial intelligence system that analyzes real-time user behavior on your website and effectively offers them the products they are looking for. Don’t hesitate to contact us if you want to find out more. We are happy to answer all your questions and help you stay ahead of the competition with the power of recommendation.