Today we are so accustomed to getting recommended for desired commodities and services on the web that the buying experience has started to seem incomplete without it. Abundant alternatives available on the internet have compelled online businesses to come up with a personalized browsing environment with user-specific recommendations. That’s where conveniently, the role of the recommendation engine comes into play. More than conversion, the goal has now become engaging the users. But, the goals of a recommendation engine are not limited to just one, there are many tasks that a recommendation engine can achieve, and they are –
Goal of Recommendation Engine
Creating Value for a Business:
Recommender systems provide immense value to users as well as service providers. They reduce the overall transaction costs of browsing through and selecting products/services in an E-commerce setting. Recommender systems have also been proved to refine the decision-making process. A recommendation engine assures high returns in the SaaS industry because they increase your products’ visibility, ensure more engagement, and maximize conversion probability. In scientific libraries, these systems assist users by enabling them to access data further than catalog searches. Hence, the importance of incorporating effective and accurate recommendation procedures within a system for users cannot be stressed enough. What makes them even more useful are the several smart decisions taken by the recommender system every millisecond. Moreover, customized newsletters, personalized promotion content, and sending push notifications urge consumers to return to the site, increasing their frequency, reducing their churn rate, and ultimately generating long-term profits.
A recommender system potentially drives more traffic to your site using personalized notifications and emails, allowing repeat visits. This is also because people tend to click on different products being recommended. According to many researches, recommendation systems increase nearly 30 percent website traffic.
Also read : How to Integrate Recommendation Engine with E-commerce Platforms
Delivers Relevant Content
A recommendation engine provides relevant product recommendations as to the consumer browses. The information is collected in real-time using usage patterns and browsing history, allowing the software to adjust to changing shopping patterns.
Increases Average Order Value and Number Of Items Per Order
The average value of orders tends to increase when recommender systems are employed. The number of items per order also usually rises because when a consumer is shown options that meet their interest, they are more likely to add additional items to their order.
Average Revenue Per User (ARPU) means the revenue an e-commerce owner earns with each visitor. It is calculated by total number of revenue divided by total number of visitors voting the website. ARPU increases when these three things happen –
- Increase in conversion
- Increase in average value of an invoice
- Increase in repeated visitors.
All of the above three things happen with a recommendation engine (we mentioned this above in our list as well).
The Generation and provision of consumer data reports is an integral part of a recommender system. Giving the client accurately and real-time information and figures allows them to make calculated decisions about his site and the direction of a campaign.
Offers Advice and Direction
An experienced service provider can provide useful guidance on effectively using the information collected and reported to the client to their benefit. Acting as their colleague and, at times, their consultant, the client will have the necessary information to help guide the E-commerce platform to become more successful in the long run.
To know more about AI-based recommender systems, try the 14-day free trial of Alie.