The Recommender Engine plays a vital role in today’s digital environment. The success rate of such a system is directly proportional to the quality of user engagement. User engagement is not only measured from immediate user responses such as clicks on the platform as well as dwell time of the recommended items, but also from long-term responses such as user’s revisit and return time intervals. Many eccentric platforms like YouTube, Netflix, Amazon, LinkedIn, and Pandora use recommendation systems to aid users to find out new and relevant items while creating a delightful user experience and increasing revenue. But, how does the recommendation engine bring in maximum user engagement? Let’s dive deep into the topic but before that, let’s find out the ABCs of user engagement and the relation between user engagement and recommendation system.
What is a Recommender Engine?
A recommender engine is an AI-powered tool that collects and analyzes data based on users’ behavior. Further, this data is used to provide personalized and relevant content to the user. Predicting users’ likes and dislikes and suggesting accordingly offers visitors an easy journey.
What is user engagement?
User engagement is defined as the time spent by any user on a digital platform in assessing various services or products. It makes for a crucial aspect, important for any online business, to measure the success of its products and services. It is calculated by tracking users’ behavior such as downloads, clicks, shares, and more.
Why is user engagement crucial?
User engagement is directly related to overall profitability. Gaining attention of the users is a top priority in checklists for businesses, and if the user chooses to spend their precious time on a particular app or website means they find value in the offerings of that particular business.
So, user engagement as an aspect allows businesses to boost their revenue from the product or services with subscription, ads, or sales. In addition to this, a highly engaged user is more likely to buy, return and share the product or service with their friends and relatives.
The product and marketing team that monitors and calculates user engagement can use analytics to realize the factors that contribute to higher engagement. .
Parameters to calculate User Engagement
Before calculating user engagement, businesses must choose what engagement means to them. For example, a media publication site might consider all activities on the website as a positive engagement, because they make money from Ads. For an e-commerce store, activity will be calculated as a positive engagement only if it comes with a positive outcome like purchases, adding items to a cart, etc. Here are some of the different parameters that can be used by different businesses to calculate engagement:
- Media site: Daily usage, views, time spent on page, clicks, searches, comments, and share
- Streaming Music Platforms – Daily usage, time spent by users in the app, songs frequently listened to, playlist creation, friend addition, etc.
- E-commerce Platforms – Monthly usage, purchases, adding items to cart
- Personal Financial App – Weekly usage, syncing bank account, creating a budget, notification authorization, viewing the dashboard.
- Enterprise software – Monthly usage, generating reports, sharing of reports, an invitation to users.
What does a recommendation system do to optimize user engagement?
To ensure long-term user engagement, a recommendation engine maximizes the total number of clicks from a population of users in a specific period. It takes care that users must not leave the platform early. To maximize the amount of time spend on the platform, the system has to not only predict its influence on a user’s immediate click but also plan it onto the future clicks if the suggestion would appeal to the user to return.
A recommender system makes it easier for the customer to access the content they prefer by shortening the search process and resulting in a purchase or consumption of products or services. Enhanced user engagement and increased sales are a result of integrating the recommendation system.
Integration and application of recommendation systems offer enhanced consumer experience and in turn boosts user engagement. Comprehensive knowledge of several customers gives businesses a significant competitive advantage over their competitors.
Choose Alie and Skyrocket your User Engagement!
Alie is an AI-powered recommendation engine that easily integrates with your website and applications to provide real-time recommendations. It helps businesses to offer personalized experience to their customers and as a result boost user engagement on their platforms. It’s advanced ML algorithms deliver recommendations based on previous interactions. Start a 14-days free trial to see how Alie can help you keep your customers engaged.
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