How Recommendation Engines Amplify CX and Sustain e-Businesses
by Rajesh Shashikant Renukdas, on Dec 9, 2020 1:21:11 PM
In today's fast-paced digital world, consumers have taken a front seat in determining the success of a business in almost every vertical. Given infinite choices and proliferating user personas, for every best possible user experience out there, there will be another which can win over. The success of eCommerce companies like Amazon and OTT giants like Hotstar, Netflix etc. is strongly linked to offering user-centric experiences, and recommendation engine is one of their most fundamental components. Recommendation engines have been doing a commendable job in creating a consistent user experience across industries.
They have not only made the lives of consumers easier but have also enabled companies to leverage cross-selling and multiply their revenue streams in a short span of time. According to a recent study, the recommendation engine Market size is projected to reach $12.03 billion by 2025 from $ 1.14 billion in 2018, with a CAGR of 32.39% during 2020-2025.
In this blog, we will glance at how a recommendation engine works and how can your business benefit from its implementation.
What is a Recommendation Engine?
A recommendation engine is simply an AI technology based component in eCommerce platforms designed to offer tailored recommendations to users based on their past activities, such as browsing history, products bought, time spent on a certain page, etc. This digital implementation is widely seen in a wide range of industries from eCommerce to OTT content streaming platforms
Primarily, there are three kinds of underlying techniques that a recommendation system works on. We will take a look at each one of them in the upcoming section:
- Content-based filtering
Content-based filtering works on data collected from a single user’s activity, preferences and historic buying behaviour. All the recommendations made under this system are result of insights gathered on user's metadata collected. The recommendations done by gauging the patterns in this metadata and align it with user’s likings or behavior. The information returned including products and services will all be exactly like a user prefers. In such a system, accuracy is directly proportional to the data a user can offer. More and authentic data improves the accuracy of such a system.
- Collaborative filtering
This is another commonly used algorithm behind recommendation engine. The Collaborative filtering has a much broader net, gathering data from the activities of multiple users to obtain common suggestions. This mechanism suggests products based on what other users in a niche are buying. For instance, using their suggestions and actions for recommending items or informing them if a certain product best works with some other products. The ‘Next buy’ and ‘Users also bought’ recommendation is a primary usage of this system.
- Knowledge-based system
In the knowledge based mechanism, recommendations are made under the influence of a user’s requirements including a certain extent of domain experience and expertise. Rules are laid to define a context for every recommendation. These recommendations are particularly useful in selling of products that involve presence of trust between selling and buying parties, for example, financial products, like , loans. It is best implemented in complex domains where items are infrequently purchased like property buying, insurance, etc.
Some Common Use Cases for AI-backed Recommendation Engine
OTT Video Streaming
Netflix is already touted for its impeccable microservices architecture backed by enhanced machine learning algorithms that keep its subscribers perpetually glued to their content. The recommendation engine of Netflix is heavily based on content-based filtering that takes in various parameters like user’s browsing history, frequently watched shows, most-watched genres to recommend irrefutably amazing content. According to McKinsey, 75 percent of content users watch on Netflix come from product recommendations.
Ecommerce has seen the widest implementation and most ROI through AI-backed recommendation engines. Amazon has long earned traction for its refined customer-centric experiences, an instrumental link to its massive success. Despite it’s distributed focus across products, it has managed to build a unique seller and buyer experience through recommendation engines. Other eCommerce giants have been following the trail to compete with a marketplace full of high-end e-retailers.
Recommendation engines have enabled people to connect better, communicate effectively and voice their opinions on matters of national and global importance. Recommended Accounts include high-profile individuals, close and distant friends, accounts of businesses, and other services that the users are interested in, etc. This has enabled people to access and understand the areas of their interest and build meaningful networks.
Recommendation engines find implementation in fintech also. For instance, Credit Karma, a fintech startup from California offers access to credit history and scores free of cost hence making money from a cohort of personalized recommendations on loans, credit cards, and other financial utilities for their users. Its recommendation system is based on huge amounts of data on credit history and financial situations of users, to recommend personalized products to the user with a high probability of approval, and far-sighted profit results.
Benefits of a recommendation engine for your OTT platform or eCommerce platform:
- Intensive personalization – the users come across the products which are according to their likes and hence will spend more time on the page.
- Improved conversion rates – Increasing relevancy of products will definitely result in a conversion for one or more recommended products. Hence, it will lead to more conversion rates.
- Improving average order value – the recommendation engines improve the AOV and amount of products bought in single transactions.
- Spike in targeted traffic – suggesting related products over banners or in-person promotional email, will narrow down the efforts needed for targeted traffic.
- Mitigated cart abandonment rate and bounce rate – These KPIs are essential to every e-commerce entrepreneur and they improve when abandonment of product rooting from irrelevancy or unsuitability is reduced.
The deployment of recommendation engines has helped businesses to capitalize on existing data and offer precise and highly-personalized services to their consumers. Moreover, a recommendation engine reforms and improves with relevant data and authentic information. Businesses that are run and strategized on this consumer data, can reap the real benefits of this technology backed selling. With smart, intuitive, visualization techniques for marketing and selling products and services, customer loyalty and retention can be won through hassle-free and targeted effort.
If you are on the look-out for a self-sustaining and ever-improving movie recommendation system for your OTT platform or a simple recommendation engine for your ecommerce platform, get in touch with Datamatics.
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