Calibrating product suggestions to the users’ preferences continuously and autonomously is the most important, core potential of a recommendation engine. When your E-Commerce store has a sense of what the customer may have in mind, it makes life easier for both your customer and for yourself. That is how you retain more customers. Moreover, with timely and accurate suggestions, recommendation engines increase cart value, which means you ship more products together and earn some cost benefits. In fact, 35% of Amazon’s total product sales are driven by recommendations.
There is a lot that you can achieve when you know your customer. However, profiling each customer manually and finding products that they might be interested in lacks practicality when you are dealing with a consumer base that is large, spread across the globe, and constantly in flux.
Making your customers fill large lists to get a hang of their preferences has lost currency years ago. Also, this approach lacks the elements of accuracy and scalability. You end up spending a lot of money without really making any significant strive towards a satisfying customer experience. Then again, a report shows that up to 52% customers switch brands because of a lack of personalized communication from the business.
A recommendation engine powered by advanced ecommerce analytics and machine learning can change things for the better for both your enterprise and your customers.
What a recommendation engine does
A recommendation engine uses confirmed information about what a set of people like to predict what else they might like based on the similarity of or relationship between items. And makes those recommendations automatically. Let us take a set of readers and a few books for instance. Someone who likes books by J.R.R. Tolkien, a great fantasy fiction writer, may also like books by J.K. Rowling, the author of the Harry Potter series. These associations are made by assigning values to the similarity between two different books. It can also determine the similarity between two sets of people by formulating features out of the patterns in their preference data. This is, of course, a very simplistic representation of the computation that goes underneath recommendation systems. Let us delve deeper into that.
How it does it
If we take Netflix’s example (because, well, they have earned it), they had started making movie recommendations based on the preferences of their customers in 2006. It was a very coarse version of what Netflix’s recommendation engine has become today, but it will serve the purpose.
They collected some information about the movie preferences of people and labeled each person and movie with some known attributes. These are called features and they added values to each feature. For instance, if a person likes comedies and hates action films then this system of value addition can represent her by some numbers. The films can be mapped in a similar way. These factors can be put into matrices and used to determine the likeness of the person liking the movie. This approach is called content filtering.
While content-based filtering is a dated practice and has its own shortcomings, sometimes it is all that you need.
Collaborative filtering takes it one step further and instead of imagining these features lets the data determine the features. Machine learning algorithms can be used to explore the patterns in preference data generated by people (actions taken on a website, social media feed, purchase history, reviews, and ratings) and come up with unlabeled associations that can compress the preference data into feature data. This uses the likeness between two people to determine the products they would like.
Why you need it
Every enterprise wants to grab an opportunity to sell more to existing customers. Your B2C E-Commerce platform gets 30-90 seconds in which the customer browses through 20 odd titles before switching to a different platform. If your website can automatically come up with near accurate recommendations for customers, your chances are much higher. Real-time recommendation is the name of the game and machine learning algorithms are your best players. Netflix saves 1billion dollars a year by reducing customer churn with the help of its recommendation engines.
A robust recommendation system makes the purchasing experience for the customers much better as well. They do not have to swipe through a ton of products to find what they are really looking for. When your recommendations are well targeted and personalized, the customer is little likely to get annoyed.
You can even use this functionality to detect anomalies. And all of this and more happens without you breaking a sweat.
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