Whether or not you run an e-commerce store, you must have shopped on Amazon at some point in your life. Have you ever noticed something like this when you view a product or add a product to your cart?
This is the recommender system of Amazon at work. It tries to lure you into increasing your average order value by offering products that go along with your current purchase. A recommender system works like a brilliant salesman who is well trained in cross selling and upselling. It uses information such as ratings and reviews other customers leave for different products to suggest good products with a higher price to you. For an online business, the aim of a good recommender system is not just to increase the average order value, but also to provide a great customer experience. In the world of accessories and accessorizing well, a good recommender system can act as a great stylist or a personal shopper for you. You do not need to waste your time on several trips to the mall or any other place to find the best matching sneakers against your attire or the best accessories for your brand new iPhone X.
Types of information a recommender system feeds off of A recommender system uses Machine Learning (ML) to draw insights and recommend appropriate products to users. But ML works on a given set of data. What kind of data would it need to confidently say "Oh! Susan might like this pair of trousers to go along with the blouse she bought"? There are two kinds of data that a computer engineer in some remote location of the world would be feeding into the system - implicit and explicit. Explicit data is what you literally tell the site by leaving ratings for a product you bought or the comments you leave. Implicit data, on the other hand, is derived from your online behaviour. If you regularly buy protein shakes from an online store, the computer engineer goes like "Ooo, we're looking at someone who probably hits the gym. Let us add gym related tags to her customer profile". Information is drawn from a lot of things like the kind of products you viewed, the products you added to your cart but later removed, your search and return history etc.
The 3 ways they recommend products to you Collaborative filtering is based on two types of assumptions: One assumption is called user-user filtering: if Susan liked half sleeved blouses, ankle length trousers and brogues and Margot liked ankle length trousers, brogues and chino pants, then it is highly likely that Susan will also like chino pants and Margot will like half sleeved blouses. The other assumption is called item-item filtering: It finds items that have some common underlying features. It then recommends look alike products to you. For instance, you buying protein shakes would prompt the recommender system to offer a shaker. Content based filtering builds user profiles based on what user liked or bought and offers similar products in the future. For example, this can be used in recommending similar types of movies or a songs. Hybrid filtering uses elements of both content and collaborative based filtering and are more accurate in nature when it comes to performance. Netflix uses this approach by comparing the watching habits of users (collaborative) and offering movies that have similar traits (content)
Recommender engines you can use for your site
You probably understand by now the various advantages of using a recommender system for your own e-commerce website. It can increase your revenue and provide a great customer experience. What are some of the available recommender systems? Software as a Service(SaaS) Recommender Systems: Instead of having a large upfront investment, you can pay as you use in a SaaS model. The integration is usually straightforward and there are continuous cycles of improvement. Some SaaS recommender systems are: SuggestGrid is a generic recommender system that can be used to recommend products and content to users. Episerver is more e-commerce centric and automatically recommends product and content items based on a visitor's past behaviour, and the behaviour of visitors similar to them. SLI Systems delivers personalisation with proven up sell and cross-sell results. Their cloud-based, artificial intelligence platform predicts what your shopper is most likely to buy right now.
End Notes Using a recommender system on your site is bound to drive up your sales, engage your customers in a better way and can help in building customer loyalty. If you are using the ecommerce platform Shopify, you can find some of the recommender apps here.