Product recommendation engine. Personalized recommendations service for ecommerce.
A product recommendation engine, although it is a sophisticated and complicated system, can be explained in fairly simple terms. You have products to sell. You want your customers to find those products. Your website has limited space. The search function can often miss the context of what your buyer is actually looking for. Our Azure-based personalized recommendation engine offers the products specific to each customer at different times. As a result, you get higher conversion rates on every view and the buyers find what they want right at the time they are ready to make a purchase.
In modern times in ecommerce customers pretty much expect the vendors to know what they might want to buy. This creates a curious situation for the retailers and an opportunity at the same time.
If a retailer can take advantage of ecommerce recommendations to tell the buyers what they might want to purchase – not only it would improve sales, but affect the overall customer experience in the most positive way.
The downside of this that you as a vendor need to be aware of is that if your recommender systems in e commerce do not correctly predict the customer’s next potential purchase, they might think twice about coming back to your store in the future.
A good ecommerce personalization algorithm would not let that happen though. It’s typically set to recommend a range of products from various categories the buyer has been browsing or shown interest in before. The goal is to put products in front of the customers’ eyes that they would be most likely to click, discover more about and ultimately purchase.
A personalized recommendation engine has one main objective. That is to increase average order value. Typical ‘Frequently bought together’ recommendation mechanisms are meant to up-sell and cross-sell the buyers by suggesting additional or similar products based on the items they clicked on or added to the basket before right by the items the customers have just put in their shopping carts or below products they’re currently viewing on the website.
Ecommerce product recommendations track and analyze products a customer has been looking at and suggest similar products of different brands, shapes, and sizes to make them consider the items that very similar to the products they’ve already shown an interest in.
Another popular way to utilize ecommerce product recommendations is by introducing “Related items”. This feature displays similar products in different brands, colors, etc., to products a customer has looked at in the past.
Similar to “Frequently bought together”, ecommerce recommendations engines show products that have been bought together by other shoppers, aiming to increase average order value through up-sells and cross-sells.
As you well know, ecommerce product recommendations have been around for a while in the eCommerce industry. So what makes product recommendation so great and why is it a smart idea to invest in a product recommendation engine for your eCommerce business?
The biggest reason to invest in a recommendation engine is that with it you can create a personalized experience for each visitor of your online store. This means that aside from having the most popular products in your inventory showcased to all the potential customers, someone who has been purchasing from your store for a while will also see personalized recommendations related to their prior searches and purchases, as well as their general taste, interests, and style preferences.
Our Azure-based self-learning ecommerce personalization module uses a number of algorithms that track various customer behaviors and build a unique experience for every customer. So, you don’t have to tag related products anymore or use additional widgets and apps that suggest related products.
Certain brands and products always tend to resonate better with the majority of customers than others. Recommender systems in e commerce can show you which products in your store are most popular with your customers. This can help you plan your inventory better and use the knowledge in your marketing strategy.
Ecommerce recommendations can help you get a better understanding of your customer’s journeys. Our product recommendation engine will show you how they browse the store, what they like, what they choose, and when most conversions happen. It can also suggest information you might not have even considered looking at, like the time when most of the transactions occur. Mapping the customer journey can also give you insights on where your conversion process gets stuck, and how to fix it.
Built using Azure Machine Learning, the Recommendations engine uses customer data (past customer activity you’ve uploaded or data collected directly from your digital store) to recommend items for your buyers. It is a powerful tool that uses gathered data and machine-learning to identify the most relevant product recommendations for each customer interaction.
Here are a few key features.
The product recommendation engine automatically identifies and recommends items from your inventory on your product page that are likely to be consumed together in the same transaction.
This feature is essentially an algorithm that enables a vendor to predict which items are likely to be bought together based on previous journeys of other customers. Using ecommerce product recommendations can help a retailer make business decisions in retail environments.
Once you know this, you can use it to your advantage in a number of ways. Ecommerce recommendations analysis would help a vendor improve sales and make calculated business decisions for customers who are in a rush, as well as those shopping leisurely.
If a customer has looked at a product even once, it means you they were at least slightly interested and ecommerce personalization engines take note of it. They will often remind the customers their browsing history in case they decide to go back and purchase something they have previously shown an interest in.
Basically, it learns from customers’ click patterns to increase your product catalog’s discoverability and boost sales.
Uses the purchase history of each specific customer to recommend products unique to that customer.
For example, after a customer purchased a phone from the store they are taken to the order processing pages, where underneath their current order they would get recommended a variety of different cases for the exact phone they have purchased aiming to encourage a second purchase.