It is commonly believed that “70% of Amazon’s machine learning is devoted to Product Recommendation”.
As a matter of fact, all of the top 10 world’s leaders in eCommerce use product recommendation to boost revenue by 300%. Personalization is at the core of the e-commerce giant’s user experience, and one of the most crucial drivers of its enormous growth.
Given how much Amazon focuses so much on product recommendation, and other data points which testify to its potential – using a product recommendation engine has now become essential for every ecommerce store.
Here’s an example from Amazon itself. Look under Related to items you’ve viewed:
We’ve put together this comprehensive post to give you a thorough understanding of the world of product recommendation. This article is divided into 4 major parts as follows:
What is Product Recommendation?
What are the benefits of using Product Recommendation?
Betaout’s Product Recommendation Engine
Effective Use-cases Of Product Recommendation
Without much further ado, let’s dive right into it:
What is Product Recommendation?
Product Recommendations are dynamic, real-time item recommendations that are generated by the users of an e-commerce store by recommendation engines. These recommendations for ecommerce firms allow customers to find the products they need and are relevant to them quickly and easily.
Recommendation engines are, at their core, information filtering tools that utilize algorithms and big data to recommend the most relevant items to a particular user in a given context.
Recommendations use the concept of attribute affinity. For e-commerce, recommendation engines essentially capture user behavior on an e-commerce store, to come up with item suggestions based on their browse and purchase behavior coupled with a vast number of individual attributes and product properties.
A customer will be more willing to make a purchase at a shop where they feel they’re getting maximum help in finding what they’re looking for as fast and easy as possible.
They’re also much more likely to return to such a shop in the future. Product Recommendation makes it really possible.
Benefits Of Product Recommendations
If you still have reservations about implementing product recommendations to your online store, here are 4 benefits which will convince you otherwise:
Creates A Personalised Experience For Your Users And Visitors Alike: One of the biggest benefits to investing in a product recommendation engine is that you can use it to create personalized experiences for your entire site audience. This means that if we were to visit your site, we would see an entirely different range of products and suggestions from someone who was already your customer. While we might see the most popular products in your inventory, someone who has been your customer for a while may see recommendations tailored to their interests, taste, and style.
Increase Conversions By Increasing Relevancy For Shoppers: Product Recommendations provide e-shoppers a quick and easy way to view relevant items. This can motivate “just-browsing” visitors to make a purchase, help “lost” customers to find their favorite items, and encourage “big” shoppers to add additional items to their carts. Since websites offer a vast range of items, product recommendations are an effective method to combat the issues such as browse abandonment.
Helps Drive And Boost Customer Loyalty: Using a product recommendation engine to suggest items ‘just for’ a customer on your website, makes them feel valued. Valued customers are more likely to stay loyal to your brand, and recommend your brand to their friends and family. This is essential because steady customers help businesses thrive in the times that are bad for business. Infact, businesses with 40% repeat customers generated nearly 50% more revenue than their counterparts with 10% repeat customers.
Understand Your Customers’ Journey Better: Using product recommendation can help you understand your customer’s journeys better by seeing how they browse, what they choose, and when most conversions happen. They can also pull out information you might not have considered looking at, like the time when most of your transactions occur, or predicting the right kind of customer that would fit a particular product you just entered into your inventory. Mapping the customer journey gives you insights on where your conversion funnel is stuck, and ideas on how to fix it.
Betaout Product Recommendation Engine
Betaout comes equipped with a powerful recommendation engine. Using the power of machine learning, artificial intelligence, and big data, Betaout empowers e-commerce stores to provide contextual, relevant and highly personalized user experience to their customers on multiple channels including On-Site Targeting, Email Marketing, Browser Push, and more.
Betaout’s Product Recommendation Engine is an intelligent, robust and highly-scalable solution. There are essentially two types of product recommendations that an e-commerce store can make use of with Betaout – Item-To-Item & User-To-Item. For further reading, please refer to these articles:
What is Item-To-Item Recommendation in Betaout?
What is User-To-Item Recommendation in Betaout?
Using Betaout’s Product Recommendation, you can dynamically recommend items to your users on the following basis. We’ve also included some use-cases that will come handy with the tool:
Viewed Also Viewed: Viewed Also Viewed simply translates to ‘customers who viewed this item also viewed.’ This type of recommendation is aimed at giving the customer more choice of options to choose from, based on how other people viewed this item. This is primarily a frequency driven algorithm.
Bought Also Bought: Bought Also Bought refers to ‘customers who bought this item also bought.’ This recommendation type helps the customer with a choice of products which other buyers have bought with or after purchasing the product. For example, if a customer buys an iPhone, bought also bought would recommend iPhone cover, cases, headphones. This is also a frequency based algorithm
Top Selling Products: As the name suggests, Top Selling Products are basically the best seller of a brand. They are the overall best sellers on your e-commerce store. Customers when recommended the top selling products are being shown the best products that your store has to offer to them, so promoting them help you in revenue dramatically.
Cross-Sell: Cross-Sell is referred to an opportunity in which the engine suggests products to a customer from categories other than the one the product is in. For example, when buying a new mobile phone Amazon suggests me a cover and memory card to use with it. Cross-sells creates opportunities for highly-relevant suggestions which the customer is interested in buying.
Up-Sell: Up-Sell is referred to when a customer is shown products in the same category, but with a higher price. Up-selling helps boost revenue since many customers act impulsively while making a purchase online and hence is a great way to boost your revenue.
Recently Viewed: Recently Viewed is referred to items which the customer has recently viewed and are recommended to him solely based on his browsing history.
Shoppers today want a “personalized” experience, it isn’t just a buzzword anymore. One that is relevant, contextual and tailored according to their pain-points and needs. Gone are the days when ‘one-size-fits-all’ solution used to be the norm. With the advent of e-commerce, consumers have now become more sophisticated in terms of how they shop, everything from groceries and plane tickets.
For e-commerce companies looking to provide consumers with an experience that is differentiated – using product recommendations is quite effective. If you’re wondering where to start, Betaout provides a powerful and scalable product recommendation tool incorporated in its all-in-one marketing automation suite for ecommerce.