Recommender Systems In eCommerce: The Quickest Way to Boost Sales

Recommender systems in eCommerce work like an ace salesperson who is well versed with upselling and cross selling concepts. –   Tweet This!

Whether or not you own a webstore, you must have bought items from Amazon or eBay at some point in your life. You might have noticed something like this (See Image Below) when you add an item in your cart or simply view a product.

The image that you see above belongs to the famous Amazon recommendation system. This is one of the best recommendation system examples in the online retail world. It tries to attract your customers and boost your store’s average order value by suggesting products that go along or perhaps pair with the currently purchased item on your webshop.

For instance, if you have recently purchased a Copper Jug from Amazon, the next time you log in, Amazon’s recommender system’s algorithms will suggest copper cups or plates to supplement your Copper Jug.

A recommender system works like an ace salesperson who is well versed with upselling and cross selling concepts. It makes use of the information such as customer purchase behavior, product reviews and ratings that other customers leave for different products to suggest similar products for you.


Are you able to showcase AI powered related products, upsell items and frequently bought together products on your online store? To get all of these and more, install Perzonalization for your store now and start your 14 day free trial.


For an eCommerce store, the ultimate objective of a robust recommender system is not to just augment the average order value, but also to offer a great and eventful shopping experience to anyone who visits the webstore.

In a busy world like today, a good recommender system can act both as a personal shopper and a shopping assistant for you.

You no longer need to waste your time on several unfruitful trips to the shopping mall to find the best accessories for your brand new laptop, clothes and household appliances, or even high-end smartphones. With the help of talented recommender systems in eCommerce, the items that you’re looking for will be at your fingertips and displayed with ease on webshops.

Recommender Systems in eCommerce: An Introduction

In this segment, we shall give you a detailed picture about a recommendation system, how recommender systems for large-scale eCommerce stores work, and a brief on recommender systems algorithms.

Recommender systems in eCommerce are transforming from just mere innovations by a few webstores, to serious business tools that are remodeling the entire eCommerce landscape. Many eCommerce giants are using recommender systems to assist their customers so that they can shop for products with ease and efficiency.

A good recommender system learns from customer buying behavior and suggests products to help customers find the most valuable items from among the available products. The products can be suggested based on a few analyses that take into account:

  • The best sellers on the webstore,
  • The best sellers in the specific category that the shopper is viewing
  • Customer demographics
  • Clickstream behavior
  • Past purchase behavior

These analyses are then used as a prediction of the customer’s future purchase behavior.

The underlying technology behind the recommender systems in eCommerce

Not all but some of the recommender systems (like the one we have here at Perzonalization) use machine learning to generate insights and suggest the right products to customers. The important thing here is that; recommender systems machine learning works on a given set of data and information.


Are you able to showcase AI powered related products, upsell items and frequently bought together products on your online store? To get all of these and more, install Perzonalization for your store now and start your 14 day free trial.


There are two types of data that is fed to the recommender system – implicit data and explicit data:

  • Implicit Data

Implicit is drawn from a customer’s online behavior. If a customer regularly purchases jeans from an online store, the data scientist gets a hint they are dealing with a person who is into clothes and fashion. Hence they assign fashion-related tags to their (customer) profile.

Information and data are drawn from a lot of factors like the kind of items a customer viewed, the products they added to their shopping cart but later removed, their shopping history and their search variables.

  • Explicit Data

Explicit data, on the contrary, is what a customer literally tells the webstore by leaving ratings and reviews for a product they recently purchased. This type of data is more upfront and easily retrieved.

How do recommender systems in eCommerce manage to increase your webstore’s sales?

Recommender systems eCommerce algorithms help webstores enhance sales in three ways:

  • Convert browsers into potential customers

Visitors to a webstore often browse through the site without ever buying anything. This is potentially the most common behavior of any customer, similar to window shopping.  Recommender systems in eCommerce can assist buyers to find products they wish to purchase by giving them the relevant recommendations.

  • Cross-sell products

Recommender systems enhance cross-selling by recommending additional products for the buyer to purchase. If the suggestions are relevant and good, the average order size should boost. For example, a webstore might suggest supplementary products in the checkout process, based on those products already existing in the buyer’s shopping cart.

  • Gain trust and increase loyalty

Recommender systems in eCommerce enhance loyalty by creating a value-added and personal relationship between the site and the buyer. eCommerce websites invest in learning about their customers, implementing recommender systems to put into action learning and present buyer interfaces that go with customer needs.


Are you able to showcase AI powered related products, upsell items and frequently bought together products on your online store? To get all of these and more, install Perzonalization for your store now and start your 14 day free trial.


Customers repay these webstores by returning to the ones that best suit their requirements. The more a buyer uses the recommendation system – instructing it what they (customers) want – the more loyal a customer is to the online shop.

Important concepts and algorithms followed by recommender systems in eCommerce

Machine learning, big data and artificial intelligence are the three most important terms used while we are talking about building and designing recommender systems. eCommerce giants like Amazon and eBay have their own data scientists to custom-design their product recommendation machine learning algorithms to match their webstore’s requirements. However, not all eCommerce businesses can find the finances to accommodate an internal personalization team to build recommender systems. They tend to employ subject matter experts like Perzonalization to manage their personalization efforts.

Broadly, product recommendation algorithms are divided into two categories – collaborative filtering and content-based filtering. Perzonalization’s contemporary recommender system is a combination of both these algorithms, known as a hybrid recommendation system.


Broadly, product recommendation algorithms are divided into two categories – collaborative filtering and content-based filtering. –   Tweet This!


  • User based Collaborative filtering

Collaborative filtering (CF) is a common product recommendation machine learning algorithm used in popular eCommerce stores. This algorithm can instantly pick up and learn to offer suitable recommendations as more data about customers is collected.

Websites like YouTube, Netflix and Amazon implement CF algorithm as part of their product recommendation systems.

Collaborative filtering recommendation systems are based on two types of assumptions:

User to user filtering – If a customer A (Peter) liked blue jeans, a casual t-shirt, trousers, and moccasin shoes while another customer B (Ronald) like black jeans, formal shirts, baggy trousers, and converse shoes, there is a probability that Peter will also like baggy trousers and Ronald will like moccasin shoes.

Item to item filtering – It finds products that have some common primary characteristics. Furthermore, it suggests similar or related products to the customer. For instance, a customer purchasing energy drinks would prompt the recommendation system to provide a sipper.


Are you able to showcase AI powered related products, upsell items and frequently bought together products on your online store? To get all of these and more, install Perzonalization for your store now and start your 14 day free trial.


  • Recommender systems content-based filtering

This recommendation algorithm is completely based on the description of a product or profile of a buyer’s preferred choices. This filtering system uses keywords to describe the products. Furthermore, a customer profile is designed to highlight the types of products this customer likes.

In other words, the algorithms used in content-based filtering try to suggest products, which are related to the ones that a customer has bought or liked in the past. The fundamental assumption of this filtering system is that if a customer likes an item, they will also like related products.

  • Hybrid recommender systems

This filtering system uses elements from both collaborative and content-based filtering. They are more precise when it comes to performance.

Netflix is known for implementing hybrid filtering in its system. The Netflix recommendation system makes suggestions by evaluating the search and watch habits of its viewers (collaborative filtering) as well as suggestions based on the ratings given for a movie (content-based filtering).

The hybrid approach we have here at Perzonalization uses machine learning to understand users’ taste profiles by analyzing product descriptions along with user behaviors in real-time. We then use collaborative filtering to micro-segment the users and come up with personalized product recommendations.

Recommender Systems In Machine Learning: The Visual Perspective

Today, respected recommender systems in eCommerce are trying to utilize machine learning-based techniques. However – as with all other technological approaches – machine learning based recommender systems have some limitations as they mostly rely on text-based product information and user searches. The fact that visual descriptions are not detailed enough to describe images, recommender systems in machine learning has to process text-based data to understand product attributes.

To overcome these challenges, some researchers have proposed image-based supervised and unsupervised recommendations. Today, the effectiveness of deep learning based visual recommender systems in eCommerce are being investigated. eCommerce owners are eager to see “pair it with” type of recommendations on their webshops however these deep learning based approaches are still taking baby steps.

Which concept must you use for your eCommerce store?

With machine learning recommendation systems being the current trend in the online business, is it necessary for you to opt for the same? The answer depends on your preference. As experts in the domain of online business, we suggest you go for AI-powered real-time product recommendation engines for your eCommerce store.

Currently, most eCommerce giants are switching from standard collaborative filtering recommendation systems to AI recommendation systems. Product recommendation engines powered by AI can offer quick and precise suggestions personalized to each customer’s tastes and preferences. It can sense what the customer needs and quickly suggest products catering to their tastes.

We at Perzonaliztion use AI to create our own product recommendation engine. Our AI-powered real-time recommendation engine helps us group products according to the behavior patterns. For instance, we are able to understand that a product is polka-dotted just by looking at visitor behavior even though this feature is not being stated on the webstore. We then use this information to display personalized items and content based on the customer’s unique shopping needs and tastes. Perzonalization’s product recommendation engine – is instantly operational, sports a perfect design suitable for your eCommerce site, customizable and offers data based on real-time filtering.

We are also able to understand the context of the visit. If the user is looking at female hoodies, then we tend to recommend similar hoodies for women. This is possible only because Perzonalization belongs to the small but respected group of context aware recommender systems in eCommerce.

Why is a recommendation system important in today’s online world?

Not that long ago, every vendor knew their customers personally and could make recommendations to them based on their personal knowledge of their previous purchases. This type of personal relationship meant that shoppers would get the best customer service, while sellers were able to reap the benefit of brand loyalty since they were well-informed about their customer’s tastes, needs, budget and preferences.


Are you able to showcase AI powered related products, upsell items and frequently bought together products on your online store? To get all of these and more, install Perzonalization for your store now and start your 14 day free trial.


In this day and age, online shopping is the current trend. Although online business is progressing rapidly, sellers cannot build the intimacy and relationship they could through a brick and mortar store. Hence, to connect with customers online, a recommendation engine was designed.

If you are reading this post, I assume you are not working for Amazon, eBay or any of the eCommerce giants out there. So for your store, it would be smarter to work with a recommender system vendor rather than trying to develop a personalization software, internally.

Here is why working with a recommendation engine vendor is important in today’s online business world:

  • e-Commerce Recommender Systems Help You Gain A Competitive Edge 

Many online businesses are vying to offer the best customer service through their payment gateways, recommendation engines, product offerings and customer care services. Working with a robust and highly-intelligent product recommendation engine can make your eCommerce store distinct from the others.

  • e-Commerce Recommender Systems Augment Sales

The ultimate objective of every eCommerce store is to improve sales. Recommendation engines offer the right suggestions to customers promoting to purchase the items. It has been proven that using the right recommendation options can boost upselling revenue and offer an enhanced shopping experience to customers.

  • e-Commerce Recommender Systems Change Shopping Trends

Serving precise content can trigger cues, build consistent habits and influence shopping practices. Recommendation engines have become the next factor for influencing and perhaps changing the buying behavior of customers.

  • e-Commerce Recommender Systems Drive Business

Lastly, eCommerce owner’s and marketing analysts can save up to 75 per cent of their time when they are offered customized suggestions necessary for building future marketing and sales  campaigns.

How can webshops benefit from recommender systems in eCommerce?

You need not undertake marketing research activities to find out whether a customer is willing to buy at a store where they are getting increased assistance in scouting the right item. With the help of a recommendation engine, customers are likely to return to your shop in the future.

Recommender systems’ benefits for online business owners and customers are huge as both parties are gaining from the usage of such a system. The customers can easily find what they are looking for and feel that the eCommerce company values them. On the other side, there are a few monetary benefits for the online business owners.

Here are a few benefits an eCommerce store can achieve using the best product recommendation engines:

  • Increases revenue

With vigorous research and experiments driven by eCommerce giants, product recommendation engines have proven to drive higher conversion rates than web stores that do not work with a recommendation engine.

  • Drives customer traffic to your webstore

Using a good recommendation system prompts customers to offer good reviews about your website on various platforms and social media. This can direct other potential leads to visit your website and buy products.

Product recommendations used on emails can easily direct re-marketing traffic to an online store, which can lead to new purchases.


Are you able to showcase AI powered related products, upsell items and frequently bought together products on your online store? To get all of these and more, install Perzonalization for your store now and start your 14 day free trial.


  • Provide reports

It is a crucial part of a recommendation system. Offering the website owner precise and up to the minute reporting lets you make robust decisions about your site and design the best marketing campaign. Based on the reports generated through recommendation systems, webstore owners can generate offers or strategize marketing campaigns for slow moving products to drive sales.

  • Increase customer satisfaction

One of the best benefits of implementing a good recommendation system on a webstore is that it makes shopping for a product easy for customers. For instance, when a recommendation system makes surprising recommendations similar to a customer’s taste and preference, they are certainly going to visit the site again to purchase the item.

Furthermore, a recommendation system acts like the best salesperson, it helps customers to purchase items based on their previous purchase. This leads to less confusion and more of post-purchase satisfaction.

  • Increases the average order value

When a recommendation engine displays personalized, relevant product options to customers, the average order value automatically shoots up. Customers are tempted to purchase a supplementary product to support their immediate or previous purchase. For instance, if a customer bought a blue dress, they would certainly look for jewelry to match the outfit.

Recommender Systems In eCommerce: The Conclusion

Recommender systems in eCommerce are the need of every online store owner, especially if they want to make a mark in the online business world. Even eCommerce giants are upgrading their legacy recommendation systems to real-time AI-powered recommendation systems.

If you want to increase your sales, stay ahead of the competition, increase customer satisfaction and brand loyalty, you will have to move ahead of the times and that is by implementing AI-powered recommendation systems in your webstore. Perzonalization offers AI-powered recommendation systems that match your needs; you can click this link to know more.

Curious to learn more? Take a look at related posts!


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