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Recommendation engines are actually a replacement for a nice shop assistant who would friendly help the visitor throughout his shopping journey. Nowadays the medium of sites that are in demand of Recommendation Engines includes mostly E-commerce websites which can be immense, selling everything from A to Z, like Amazon or Aliexpress and smaller ones, more category-specified stores like those selling clothes, jewelleries etc.


A case study, written by Senior Data Scientist at Lynchpin Analytics for a large online retailer, illustrated how Recommendation Engine algorithms can make a 32% conversion rate uplift and 23% uplift in revenue. The biggest Online cinema – Netflix that launched a Netflix prize company in 2006, ended with a team of machine learning experts improving the previous recommendation system accuracy only by 10%. It may seem like a small number, but it resulted in a huge ROI increase for the company in future with 32.3% every year growth and 5.2 million new subscribers.


Let’s get started with a major field – online stores. To understand how critical it is for online commerce, consider Recommendation engine examples of Internet giants, such as Amazon that claims to have a 29% sales increase (almost $13 billion), achieved due to Amazon having integrated recommendations into almost every part of the purchasing process.


Recommendation system for E-commerce websites



Even if personalized offers are a best choice, not always this way of recommending things can actually be used, for example, when there is an obvious lack of data on your customers. Until your online store database collects enough data to extract patterns and sequences, you will have to use unpersonalized recommendation type for a “cold start” (a lack of historical information on customers/their preferences).


Unpersonalized: Popular products

Using this method means to use the sales data in order to identify what products are best selling. With the lack of data on unique customers you can address the purchase history of others and identify the most popular items. These types of recommendation are usually sent via e-mail as newsletter, but also you can send them as bulk letters. With the fact that you do not have enough Data on your new customers of your online store, your ideas about what they may prefer are far too vague. Promoting items that are the best choice for other customers towards the newcomers gives an overall positive impression of your store assortment.


Unpersonalized: New Products

A letter with the new products recommendation sent to your potential buyers or the same items put into their layouts on the landing page is a good alternative to Popular products. Moreover, even if you have enough data to build a personalized recommendation approach, it would not hurt to send notifications about new products in stock from time to time.


Unpersonalized: Products of a lower demand

While some of the items hit top in sales, some may be staying in the shade and untouched by most customers. You can change the situation by making them “hot” and drop their price with, say, a 20% reduction. Recommending less popular products with a sale to customers might speed up your slow moving items. The best way to get your customers acquainted with the discounted products is by e-mail as in the case of new products. Once you have a preference history of your clients, make sure to personalize such offers by sending sale e-mails with a relevant stuff. You can monitor the client’s interest in an item through whether his adding of the item to a user basket, either a simple “click” on it.


This is how Netflix uses recommendation engine



On the contrary to unpersonalized recommendations, personalized are based on the history of any users’ interactions with the site and items on it. It is proven that about 70% of what users click is brought by recommendations. Once you have insights, users’ engagement and purchase history, next step is to pick a relevant and preferable content for them to recommend. It is obvious that more than 50% of users would be frustrated to see things not corresponding to their tastes (for e. g. a bulk stuff instead of relevant items) in the recommendation layouts.

Personalized: Recently viewed

This one in actually the easiest to understand and implement, also it does not need in-depth data on your clients. It recommends items that your site users already engaged with while surfing the store. This tactic works if a visitor might have been interested by a certain product, although got distracted in the process of browsing. You can use emailing to remind users’ of the item they considered and potentially receive a conversion.


Personalized: Recommendation based on similarity

After you have “recently viewed” users’ items, the system will start to select other offers of similar products. Your recommendation engine makes an effort to figure out what items, resembling the following one, were viewed by others. This surely needs some amount of product data comprising other users’ habits.
In case your visitors left the site because of not finding exactly what they wanted, showing them items with similar characteristics, but not the same, may be a better idea.


Personalized: Frequently bought together

Recommending complementary items bought by identical customers is a great way to cross-sell and encourage more purchases. You can make it even more efficient by sending the recommendations in post-purchase emails, or for instance, in messages, confirming the order. It does not necessarily mean that customers would be eager to buy these complementary items at once, though they might feel a need in having a full set and think of eventually purchasing them.


Personalized vs. unpersonalised offers

However it still makes sense to show up bestsellers, new products or discounted items, a better idea is to make personalized offers once you collected sufficient insights on customers’ activity, habits and purchases. As long as targeted content works much better for increasing the conversion, your subscribers would be happier to see items that cater their tastes. These campaigns along with bulk newsletter can be used in welcome emails, browse abandonment emails and post-purchase letters.


E-commerce engines could be used for shop assistance



Powerful and insightful Recommendation Engine services built with the involvement of Machine Learning are used by all E-commerce giants, such as Amazon, Netflix, Aliexpress, Alibaba, bringing them almost 10% revenue increase each year. Unpersonalized offers are good to draw the attention of new buyers and personalized can really pull off your conversion in the future to a brand new level.


If you need a consultation or a solution for your online site Recommendation system or any other matters that can be addressed with Machine learning and Artificial Intelligence – contact us anytime through our chatbot on the main page.