A recommendation system is a piece of AI software that uses Machine Learning and Big Data and suggests additional products to consumers based on past behaviour, product features and other data collected from ecommerce systems. These can include past purchases, demographic info or their search history. 

The idea behind this is that we can limit the pool of content or products to show to the consumer which results in higher conversions and profitability.  

The Challenge

In recent times, globalization has changed quite a lot. Though the percentage of productivity has massively increased, we have however lost the close relationships that used to exist between clients and vendors. This however does not change the fact that key to successful sales in understanding the consumer’s problem and therefore we need to learn more the consumers problem even if that personal relationship has been lost. 

With modern AI technology we can build smart algorithms known as recommendation system that can fill that gap!


The Process

Recommender systems make use of a great sea of content and make the selection process easier by providing specific content that is relevant to the user. A relevant example of a recommender system is LinkedIn’s recommendation system for people you might know. There are about 500 million users registered on the site and it wouldn’t be cool suggesting an unlimited number of accounts to connect. Hence the recommendation system algorithm helps to filter the pool of availability to a few options that the user may actually know in order to grow a network on the site.


The Technical Bit

There are different types of recommender systems.

It is very important to choose the right type of recommender system when choosing it for your business. Let’s look at the basic types of recommender systems available.

Collaborative Filtering

The backbone of this recommendation system depends on the feedback of other users on certain items. It basically depends on the interaction of users on the platform. If a certain group of people have the same preference as regards certain choices in the past, then there is a high possibility that in future, they would prefer the same additional selections.

Content-Based Filtering

This type of recommender system is quite different from the collaborative systems. It strictly focuses on a user’s behaviour. It studies the user and determines the possible items that the user would like. An example is a site that uses a keyword system to suggest items with similar keywords to a user due to the keywords in previous items purchased.

Demographic Based Filtering

This type of system makes recommendations based on the demographic info of the suggested to the user. The items suggested are items that have been previously selected by users of the same demographic category.

Utility-Based Filtering

The utility-based recommendation is the type that calculates how useful the product is to the user. It does this using value like product availability, vendor’s ranking and reliability as well as comparison with similar considered products.

Knowledge-Based Filtering

This is majorly dependent on the user behaviour which is used to suggest the assumed needs and preferences of the user. Knowing what the consumer has purchased in the past, the system can make attempts to predict the future needs of the user.

Hybrid Filtering

This type of system has to do with a combination of multiple recommender techniques to create a more effective recommending system. For example, you can merge collaborative filtering with content-based filtering or vice-versa.


The Results

The market is changing. To stay relevant in this dynamic changing environment you have to be dynamic as well. You must also learn how to engage with your customers in the best way possible with the best content or products. 

Recommendation systems can help drive a high conversion rate which leads to an increase in sales. Your brand will be trusted by consumers, if your website services are well suited to a user’s preference. If your brand understands your customers, they will stay loyal and will keep buying things on your website.

A recent study by McKinsey showed that Netflix’s recommender system is responsible for 75% of what consumers watch. The recommender system not only leads consumers to more movies to watch, but it also helps the company save a large amount of cash they would have spent in marketing. An interview with Netflix executives- Carlos A. Gomez-Uribe and Neil Hunt disclosed that their recommender system saves the company around $1 billion every year.

Amazon spends 35% of their revenue on recommendation systems. This is enough to tell you why you should adopt this AI technology solution. In order to achieve this, Amazon combines several recommender systems together. They provide users with product suggestions based on the items that are frequently purchased along with the items in their cart. Users are also shown items similar to the products they are presently viewing or have viewed in the past. An interesting fact is that they use recommender systems both onsite and offsite. Offsite recommendations are through emails and are known to produce a higher conversion rate than those onsite recommendations. In 2016, Amazon recorded a 29% increase in total sales with credit to recommender systems. That year, they had total sales of $135.99 Billion.