Not that long ago, people lived and functioned in tight communities. Every vendor knew their customers personally and could make recommendations to them based on a personal knowledge of past purchases. This type of personal relationship meant that customers would receive great customer service, while vendors were able to reap the benefit of brand loyalty since they understood their customer's needs, preferences, and even their budget.

shopkeeper

Fast forward to today, and our modern-day globalization of services and we see that things have changed. While we gain a lot regarding productivity and availability of options, we lose that intimacy of personal client-vendor relationships.

However, this lack of personalized service doesn’t change the very important fact that the key to successful sales is understanding a person’s problems.

And there is an algorithm for that!

What are recommender systems?

Simply put, recommender system is an AI algorithm (usually Machine Learning) that utilizes Big Data to suggest additional products to consumers based on a variety of reasons. These recommendations can be based on items such as past purchases, demographic info, or their search history.

The idea is that if you can narrow down the pool of selection options for your customers to a few meaningful choices, they are more likely to make a purchase now, as well as come back for more down the road.


How they work?

Recommender systems take a large pool of available data and make the decision-making process easier by providing just a few targeted selections. A great example of a recommender system at work is LinkedIn’s recommendation system for people you might know. Instead of suggesting an unlimited number of possible connections (like the 500 million users currently registered on the site), the algorithm is able to narrow down the pool of availability to a few options based on Big Data that it collects so that you can connect with more people that you may actually know and grow your network on the site.

There are many types of recommender systems available

Choosing the right type of recommender system is as important as choosing to utilize one in the first place. Here is a quick overview of the options available to you.

Collaborative Filtering

This type of recommender system uses the recommendations of other users to make suggestions for specific items. The idea behind this type of recommender is that if some people have made similar selections in the past, for example, movie choices, then there is a high probability that they would agree on additional selections in the future.

Content-Based Filtering

This type of recommender system creates a user profile based on a learning method to determine items that a particular user would like. For example, the site may utilize a keyword system that suggests items with similar keywords in its description to an item the user has previously purchased.

Demographic Based Filtering

In this type of system, recommendations are made to the user based on their demographic info. The system will suggest items that have been selected by other users that fit the same demographic profile.

Utility-Based Filtering

This type of system is based on the utility that the user will get from the product. It can include things like vendor reliability and availability to make sure it recommends products easily obtained by the user.

Knowledge-Based Filtering

This type of system recommends selections based on a user’s known preferences and buying patterns. Since the system will know what the consumer purchased in the past, it can make recommendations based on what might fill those needs in the future.

Hybrid Filtering

This type of system combines two or more different recommender techniques to create a more thorough recommending system.

What's the impact?

In today’s fast-paced market, it’s important to stay relevant to the marketplace and engage with your customers. Recommender systems can help you retain customers by providing them with tailored suggestions specific to their needs. They can help you increase sales and can also help you create brand loyalty through relevant personalization. When a customer feels as though they are understood by your brand, they are more likely to stay loyal and continue purchasing through your site.

Netflix

According to a recent study by McKinsey, up to 75% of what consumers watch on Netflix comes from the company’s recommender system Not only are consumers finding more to watch, but recommender systems can save the company a ton of cash in marketing. How much? Well, according to Netflix executives Carlos A. Gomez-Uribe and Neil Hunt their recommender system saves them about $1 billion (yes, you read that right) each year.

Amazon

When a retail giant like Amazon credits recommender systems with 35% of their revenue, it’s time to take note of this highly adaptable AI solution. Amazon uses several different recommender systems to achieve this. First of all, they show users items that are frequently purchased with items already in their cart. They will also show users items that are similar to ones they’ve recently viewed, and they make suggestions on upgrades to items that they currently own. Interestingly, they are using recommender systems both onsite and offsite through email and the recommendations through email convert at a higher rate than those onsite. They credit recommender systems with a 29% increase in total sales, bringing their yearly sales volume in 2016 up to (wait for it) 135.99 Billion.

Best Buy

Best Buy is another retailer who has boasted big returns from their recommender systems. The company began using recommender systems in 2015 at a time when many believed that Amazon would potentially take the lion’s share of their business. Instead of giving up, Best Buy decided to focus on their online sales, and in 2016’s second quarter they reported a 23.7% increase, thanks in part to their recommender system.