Artificial intelligence business process

How Machine Learning Can Build the Perfect Recommendation Engine

Recommendation engines have become a core part of any e-commerce or digital business, as they can significantly increase both sales and engagement. If you’ve ever seen Amazon’s “Recommended for You” section, then you’ve seen a recommendation engine in action. Netflix, Facebook, LinkedIn, Twitter, and many other companies use this type of software to serve relevant information, contacts, etc. to their users, which in turn encourages users to continue engaging with their content and services.

The most powerful recommendation engines today rely on a subset of artificial intelligence (AI) known as machine learning. Let’s take a closer look at how machine learning works and how it’s being applied to help businesses keep customers interested and close more sales.

What is Machine Learning?

What is machine learning? Machine learning uses statistical analysis to develop algorithms that perform specific tasks without explicit instructions or human interaction. In other words, machine learning software analyzes data and builds a mathematical model (i.e. an algorithm) to predict an outcome or make a decision. The key part here is that the computer develops – and refines – the algorithm without any human input other than providing the data.

How does machine learning work with recommendation engines?

Originally, recommendations on most e-commerce sites were based on data tags and categories. For example, if someone bought a romance book on Amazon, Amazon would recommend other romance books. As Amazon (and other web-based services) grew, adding more products and customers, tagging and categorization of items became insufficient for providing accurate recommendations. What’s more, just because a person has bought a product one day doesn’t necessarily mean they’re going to buy a similar product another day. For example, if you bought a television on Amazon, you likely will not be buying another, similar television any time soon.

Machine learning solves this issue by analyzing both individual purchases as well as all purchases from all customers to develop rules on what products to suggest. That’s how Amazon generates its “Customers who bought X also bought Y” recommendations.

This is what’s known in AI as an “apriori algorithm.” This has been one of the most types of algorithms in data science, and it basically is an algorithm that uses previously acquired data to develop its rules for making decisions.

Machine learning can also use other data beside purchase history to refine product recommendations. For example, a machine learning system on an e-commerce engine will likely also analyze data related to website navigation in order to identify any possible correlations between pages viewed and products purchased. A significant part of this process is calculating the probability that a customer will buy a product when he or she views (and possibly buys) another product.

How can businesses make the best use of machine learning?

One of the interesting aspects of machine learning is that these systems can continually refine their own algorithms. That means that the more data they analyze, the higher the quality of recommendations. This will ultimately lead to more sales. What’s more, this process can be fully automated, so that machine learning systems can systematically deliver recommendations that will more likely become a sale, and they can deliver those promotional emails based on times and days when a customer may be most receptive to making those purchases. In other words, machine learning can optimize the entire upselling/cross selling process.

If you’d like to learn more about how a machine learning recommendation engine can help your business, contact us. We’re happy to help you strategize, develop, and deploy a recommendation that will be boosting your revenues in no time!

Best regards,
Florian Horn

Your data will be collected and processed in accordance with our privacy policy.
0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply

Your email address will not be published. Required fields are marked *