The secret behind recommendation engines

The power of recommendation engines

When you look at some of the biggest companies like Netflix, Amazon and Airbnb, they all have one thing in common: Their ability to use data analytics technology to maximise customer retention and acquisition.

At the heart of this strategy is the recommendation engine.

You may have seen a recommendation engine in action. If you have a Netflix account, you would have seen a ‘Recommended For You’ section containing an assortment of TV shows and movies that you might like, based on your previous choices.

It’s an incredibly effective marketing strategy to retain customers and its one of the reasons why Netflix grew from a DVD-rental company into a streaming giant that pumps millions of dollars into creating their own content.

Recommendation engines, when used properly, can be one of the best assets a company can have, which is why we are going to explain how to create them in our latest webinar, ‘The science behind recommendation engines.

What are the details of this webinar?

On 29th July 2020, we will explain how you can simulate a recommendation engine using Selerity BA and SAS analytics. We are going to examine the underlying logic that determines how the engine uses data and machine learning to generate feedback that provides immense value for the end-user.

Just a disclaimer, we are not saying that you will see similar success like Netflix or Amazon. However, if you are interested in staying one step ahead of your competitors and providing more value to your customers, then this webinar is for you.

Why did we choose this topic?

We choose this topic because we noticed a lot of interest in one of our previous blog posts that discussed Netflix and big data analytics.

The interest in Netflix’s success tells us that a lot of businesses are interested in replicating some of the streaming giant’s tactics or at least understanding the technology they use.

So we decided to put this webinar together to explain how recommendation systems work and how they can be used in different cases.

Recommendation systems operate using a combination of machine learning and data analytics. To build and utilise these systems, you not only need to have a sophisticated data infrastructure in place but also the means to collect and analyse the data.

You also need to establish how the engine works. Depending on your business objectives, recommendation engines can be tweaked to complement your operations.

In this webinar, we will be exploring how you can set up the necessary infrastructure that will feed your recommendation engines using machine learning and data analytics, as well as how the engine will operate. The approaches we will be covering in this webinar include the collaborative filtering approach, the content-based approach and the hybrid approach.

Most importantly, we will be explaining how you can create a recommendation system that fits your business, using Selerity BA and SAS analytics.

We hope to see you there!

We look forward to seeing you at the webinar. If you are interested and would like to sign up or learn more, then have a look at our registration page to learn more.

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