In today’s digital world, a recommendation engine is one of the most powerful tools at a company’s disposal. A recommendation engine is an information filtering system composed of machine learning algorithms that predict a given customer’s ratings or preferences for an item.
The idea of recommendation engines is something you might be familiar with. Whether it is product recommendations on Amazon, movie recommendations on Netflix or video suggestions on YouTube, recommender systems are already a crucial aspect of your online experience and are the bedrock of progress for most of these companies.
When used properly, these engines can increase revenue, customer satisfaction, and marketing efforts. McKinsey estimated that thirty-five per cent of consumer purchases on Amazon come from product recommendations.
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. Reports show that recommendation engine systems can improve customer service through personalisation and make it a more enjoyable experience.
Organisations can also use these engines to promote their product range without spending much on marketing because they can recommend new products based on a customer’s purchase history. It’s an incredibly effective strategy to retain customers and it’s one of the reasons why Netflix grew from a DVD-rental company into one of the biggest brands in entertainment.
Given the exceptional benefits of recommendation engines, we decided to conduct this three-part webinar series to explain how organisations can create their own recommendation engine.
We decided to run a three-part webinar series explaining how organisations can create a recommendation engine using SAS technology. We chose to split the webinar into three different parts to give proper attention to the complex nature of recommendation engines.
In part one of our recommendation engines webinar, we explained the basics of how to create an engine using Selerity BA and SAS Business Analytics. The second webinar was built on the foundation set in the first part and explains how you can build recommendation engines that are more powerful, dynamic, and better suited for working with complex datasets (you can find these webinars on our website).
For our third and final webinar on recommendation engines. Our keynote speaker, Michael Esposito, will explain how to refine your engine to deliver the accurate readings you need to support decision-making. Furthermore, he will also dive into how to leverage your findings to improve business operations and use data analytics technology to maximise customer retention and acquisition.
The webinar will take place on 3rd December 2020 at 10:30 AM!
Want to sign up for the session?
Visit our webinar page to get the information you need and reserve your seat! We look forward to seeing you there and hope you walk away with a better understanding of how recommendation engines work and how it can work for you.
Recommendation engines are a useful asset for any business. In our previous blog post, we talked about how Amazon and Netflix used technology to become the most well-known brands in e-commerce and entertainment.
However, this is no coincidence. Yes, Netflix and Amazon have strong brand recognition, due in part to being pioneers in their respective industries. However, there are several benefits you can derive from recommendation engines that boost earnings, traffic, customer satisfaction or any other KPIs you are measuring.
Given these benefits to organisations, we decided to run a three-part webinar series explaining how organisations can create a recommendation engine using SAS technology.
In part one of our recommendation engines webinar, we explained the basics of how to create an engine using Selerity BA and SAS Business Analytics. However, we also know that most organisations are handling complex datasets with far more variables than what we covered in our previous webinar.
The second webinar builds on the foundation set in the first part to explain how you can build recommendation engines that are more powerful, dynamic and better suited for your needs.
In this 45-minute session, we are going to explain how to create a recommendation engine using SAS Science Data Starter (a combination of SAS Visual Analytics and SAS Visual Statistics) along with SAS Business Analytics to create recommendation engines that are more powerful, dynamic and better suited for more complex, varied datasets.
The webinar is scheduled for September 16th.
Recommendation engines are a useful asset for an organisation. When used properly, these engines can increase revenue generated, the degree of customer satisfaction and promote their inventory more effectively.
Consumer reports show that recommendation engine systems can improve customer service through personalisation. If there is one thing I have learnt over the years, its that customers respond positively to personalised service, and satisfied customers return to spend more money. Furthermore, businesses can promote their product range without expending too many resources on marketing because they can recommend new products based on a customer’s purchase history.
However, there is no denying their technical complexity. In fact, as we dove deeper into the topic, we realised that the technology behind recommendation engines cannot be explained in one session. Dividing the webinar into three different parts allowed us to address the details with the depth they deserve instead of just skimming over the details.
Want to sign up for the session? Visit our webinar page to get all the information you need and reserve your seat! We look forward to seeing you there and hope you walk away with a better understanding of how recommendation engines work.
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.
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.
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 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.