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.