Recommender systems – How they can help retailers deliver personalized shopping experiences

About Webinar

Recommender systems are a must-have for retailers today who want to get a slice of the lucrative online market. These systems can help your website visitors find relevant products and services by displaying personalized recommendations. If you think that recommender systems are just for the big guys such as Facebook, Amazon and LinkedIn, then you are in for a pleasant surprise.

In our webinar, we will show you how any business with an online presence and an extensive product lineup can benefit from recommender systems. With the right team of data mining and machine learning experts you can build your customized model that gives your website visitors a system that has a high degree of accuracy and is easy to use for you.

Webinar agenda

• What are recommender systems?
• Different types of recommender systems
• Use cases of recommender systems and how to create one
• Difficulties of building the right system and how to overcome them
• How a customized recommender system can benefit your business

Questions & Answers

The following are the answers to the questions that were asked during the live webinar.

Question 1: How do I go about implementing a recommender system in my stores?

Answer 1: If you are talking about just physical stores and are considering our solution for your business, then you have to put in place beacons, cameras and other data acquiring units. Along with this, you need a mobile application that will act as the interface for dynamic recommendations. Point-of-Sale integration will also be a crucial piece to get this solution in place.

You can contact us to discuss other scenarios surrounding this question. Our Smart Retail platform has all these components integrated and can be molded as per your needs.

Question 2: Is this an off-the-shelf system that you are offering?

Answer 2: Most of the solution carries off-the-shelf components but it has been customized to fit into any specific store setup as they all are different in one way or the other. E.g. Your PoS system should be able to gel with our Smart Retail platform to have a complete solution in place.

Question 3: Does the model need to be continuously improved or will this be a one-time setup?

Answer 3: The first cycle of building models with your existing data and deployment will be quite intensive. However, going forward with new data, our iteration will not be as intensive but it will be crucial to keep iterating for continuous improvement.

Question 4: How do I know that my data is large enough to overcome the cold start problem?

Answer 4: The very first point is that you have to start somewhere. You do not need to worry if you have a small number of users. We can still build item-based recommender systems using your transactions data. Meanwhile, as your user base grows, you can introduce user-based recommendations any time and make your system more robust.

Question 5: Is this technology easy to integrate in my existing eCommerce site? Is the interface application easy to use for people who are not data scientists?

Answer 5: The answer is yes to both the questions. Our data science interface for the recommender system will be a set of intuitive APIs and quite easy for your eCommerce site developers to understand and integrate.

Question 6: Are collaborative filter based recommendations applicable outside retail?

Answer 6: Yes, they can be. The recommender system paradigms of data science have a wide range of applications across all industries. The implementation methods may vary, but the core concepts of collaborative filtering stay the same.