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Ask the Expert: Build a Churn Use Case. August 9th, 2022

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Blue LED

Ask the Expert: Build a Churn Use Case. August 9th, 2022

Register and attend the next "Ask the Expert" session: Build a Churn Use Case.

 

In this session, @jake  and @Olga Shpyrko  will provide a deep dive into how to predict which customers will churn and how to use machine learning to choose the optimal retention strategies. They will show how critical it is to frame the churn problem appropriately,  give an example with DataRobot, then discuss how to manage the use case in production and how to measure its success!

 

This session is relevant for any user who has a basic familiarity with data science and the DataRobot platform and is interested in building a churn model! It does not require advanced knowledge of the platform or of churn modeling.

 

You can register here!

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3 Replies
jake
Data Scientist
Data Scientist

My name is Jake, I am the Director of Internal Data Science at DataRobot. I build and maintain our internal AI models that we use every day to make decisions about our customers and our business. I will be the host of this session on churn, where we will cover every step of this use case from problem framing to productionalization and measuring success. Please feel free to post any questions about the topic on this thread and I will make sure to answer them during the session!

Olga Shpyrko
Data Scientist
Data Scientist

Hello,
I'm Olga. I work as a data scientist at DataRobot, helping our customers in various industries to build and implement machine learning models. This session will be focused on churn management, one of the most important areas for many businesses. How do you define churn? What is the benchmark for model accuracy? How to ensure that your AI solution is effective? We will be pleased to take your questions in this thread or live during the session. See you there!

scottwinkleT
Blue LED

I am excited about this session!
I was involve in a churn project in my previous job and we had multiple discussions about hwo to define the time horizon of the predictions, for instance whether it is more useful to predict churn at 3 months, 6 months, 1 year... That was for a broadband and TV provider.

 

Will this be covered in the session?

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