Live Webinar February 28th, 2017 1:00 PM – 2:00 PM EST
Activity Type: Education – Course or Training 1 Hour 1 PDU free
Provider: O’Reilly
Customer lifetime value models (CLVs) are powerful predictive models allowing a data scientist to forecast how much customers are worth to the business.
CLV models provide crucial inputs to inform marketing acquisition decisions, retention measures, customer care queuing, demand forecasting, etc.
Historical or retrospective CLV models focus only on the past and do not attempt to predict the future purchases of customers who have been acquired recently.
This could lead to severe bias and selection effects on estimated CLV.
In contrast, predictive CLV models are particularly valuable as they attempt to forecast the future value of a customer.
In this webcast, Jean-René Gauthier (LinkedIn profile) will explain the ins and outs of probabilistic models that can be used to quantify the future value of a customer, and demonstrate how e-commerce companies are using the outputs of these models to identify, retain, and target high-value customers.
Specifically, in this webcast, you will:
- Learn the difference between historical versus predictive CLV and why predictive CLV is a better approach
- Get an overview of the various predictive CLV modeling techniques and which ones apply to different business cases;
- Know the minimum data requirements to build a reliable CLV model
- Get a solid overview of probabilistic models for non-contractual business settings and understand the limitations of such probabilistic models.
In the second part of the webcast, Jean-René will guide you through a hands-on Python tutorial on how to implement, train, and validate CLV models on a retail dataset.
Presenter: Jean-René Gautier, Lead Data Scientist, DataScience manages a team of data experts in developing algorithms and analytics models to solve customers’ unique business problems. He is also responsible for educating clients on these algorithms and models, ensuring that they are incorporated into the business to add maximum value. Prior to this Jean-René was a data scientist at AuriQ Systems where he focused on online marketing analytics and data engineering, often involving high-speed processing of massive data sets. He has a PhD in astrophysics from the University of Chicago and was a postdoctoral fellow at the California Institute of Technology.
Click to register for:
An Intro To Predictive Modeling For Customer Lifetime Value
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