An Intro to Predictive Modeling for Customer Lifetime Value
Date: This event took place live on February 28 2017
Presented by: Jean-René Gauthier
Duration: Approximately 60 minutes.
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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, we 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:
After the presentation, a Jupyter notebook file (.ipynb) will be provided to attendees along with a sample data file. The notebook file will contain code to allow you to train one of the models discussed in the webcast.
About Jean-René Gautier, Lead Data Scientist, DataScience
Jean-René Gauthier, Lead Data Scientist at 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 his two years at DataScience, 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.