Using survival analytics to estimate lifetime value
Abstract
Typically, lifetime value (LTV) is merely a calculation using descriptive/ historical data. This calculation makes some rather heroic assumptions to project into the future but most importantly gives no insights into why a customer is, for example, lower valued, or how to make a customer higher valued. That is, descriptive techniques offer no insights into predicting, incentivising or changing customer behaviour. Using predictive techniques — in this case survival analysis — can give an indication into what causes purchases to happen. This means marketers get insights — levers — into how to increase LTV. This predictive modelling is strategically lucrative. This paper appeared in a different format in Marketing Analytics, Kogan Page, June, 2015.
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Author's Biography
Mike Grigsby has worked in marketing analytics for nearly 30 years, working at Sprint, Dell, HP and the Gap. He is now an analytic consultant at Targetbase, focusing on the retail industry. Mike has been a frequent speaker at trade/academic conferences. He teaches marketing analytics at UTD’s graduate school. Mike has published in Marketing Insights, Canadian Journal of Marketing Research, Marketing Management and similar journals. His book, ‘Marketing Analytics’, was recently published by Kogan Page.
Citation
Grigsby, Mike (2015, July 29). Using survival analytics to estimate lifetime value. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 1, Issue 3. https://doi.org/10.69554/JDRV7764.
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