Using survival analytics to estimate lifetime value

Mike Grigsby
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Mike Grigsby: Associate Vice President of Marketing Analytics, Caliber Home Loans, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2015, vol. 1, issue 3, 221-225

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.

Keywords: lifetime value; LTV; predicting next purchase; time until purchase; survival modelling; retail analytics; predictive modelling; targeting; consumer behaviour; financial implications (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2015
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