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Using survival analytics to estimate lifetime value

Author

Listed:
  • Grigsby, Mike

    (Associate Vice President of Marketing Analytics, Caliber Home Loans, USA)

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.

Suggested Citation

  • Grigsby, Mike, 2015. "Using survival analytics to estimate lifetime value," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 1(3), pages 221-225, July.
    • Handle: RePEc:aza:ama000:y:2015:v:1:i:3:p:221-225
    as

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    More about this item

    Keywords

    lifetime value; LTV; predicting next purchase; time until purchase; survival modelling; retail analytics; predictive modelling; targeting; consumer behaviour; financial implications;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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