Transcription of Probability Models for Customer-Base Analysis
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Probability Models for Customer-Base Analysis
Probability Models for customer -BaseAnalysisPeter S. FaderUniversity of HardieLondon business Annual Advanced Research Techniques ForumJune 14 17, 2009 2009 Peter S. Fader and Bruce G. S. Hardie1 Agenda Introduction to Customer-Base Analysis The right way to think about computing CLV Review of Probability Models Models for contractual settings Models for noncontractual settings The BG/BB model The Pareto/NBD model The BG/NBD model Beyond the basic models2 Customer-Base Analysis Faced with a customer transaction database, we maywish to determine which customers are most likely to be active in thefuture, the level of transactions we could expect in futureperiods from those on the customer list, bothindividually and collectively, and individual customer lifetime value (CLV). Forward-looking/predictive versus of Modelling Approaches- PastFuturelatentcharacteristics( )66 Traditional approachfuture =f(past) Probability modelling approach =f(past)- future =f( )4 Classifying Analysis SettingsConsider the following two statements regarding the size ofacompany s customer base : Based on numbers presented in a January 2008 pressrelease that reported Vodafone Group Plc s third quarterkey performanc
Probability Models for Customer-Base Analysis Peter S. Fader University of Pennsylvania www.petefader.com Bruce G.S. Hardie London Business School www.brucehardie.com
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