Revisiting customer value analysis in a heterogeneous market

Wayne S. Desarbo, Peter Ebbes, Duncan K.h. Fong, Charles C. Snow

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Purpose – Customer value has recently become a primary focus among many strategy researchers and practitioners as an essential element of a firm's competitive strategy. Many firms are engaged in some form of customer value analysis (CVA), which involves a structural analysis of the antecedent factors of perceived value (i.e. perceived quality and perceived price) to assess their relative importance in the perceptions of their buyers. Previous CVA research has focused upon using aggregate market or market segment level analyses. The purpose of this paper is to expose the limitations of implementing CVA on either an aggregate or market segment level basis, and propose an alternative individual level approach. Design/methodology/approach The paper develops an extended hierarchical Bayesian approach for crosssectional data with one observation per response unit, which allows for estimation at the individual firm level to make CVA more useful. This paper demonstrates the utility of the proposed Bayesian methodology involving a CVA study conducted for a large electric utility company. It also compares the empirical results from aggregate, market segment, and the proposed individual level analyses, and show how traditional approaches mask underlying price and quality importance. Findings Marketing and management strategy researchers need to exhibit care when conducting such CVA analyses as underlying heterogeneity can be masked when aggregate market or segment level analyses are conducted. Originality/value This paper provides a new hierarchical Bayes recursive simultaneous model formulation for CVA analyses to provide individual level insights with crosssectional data.

Original languageEnglish (US)
Pages (from-to)8-24
Number of pages17
JournalJournal of Modelling in Management
Issue number1
StatePublished - Mar 16 2010

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Strategy and Management
  • Management Science and Operations Research


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