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Share of Wallet Analysis/证据
方法证据记录

Share of Wallet Analysis

Share of wallet (SOW) analysis measures the proportion of a customer's total category spending that a particular brand or firm captures, shifting attention from how many customers a firm has to how much of each customer it owns. Unlike overall market share, share of wallet is a customer-level loyalty metric: a customer might buy from you regularly yet give most of their category budget to a competitor, a vulnerability that absolute sales figures hide. Bruce Cooil, Timothy Keiningham, Lerzan Aksoy and colleagues established in longitudinal work that changes in customer satisfaction drive changes in share of wallet, moderated by customer characteristics. Building on this, Keiningham and colleagues introduced the Wallet Allocation Rule, which predicts a customer's share of wallet from how the brand ranks against the competitors that customer uses and how many brands they use, arguing that relative rank, not absolute satisfaction, is what governs spending allocation. Share of wallet analysis thus combines measurement (estimating each customer's category spend and the slice you capture) with a predictive rule that turns competitive standing into expected wallet share, helping firms find growth inside their existing customer base.

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Share of Wallet (SOW) Analysis and the Wallet Allocation Rule
分类方法记录 · process-pipeline / marketing
  • Cooil, B., Keiningham, T. L., Aksoy, L., & Hsu, M. (2007). A Longitudinal Analysis of Customer Satisfaction and Share of Wallet: Investigating the Moderating Effect of Customer Characteristics. Journal of Marketing, 71(1), 67-83. · DOI 10.1509/jmkg.71.1.067
  • Keiningham, T. L., Aksoy, L., Williams, L., & Buoye, A. J. (2015). The Wallet Allocation Rule: Winning the Battle for Share. Wiley. · ISBN 9781119037316
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Same method familyCustomer Equity Modelingmachine-suggested · Relational suggestion, not evidence.Same method familyCustomer Lifetime Valuemachine-suggested · Relational suggestion, not evidence.Used in the same domainNBD-Dirichlet Modelmachine-suggested · Relational suggestion, not evidence.Same method familyRFM Analysismachine-suggested · Relational suggestion, not evidence.

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