Compare methods

	NBD-Dirichlet Model	Share of Wallet Analysis
Field	Marketing	Marketing
Family	Regression model	Process / pipeline
Year of origin	1984	2007
Originator	Gerald J. Goodhardt, Andrew S. C. Ehrenberg & Christopher Chatfield	Bruce Cooil, Timothy Keiningham, Lerzan Aksoy & colleagues
Type	Stochastic model of category purchase incidence and brand choice	Loyalty and category-spend measurement pipeline
Seminal source	Goodhardt, G. J., Ehrenberg, A. S. C., & Chatfield, C. (1984). The Dirichlet: A Comprehensive Model of Buying Behaviour. Journal of the Royal Statistical Society: Series A (General), 147(5), 621-655.	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.
Aliases	Dirichlet Model, NBD-Dirichlet, Goodhardt-Ehrenberg-Chatfield Model, Dirichlet Model of Buying Behaviour	SOW Analysis, Share-of-Wallet Measurement, Wallet Share Analysis, Wallet Allocation Rule
Related	4	4
Summary	The NBD-Dirichlet model is the canonical stochastic model of repeat buying and brand choice in stationary, competitive consumer-goods markets. Introduced by Gerald Goodhardt, Andrew Ehrenberg and Christopher Chatfield in their 1984 Journal of the Royal Statistical Society paper "The Dirichlet," it integrates two processes: how often households buy in a product category, modeled by the negative binomial distribution (NBD), and how those purchases are split across competing brands, modeled by a multinomial-Dirichlet process. From just a few parameters, the model reproduces a remarkably wide set of empirical regularities, including each brand's penetration (how many people buy it), its buyers' purchase frequency, repeat-purchase rates, the share of category requirements each brand earns, and the duplication of purchase between brands. The model encodes Ehrenberg's classic 'laws' of buying behavior, most famously double jeopardy, whereby small brands suffer twice over by having both fewer buyers and slightly less loyal buyers. It assumes a stationary, non-partitioned market with brand choice that looks like sampling 'as if from an urn,' and it serves as a benchmark of what normal, no-loyalty-segmentation buying looks like, against which deviations such as genuine partitioning or excess loyalty can be detected.	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.