NBD-Dirichlet Model
Also known as: Dirichlet Model, NBD-Dirichlet, Goodhardt-Ehrenberg-Chatfield Model, Dirichlet Model of Buying Behaviour
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.
Key highlights
- Reproduces a wide range of empirical brand-performance regularities, including penetration, frequency, repeat, share of requirements and duplication, from very few parameters.
- Provides rigorous theoretical norms that turn brand metrics into diagnostics, revealing whether observed loyalty or duplication is normal for a brand's size.
- Formalizes Ehrenberg's laws such as double jeopardy and the duplication-of-purchase law within a single coherent stochastic framework.
- Acts as a powerful 'no special effects' benchmark, so that genuine market partitioning or excess loyalty shows up clearly as deviation from the model.
Intuition
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How it works
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When to use it
Use the NBD-Dirichlet model to establish theoretical norms for brand-performance measures in a stationary, competitive, repeat-purchase market, typically frequently bought consumer packaged goods analyzed from household panel data. It is appropriate when you want to know how penetration, purchase frequency, loyalty, share of category requirements and brand duplication should look in a 'no special effects' market, and to benchmark observed brand performance against those norms. The model is the right tool for testing for double jeopardy, detecting market partitioning (where duplication departs from proportionality), and assessing whether a brand's loyalty or repeat rate is normal for its size. It assumes a near-stationary market with no strong trends, no segmentation in the timing of buying, and brand choice that is close to zero-order (little genuine state dependence), so it is less suitable for markets in rapid growth or decline, for durable or infrequently purchased goods, or where strong functional partitioning or true loyalty dominate, although deviations from the Dirichlet are themselves often the finding of interest.
Strengths & limitations
- Reproduces a wide range of empirical brand-performance regularities, including penetration, frequency, repeat, share of requirements and duplication, from very few parameters.
- Provides rigorous theoretical norms that turn brand metrics into diagnostics, revealing whether observed loyalty or duplication is normal for a brand's size.
- Formalizes Ehrenberg's laws such as double jeopardy and the duplication-of-purchase law within a single coherent stochastic framework.
- Acts as a powerful 'no special effects' benchmark, so that genuine market partitioning or excess loyalty shows up clearly as deviation from the model.
- Assumes a stationary market, so it does not describe brands in strong growth or decline or markets undergoing structural change.
- Treats brand choice as essentially zero-order, ignoring genuine state dependence, switching dynamics and the effects of price and promotion.
- Applies most naturally to frequently purchased, non-durable, low-involvement categories rather than durables or infrequent purchases.
- Requires reasonably rich panel data and can be sensitive to how the category and competitive set are defined.
Common pitfalls
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Applications
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Frequently asked
What is 'double jeopardy' and why does the Dirichlet model predict it?
Double jeopardy is the empirical pattern that small brands are penalized twice: they have far fewer buyers than large brands, and those buyers also buy them slightly less often and are slightly less loyal. The NBD-Dirichlet model predicts this as a near-mathematical consequence of brand size under near-random brand choice. When households choose among brands roughly in proportion to stable preferences with little true loyalty, a brand with a small share simply gets fewer occasions from most households, which lowers both its penetration and the average frequency and loyalty of its buyers. The practical implication is profound: differences in loyalty across brands of different sizes are usually normal and structural, not evidence that big brands have better loyalty programs or that small brands have failed.
How does the model help detect market partitioning?
In an unpartitioned market, the duplication-of-purchase law says that the proportion of brand A's buyers who also buy brand B is roughly proportional to brand B's penetration, regardless of which brand A is. The NBD-Dirichlet model formalizes this expectation. To detect partitioning, an analyst compares observed brand-to-brand duplication against the model's predicted duplications. If a subset of brands shares customers much more than the model predicts, while sharing less with brands outside the subset, that excess duplication signals a genuine submarket or partition, for example diet versus regular variants, or premium versus value tiers. Thus the Dirichlet acts as the 'no partition' null hypothesis, and systematic deviations from it are the evidence of structure.
What kind of data and markets is the model designed for?
The model is built for stationary, competitive, frequently purchased categories, classically consumer packaged goods like detergents, cereals or beverages, analyzed from consumer or household panel data that records repeated category purchases and the brands chosen over a period. It assumes the market is roughly in equilibrium (no strong growth, decline or structural upheaval), that brand choice is close to zero-order with limited true loyalty, and that there is no strong segmentation in buying timing. It is not designed for durable or infrequently purchased goods, for markets in rapid transition, or for situations dominated by promotions and price dynamics. When these assumptions hold, the model fits strikingly well; when they do not, the misfit itself can be informative about what is unusual in the market.
Sources
- 1.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.
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ScholarGate. (2026, June 23). NBD-Dirichlet Model. ScholarGate. https://scholargate.app/marketing/nbd-dirichlet-model