Sammenlign metoder

	NBD-Dirichlet Model	RFM Analysis
Fagområde	Markedsføring	Markedsføring
Familie	Regression model	Process / pipeline
Oprindelsesår	1984	2006
Ophavsperson	Gerald J. Goodhardt, Andrew S. C. Ehrenberg & Christopher Chatfield	Arthur M. Hughes (popularizer); roots in direct-mail catalog marketing
Type	Stochastic model of category purchase incidence and brand choice	Behavioral customer-segmentation and scoring pipeline
Oprindelig kilde	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.	Hughes, A. M. (2006). Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program (3rd ed.). McGraw-Hill. ISBN: 9780071457507
Aliasser	Dirichlet Model, NBD-Dirichlet, Goodhardt-Ehrenberg-Chatfield Model, Dirichlet Model of Buying Behaviour	RFM Segmentation, Recency-Frequency-Monetary Analysis, RFM Scoring, RFM Model
Relaterede	4	4
Resumé	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.	RFM analysis is a long-standing, behavior-based method for scoring and segmenting customers by how recently they purchased (Recency), how often they purchase (Frequency), and how much they spend (Monetary value). Rooted in catalog and direct-mail marketing and popularized in Arthur Hughes's Strategic Database Marketing, it rests on the empirical observation that customers who bought recently, buy frequently, and spend more are the most likely to respond to the next offer. The classic procedure ranks customers into quintiles on each of the three dimensions, assigns each a score from 1 to 5, and combines the scores into cells, typically a 5x5x5 grid of 125 segments. Campaign managers then measure historical response rates per cell, compare them to a break-even threshold derived from contact cost and order margin, and target only the cells that are profitable to contact. Despite its simplicity, RFM is remarkably effective and cheap to run, requiring only transaction history. It remains a workhorse for segmentation and a natural precursor to model-based customer-base analysis and lifetime-value estimation.