Compara mètodes

	Pareto/NBD Model	RFM Analysis
Camp	Màrqueting	Màrqueting
Família	Regression model	Process / pipeline
Any d'origen	1987	2006
Autor original	David C. Schmittlein, Donald G. Morrison & Richard Colombo	Arthur M. Hughes (popularizer); roots in direct-mail catalog marketing
Tipus	Probabilistic buy-till-you-die model with continuous-time dropout	Behavioral customer-segmentation and scoring pipeline
Font seminal	Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting Your Customers: Who Are They and What Will They Do Next? Management Science, 33(1), 1-24.	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
Àlies	Pareto/NBD, Schmittlein-Morrison-Colombo Model, Counting Your Customers Model, SMC Model	RFM Segmentation, Recency-Frequency-Monetary Analysis, RFM Scoring, RFM Model
Relacionats	4	4
Resum	The Pareto/NBD model is the foundational buy-till-you-die model of customer-base analysis, answering the question of which customers are still active and how many transactions they will make in the future from a non-contractual purchase history. Introduced by David Schmittlein, Donald Morrison and Richard Colombo in their 1987 Management Science paper "Counting Your Customers," it combines two stochastic stories: customers buy according to a Poisson process while alive, and each customer has an unobserved lifetime after which they are permanently inactive. Purchasing rates vary across customers by a gamma distribution, producing the negative binomial (NBD) for counts, and dropout rates also vary by a gamma distribution, producing a Pareto distribution of lifetimes, which gives the model its name. Unlike later discrete-dropout variants, the Pareto/NBD allows a customer to become inactive at any instant in continuous time, not only after a purchase. From only each customer's recency, frequency and tenure, the model yields a probability that the customer is still alive and an expectation of their future buying. Its main cost is computational: estimation involves Gaussian hypergeometric functions and careful numerical integration, which historically made it hard to apply.	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.