Sammenlign metoder

	BG/NBD Model	RFM Analysis
Fagfelt	Markedsføring	Markedsføring
Familie	Regression model	Process / pipeline
Opprinnelsesår	2005	2006
Opphavsperson	Peter S. Fader, Bruce G. S. Hardie & Ka Lok Lee	Arthur M. Hughes (popularizer); roots in direct-mail catalog marketing
Type	Probabilistic buy-till-you-die model of repeat transactions	Behavioral customer-segmentation and scoring pipeline
Opprinnelig kilde	Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). "Counting Your Customers" the Easy Way: An Alternative to the Pareto/NBD Model. Marketing Science, 24(2), 275-284.	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
Alias	Beta-Geometric/NBD Model, BG/NBD, Buy-Till-You-Die Model, Fader-Hardie-Lee Model	RFM Segmentation, Recency-Frequency-Monetary Analysis, RFM Scoring, RFM Model
Relaterte	4	4
Sammendrag	The BG/NBD (Beta-Geometric/Negative Binomial Distribution) model is a probabilistic buy-till-you-die model that predicts how many times a customer will transact in the future and whether that customer is still active, using only their past purchase recency and frequency. Introduced by Peter Fader, Bruce Hardie and Ka Lok Lee in their 2005 Marketing Science paper "Counting Your Customers the Easy Way," it was designed as a far simpler alternative to the Pareto/NBD model of Schmittlein, Morrison and Colombo while delivering comparable forecasts. The model couples a Poisson purchasing process, whose rate varies across customers by a gamma distribution, with a geometric dropout process governed by a beta-distributed dropout probability. The key behavioral story is that customers buy at a steady individual rate while alive and become permanently inactive with some probability immediately after any purchase. Because the latent attrition is unobserved, the model infers each customer's probability of still being alive from how recently and how often they bought. Its estimation requires only the (x, t_x, T) summary per customer and can even be fit in a spreadsheet, which made customer-base analysis practical for ordinary analysts.	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.