Compara mètodes

	Gamma-Gamma Spend Model	RFM Analysis
Camp	Màrqueting	Màrqueting
Família	Regression model	Process / pipeline
Any d'origen	2013	2006
Autor original	Peter S. Fader & Bruce G. S. Hardie	Arthur M. Hughes (popularizer); roots in direct-mail catalog marketing
Tipus	Probabilistic model of monetary value per transaction	Behavioral customer-segmentation and scoring pipeline
Font seminal	Fader, P. S., & Hardie, B. G. S. (2013). The Gamma-Gamma Model of Monetary Value. Technical note, www.brucehardie.com/notes/025/.	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	Gamma-Gamma Model, Gamma/Gamma Spend Model, Monetary Value Model, Average Transaction Value Model	RFM Segmentation, Recency-Frequency-Monetary Analysis, RFM Scoring, RFM Model
Relacionats	4	4
Resum	The Gamma-Gamma model of monetary value is the standard companion to buy-till-you-die transaction models, estimating how much a customer spends per transaction so that purchase-count forecasts can be turned into monetary customer lifetime value. Formalized by Peter Fader and Bruce Hardie in a widely cited technical note, it assumes that each customer's individual transactions vary around their own average spend according to a gamma distribution, and that these per-customer average-spend levels themselves vary across the population according to a second gamma distribution, giving the model its name. A central assumption is that a customer's monetary value is independent of their transaction frequency, which lets the spend model be estimated and combined separately from a frequency model such as BG/NBD or Pareto/NBD. The model produces, for each customer, a Bayesian estimate of expected spend that shrinks a customer's noisy observed average toward the population mean, with more shrinkage for customers who have made fewer transactions. This guards against over-trusting the average order value of a customer seen only once or twice. The result feeds directly into the residual-lifetime-value calculation that powers customer-base analysis.	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.