Compare methods

	Share of Wallet Analysis	RFM Analysis
Field	Marketing	Marketing
Family	Process / pipeline	Process / pipeline
Year of origin	2007	2006
Originator	Bruce Cooil, Timothy Keiningham, Lerzan Aksoy & colleagues	Arthur M. Hughes (popularizer); roots in direct-mail catalog marketing
Type	Loyalty and category-spend measurement pipeline	Behavioral customer-segmentation and scoring pipeline
Seminal source	Cooil, B., Keiningham, T. L., Aksoy, L., & Hsu, M. (2007). A Longitudinal Analysis of Customer Satisfaction and Share of Wallet: Investigating the Moderating Effect of Customer Characteristics. Journal of Marketing, 71(1), 67-83.	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
Aliases	SOW Analysis, Share-of-Wallet Measurement, Wallet Share Analysis, Wallet Allocation Rule	RFM Segmentation, Recency-Frequency-Monetary Analysis, RFM Scoring, RFM Model
Related	4	4
Summary	Share of wallet (SOW) analysis measures the proportion of a customer's total category spending that a particular brand or firm captures, shifting attention from how many customers a firm has to how much of each customer it owns. Unlike overall market share, share of wallet is a customer-level loyalty metric: a customer might buy from you regularly yet give most of their category budget to a competitor, a vulnerability that absolute sales figures hide. Bruce Cooil, Timothy Keiningham, Lerzan Aksoy and colleagues established in longitudinal work that changes in customer satisfaction drive changes in share of wallet, moderated by customer characteristics. Building on this, Keiningham and colleagues introduced the Wallet Allocation Rule, which predicts a customer's share of wallet from how the brand ranks against the competitors that customer uses and how many brands they use, arguing that relative rank, not absolute satisfaction, is what governs spending allocation. Share of wallet analysis thus combines measurement (estimating each customer's category spend and the slice you capture) with a predictive rule that turns competitive standing into expected wallet share, helping firms find growth inside their existing customer base.	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.