Counting Your Customers the Easy Way: An Alternative to the Pareto/NBD Model

By Peter S. Fader, Bruce G. S. Hardie, Ka Lok Lee. 2005

501 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/eabacb3527277465831c12742a779304bdfd5fa4

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Citations in the corpus

A Model to Determine Customer Lifetime Value in a Retail Banking Context (2007, background): “Similarly, Fader et al. (2005b) predicted future customer value based on information on past customer recency, frequency and monetary value with high accuracy.”
New Product Diffusion with Influentials and Imitators (2007, background): “Maximumlikelihood estimation is known to be troublesome as well, even when the parameters of interest enter the function linearly rather than nonlinearly as in the AIM (e.g., Fader et al. 2005).”
Managing retailer profitability - one customer at a time! (2006, methodology): “Fader et al. (2005) proposed a simpler alternative to areto/NBD model in the form of a beta-geometric/NBD odel for predicting customer’s future purchase contingent o past purchase behavior.”
Customer Equity: Measurement, Management and Research Opportunities (2007, methodology): “Therefore, formal statistical models can do much better in optimizing the firm’s retention spending, such as the one developed by Fader et al. (2005b).”
RFM and CLV: Using Iso-Value Curves for Customer Base Analysis (2005, methodology): “The sample log-likelihood function is given by This can be maximized with standard numerical optimization routines (see Fader, Hardie, and Lee 2005a).”
Instant Customer Base Analysis: Managerial Heuristics Often “Get it Right”: (2008, methodology): “= x, t, T, T*) is the expected number of transactions X* of a random customer with purchase pattern (X = x, t, T) (and unknown individual purchase rate and dropout rate) in time (T, T + T*] (see Fader, Hardie, and Lee 2005a, Equation 10).”
Fast and frugal forecasting (2009, background): “Wübben and Wangenheim (2008) have tested the Pareto/NBD model (described in the previous section), in addition to a somewhat simpler variant called the BG/NBD model (Fader et al., 2005), on the task of predicting from historical data which customers will emerge as the best customers in the future.”
Customer Complaints and Recovery Effectiveness: A Customer Base Approach (2014, methodology): “Second, we fit the BG/NBD model (Fader, Hardie, and Lee 2005a), which uses the prior purchase summary statistics recency and frequency to forecast future purchasing.”