RFM and CLV: Using Iso-Value Curves for Customer Base Analysis

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

623 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/005fff25e3a2468d7e6e0b16fd18c068b797eef0

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Citations in the corpus

Consumer Heterogeneity and Paid Search Effectiveness: A Large Scale Field Experiment (2014, background): “These are traditionally considered a firms “worst” customers, and advertising is often aimed a high value repeat consumers (Fader et al., 2005). This evidence supports the informative view where ads affect consumption only when they update a consumer’s information set. Bluntly, search advertising on”
Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics (2016, background): “…into a relatively small set of variables summarizing properties such as the transactions’ recency (e.g., when was the most recent transaction), frequency (e.g., what is the frequency of the transactions), and monetary value (e.g., what is the monetary value of the transactions) (Fader et al. 2005).”
LaMP: When Large Language Models Meet Personalization (2023, methodology): “Note that recency is considered as a simple yet strong personalization signal in search and recommendation (Fader et al., 2005; Reinartz and Kumar, 2000, 2003).”
Domain knowledge integration in data mining using decision tables: case studies in churn prediction (2009, background): “Customer lifetime value (CLV) (Jain and Singh, 2002; Fader et al , 2005; Malthouse and Blattberg, 2005) represents the present value of the expected benefits minus the costs of initialising, maintaining and developing the customer relationship.”
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.”
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).”
Modeling Customer Lifetime Value (2006, background): “Whereas RFM or other scoring models attempt to predict customers’ behavior in the future and are therefore implicitly linked to CLV, they have several limitations (Fader, Hardie, and Lee 2005; Kumar 2006a).”
Predictive Modeling With Big Data: Is Bigger Really Better? (2013, neutral): “In these example applications, the features tend to be demographic, geographic, and psychographic characteristics of individuals, as well as statistics summarizing particular behaviors, such as their prior purchase behavior with the firm.(7) Recent work has shown that even with rich descriptive data”
A stochastic patent citation analysis approach to assessing future technological impacts (2012, background): “(2005) [53].”