A Hidden Markov Model of Customer Relationship Dynamics

By Oded Netzer, James M. Lattin, V. Srinivasan. Marketing science (Providence, R.I.), 2008

403 citations recorded by the source corpus.

Subfields: Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/e29923c461d653b101bba582b2ecac49c12e7078

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Citations in the corpus

Sentence-Based Text Analysis for Customer Reviews (2016, methodology): “Restricted versions of the assumption made by the LDA model for observational heterogeneity include models of change points (DeSarbo et al., 2004) and latent Markov models (Fader et al. 2004; Netzer et al. 2008; Montoya et al. 2010).”
Website Morphing (2009, background): “Gittins’ (1979) proof of indexability is beyond the scope of this paper.”
Disconfirmation Effect on Online Rating Behavior: A Dynamic Analysis (2014, methodology): “Given the nature of parameter hierarchy, the parameters in our model can be divided into two groups (Netzer et al. 2008): (1) " random-effect " parameters that vary across individuals (denoted by θ i ); and (2) " fixed " parameters that do not vary across individuals (denoted by ψ).”
An Empirical Study of Online Supports among Patients (2010, neutral): “For example, Netzer et al. (2008) capture customers’ dynamic relationships by modeling latent relationship states.”
Machine learning and AI in marketing – Connecting computing power to human insights (2020, neutral): “For example, the hidden-Markov model (HMM) used in numerous marketing studies (e.g. Ma, Sun, & Kekre, 2015; Netzer, Lattin, & Srinivasan, 2008) is a speci ﬁ c type of PGM. Deep neural networks , i.e. ANNs with more than one hidden layer, have been a main driver behind of recent surge in AI.”
Marketing in the Sharing Economy (2019, background): “…from Oliver’s (1980) expectancy-disconfirmation model, to Bolton’s (1998) dynamic model that focuses on the relationship between customers and service providers, to hidden Markov models that classify customers over the course of their experience with a firm (Netzer, Lattin, and Srinivasan 2008).”
Transformational Relationship Events (2015, positive): “Although prior research has provided evidence of dramatic relationship change (Jap and Anderson 2007; Netzer, Lattin, and Srinivasan 2008), by introducing TREs, we can explain why certain exchange events dramatically change relationships and how this change affects exchange performance.”
Cross-Selling the Right Product to the Right Customer at the Right Time (2011, methodology): “…(2005) Rust and Verhoef (2005) Venkatesan and Kumar (2004) Kumar, Venkatesan, and Reinartz (2008) Venkatesan, Kumar, and Bohling (2007) ✓ ✓ ✓ Du and Kamakura (2006) Netzer, Lattin, and Srinivasan (2008) ✓ ✓ ✓ Our study ✓ ✓ ✓ ✓ ✓ ✓ 1Checking includes various types of checking accounts; savings…”
Recurrent Reinforcement Learning: A Hybrid Approach (2015, background): “This simulator thus creates a realistic scenario that allows hidden states and long-term effects on customers (Netzer et al., 2008).”