Stochastic process customer lifetime value models with time-varying covariates

Harman, David M.

doi:10.17077/etd.zjhm23na

Cited by 3 publications

(4 citation statements)

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“…It is geared toward accurate forecasts for specific time periods to aid and improve the timing of marketing actions. It generalizes previous post hoc seasonal adjustment (Zitzlsperger, Robbert, and Roth 2009) and seasonal dummy-variable approaches (Fader, Hardie, and Huang 2020; Harman 2016; McCarthy and Fader 2018; Schweidel and Knox 2013) to improve forecast accuracy.…”

Section: Discussionmentioning

confidence: 72%

“…However, these models from the literature stream on probability models for customer-base analysis typically sidestep the issue of seasonality, as a product's “seasonality” is usually not a readily available product attribute (Trusov, Bodapati, and Cooper 2006). Seasonality, if addressed at all, is often modeled through post hoc seasonal adjustment (Zitzlsperger, Robbert, and Roth 2009), the inclusion of seasonal dummy variables (Fader, Hardie, and Huang 2020; Harman 2016; McCarthy and Fader 2018; Schweidel and Knox 2013), or contextual factors (Bachmann, Meierer, and Näf 2021). These approaches are unable to capture the hierarchical structure that relates individual to cross-sectional seasonal effects.…”

Section: Background Of Customer-base Analysismentioning

confidence: 99%

“…Schweidel and Knox (2013) improve model fit by including a winter dummy variable in their latent attrition model. Harman (2016) shows that incorporating time-varying covariates as a seasonal correction into the transaction process through a proportional hazards formulation reduces bias in his study on soup customers. Dew and Ansari (2018) find that “isolating calendar time variability from the natural ebb and flow of purchasing is important for accurately assessing the influence of calendar time shocks to the spending process” (p. 216).…”

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confidence: 99%

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A Seasonal Model with Dropout to Improve Forecasts of Purchase Levels

Wünderlich

Wangenheim

2022

Journal of Interactive Marketing

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Predicting future purchase levels is an important and constant challenge for marketing professionals, as purchase patterns often vary over time and across customers. Moreover, purchases often follow individual and cross-sectional seasonal patterns, which affect forecasts of purchase propensity and customer dropout. The authors develop the hierarchical Bayesian seasonal model with dropout (HSMDO), which captures the interrelation between individual and cross-sectional seasonality, purchase, and dropout rates, with the aim of improving forecast accuracy at specific points in time. They perform (1) a parameter recovery analysis with synthetic data; (2) an empirical validation on three noncontractual retail data sets; (3) an analysis of different model variants to isolate the effects of dropout, seasonality, and hierarchical seasonality; and (4) a comparison with several probabilistic models from the marketing literature. The results demonstrate that the HSMDO provides increased forecast accuracy and that tracking errors decrease further with data exhibiting strong seasonality and high customer retention. The HSMDO yields a measure of individual seasonality that has high discriminative power even with sparse data sets and is useful to customer relationship management analysts for customer segmentation, portfolio management, and improvement in the timing of marketing actions.

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Section: Discussionmentioning

confidence: 72%

Section: Background Of Customer-base Analysismentioning

confidence: 99%

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confidence: 99%

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A Seasonal Model with Dropout to Improve Forecasts of Purchase Levels

Wünderlich

Wangenheim

2022

Journal of Interactive Marketing

View full text Add to dashboard Cite

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“…The authors use the model to evaluate the impact of a customer's service experience at the time of purchase. Using a MCMC approach, Harman (2016) includes time-varying contextual factors in the transaction process of the BG/NBD model. McCarthy and Fader (2018) develop an approach for customer-based corporate valuation in noncontractual settings.…”

Section: Related Researchmentioning

confidence: 99%