Model-based Dashboards for Customer Analytics
Ryan Dew, Asim Ansari

TL;DR
This paper introduces a probabilistic Gaussian process-based framework for customer analytics dashboards that models and predicts individual spending behavior over time, capturing complex event impacts and outperforming traditional models.
Contribution
The paper presents the Gaussian Process Propensity Model (GPPM), a novel nonparametric approach that integrates calendar time, recency, and lifetime effects for customer spending analysis.
Findings
GPPM effectively captures spending dynamics influenced by unknown events.
GPPM outperforms hazard and buy-till-you-die models in fitting and forecasting.
The dashboard provides an intuitive visual representation of customer purchase behavior.
Abstract
Automating the customer analytics process is crucial for companies that manage distinct customer bases. In such data-rich and dynamic environments, visualization plays a key role in understanding events of interest. These ideas have led to the popularity of analytics dashboards, yet academic research has paid scant attention to these managerial needs. We develop a probabilistic, nonparametric framework for understanding and predicting individual-level spending using Gaussian process priors over latent functions that describe customer spending along calendar time, interpurchase time, and customer lifetime dimensions. These curves form a dashboard that provides a visual model-based representation of purchasing dynamics that is easily comprehensible. The model flexibly and automatically captures the form and duration of the impact of events that influence spend propensity, even when such…
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Taxonomy
TopicsConsumer Market Behavior and Pricing · Customer churn and segmentation · Forecasting Techniques and Applications
MethodsGaussian Process
