Modern Approaches to Causal Modelling in Customer Experience Measurement

Modern Approaches to Causal Modelling in Customer Experience Measurement

Presented at the 6th Annual BayesiaLab Conference in Chicago, November 1-2, 2018.


Traditionally, statistical techniques such as correlation analysis and linear regression were used to perform key driver analysis on survey data, where a set of attributes and outcome variables such as overall satisfaction or likelihood to purchase are rated using a scale-based question. However, survey data, particularly scale-type questions, sometimes introduce peculiar data conditions that are not accommodated by traditional approaches.

Presently, due to the availability of high computing power and advanced software, it is possible for us to use more robust statistical techniques, such as Bayesian methods, which allow for the development of robust driver models. In our presentation, we will showcase how we applied Bayesian technologies to help a major utility company solve complex causal modeling of customer experience management, providing required diagnostics on the model to obtain actionable insights to optimize the customer experience.

Presentation Video

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