Conceptually straight forward but practically difficult to handle, customer lifetime value (LTV) is a well-known form of long term analysis with a wide range of applications, from marketing and finance, to engineering and sales. Conventional approaches find themselves not capable of satisfying the following design requirements for Azure cloud-computing customer lifetime value prediction:
Chao Zhong offers an overview of a new predictive model for customer lifetime value (LTV) in a cloud-computing business. This model is also the first known application of the Fader RFM approach to a cloud business (based on the 2010 Fader-Hardie-Shang paper that describes the discrete purchase decision-making process of noncontract customers)—a Bayesian approach that predicts a customer’s LTV by making and updating assumptions on the distributions of customers’ latent variables. So far the model has achieved an overall symmetric absolute percentage error (SAPE) of 3% over an out-of-time testing dataset, with a minimum train:test ratio of 5:81.
This model is only the first phase of a long-term plan. Chao ends by briefly sharing the next phases of the model, which will be dynamic (allowing the product and population to change) and interactive (proving causality and providing prescriptive analysis).
Chao Zhong is a senior data scientist at C+E Analytics and Insights within Microsoft. His current research interests include (deep) machine learning for customer journey and customer lifetime value and (deep) reinforcement learning for interactive customer behavior modeling. Previously, Chao was the lead data scientist at Scopely, a mobile gaming company in LA. Chao was an ABD (all but dissertation) PhD candidate in mathematics at Michigan Technological University. He holds an MS degree in financial engineering from Temple University and a BS degree in computer science from Beijing University of Aeronautics and Astronautics.
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