Validating Goal-Oriented Hypotheses of Business Problems Using Machine Learning: An Exploratory Study of Customer Churn

Organizations are investing in Big Data and Machine Learn-
ing (ML) projects, but most of these projects are predicted to fail. A
study shows that one of the biggest obstacles is the lack of understand-
ing of how to use data analytics to improve business value. This paper
presents Metis, a method for ensuring that business goals and the corre-
sponding business problems are explicitly traceable to ML projects and
where potential (i.e., hypothesized) complex problems can be properly
validated before investing in costly solutions. Using this method, business
goals are captured to provide context for hypothesizing business prob-
lems, which can be further rened into more detailed problems to identify
features of data that are suitable for ML. A Supervised ML algorithm is
then used to generate a prediction model that captures the underlying
patterns and insights about the business problems in the data. An ML
Explainability model is used to extract from the prediction model the
individual features and their degree of contribution to each problem. The
extracted weighted data feature are then fed back to the goal-oriented
problem model to validate the most important business problems. Our
experiment results show that Metis can detect the most in
uential problem when it was not apparent through data analysis. Metis is illustrated using a real-world customer churn (customer attrition) problem for a
bank and a publicly available customer churn dataset.