Matogen Applied Insights (MAI) employed machine learning to build a predictive model to identify individuals who were likely to churn, as well as anticipate customer spending.
Churn is defined as the percentage of customers who moved to a different service provider within a given time period. It is a particular feature of the telecommunications industry that is of concern to service providers, as it has profound impact on income and profitability.
An MAI client in the telecoms sector saw considerable potential in leveraging their warehoused data to manage voluntary churn. Initially, the project aimed to discover and examine the patterns of behaviour associated with churn. The MAI data scientists used weights of evidence, logistic regression and random forests to predict customers that were highly likely to churn, and in conjunction with an expert-based approach, alerts were triggered to highlight particular accounts. These accounts were forwarded into a retention queue for proactive attempts to retain them.
The machine learning techniques employed by the MAI team not only helped to manage voluntary churn, but the improved profiling of customers enables more targeted and relevant product and service offerings in the prepaid customer base especially. This also results in an even better understanding of the customer due to a longer and deeper relationship.
After profiling customers in new and insightful ways, the project expects to start seeing tangible value from the three selected initial use cases. The next steps will be to increase the sophistication of the modelling and automate the process to a larger extent.