Case study: Customer Churn & predictive analysis2019-02-11T17:50:15+10:00

Customer Churn & Predictive Analysis

Accurately forecast customer churn through Advanced Analytics

Problem

Our client, a multinational telecommunications company was presently facing above-average levels of customer turnover, compared with industry levels. With the increasing price competition from new companies and resellers, the commoditisation of telecommunication services and consequently declining margins, customer retention and accurate churn forecasting are of critical importance.

High levels of customer churn result in a declining market share, loss of revenue and deterioration of brand equity. Accurate churn forecasting enables senior management, through the use of advanced analytics and data science, to take required actions.

Solution

Through the application of Statistics and Data Science, we were able to identify key variables driving customer defection in the company’s most profitable business units.

In order to determine which variables were driving this behaviour, we reviewed customer satisfaction data across the most relevant business units. Having determined the different components in the data, we proceeded to develop a churn prediction model that isolated the key drivers behind the customer dissatisfaction. The model was rolled out for the entire organisation.

Result: according to our model, clients who experienced delays in delivery or extended time awaiting customer service were several times more likely to defect to a competitor. This enabled senior management to take corrective actions that curved the number of clients experiencing these problems. The application of advanced analytics, therefore, has the potential to improve revenue and customer retention, providing the client with a source of competitive advantage.

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