Harnessing Actionable Analytics to Reduce Attrition
Our journey with Actionable Analytics started a while back. We were working with a large telecommunications provider who had a major churn problem. Churn rate, for those who are not familiar with the term, refers to the proportion of subscribers who leave a supplier during a given time period. In this case, the company was experiencing attrition at over 25% in some of its customer segments.
Starting with this problem, we needed to achieve two goals:
- Predict which Customers are likely to leave
- Take Proactive Action to keep them
To address the first goal, we needed to establish patterns of behavior that allow us to answer the question: “How likely is this Customer to leave?”
Answering this question meant mining massive amounts of data about customers who have left to identify common threads in customer service interactions, usage patterns, incidents and resolutions, and network events. What made this problem more challenging was the fact that the company’s customer data was dispersed across 14 different systems with a variety of underlying technologies.
The first step in this exercise was sourcing the data from the multiple systems and correlating it. Not only was this a complex integration problem, but we needed to make sure that all the raw data was available for the next step.
The next step was to run analysis on the correlated data to determine the patterns. This exercise yielded two algorithms that can be applied to answer the question above.
But simply knowing that a Customer may leave does not solve the problem, doing something impactful with that information is what counts. This is where our solution had to prescribe Proactive Actions to be taken by Customer Service to keep the Customer.
Before this solution was implemented, the only tool that Customer Service Reps had was to offer the Customer a retention rate to try to keep them. This rarely yielded a positive outcome, because by the time a Customer called to disconnect, his/her mind was already made up and little could be done to change it.
Taking a Proactive approach to the problem led to an incredible change in Customer loyalty and a significant reduction in churn.
Today we have incredible tools for managing, analyzing and visualizing Big Data. Many organizations are using these tools to simply view their data in what we call Descriptive Analytics. Descriptive Analytics are good at showing what has happened and potential root causes, they lack the actionable characteristics that are essential to affecting change in Customer behavior. In many cases, companies think that they need major investments in technology and Data Scientists with PhDs to take their analytics to the next level, but the reality is what you need is focus and determination to take action. In the case of our Telecommunications Provider, the solution had a significant positive effect on the bottom line.
Engage the Dante team to help you harness the power of Actionable Analytics.
