Communications, Media and Technology Client Story

Advanced Analytics Identifies “At Risk” Customers

10%

reduction in monthly disconnects as a result of targeted churn prevention communications

1st

time in years that overall customer totals have stabilized thanks to greater retention of existing customers

5%

increase in average revenue per customer due to improved retention of high value customers

The growing problem of customer churn was one our client found themselves in the middle of. We were able to provide help by granting a better understanding of their customers and therefore reducing churn.

Customer churn is a growing problem in the increasingly competitive telecommunications and media industry. In the last few years, the industry has witnessed an explosion in the number of firms that now provide customers with an array of ways to consume high quality video content, including offerings from Hulu, Roku, Apple TV, Google Chromecast, and Amazon Fire TV. The battle for market share has spawned opposing claims of product superiority, price wars, and customer service guarantees. And consumers are embracing the change.

One of our clients, a major multiple service operator, found themselves in the middle of this vortex and responded with major investments in networking capabilities, product innovation, and customer experience management. They also redoubled their efforts to use analytics to drive better decision making across the enterprise – especially churn.

They partnered with us to advance their understanding of their customers and reduce churn. In response, we built a first-of-its-kind, integrated data repository of customer information. This asset contains virtually everything the client captures about individual customers, including data about purchases, product usage, engagement, customer care interactions, and profitability. This information covers both existing and former customers - some with histories dating back 25 years or more. Once the data was assembled, the project team explored the relationships that customers had with the client, identifying those factors that were highly predictive of a customer’s likelihood to churn in time periods, ranging from 30 days to 12 months. The team then developed statistical models to estimate the likelihood to disconnect for reasons like relocation, non-payment or switching to a competitor.

Armed with this intelligence, our client can now proactively communicate with valuable customers who are high churn risks in general, as well as respond quickly when a customer trips a trigger signaling a more imminent disconnect possibility. This solution has greatly advanced the client’s capability to address churn and has led to the creation of a permanent analytic data repository to enable timely updates of customer churn scores.

 
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