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CASE STUDY - Cablecom Reduces Churn with the Help of Predictive Analytics

Commentary by Federico Cesconi, Head of Customer Insight and Retention, Cablecom GmbH

Cablecom GmbH is Switzerland’s largest cable network operator. Broadcasting to 1.6 million homes across the country, Cablecom offers customers cable television, broadband Internet, and mobile and fixed-network telephony.

Cablecom’s reputation as a market innovator is mirrored in house, where the company uses the very latest technology to better serve its customers.


In the broadcast and telecommunications space, where churn is a major global issue, it is vital to be able to target customers efficiently with tailored marketing offers.

“Seeing the world through the customers’ eyes is at the heart of our business,” said Federico Cesconi, head of customer insight and retention at Cablecom. “Customer information and feedback is key to this process and is enabling us to take a proactive approach to one of our industry’s most pressing problems.”

“It’s much easier to retain a customer than to try to win him or her back. Many win-back activities and offers are often too late for the customer looking to switch providers. In many cases, the decision has been made weeks before, and it is costly and difficult to reverse,” explained Cesconi.


Cablecom recognized the key to tackling churn was to identify the point at which customers become dissatisfied—before they make the decision to switch to an alternative provider. It opted to use SPSS’s best-of-breed predictive analytics technologies to assess and analyze customer feedback and to evaluate it alongside existing behavioral and demographic data sets. Cablecom’s aim is to predict future behavior and proactively meet customer demands, and therefore reduce churn.

At significant points across the customer lifecycle, Cablecom solicits detailed customer feedback. Initial results showed a peak in churn behavior between 12 and 14 months into the lifecycle, but the decision to churn was actually made around the ninth month into a contract.

As a result, the company targeted customers who had been with the company for about seven months with an online satisfaction survey. Dissatisfied customers most at risk of switching were then targeted by a dedicated customer retention team that proactively reached out to persuade them to stay.

As is typical with surveys, though, only a minority of customers responded. Using SPSS’s data mining solution, Cablecom combined the survey results with other customer data and built predictive models that could score all customers on their likelihood to churn. They found more than 100 factors that indicate if a customer is likely to churn, including initial activation period, number of customer service queries, price band, and original sales channel. This insight was applied across the entire customer base to predict which customers would churn and enable Cablecom to take proactive steps to retain them—as well as identify a segment likely to have high satisfaction as a target for cross- and up-sell campaigns.


Data from the customer feedback program gives an accurate picture of the traits of satisfied and dissatisfied customers. Across the wider customer base, Cablecom can identify satisfaction levels (with a 78 percent degree of accuracy) and take appropriate action to retain or even cross- or up-sell customers.

SPSS’s technology enables Cablecom to identify more accurately those customers who are likely to churn and take proactive action to improve retention. Early pilot studies show a drop in churn rate from 19 percent to an impressive two percent among treated customers.

Cablecom can also provide better leads for cross- or up-sell campaigns by targeting a customer segment most likely to accept any such offer.

As a result, Cablecom has achieved substantial results that directly affect its bottom line.

This article originally appeared in the issue of .

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