Calix Marketing Cloud Adds Machine Learning Capabilities for Behavioral Analytics-Based Churn Prediction

Calix Marketing Cloud Adds Machine Learning Capabilities for Behavioral Analytics-Based Churn Prediction

New Capability Leverages Advanced Analytics to Identify At-Risk Subscribers, Helping Communications Service Providers of All Sizes Reduce Churn and Improve Profitability

Calix, Inc. announced a new machine learning capability in the latest release of Calix Marketing Cloud (CMC) to reduce subscriber churn. The new churn prediction dashboard, with algorithms that learn and improve over time, allows Communication Service Providers (CSPs) to shift from simply gathering data to acting with foresight. The new churn prediction capabilities create an experience scorecard to assess subscriber satisfaction based on dozens of factors, including behaviors of subscribers who have already churned. The experience scorecard flags those subscribers who are at-risk, thereby giving CSPs – of all sizes – the opportunity to act and prevent churn in real-time without the need for a costly data warehouse and the expensive data science resources that support it.

Churn is a significant risk for all CSPs. A recent study by Independence Research LLC revealed that 70 percent of broadband subscribers had either recently changed their CSP or considered switching to a new CSP. Leading CSPs who invest in subscriber retention significantly improve profitability. According to Harvard Business Review, a 5 percent increase in customer retention rates can increase profitability by as much as 95 percent.

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“To fend off competition and deliver the level of service our subscribers demand, we had to be smarter with data, and that led us to Calix,” said Rob McNabb, CEO for Cayman Islands-based Logic. “Calix Marketing Cloud has been instrumental in enabling us to keep track of our subscribers’ usage patterns and deliver targeted services accordingly. We look forward to taking the next step and leveraging new predictive churn capabilities to engage our subscribers before they become unhappy and decide to leave.”

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The Calix Cloud platform first delivered machine learning capabilities to CSPs to enable network self-heal via Calix Support Cloud. Now, the churn prediction capabilities in CMC deliver machine learning to help CSPs anticipate which subscribers are most at-risk based on the behaviors of their subscribers who recently churned. The CMC churn prediction algorithms:

  • Analyze more than 30 service experience and subscriber behavioral attributes that are known to trigger churn from live and recently churned subscribers.
  • Automatically segment at-risk subscribers, categorizing them as high, medium, or low churn risks.
  • Surface these insights through pre-configured dashboards that are designed specifically for the marketing team.

Calix has also updated its CMC Education Courses, which are accessible from the Calix Education Services webpage, to help marketing teams quickly embrace these new capabilities. The majority of Calix customers are leveraging the CMC Education Courses and Calix Customer Success services to help their teams quickly update their marketing programs to drive revenue growth.

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“The reality is that customer retention is the easiest and most powerful way to drive growth,” said Michael Weening, executive vice president of field operations for Calix. “While customer acquisition is important, upselling and cross-selling existing customers is where everyone needs to start as it yields the greatest return on investment. Predictive analytics completely transform how service providers engage with subscribers enabling dramatic improvements in experience and retention. However, very few CSPs can afford to invest in expensive data science teams and build massive data warehouses. By automatically identifying at-risk subscribers through real-time behavioral analytics, Calix Marketing Cloud provides every CSP, regardless of size, with a simple, cost-effective way to mitigate churn and significantly improve their top and bottom-line performance.”

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