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Case Study:

Demand Forecasting / Global Internet Network & Security Provider

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Global IT Services Provider Slashes Customer Churn by Leveraging Data Analytics

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Challenge:

Commoditization and market disruption induced unprecedented customer churn

Customer churn may be caused by a multitude of diverse factors and is therefore difficult to predict. In addition, survey data on customer satisfaction are oFen biased or incomplete, and therefore cannot represent the main source of information when trying to understand and ultimately predict customer behavior. A better approach is to use data that reflect behavior, as well as exogenous data such as financial metrics reported by public companies, and integrate them all into a comprehensive AI-based churn prediction model.

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Solution:

Data-driven predictive model based on customer historical behavior

1. 1. Teranalytics developed an algorithm to predict customer churn based on internet traffic data, feature usage, and payment history. Customers with highest churn likelihood are flagged with a lead time sufficient to implement marketing action in line with the drivers identified and selected by the custom algorithm. Efficiency has been demonstrated by comparing a control group (no action) vs a group where marketing outreach has been implemented as per the algorithm recommendation.
   2. Teranalytics centralized thousands of B2B time series reflective of customer behavior across AMER, LATAM, MEA, and APJ regions, merged these data with publicly available financial reports, and derived a series of financial metrics to quantify behaviors along multiple dimensions.
   3. The resulting data were used to build an AI predictive engine that provides:
      • (1) A probability of churn with a certain lag parameter (e.g. predicting churn at 3 months, at 6 months, etc)
      • (2) A driver analysis highlighting the most likely reason for the customer churn
   4. The findings, based on a Random Forest classification coupled with financial analysis, can be used to tailor marketing strategies to specific customer needs.

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Results:

Projected 15% Customer churn reduction with targeted marketing campaigns, protecting millions of revenue dollars

• Efficiently and effectively identify customers exhibiting leading indicators of probable churn
• Pinpointing why these customers are likely to leave
• Suggesting pre-emptive measures and targeted marketing messages
• Customize recommendations to specific geographies, industries, and timeframes
• Inform marketing team of optimal retention campaign prioritization
• Provide a business decision tool for executives to assess financial impact of churn reduction
• Enhance understanding of customer behavior, from internal to external metrics
• Provide predictive data to decision makers to support experimentation and prioritization

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Client Feedback:

“We’ve been going at this issue for a couple of years; nobody had presented the problem this way, and then solved it so quickly.”

— Senior Vice President

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