Customer retention is a top priority for most businesses as the cost of acquiring new customers is high and constant churn creates a drag on profitability. Understanding why customers churn with AI delivers a clearer path to reliable results.
Learn how Verizon Wireless gained actionable insights from data and proactively identified 30% of churn.
The Challenge
- Pre-paid churn rates higher than post-paid
- Predict churn-likely customers before next bill
- Target them with remediation or marketing
- 32 million prepaid transactions daily
- 40 different channels
- 100+ transaction types
The Solution
- Capture all real-time data and aggregate
- Complex pre-processing process
- In-depth feature engineering process:
- 200 attributes collected; 80 generated
- Compared 7 models – GBT selected:
- Accuracy
- Model confidence
Your Impact
- Gained actionable insights from data
- Identified leading indicators of churn for:
- Pre-paid customers
- Post-paid customers
- Were able to proactively identify 30% of churn
- Able to challenge fundamental assumptions
- What was causing the churn?
- How addressable is the problem?
Learn more about churn prevention with RapidMiner
Related Resources. Take a Look!
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Solving Customer Churn: A Telecom Use Case
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