Vodafone Churn Demo

Part I + Part II

AI-Powered Churn Prediction and Retention Activation

Predict churn risk, identify key churn drivers, and activate segment-specific, brand-aligned retention messaging.

PoC context: This demo uses a relatively small sample dataset to validate approach and workflow. A production-scale model trained on larger, richer data is expected to improve predictive performance and segment quality.

So what: We can identify high-risk customers with solid discrimination and turn predictions into actionable retention communication.

1. Can We Classify Churn Well?

Results from tuned Logistic Regression and Random Forest, evaluated with imbalance-aware metrics.

Model Comparison

Model ROC-AUC PR-AUC Churn Precision Churn Recall

Recommendation by objective: RF for ranking quality, LR when catching churners is prioritized.

Performance Snapshot

So what: In this PoC both models are viable; at larger production scale we expect stronger ranking and calibration, with final model/threshold selection aligned to campaign cost vs missed-churn trade-off.

2. What Factors Influence Churn, and in What Direction?

Top Churn-Increasing Signals (LR Direction)

    Top Churn-Reducing Signals (LR Direction)

      Most Influential Features (RF Importance)

        Caution: charge-related variables and tenure are correlated, so business actions should be validated with controlled tests.

        So what: Contract type, tenure, and support/security consistently explain churn risk and directly inform retention strategy.

        3. LLM Activation: From Risk Score to Retention Email

        1

        Score customers with churn model

        2

        Assign segment by profile and risk pattern

        3

        Use segment-specific prompt template

        4

        Generate Vodafone-style retention email

        Segment Prompt Strategy (3 segments)

        • Price-sensitive monthly: savings, plan flexibility, billing transparency.
        • Service-support gap: reliability reassurance, support/security improvements.
        • Loyal renewal: recognition, perks, premium continuation offers.

        Sample Email (Support Gap Segment)

        So what: This closes the loop from analytics to customer communication with consistent tone and higher relevance.

        4. Brand Control and Safe Operations

        Generation Controls

        • Fixed structure (subject, intro, bullets, CTA, close).
        • Tone constraints (friendly, clear, positive, professional).
        • Forbidden content rules (no mention of model/churn prediction).

        Validation Controls

        • Automatic checklist pass/fail after generation.
        • Auto-regenerate with targeted feedback if failed.
        • Sample manual QA with brand/marketing approval gates.

        So what: The solution is not only effective, but also governable and brand-safe for scaled deployment.

        5. Recommendation and Rollout Plan

        Recommended Operating Choice

        • Use Random Forest for ranking and prioritization quality.
        • Keep Logistic Regression as a challenger when recall-heavy strategy is needed.
        • Set campaign thresholds based on outreach capacity and expected retention ROI.

        30-60-90 Plan

        • 30: pilot with one segment and controlled audience.
        • 60: threshold optimization and A/B tests on offer framing.
        • 90: multi-segment rollout with quality and uplift dashboards.

        Scale-up Positioning

        • This presentation demonstrates feasibility on a small PoC dataset.
        • Next phase trains the same architecture on larger, broader historical data.
        • Expected gains: stronger churn recall/precision balance, richer segmentation, and higher retention uplift per campaign.

        So what: We can move from model insight to measurable retention impact with a low-friction phased rollout.