Predictive Churn Prevention

Executive Summary

Churn is expensive because it is discovered late. By the time a cancellation request arrives, retention options are limited and costly. A predictive, proactive approach changes the timing. An AI model continuously scores customers for churn risk with interpretable reason codes (billing friction, service quality, price sensitivity, contract phase). When risk exceeds a threshold—or when a cancellation trigger occurs—a conversational voice assistant places an outbound call, runs a short satisfaction check, confirms the root cause, and offers policy-aligned retention options in real time. Humans approve sensitive actions and complex cases; every step is logged for audit and learning.

The problem today

Retention teams work reactively off ticket queues and cancellation notices. Agents spend cycles re-collecting context that already exists in CRM, billing, and network telemetry. Outreach happens after sentiment has hardened, so even generous offers underperform. Decision-makers lack a consistent view of which segments are at risk and why, which makes budget and capacity planning guesswork.

The AI-led flow

1. Risk scoring with reason codes: A supervised model learns from historical churn, usage, trouble tickets, NPS/CSAT, plan changes, price rises, tenure, and local network quality. Output is a risk score plus top reason codes and confidence.
2. Triggering conditions: Exceed risk threshold, contract end nearing, repeated QoS alarms, billing disputes, or explicit cancellation intent.
3. Proactive outreach (voice assistant): A compliant, branded voice agent calls at suitable times, introduces itself, and conducts a 60–120s diagnostic: service quality, billing issues, price/plan fit, move/relocation. The assistant can schedule technicians, propose plan optimizations, offer goodwill credits within configured limits, or warm-transfer to a specialist.
4. Human-in-the-loop controls: High-value accounts, vulnerable customers, and low-confidence conversations route to human retention specialists. All offers follow a rules engine with caps, eligibility, and approval steps.
5. Real-time actions & follow-up: The workflow writes notes and outcomes to CRM, opens or updates tickets, sends confirmation SMS/email, and schedules follow-up surveys. Results feed back into the feature store to improve next-week scoring.
6. Observability & governance: Dashboards track at-risk base, contact rate, save rate, offer mix, and net revenue saved. All calls, decisions, and model versions are auditable.

Privacy-by-design, compliance-aligned: Consent and do-not-call lists are enforced; data minimization and role-based access apply; processing can be region-bound (e.g., EU). Recordings and transcripts follow retention policies. This is decision support, not legal or financial advice; humans remain accountable for final decisions and exceptions.

Pilot scope (30–60 days)

• Scope: One region, broadband segment, top 3 churn drivers.
• Channels: Outbound voice first; SMS/email follow-ups for unreachable customers.
• Interfaces: Read-only CRM/billing/OSS feeds, write-back for outcomes; telephony via SIP/PSTN.
• Success criteria: Contact rate, qualified risk confirmation rate, save rate within 7 days, net revenue saved per 1,000 calls, and customer sentiment (CSAT post-call).

Hypothesis metrics (illustrative, not guaranteed):

• Contact rate 45–65%; of those, risk confirmation 60–75%.
• Save rate on reached, at-risk customers 15–30% depending on driver.
• NPS/CSAT uplift +5–10 points on proactive contacts.

Quick ROI math (scenario):
Assume 1,000,000 subscribers, monthly churn 2% → 20,000 would leave each month.
If the system reaches 50% of at-risk customers and saves 20% of those, that is 2,000 saves/month.
With ARPU €30, gross margin 60%, and an average 6 months of retained life, incremental gross margin ≈
2,000 × €30 × 0.6 × 6 = €216,000/month (~€2.6M/year).
Operating costs (telephony, compute, tuning) are typically a small fraction of recovered margin in this scenario.

Risks & mitigations

• Model bias or drift: Use reason codes, fairness checks, and rolling retrains; monitor calibration.
• Compliance & consent: Enforce DNC lists, quiet hours, and consent flags; provide instant opt-out.
• Offer misuse or over-discounting: Hard policy caps with approvals for high-value incentives; A/B guardrails.
• Customer experience risk: Natural, concise scripts; fall back to human agents on frustration signals.

From pilot to scale

Add mobile segments and enterprise accounts; expand to in-app outreach and retail handoffs. Introduce dynamic discounting tied to LTV and network cost-to-serve. Feed save outcomes into pricing and network investment planning. Over time, retention becomes continuous, data-driven, and less dependent on large reactive call centers.

Expected impact (illustrative):

• Increased customer retention and reduced churn-related revenue loss.
• Lower acquisition costs by keeping existing customers longer.
• Improved customer satisfaction through proactive engagement.
• Automated retention process reduces manual call center workload.
• ROI measurable as additional revenue growth within months.