AI Customer Retention
Predict churn early and trigger the right save play before customers leave.
- Project range
- $7,000–15,000
- AWS running cost
- $60–290/mo
- Time to deploy
- 4–6 weeks
- Best-fit industries
- SaaS, Subscription services
Executive summary
A retention engine that continuously scores each customer's health from usage, support, billing, and sentiment signals, predicts churn risk, and triggers the right intervention — a check-in, an offer, or a human escalation — at the right moment. It turns retention from a reactive scramble after a cancellation into a proactive, automated discipline.
Business problem
Churn is usually noticed only when a customer cancels, far too late to save them. The warning signs — declining usage, unresolved tickets, missed payments — sit unwatched across separate systems, and no one has time to monitor every account or personalize outreach at scale.
Architecture
AWS services
Amazon S3
Storage- — Unified signal lake across usage, support, and billing
AWS Lambda
Compute- — Run health scoring and play selection
- — Coordinate interventions
Health + churn model
Compute- — Score account health and predict churn risk
- — Segment by risk and value
Amazon Bedrock
AI / ML- — Explain the drivers of risk per account
- — Draft personalized retention outreach
Amazon DynamoDB
Database- — Account health, risk history, and play state
Amazon EventBridge
Messaging- — Schedule scoring and trigger plays
Amazon SNS
Messaging- — Fire save-play notifications and escalations
CRM / email adapters
Integration- — Trigger outreach and alert the CS team
Amazon CloudWatch
Observability- — Logs, metrics, and cost alarms
Data flow
- 1
Usage, support, billing, and sentiment signals consolidate into an S3 lake.
- 2
The health model scores each account and predicts churn risk, segmented by risk and value.
- 3
Bedrock explains the drivers per account and drafts a personalized intervention.
- 4
Account health and play state are tracked in DynamoDB; EventBridge triggers the right save play.
- 5
Low-touch plays run automatically via CRM/email; high-value risks escalate to the CS team.
Security considerations
- Customer data encrypted at rest and in transit; access-controlled.
- Risk scores are explainable, not black-box, to guide fair action.
- Least-privilege IAM; CRM and email credentials in Secrets Manager.
- Consent-aware outreach with opt-out honored.
Cost considerations
- Bedrock explanation and outreach drafting is the main variable cost.
- Scheduled scoring keeps compute predictable.
- S3, DynamoDB, and EventBridge are inexpensive at rest.
Scalability
- Serverless throughout; scales across the customer base.
- New signals and play types attach without core rewrites.
- Risk thresholds and playbooks configurable per segment.
Deployment roadmap
Phase 1 — Signals & plays
Weeks 1–2- — Connect usage, support, and billing; define save plays
- — Provision AWS foundation and signal lake
Phase 2 — Build & validate
Weeks 3–5- — Build health/churn scoring and play triggers
- — Validate against historical churn
Phase 3 — Launch & tune
Week 6- — Launch with a subset of plays
- — Tune thresholds and personalization
Future enhancements
- Expansion and upsell scoring alongside churn risk.
- Automated win-back campaigns for lapsed customers.
- Lifetime-value modeling to prioritize effort.
- Retention and cohort analytics into the executive dashboard.