AI Readiness Score
SaaS company with structured customer, billing, and engagement data across systems.
Technical team familiar with APIs and analytics, but limited AI automation experience.
Budget range appropriate for SaaS tooling and AI implementation at this scale.
Strong data foundation with HubSpot, Stripe, and existing analytics capabilities. Clear metrics-driven use cases.
3-6 month timeline realistic for phased rollout of churn prevention system.
All key systems have robust APIs. HubSpot MCP available. Some custom data modeling needed.
How This System Works
Architecture
Three-agent system focused on customer lifecycle intelligence: daily churn risk analysis, weekly feature adoption tracking, and comprehensive customer success reporting. All agents leverage existing data from HubSpot, Stripe, and PipelineIQ's internal analytics.
Data Flow
Customer data flows from HubSpot (CRM) and Stripe (billing) into daily churn analysis, while PipelineIQ usage data feeds weekly adoption tracking. Both systems update HubSpot with insights and alert teams via Slack. Weekly intelligence reports synthesize all data sources for strategic CS planning.
Implementation Phases
Implement daily churn prediction with basic risk scoring
Add feature usage analysis and expansion opportunity detection
Implement comprehensive weekly reporting and insights
Prerequisites
- -PipelineIQ internal API for usage data
- -HubSpot custom properties for risk and adoption scores
- -Slack workspace with appropriate channels
Assumptions
- -PipelineIQ tracks detailed feature usage
- -Customer success team actively uses Slack
- -HubSpot contains complete customer contact data
Recommended Agents (3)
How It Works
- 1Pull customer data from HubSpot and Stripe
Get account activity, contacts, deals, billing status
HubSpot API - 2Analyze usage patterns from PipelineIQ
Login frequency, feature adoption, report generation
Internal API - 3Calculate churn risk score using AI
Weighted scoring based on engagement, billing, support patterns
Claude - 4Create risk alerts in Slack
Tag CSM with specific risk factors and recommended actions
Slack API
Data Flow
Inputs
- HubSpot — Account properties, contact activity, deal stages(JSON)
- Stripe — Billing history, payment status, subscription changes(JSON)
- PipelineIQ — Product usage metrics, login patterns(JSON)
Outputs
- Slack — Risk alerts with account details and action items(Message)
- HubSpot — Churn risk score and last analysis date(Contact Property)
Prerequisites
- -PipelineIQ usage tracking API
- -HubSpot custom properties for risk scoring
Error Handling
Implement exponential backoff
Flag for manual review
Integrations
| Source | Target | Data Flow | Method | Complexity |
|---|---|---|---|---|
| HubSpot | Churn Risk Analyzer | Account and contact data | api | low |
| Stripe | Churn Risk Analyzer | Billing and subscription data | api | low |
| PipelineIQ | Feature Adoption Tracker | Usage analytics | api | moderate |
| Agents | Slack | Alerts and reports | webhook | low |
Schedule
0 8 * * *0 9 * * 10 10 * * 1Recommended Models
| Task | Recommended | Alternatives | Est. Cost | Why |
|---|---|---|---|---|
| Churn risk analysis | Claude Sonnet 4 | GPT-4 | $50-80/month | Complex pattern recognition across multiple data sources requires reasoning capabilities |
| Feature adoption scoring | Claude Haiku | GPT-3.5-turbo | $20-30/month | Structured analysis task suitable for faster, cost-effective model |
| Report generation | Claude Sonnet 4 | GPT-4 | $30-50/month | High-quality written reports with strategic insights require advanced language model |
Impact
What Changes
Quality Gains
- ✓Earlier churn intervention
- ✓Data-driven expansion conversations
- ✓Proactive customer success
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