How do we model expansion revenue from self-serve users in the first 90 days?

Question

Pulse RevOps · The Machine · Accepted Answer

## Expansion Math on Self-Serve Base

Capture baseline usage & product-indicated account value (ACV) markers within 30-day onboarding window.

### Key Metrics to Track

- **Activation Day 0–7**: API call velocity, module adoption, team seats invited
- **Cohort Day 30–60**: Feature depth (nested objects created, workflows triggered, integrations connected)
- **Day 90 threshold**: Free-tier resource limits hit (seat cap, query rate, storage) = expansion signal

### Expansion Conversion Model

Define **three buckets** based on Pavilion / OpenView research:

1. **Self-serve only** (70%): Low-touch expand at limit hit; email + in-app upsell
2. **Sales-assist ready** (20%): Mid-market signals (5+ seats, cross-functional use); sales-dev touch after Day 45
3. **Enterprise negotiation** (10%): Volume commitments, compliance requirements; full CRM handoff

### Revenue Attribution

```mermaid
gantt
    title Expansion Revenue Attribution (90-Day Window)
    section Self-Serve
    Onboard & Activate :s1, day0, 7d
    Feature Adoption :s2, day7, 23d
    Limit Hit Signal :s3, day30, 30d
    Auto-expand Billing :s4, day60, 30d
    section Sales-Assist
    Monitor Day 45+ :sa1, day45, 15d
    Dev Intro Call :sa2, day60, 10d
    Expansion Quote :sa3, day70, 20d
```

Track **$ACV uplift per cohort day** by feature adoption depth. Use **SQL window functions** to compute rolling 30-day expansion rate. Pavilion reports **18–22% of enterprise SaaS** expansion comes from freemium user bases within 6 months of initial signup.

TAGS: plg-expansion,freemium-math,90-day-cohort,seat-expansion,self-serve-upsell,product-signals

How do we model expansion revenue from self-serve users in the first 90 days?

Expansion Math on Self-Serve Base

Key Metrics to Track

Expansion Conversion Model

Revenue Attribution

What does the score mean?