Same automation. Four different dashboards.
Tuesday you saw the code. Today you see how product managers, analysts, ops leads, and AI agents each use it differently. Same data, different jobs, better outcomes.
Team Workflows
See how different roles use the same system to transform their daily work.Click each role below
Before Automation
With Automation
Workflow Process
Impact By The Numbers
"I finally have time to think about why users drop off, not just where they drop off."
— Product Manager, 6 years SaaS
How Roles Work Together on One User
Watch how the system and team collaborate to re-engage Sarah within 90 minutes.
Sarah signs up at 2pm. Completes Step 1 (profile), skips Step 2 (integration), logs out.
✨ Scroll here to watch the workflow
Team-Wide Impact (First 60 Days)
| Metric | Before | After | Improvement |
|---|---|---|---|
| Activation Rate (Day 7) | 42% | 67% | +25 pts |
| Time to First Value | 8.3 days avg | 4.2 days avg | 49% faster |
| Drop-off Detection Time | 3-7 days (weekly reports) | 2 hours (real-time) | 98% faster |
| Team Hours on Onboarding | 26 hours/week | 4.75 hours/week | 82% reduction |
Getting Your Team to Actually Use It
Product Manager: 'AI can't understand user psychology like I can.'
True. AI finds patterns (what users do). You provide psychology (why they do it). Run parallel for 2 weeks: your intuition vs AI patterns. Compare accuracy.
PMs see AI catches 3x more drop-offs. They focus on 'why' (strategy), AI handles 'what' (detection).
Analyst: 'I'll lose my job if AI does all the reporting.'
AI does weekly status reports. You do the analysis humans can't: 'Why did cohort X activate 2x faster?' That's the valuable work.
Analysts become strategic advisors, not data janitors. Promotions follow insights, not reports.
Operations: 'Automation will send the wrong message and upset users.'
Start with AI recommendations, human approval. After 30 days, review: how many AI suggestions did you reject? Usually <5%. Then enable auto-send for low-risk messages.
Ops sees AI is conservative (suggests proven tactics). They approve auto-send for 80% of cases, focus on edge cases.
Leadership: 'This sounds expensive and risky.'
30-day pilot: 5 users, one onboarding flow. Measure activation rate before/after. If <10% improvement, cancel and get refund. Typical result: 20-30% improvement.
CFO sees 25% activation lift = $47K additional MRR in 60 days. Approves company-wide rollout.
Engineering: 'Another tool to integrate and maintain.'
Uses existing data sources (Segment, Amplitude, etc). No new tracking code. 2-hour setup: connect APIs, map events. We handle maintenance.
Engineering spends 2 hours on setup, zero hours on maintenance. They're happy.
Investment & ROI
Typical payback in 30-45 days through activation rate improvements
Pricing
ROI Calculator
Proven Results
From Demo to Live in 3 Weeks
From demo to production in just 3 weeks
- Connect data sources (Segment, Amplitude, analytics tools)
- Map onboarding events and user properties
- Configure role-specific dashboards (PM, analyst, ops views)
- Import 90 days historical data for baseline
- Train each role on their workflow (4hr session per role)
- Run pilot with 5 users per role on one flow
- AI analyzes first week of data, generates initial recommendations
- Team reviews AI suggestions, provides feedback for tuning
- Roll out to all users and flows
- Enable auto-interventions for low-risk messages
- Daily check-ins for first week to catch issues
- Measure baseline metrics vs new performance (activation rate, time to value)
Enterprise deployments: 4-6 weeks for custom ML models and compliance reviews
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