Same retention data. Four different workflows.
Tuesday you saw the automation. Today you see how managers, analysts, ops, and specialists each use it differently. Each role has different dashboards, different actions, different wins.
Team Workflows
See how different roles use the same system to transform their daily work.Click each role below
Before Automation
With Automation
Dashboard Metrics
Impact By The Numbers
"I finally spend time on strategy instead of begging analysts for data."
— Retention Manager, 6 years SaaS growth
How Roles Work Together Through the System
Watch how the system coordinates all four roles in real-time to save a $50K/year account.
High-value customer shows early churn signals
✨ Scroll here to watch the workflow
Team-Wide Impact
| Metric | Before | After | Improvement |
|---|---|---|---|
| Time to Launch Campaign | 5-7 days (request → analysis → approval → execution) | 18 minutes (alert → review → launch) | 99% faster |
| Campaigns Per Week | 2-3 campaigns (limited by analyst bandwidth) | 15-20 campaigns (automated cohort generation) | 6x volume |
| Customer Save Rate | 42% (slow response, generic outreach) | 68% (fast personalized intervention) | +26 points |
| Team Capacity | 4 people handling 500 at-risk users/month | 4 people handling 1,800 at-risk users/month | 3.6x throughput |
Getting Your Team On Board
Analysts worry AI will make them redundant
Show them they'll shift from SQL queries to strategic analysis. 'You'll spend 10 hours/week on insights that drive revenue, not pulling reports.' Frame as promotion, not replacement.
Analysts become retention strategists. One analyst told us: 'I finally have time to find the why, not just the what.'
Managers don't trust AI-generated segments
Run parallel for 30 days: manual cohorts vs AI cohorts. Track which performs better. Show data: AI segments have 94% accuracy vs 67% manual.
After 2 weeks, managers stop checking AI's work. Trust builds through results.
Operations worried about losing control of messaging
AI generates templates, ops approves and personalizes. Show them: 'You'll review 10 campaigns in the time you used to build 1.' Control increases, not decreases.
Ops becomes quality control + optimization. One specialist: 'I went from copywriter to campaign strategist.'
Specialists fear losing personal touch with customers
AI handles research, specialist handles relationship. Show time breakdown: 'You'll spend 85% of time talking to customers, not researching them.'
Specialists reach 3.75x more customers with better context. Save rate jumps from 42% to 68%.
Leadership worries about upfront cost and ROI timeline
Calculate team cost: 4 people × $80K avg = $320K/year. System saves 60% of their time = $192K/year value. Subscription is $60K/year. ROI is 3.2x in year one.
Payback in 4 months. Most teams expand to more use cases after seeing results.
Investment & ROI
Typical payback in 3-4 months through team efficiency gains
Pricing
ROI Calculator
Proven Results
From Demo to Live in 3 Weeks
From demo to production in just 3 weeks
- Connect data sources (analytics, CRM, email tools)
- Import historical user data for baseline cohorts
- Configure role-specific dashboards
- Set up notification preferences per role
- Role-specific training (2hr sessions per role)
- Run pilot with 1 campaign per role
- Test cross-role collaboration on live scenario
- Gather feedback, adjust workflows
- Roll out to all team members
- Launch 5-7 retention campaigns
- Daily check-ins for first week
- Measure baseline metrics vs new performance
Enterprise deployments take 6-8 weeks for custom model training and integrations
2026 Randeep Bhatia. All Rights Reserved.
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