Demo Case Study: This is a fictional example created to demonstrate Mapster's PMF analysis methodology. The company, data, and insights are illustrative examples showing how segmented PMF measurement works in practice.
Demo Case Study
How Mappbook Achieved 52% PMF score and Reduced Churn by 31%
A demo case study on systematic Product Market Fit measurement across customer segments, geographies, and acquisition channels
Mappbook
B2B Project Management SaaS
Company Overview
About Mappbook
- Stage: Series A
- ARR: $8M (at start)
- Active Users: 15,000 across 2,500 companies
- Markets: US, UK, Germany, Australia
The Problem
Mappbook had solid topline growth but struggled with 8% monthly churn. They didn't know which customer segments actually loved their product, why expansion into Europe was underperforming, or what was driving the gap between power users and churned customers.
The Challenge: Growth Without Clarity
What They Had
- 15% MoM user growth
- $8M ARR
- Good press coverage
What They Didn't Know
- ✕8% monthly churn (unsustainable)
- ✕Which segments actually loved them
- ✕Why Europe was underperforming
- ✕What separated power users from churners
"We're growing, but are we actually building something people love? Or just something people kind of use?"
— Andrew Walker, Co-Founder & CEO
The Approach: Systematic PMF Measurement
Month 1: Initial PMF Baseline
- Sean Ellis PMF Survey sent to 8,000 active users
- 2,847 responses (35.6% response rate)
- Question: "How would you feel if you could no longer use Mappbook?"
User Attributes Tracked
Well below the 40% threshold for strong Product Market Fit
Key Findings: The PMF Reality Check
The segmented analysis revealed the real story behind the numbers. Mappbook's overall PMF masked dramatic differences across segments, geographies, and channels.
PMF by Customer Segment
| Segment | PMF Score | Sample Size | Key Insight |
|---|---|---|---|
| Marketing Agencies (11-50) | 58% ✓ | 847 | Loved time tracking + client reporting |
| Software Dev Teams (11-50) | 42% ~ | 623 | Used for sprint planning |
| Consultants (Solo/Small) | 19% ✕ | 412 | Found it "too complex" |
| Enterprise (200+) | 15% ✕ | 521 | Lacked enterprise features |
User Type Layer: Power User Conversion by Segment
Marketing agencies didn't just have higher PMF - they also had the highest power user conversion rate at 34%. Users in this segment were 3.2x more likely to adopt 3+ integrations compared to other segments.
✓ Actions Taken
- →Doubled down on marketing agencies (11-50 employees)
- →Built GitHub integration and sprint templates for dev teams
- →Stopped targeting solo consultants and enterprise
Results
- 📈ICP clarity increased sign-up quality by 34%
- 📈Reduced CAC by 42% (stopped wasteful targeting)
- 📈Dev team PMF improved from 42% → 51% after features
See the exact survey Mappbook used to measure PMF by segment
PMF by User Type: The Power User Paradox
Mappbook segmented PMF scores by user type to understand how product value perception changed from free trial through paid tiers to power users. The results challenged conventional wisdom.
| User Type | PMF Score | Sample | 30-Day Retention | Key Characteristic |
|---|---|---|---|---|
| Power Users (3+ integrations) | 71% ✓ | 412 | 96% | Deep integration in workflow |
| Paid (Pro Plan) | 54% ✓ | 687 | 88% | Uses advanced features |
| Paid (Basic Plan) | 42% ~ | 834 | 72% | Core features only |
| Free Trial (Active) | 31% ~ | 614 | 47% | Evaluating fit |
| Free Trial (Inactive) | 12% ✕ | 300 | 8% | Never activated |
The Power User Insight
Power users who adopted 3+ integrations showed 71% PMF - dramatically higher than even paid Pro users (54%). The path to "very disappointed" wasn't just about paying, it was about depth of integration into daily workflow.
Meanwhile, 31% of active free trial users already showed moderate PMF, revealing that activation quality mattered more than paywall friction for conversion.
Actions Taken
- →Redesigned free trial onboarding to showcase integrations early
- →Built "activation score" to identify trial users ready to convert
- →Created upgrade prompts when users hit collaboration features
- →Added "power user path" in-app guidance for Basic plan users
Results After 3 Months
PMF by Geographic Location
| Location | PMF Score | MRR | Churn Rate |
|---|---|---|---|
| 🇺🇸 United States | 47% ✓ | $4.2M | 5.2% |
| 🇬🇧 United Kingdom | 38% ~ | $1.8M | 7.8% |
| 🇩🇪 Germany | 18% ✕ | $1.2M | 12.4% |
| 🇦🇺 Australia | 41% ✓ | $0.8M | 6.1% |
Qualitative Feedback from Germany:
"We want to use it but compliance team won't approve - no data residency options"
"The workflow doesn't match how German teams operate"
🔗 User Attributes → Product Decision
Segmenting German users by segment showed that regulated industries (fintech, healthcare) had 14% PMF due to compliance blockers, while creative agencies had 24% PMF citing workflow mismatches.
Decision: Prioritized EU data residency and GDPR features first (unblocking regulated industries), then localized workflows. This dual approach addressed both segments, resulting in 18% → 43% PMF improvement.
Actions for Germany
- 🇪🇺Built EU data residency (Frankfurt hosting)
- 🇪🇺Added GDPR-first features
- 🇪🇺Hired German-speaking CS team
Results After 4 Months
Channel Attribution: Which Channels Bring Champions?
| Channel | PMF Score | CAC | LTV | LTV/CAC |
|---|---|---|---|---|
| Referrals | 67% ✓ | $45 | $2,840 | 63x |
| Paid (Google) | 51% ✓ | $12 | $1,620 | 135x |
| Organic (SEO) | 44% ~ | $89 | $2,140 | 24x |
| Paid (LinkedIn) | 22% ✕ | $340 | $720 | 2.1x |
| Product Hunt | 19% ✕ | $280 | $580 | 2.0x |
Channel Quality: Power User Conversion
Channel quality wasn't just about PMF scores - it was about user type quality. Referrals brought 2.8x more power users (users who adopted 3+ integrations) than paid channels, explaining their dramatically higher LTV.
Key Insight
Referrals and Google Ads brought users with 3x higher PMF scores than paid channels. Paid ads were attracting the wrong customer segments with poor retention.
Actions:
- → Shifted 60% of paid budget to referral program
- → Built in-app referral prompts
- → Paused broad campaigns
Results:
- ✓ CAC dropped 42%
- ✓ New user PMF: 28% → 39%
- ✓ Referral rate increased 3.2x
See how to track which channels bring your best customers
Leading Indicators of Churn
Mappbook correlated PMF survey responses with actual churn behavior over 90 days:
| PMF Response | 90-Day Churn Rate | Prediction |
|---|---|---|
| "Very Disappointed" | 4.2% | High retention |
| "Somewhat Disappointed" | 18.7% | At-risk (41% of users) |
| "Not Disappointed" | 64.3% | Will churn |
Breakthrough Insight
"Somewhat disappointed" users were 4.5x more likely to churn than "very disappointed" users - and they represented 41% of the user base.
Follow-up Question to "Somewhat Disappointed" Users:
"What's the main thing preventing you from being 'very disappointed' if Mappbook disappeared?"
🔗 User Attributes → Product Decision
By segmenting "somewhat disappointed" responses by user attributes, Mappbook discovered that marketing agencies (their ICP) were asking for Slack/Asana integrations, while solo consultants (not ICP) wanted pricing changes.
Decision: Built Slack & Asana integrations first (serving 38% of at-risk ICP users), deferred pricing changes (requested by non-ICP). This targeted intervention moved 34% of at-risk ICP users from "somewhat" → "very disappointed."
Actions Taken
- ✓ Built Slack & Asana integrations (6 weeks)
- ✓ Rebuilt UI and UX
- ✓ Introduced usage-based pricing tier
Results
- 📈 34% moved from "somewhat" → "very disappointed"
- 📈 Overall churn dropped from 8% → 5.5%
- 📈 31% total churn reduction
Identifying Friction Points in User Journey
Mappbook sent Customer Effort Score (CES) surveys at key milestones in the user journey:
| Journey Stage | CES Score | % "Very Difficult" | Main Friction |
|---|---|---|---|
| Initial Signup | 6.2/7 ✓ | 8% | Smooth |
| First Project Setup | 4.1/7 ✕ | 41% | Too many fields, unclear workflow |
| Inviting Team Members | 5.8/7 ~ | 15% | Permission settings confusing |
| Generating Reports | 3.2/7 ✕ | 58% | Can't find button, export broken |
Specific User Quote:
"I wanted to create a simple task list but was forced to fill out 12 fields including 'project code' and 'budget allocation' - I'm a 3-person agency, I don't have a budget allocation!"
🔗 User Attributes → Product Decision
CES responses segmented by team size revealed that teams of 11-50 (their ICP) found project creation "very difficult," while teams of 200+ (enterprise, not ICP) complained about missing advanced fields.
Decision: Simplified for ICP segment (11-50 teams) by reducing required fields from 12 → 3, added "advanced mode" toggle for enterprise. This improved activation rate for ICP by 28% while maintaining enterprise usability.
Actions Taken
- ✓Simplified project creation: 12 fields → 3 required
- ✓Rebuilt reporting UI with dashboard widget
- ✓Added tooltips for permission settings
Results
See how to measure friction at key user journey milestones
Discovering True ICP Through PMF + Behavioral Data
Mapster allowed Mappbook to layer PMF scores with usage data to discover their champion profile:
Champion User Profile
- 🏢 Industry: Marketing agencies, creative studios
- 👥 Team Size: 11-50 employees
- 📍 Location: US, UK, Australia
- 🚀 Channel: Referral, Google Ads, organic
- 💼 Use Case: Client project management + time tracking + billing
- 💳 Subscription: Upgrades to Pro within 45 days (avg 28 days)
- ⚡ Power User: Adopts 3+ integrations, 34% become power users
- 📊 Behavior: Uses time tracking daily, invites 5+ team members, exports weekly reports
Low-PMF User Profile
- 🏢 Industry: Solo consultants, enterprise IT
- 👥 Team Size: 1-2 or 200+
- 📍 Location: Germany (pre-localization)
- 🚀 Channel: Paid ads (broad targeting)
- 💼 Use Case: Personal task management OR complex enterprise
- 💳 Subscription: Stays on free tier, churns before upgrade opportunity
- ⚡ Power User: Only 7% adopt integrations, never become power users
- 📊 Behavior: Uses basic features, never invites team, churns <60 days
Marketing Strategy Changes
- 🎯 Updated positioning: "Built for growing creative teams"
- 🎯 Changed hero: "Project Management for Everyone" → "Client Work Management for Agencies"
- 🎯 Launched agency case studies
Results
Growth & Expansion: What Drives Revenue Growth
Beyond new customer acquisition, Mappbook analyzed how PMF scores correlated with expansion revenue. The discovery: users with >50% PMF had 4.2x higher expansion rates - and specific behavioral signals predicted who would upgrade.
PMF Correlation with Expansion Revenue
| PMF Segment | Avg Expansion ARR | Upgrade Rate | Time to Upgrade |
|---|---|---|---|
| Very Disappointed (>50% PMF) | $1,840 | 67% | 28 days |
| Somewhat Disappointed (30-50% PMF) | $620 | 31% | 89 days |
| Not Disappointed (<30% PMF) | $140 | 8% | Never* |
*92% of users with <30% PMF never upgraded before churning
🚀Expansion Champions
- 1.Team collaboration needs (42% of upgrades) - Hit team member limits, wanted permissions
- 2.Client reporting demands (31% of upgrades) - Needed white-label reports, export customization
- 3.Integration requirements (27% of upgrades) - Wanted Slack/Asana/GitHub integrations
Users who invited 3+ team members within first 14 days had 78% upgrade rate vs 12% for solo users
⚠️"Stuck" Users
- ✕Solo usage pattern - Never invite team members, treat as personal tool
- ✕Low PMF score - 18% average PMF, using out of habit not love
- ✕Basic features only - Never explore advanced capabilities or integrations
"Stuck" users had 23% PMF vs 58% for expansion champions - PMF predicted expansion better than usage metrics
Actions Taken
- →Built upgrade prompts at "expansion moments" (team limits, export attempts)
- →Created "expansion score" to identify ready-to-upgrade users
- →Redesigned pricing page to emphasize team collaboration benefits
- →Added in-app feature discovery for Basic users (showcase Pro features)
Results
NPS vs PMF Correlation
Key Learning: NPS strongly correlated with PMF scores. Use PMF for product decisions, NPS for customer health tracking.
Churn Reason Analysis
Key Finding: 72% of churn came from users with <25% PMF
"Missing features" churn was preventable - users at 34% PMF who churned for 1-2 specific gaps
See how to measure customer loyalty and satisfaction
6-Month Results: The Transformation
Before Mapster
Month 0
After Systematic PMF Measurement
Month 6
Key Takeaways: What Mappbook Learned
"Overall PMF" is meaningless
Mappbook had 28% overall but 58% in their ICP segment. Segment-level analysis is critical.
Geography matters more than expected
Germany looked like revenue but had 12% churn until localized for GDPR and workflow preferences.
Channel quality > channel volume
Referrals had 3x higher PMF than paid ads. Focus on channels that bring champions.
"Somewhat disappointed" users are at-risk
They represent 40% of users and 18% churn rate. This segment needs targeted intervention.
PMF predicts churn better than behavior
Low-PMF users churned even with okay usage metrics. PMF score is a leading indicator.
Fix friction at activation, not at churn
CES surveys at Day 3/7 revealed onboarding issues causing silent churn later.
Stop building for everyone
Mappbook stopped targeting enterprise and solo consultants (no PMF), doubled down on agencies (58% PMF). ICP clarity drove all growth metrics.
Similar Discovery: Mapera's creator platform faced the same decision - stop targeting "all creators" and focus on newsletter/course creators in B2B niches (62% PMF vs 18% for video creators).
See how creator platforms segment for PMF →Founder's Perspective
Note: Fictional founder perspective created for illustrative purposes
"Before Mapster, we were flying blind. We knew churn was high but didn't know why. We thought we needed more features, but the real problem was we were building for the wrong customers.
The PMF surveys gave us permission to stop trying to be everything to everyone. We narrowed our ICP, fixed the onboarding friction, and focused on the segments where we already had strong PMF.
Six months later, we're at 52% PMF, churn is down 31%, and we have a clear roadmap based on what our champions actually need. This is the data I wish I had on Day 1."
Methodology
Survey Frequency
- PMF Survey: Monthly for first 3 months, then quarterly
- CES Survey: Triggered at key milestones
- NPS Survey: Quarterly
- Churn Survey: All churned users
Response Rates
- PMF Survey: 35.6% average
- CES Survey: 42.1% average
- Churn Survey: 28.3% average
Survey Tools
- Platform: Mapster PMF Survey
- Segmentation: 8 user attributes tracked
Strategic Survey Milestones: Surveying at Moments of Truth
Mappbook didn't just send surveys randomly - they triggered them at strategic moments in the user journey where feedback would be most actionable and authentic.
📊 PMF Survey Timing
Sent monthly to rapidly iterate and understand segment-level patterns. High frequency enabled quick hypothesis testing.
Shifted to quarterly once ICP was identified and core issues addressed. Focus moved from discovery to tracking improvements.
Trigger: Sent to users with 30+ days usage and 5+ active sessions
⚡ CES Survey Moments
Captured activation friction - revealed 12-field form was blocking ICP segment
Measured collaboration ease - found permission settings confusing
Assessed value delivery - discovered export UX was broken
Philosophy: Survey right after critical actions, not days later when memory fades
📈 Growth Intent Survey
Users reach team member limits, storage caps, or feature restrictions - perfect upgrade moment
Users exporting reports 2+ times/week signal need for advanced features
Goal: Understand expansion intent when users are experiencing growth needs
🔴 Churn Survey
Triggered within 1 hour of cancellation to capture authentic reasons while top-of-mind
Analyzed churn reasons differently for high-PMF users (preventable) vs low-PMF (inevitable)
Discovery: 72% of churn came from users with <25% PMF - focus retention on saveable users
Key Principle: Survey at Decision Points
Mappbook surveyed users at moments of truth - right after activation steps, during growth needs, and at churn. This timing ensured feedback was specific, actionable, and directly tied to product improvements.
Sample Size: 2,847 initial PMF survey responses from a base of 8,000 active users (30+ days usage). Segmentation analysis included customer_id, segment, user_type, attribution_channel, usage_frequency, and features_used. All data was anonymized and aggregated for analysis.
Related PMF Case Studies
See how the same segmented PMF methodology works across different industries
This is a demo case study created to showcase Mapster's PMF analysis methodology. All companies, names, and data are fictional and for illustrative purposes only.Start measuring your own PMF →
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