Customer Success Story
How Loom Identified & Turned Must-Have Users Into
Their Most Powerful Growth Channel
A video messaging platform's journey from measuring PMF to building systematic advocacy using Mapster's Perfect Customer Loop
The Advocacy Breakthrough
Loom, a video messaging platform used by millions, had achieved strong product-market fit with a 45% must-have score. But they knew their best growth opportunity wasn't paid ads or outbound sales—it was word-of-mouth from passionate users.
The challenge? Most companies know advocacy is important, but few know how to systematically build it. Loom needed to understand: What turns a satisfied user into an active advocate?
Using Mapster's Perfect Customer Loop framework, Loom discovered the exact moments and user attributes that predicted advocacy—and built a repeatable system to create more advocates.
The Challenge: From Must-Have to Advocacy
Loom had the foundation—a 45% must-have score from their PMF survey showed strong product-market fit. But they wanted to go deeper: Who were these must-have users, and how could Loom turn more of them into advocates?
The Advocacy Framework Challenge:
Identify Must-Have Users
Filter PMF survey respondents to find users who would be "very disappointed" without the product. Then segment by user attributes to understand who these super fans are.
Discover the Aha Moment
Understand what must-have users experienced that made them fall in love with the product. What was their Aha Moment?
Measure and Amplify Advocacy
Track which must-have users become active advocates (referrals, testimonials, social shares) and identify the patterns that drive advocacy behavior.
Why Loom Chose Mapster
Mapster's PMF survey template + user attribute filtering made it possible to identify must-have users instantly and track their journey to advocacy—without spreadsheet hell.
💡 Plus: Follow-up survey triggers, NPS integration, and geographic analytics
The Perfect Customer Loop in Action
Loom's Advocacy System with Mapster
Must-Have Score (PMF)
User Attributes Tracked
Increase in Referrals
The 4-Step Process:
Step 1: PMF Survey to Identify Must-Have Users
Deployed Mapster's PMF survey template asking: "How would you feel if you could no longer use Loom?" with options: Very disappointed, Somewhat disappointed, Not disappointed.
💡 45% of respondents said "Very disappointed"—these were the must-have users
Step 2: Enriched with User Attributes
Passed behavioral data via Mapster's widget: videos_sent, days_since_signup, team_size, plan_type, feature_usage.
💡 This revealed which behaviors correlated with must-have status
Step 3: Follow-Up Survey to Find Aha Moment
Automatically triggered a follow-up survey for must-have users asking: "What was the first moment you realized Loom was valuable?"
💡 Discovered the pattern: sending 3+ videos in the first week = 85% must-have rate
Step 4: Track Advocacy Behavior
Used Mapster to segment must-have users by advocacy actions (referrals, testimonials, social shares). Found that users who experienced the aha moment were 3x more likely to refer others.
Mapster Features That Powered Advocacy
PMF Survey Template
Pre-built Sean Ellis Test survey with the core question: "How would you feel if you could no longer use this product?" Instantly identifies must-have users.
💡 40%+ must-have score = strong PMF
User Attribute Filtering
Filter survey responses by any attribute in real-time. Compare must-have vs. disappointed users by behavior, plan type, usage patterns, etc.
💡 Discover what behaviors predict must-have status
Manual Survey Triggers
Programmatically trigger follow-up surveys for must-have users to discover their aha moment. Target the right users at the right time.
💡 Ask deeper questions to your best users
Geographic & Segment Analytics
Visualize must-have users on a map and compare advocacy rates across segments, teams, and regions.
💡 Find your highest-concentration advocate markets
The Aha Moment Discovery
"Sending 3+ Videos in Week 1 = 85% Must-Have Rate"
By filtering PMF survey results by videos_sent and days_since_signup attributes, Loom discovered their Aha Moment: users who sent 3 or more videos in their first week had an 85% must-have score, compared to just 15% for users who sent fewer.
The Pattern
Users who experienced the value of async video communication early became must-have users and natural advocates
The Action
Loom optimized onboarding to get new users to send their 3rd video faster, dramatically increasing must-have rates
This discovery transformed Loom's strategy:
From Guesswork to Data-Driven Advocacy
"Mapster's user attribute filtering let us connect PMF survey responses with actual user behavior. We went from guessing what makes users love our product to having data-driven proof. Now we know exactly which behaviors predict advocacy."
— Loom Growth Team
The Perfect Customer Loop framework gave Loom a systematic process to build advocacy. Instead of hoping users would become advocates, they could now:
❌ Before Mapster
- • Generic onboarding for all users
- • No clear aha moment definition
- • Advocacy was random and unpredictable
- • Couldn't identify best advocates
- • NPS scores without context
✅ With Mapster
- • Optimized onboarding for aha moment
- • Data-driven aha moment: 3 videos/week 1
- • Systematic advocacy pipeline
- • Filter must-have users by attributes
- • Segment-specific advocacy tracking
Key Takeaways for Building Advocacy
Start with Must-Have Users
Use a PMF survey to identify users who would be "very disappointed" without your product. These are your potential advocates. A 40%+ must-have score indicates strong PMF and advocacy potential.
Discover Your Aha Moment
Filter PMF survey results by user behavior attributes to find patterns. What do must-have users do differently? This is your aha moment—the experience that creates must-have users.
Optimize for Aha, Not Just Activation
Traditional activation metrics (signup, first action) don't predict advocacy. Optimize onboarding to get users to their aha moment faster. This creates must-have users who become natural advocates.
Connect Survey Data with Behavior
Pass user attributes (usage data, plan type, feature adoption) with survey responses. This lets you compare must-have vs. disappointed users by actual behavior, not just what they say.
Build a Systematic Advocacy Loop
Use the Perfect Customer Loop: Promise → Aha → Must-Have → Advocacy. Track users through this journey, identify bottlenecks, and systematically convert more must-have users into active advocates.
How to Run Your Own Advocacy Program
Deploy PMF Survey
Use the Sean Ellis Test: "How would you feel if you could no longer use [product]?" Survey active users who have experienced your core value proposition.
Enrich with Behavioral Data
Pass user attributes via widget or URL parameters: feature usage, signup date, plan type, key actions completed, engagement metrics, etc.
Segment and Discover Patterns
Filter responses to compare must-have users vs. disappointed users. Look for behavioral differences that predict must-have status. This reveals your aha moment.
Follow Up with Must-Have Users
Trigger follow-up surveys for must-have users asking about their aha moment, what problem you solve, and if they'd recommend you. These are your potential advocates.
Optimize for Aha Moment
Redesign onboarding to get users to their aha moment faster. Track the % of users who reach the aha moment and their progression to advocacy (referrals, testimonials, social shares).
Build Your Advocacy Engine with Mapster
Use the same Perfect Customer Loop framework Loom used. Identify must-have users, discover your aha moment, and systematically build advocacy.