Churn is the silent killer of SaaS. You can have a brilliant product and a slick marketing funnel, but if your users are slipping out the back door, you're just filling a leaky bucket. We recently partnered with a B2B SaaS client facing this exact problem, and in six months, we built a system that increased their customer retention by 35%.
This isn't a story about massive marketing budgets or a huge sales team. It's a technical case study about how we used simple, event-driven logic to understand user behavior and proactively deliver value. Here's how we did it.
The Challenge: A High-Churn, Low-Visibility Problem
Our client, a growing project management SaaS, had a solid product but a troubling churn rate hovering around 8% monthly. For a B2B company, that's a five-alarm fire.
Their customer success was entirely reactive. They only heard from customers when something was broken or when they were cancelling. They had no visibility into which accounts were healthy and which were quietly fading away. They were flying blind.
The core problems were:
- No Proactive Engagement: They had no system to identify at-risk customers before they churned.
- Generic Onboarding: All users got the same welcome emails, regardless of their role or what they were trying to achieve.
- Data Silos: User activity data was locked away in product logs, inaccessible to the customer success team.
The Hypothesis: From Reactive to Proactive
Our hypothesis was simple: If we can identify leading indicators of churn based on in-app behavior, we can trigger personalized, automated interventions to guide users back to value and prevent them from leaving.
We didn't need a complex AI model (at least not at first). We just needed to listen to what the data was telling us.
The Solution: A Data-Driven Engagement Engine
We broke the solution down into three technical steps: capturing the right data, scoring user health, and automating the outreach.
Step 1: Tracking Value-Driving Events
First, we needed to stop tracking just vanity metrics like logins. A user can log in every day and still get zero value from your product. We needed to track value milestones.
Working with their team, we identified key events that correlated with long-term success:
-
projectCreated -
teamMemberInvited -
taskCompletedWithComment -
integrationConnected(e.g., Slack, GitHub) -
reportGenerated
We used a simple Javascript event tracking setup. You could do this with a tool like Segment, or roll your own. The key is consistency.
// Example: Tracking an event when a user invites a teammate
// This could be called from the frontend after a successful API response
const trackEvent = (userId, eventName, properties) => {
// In a real app, this would send data to your analytics service
// (Segment, Mixpanel, internal endpoint, etc.)
console.log(`Tracking event for user ${userId}:`, {
event: eventName,
properties: {
...properties,
timestamp: new Date().toISOString(),
url: window.location.pathname,
}
});
};
// --- In the application code ---
const handleInviteTeammate = (currentUser, invitedEmail) => {
// ... API call to invite the user ...
// On success, track the value event
trackEvent(currentUser.id, 'teamMemberInvited', {
plan: currentUser.plan,
invitedUserRole: 'editor', // Add relevant context
});
};
This gave us a clean, reliable stream of the most important user actions.
Step 2: Developing a Customer Health Score
With the event stream in place, we needed to quantify "health." We created a simple, weighted scoring model. Not every event is created equal. Inviting a teammate is a much stronger signal of engagement than just creating an empty project.
Our first-pass scoring logic looked something like this (this could run in a serverless function nightly or in real-time):
// A simplified function to calculate a health score for a user
const calculateHealthScore = (userEvents) => {
const eventWeights = {
'teamMemberInvited': 20,
'integrationConnected': 15,
'reportGenerated': 10,
'projectCreated': 5,
'taskCompletedWithComment': 2,
'login': 1, // We still track it, but give it low weight
};
const SCORE_DECAY_RATE = 0.95; // Score decays by 5% each day
let healthScore = 0;
let lastEventDate = null;
// In a real system, you'd fetch events from the last 30 days
userEvents.forEach(event => {
if (eventWeights[event.name]) {
healthScore += eventWeights[event.name];
}
lastEventDate = new Date(event.timestamp);
});
// Apply time decay based on the last activity
const daysSinceLastEvent = (new Date() - lastEventDate) / (1000 * 3600 * 24);
const decayMultiplier = Math.pow(SCORE_DECAY_RATE, daysSinceLastEvent);
return Math.round(healthScore * decayMultiplier);
};
// Example Usage:
// const userHealth = calculateHealthScore(recentUserEvents);
// console.log(`User health score: ${userHealth}`);
This gave every customer a score. We categorized them into "Healthy" ( > 75), "At-Risk" (30-74), and "Critical" ( < 30). Now, the customer success team had a dashboard where they could see who needed help right now.
Step 3: Triggering Automated, Personalized Interventions
This is where the magic happened. When a user's health score dropped from "Healthy" to "At-Risk," we didn't just flag it. We triggered an automated action.
For example, if a user signed up, created a project, but never invited a teammate after 7 days (a key indicator), we'd automatically send them a helpful, targeted email.
Here's a conceptual snippet of how a webhook listener might handle this score change:
// This could be an AWS Lambda or Google Cloud Function
// that listens for 'healthScoreUpdated' events.
async function handleHealthScoreChange(event) {
const { userId, oldScore, newScore, userDetails } = event;
const wasHealthy = oldScore > 75;
const isNowAtRisk = newScore <= 75 && newScore > 30;
// Trigger: User dropped from Healthy to At-Risk
if (wasHealthy && isNowAtRisk) {
// Logic to determine the 'next best action'
// For this example, we see they haven't invited anyone.
if (!userDetails.hasInvitedTeam) {
const emailPayload = {
to: userDetails.email,
from: 'success@saas-client.com',
subject: `Get more from [Product Name] by collaborating with your team`,
body: `Hi ${userDetails.firstName}, I noticed you've set up your first project. Did you know projects are 3x more successful when you invite a teammate? Here's a quick guide on how to do it...`
};
// await sendEmailWithSendGrid(emailPayload);
console.log('Sent "Invite Teammate" nudge email to:', userDetails.email);
}
}
}
This is a simple example, but we built dozens of these "playbooks" for different scenarios. The key was that every communication was timely, relevant, and helpful, not generic marketing spam.
The Impact: 35% Retention Lift and Beyond
The results were dramatic.
- Monthly Churn Reduced: We brought their monthly churn rate down from 8% to just over 5%, which translates to a 35% increase in customer retention.
- Increased MRR: Reducing churn has a compounding effect on Monthly Recurring Revenue. They were not only keeping more customers but also had more opportunities for expansion revenue.
- Efficient Customer Success: The CS team shifted from firefighting to strategic account management. They could now focus their human touch on the accounts that needed it most, armed with data about what those users were (or weren't) doing.
This simple, data-driven system fundamentally changed their business trajectory.
3 Key Takeaways for Fellow Builders
If you're looking to tackle churn in your own SaaS, here's where to start:
- Instrument Value, Not Just Activity: Don't just track logins and pageviews. Identify the 3-5 key actions that signal a user is getting real value from your product and track those religiously.
- Start with Simple Heuristics: You don't need a PhD in data science to build a V1 health score. A weighted model based on key events is a powerful and achievable starting point. You can get more complex later.
- Automate with Context: The goal of automation should be to deliver a personalized, helpful message at the exact moment a user needs it. Use the data you've collected to make your outreach hyper-relevant.
Churn isn't inevitable. By listening to your user behavior data and acting on it proactively, you can build a stickier product and a healthier business.
Originally published at https://getmichaelai.com/blog/case-study-how-we-increased-customer-retention-by-35-for-a-s
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