---
title: "Churn Analysis: Paid Users Drop-off"
date: "2026-04-26"
author: "Wisanna AI Analyst"
tags: ["churn", "retention", "behavior", "usage"]
---

<h1>Churn Analysis: Paid vs Retained Users</h1>
<p>This report analyzes the behavioral differences and drop-off patterns of users who previously paid but subsequently churned compared to retained paid (pro, team, enterprise) users.</p>

<h2>Overview</h2>
<ul>
    <li><strong>Retained Cohort:</strong> 33 active paid users</li>
    <li><strong>Churned Cohort:</strong> 3 recent previously paid users</li>
    <li><strong>Average Lifespan:</strong> Churned users stayed for an average of ~2.7 months before leaving, which closely matches the retained cohort's average lifetime to-date (2.7 months).</li>
</ul>

<h2>Feature Adoption Differences</h2>
<p>A stark contrast exists in how users interact with the platform based on their cohort:</p>
<ul>
    <li><strong>Retained Users:</strong> Their top activities involve deep system interaction — specifically running the core multi-query user state function (<code>getUserCompositeView</code> requests) which signals heavy utilization of the app's advanced views.</li>
    <li><strong>Churned Users:</strong> Their usage logs are dominated by login events (<code>auth.login</code>), basic UI navigation, and chat module selections, with far fewer deep usage actions. Retained users log an average of <strong>178 events per active day</strong> compared to just <strong>94 events</strong> for churned users.</li>
</ul>

<h2>Drop-off Patterns</h2>
<p>The churned users exhibited an engagement cliff—a sudden stop in activity. Looking at their last 7 days of active usage vs the prior 7 days:</p>
<ul>
    <li>User A: 1475 events prior week &rarr; 886 events last week</li>
    <li>User B: 242 events prior week &rarr; 160 events last week</li>
    <li>User C: 171 events prior week &rarr; 99 events last week</li>
</ul>
<p>Their activity dropped by nearly half in their final week before churning.</p>

<h2>Key Takeaways</h2>
<ol>
    <li><strong>Value Realization:</strong> Users who fail to utilize the heavier data-views (like <code>getUserCompositeView</code> and full workspace workflows) are at high risk of churning. They log in, check basic UI/chat, and leave.</li>
    <li><strong>Warning Signs:</strong> A sudden 40-50% drop in active daily events over a 7-day period is a massive predictive indicator of imminent churn. Proactive interventions (e.g. usage tips or success reach-outs) must happen as soon as velocity drops, not after their bill fails or they explicitly cancel.</li>
</ol>