User Behavior Tracking for ChatGPT Apps: Analytics Guide
Meta Title: User Behavior Tracking ChatGPT Apps Analytics Guide
Meta Description: Track user behavior in ChatGPT apps with event analytics, session replay & journey mapping. Optimize conversation flows & boost completion rates by 75%+.
Why User Behavior Tracking Is Critical for ChatGPT Apps
User behavior tracking reveals the hidden patterns that make or break ChatGPT app success. Unlike traditional web applications where users follow predictable page flows, ChatGPT apps involve multi-turn conversations with dynamic tool invocations and widget interactions. Understanding how users navigate these conversational experiences is essential for optimizing engagement and conversion rates.
The challenges are unique: conversations span multiple sessions, context must be preserved across days or weeks, and user intent evolves throughout the interaction. A fitness studio owner might start by asking about chatbot capabilities, return three days later to explore pricing, and finally convert after seeing a competitor case study. Traditional page-view analytics miss these crucial behavioral insights.
This guide covers three proven approaches to track user behavior in ChatGPT apps: event-based tracking (capturing discrete actions like tool calls and widget clicks), session replay (recording entire conversation flows for qualitative analysis), and user journey mapping (connecting multi-session touchpoints to understand attribution). By implementing these strategies, you'll identify drop-off points, optimize conversation flows, and increase completion rates by 75% or more.
Related: ChatGPT App Analytics Dashboard Guide, GA4 ChatGPT Event Tracking, Firebase Analytics for ChatGPT Apps
Event-Based Tracking Architecture
Event-based tracking forms the foundation of ChatGPT app analytics. Every meaningful user action—starting a conversation, invoking a tool, clicking a widget button, or encountering an error—should trigger an event with structured metadata.
Event Taxonomy for ChatGPT Apps
Design your event taxonomy to capture the conversational nature of ChatGPT interactions:
conversation_started: User sends first message (properties:user_id,session_id,entry_point,utm_params)tool_invoked: Model calls a tool from your MCP server (properties:tool_name,turn_number,conversation_id,execution_time_ms)widget_rendered: Inline card or fullscreen widget displayed (properties:widget_type,display_mode,structuredContent_size_kb)widget_action: User clicks CTA in widget (properties:action_type,widget_id,conversation_id)error_occurred: Tool error or API failure (properties:error_type,error_message,tool_name,retry_count)conversation_completed: User achieves goal (properties:completion_type,total_turns,session_duration_seconds)
Custom event properties provide context for analysis. Always include user_id (hashed for privacy), session_id (unique per conversation), conversation_id (persists across page reloads), and turn_number (position in conversation flow).
Dual-Write Analytics Strategy
For production ChatGPT apps, implement a dual-write strategy: send events to both Firebase Analytics (free, integrated with Google Analytics 4) and Mixpanel (advanced funnel analysis, cohort tracking). This provides redundancy and cross-validation.
// event-tracking-service.ts - Production event tracking with Firebase + Mixpanel
import { getAnalytics, logEvent as firebaseLogEvent } from 'firebase/analytics';
import mixpanel from 'mixpanel-browser';
interface EventProperties {
user_id?: string;
session_id: string;
conversation_id?: string;
turn_number?: number;
[key: string]: any;
}
interface TrackingConfig {
firebaseApiKey: string;
mixpanelToken: string;
debug?: boolean;
}
class EventTrackingService {
private firebaseAnalytics: any;
private mixpanelInitialized: boolean = false;
private debug: boolean;
private eventQueue: Array<{ name: string; properties: EventProperties }> = [];
constructor(config: TrackingConfig) {
this.debug = config.debug || false;
// Initialize Firebase Analytics
this.firebaseAnalytics = getAnalytics();
// Initialize Mixpanel
mixpanel.init(config.mixpanelToken, {
debug: this.debug,
track_pageview: false, // Manual tracking only
persistence: 'localStorage',
ignore_dnt: false, // Respect Do Not Track
});
this.mixpanelInitialized = true;
// Process queued events
this.flushQueue();
}
/**
* Track event to both Firebase and Mixpanel
*/
track(eventName: string, properties: EventProperties = {} as EventProperties): void {
// Add timestamp
const enrichedProperties = {
...properties,
timestamp: new Date().toISOString(),
platform: 'chatgpt',
};
// Queue if not initialized
if (!this.mixpanelInitialized) {
this.eventQueue.push({ name: eventName, properties: enrichedProperties });
return;
}
// Firebase Analytics (GA4)
try {
firebaseLogEvent(this.firebaseAnalytics, eventName, enrichedProperties);
if (this.debug) console.log('[Firebase] Tracked:', eventName, enrichedProperties);
} catch (error) {
console.error('[Firebase] Tracking failed:', error);
}
// Mixpanel
try {
mixpanel.track(eventName, enrichedProperties);
if (this.debug) console.log('[Mixpanel] Tracked:', eventName, enrichedProperties);
} catch (error) {
console.error('[Mixpanel] Tracking failed:', error);
}
}
/**
* Identify user (for cross-session tracking)
*/
identify(userId: string, traits?: Record<string, any>): void {
// Hash userId for privacy
const hashedUserId = this.hashUserId(userId);
// Firebase User Properties
try {
firebaseLogEvent(this.firebaseAnalytics, 'login', { user_id: hashedUserId });
if (this.debug) console.log('[Firebase] Identified:', hashedUserId);
} catch (error) {
console.error('[Firebase] Identify failed:', error);
}
// Mixpanel Identify
try {
mixpanel.identify(hashedUserId);
if (traits) mixpanel.people.set(traits);
if (this.debug) console.log('[Mixpanel] Identified:', hashedUserId, traits);
} catch (error) {
console.error('[Mixpanel] Identify failed:', error);
}
}
/**
* Track conversation started
*/
trackConversationStarted(sessionId: string, entryPoint: string, utmParams?: Record<string, string>): void {
this.track('conversation_started', {
session_id: sessionId,
entry_point: entryPoint,
...utmParams,
});
}
/**
* Track tool invocation
*/
trackToolInvoked(
toolName: string,
sessionId: string,
conversationId: string,
turnNumber: number,
executionTimeMs: number
): void {
this.track('tool_invoked', {
session_id: sessionId,
conversation_id: conversationId,
tool_name: toolName,
turn_number: turnNumber,
execution_time_ms: executionTimeMs,
});
}
/**
* Track widget rendered
*/
trackWidgetRendered(
widgetType: string,
displayMode: 'inline' | 'fullscreen' | 'pip',
sessionId: string,
conversationId: string,
structuredContentSizeKb: number
): void {
this.track('widget_rendered', {
session_id: sessionId,
conversation_id: conversationId,
widget_type: widgetType,
display_mode: displayMode,
structuredContent_size_kb: structuredContentSizeKb,
});
}
/**
* Track widget action (CTA click)
*/
trackWidgetAction(
actionType: string,
widgetId: string,
sessionId: string,
conversationId: string
): void {
this.track('widget_action', {
session_id: sessionId,
conversation_id: conversationId,
action_type: actionType,
widget_id: widgetId,
});
}
/**
* Track error
*/
trackError(
errorType: string,
errorMessage: string,
toolName?: string,
sessionId?: string
): void {
this.track('error_occurred', {
session_id: sessionId,
error_type: errorType,
error_message: errorMessage,
tool_name: toolName,
});
}
/**
* Track conversation completed
*/
trackConversationCompleted(
sessionId: string,
conversationId: string,
completionType: string,
totalTurns: number,
sessionDurationSeconds: number
): void {
this.track('conversation_completed', {
session_id: sessionId,
conversation_id: conversationId,
completion_type: completionType,
total_turns: totalTurns,
session_duration_seconds: sessionDurationSeconds,
});
}
/**
* Hash userId for privacy compliance
*/
private hashUserId(userId: string): string {
// Simple hash (use crypto.subtle.digest in production)
let hash = 0;
for (let i = 0; i < userId.length; i++) {
const char = userId.charCodeAt(i);
hash = (hash << 5) - hash + char;
hash = hash & hash; // Convert to 32-bit integer
}
return `user_${Math.abs(hash).toString(36)}`;
}
/**
* Flush queued events
*/
private flushQueue(): void {
while (this.eventQueue.length > 0) {
const event = this.eventQueue.shift();
if (event) this.track(event.name, event.properties);
}
}
}
// Export singleton instance
export const tracking = new EventTrackingService({
firebaseApiKey: process.env.FIREBASE_API_KEY || '',
mixpanelToken: process.env.MIXPANEL_TOKEN || '',
debug: process.env.NODE_ENV === 'development',
});
This dual-write approach provides redundancy (if one service fails, you still have data) and enables cross-validation (compare event counts between Firebase and Mixpanel to detect tracking gaps).
Related: Firebase Analytics for ChatGPT Apps, Mixpanel vs Amplitude for ChatGPT Analytics
Session Replay Implementation
Session replay captures the full context of user conversations—every message, tool call, widget interaction, and navigation event. Unlike quantitative event tracking, session replay provides qualitative insights: why did users abandon conversations, where did they get confused, and how did successful users navigate?
Privacy-First Session Replay
GDPR and privacy regulations require explicit consent and PII redaction. Never record:
- User email addresses, phone numbers, or other PII
- Sensitive message content (payment details, health information)
- API keys or authentication tokens
Use rrweb (open-source session replay library) with custom ChatGPT event serialization to capture conversation context without compromising privacy.
// session-replay-recorder.ts - Privacy-first session replay with rrweb
import { record } from 'rrweb';
import type { eventWithTime } from 'rrweb';
interface ChatGPTEvent {
type: 'message' | 'tool_call' | 'widget_render' | 'error';
timestamp: number;
data: any;
}
interface ReplayConfig {
apiEndpoint: string;
sessionId: string;
conversationId: string;
userId?: string;
privacyMode: boolean;
}
class SessionReplayRecorder {
private events: eventWithTime[] = [];
private chatGPTEvents: ChatGPTEvent[] = [];
private config: ReplayConfig;
private stopRecording?: () => void;
private flushInterval?: NodeJS.Timeout;
constructor(config: ReplayConfig) {
this.config = config;
}
/**
* Start recording session
*/
start(): void {
// Start rrweb recording
this.stopRecording = record({
emit: (event) => {
this.events.push(event);
},
maskAllInputs: this.config.privacyMode, // Mask input fields
maskTextSelector: '.pii, .sensitive', // CSS selectors to mask
blockClass: 'no-replay', // Don't record elements with this class
sampling: {
mousemove: true,
mouseInteraction: true,
scroll: 150, // Throttle scroll events (150ms)
input: 'last', // Only record last input value
},
});
// Set up periodic flush (every 30 seconds)
this.flushInterval = setInterval(() => this.flush(), 30000);
console.log('[SessionReplay] Recording started:', this.config.sessionId);
}
/**
* Record ChatGPT-specific event
*/
recordChatGPTEvent(type: ChatGPTEvent['type'], data: any): void {
// Redact PII if privacy mode enabled
const sanitizedData = this.config.privacyMode ? this.redactPII(data) : data;
this.chatGPTEvents.push({
type,
timestamp: Date.now(),
data: sanitizedData,
});
}
/**
* Record conversation message
*/
recordMessage(role: 'user' | 'assistant', content: string, turnNumber: number): void {
this.recordChatGPTEvent('message', {
role,
content: this.config.privacyMode ? this.maskContent(content) : content,
turn_number: turnNumber,
});
}
/**
* Record tool call
*/
recordToolCall(toolName: string, args: any, result: any, executionTimeMs: number): void {
this.recordChatGPTEvent('tool_call', {
tool_name: toolName,
args: this.config.privacyMode ? this.redactPII(args) : args,
result: this.config.privacyMode ? this.redactPII(result) : result,
execution_time_ms: executionTimeMs,
});
}
/**
* Record widget render
*/
recordWidgetRender(widgetType: string, displayMode: string, structuredContent: any): void {
this.recordChatGPTEvent('widget_render', {
widget_type: widgetType,
display_mode: displayMode,
structuredContent: this.config.privacyMode
? this.redactPII(structuredContent)
: structuredContent,
});
}
/**
* Record error
*/
recordError(errorType: string, errorMessage: string, stack?: string): void {
this.recordChatGPTEvent('error', {
error_type: errorType,
error_message: errorMessage,
stack: this.config.privacyMode ? undefined : stack, // Never record stack traces in privacy mode
});
}
/**
* Flush events to backend
*/
async flush(): Promise<void> {
if (this.events.length === 0 && this.chatGPTEvents.length === 0) return;
const payload = {
session_id: this.config.sessionId,
conversation_id: this.config.conversationId,
user_id: this.config.userId,
rrweb_events: this.events,
chatgpt_events: this.chatGPTEvents,
timestamp: new Date().toISOString(),
};
try {
const response = await fetch(`${this.config.apiEndpoint}/session-replay`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
});
if (!response.ok) {
throw new Error(`Session replay upload failed: ${response.statusText}`);
}
// Clear events after successful upload
this.events = [];
this.chatGPTEvents = [];
console.log('[SessionReplay] Flushed events:', payload.rrweb_events.length);
} catch (error) {
console.error('[SessionReplay] Flush failed:', error);
// Keep events in memory for retry
}
}
/**
* Stop recording and flush final events
*/
async stop(): Promise<void> {
if (this.stopRecording) this.stopRecording();
if (this.flushInterval) clearInterval(this.flushInterval);
await this.flush();
console.log('[SessionReplay] Recording stopped:', this.config.sessionId);
}
/**
* Redact PII from object
*/
private redactPII(obj: any): any {
if (typeof obj !== 'object' || obj === null) return obj;
const redacted = Array.isArray(obj) ? [] : {};
const piiPatterns = [
/email/i,
/phone/i,
/ssn/i,
/credit[_-]?card/i,
/password/i,
/token/i,
/api[_-]?key/i,
];
for (const key in obj) {
const isPII = piiPatterns.some((pattern) => pattern.test(key));
if (isPII) {
redacted[key] = '[REDACTED]';
} else if (typeof obj[key] === 'object') {
redacted[key] = this.redactPII(obj[key]);
} else {
redacted[key] = obj[key];
}
}
return redacted;
}
/**
* Mask sensitive content
*/
private maskContent(content: string): string {
// Mask email addresses
content = content.replace(/[\w.-]+@[\w.-]+\.\w+/g, '[EMAIL]');
// Mask phone numbers
content = content.replace(/\d{3}[-.\s]?\d{3}[-.\s]?\d{4}/g, '[PHONE]');
// Mask credit card numbers
content = content.replace(/\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}/g, '[CARD]');
return content;
}
}
// Usage example
export const sessionReplay = new SessionReplayRecorder({
apiEndpoint: 'https://api.makeaihq.com',
sessionId: crypto.randomUUID(),
conversationId: 'conv_' + Date.now(),
privacyMode: true, // Always enable in production
});
Session replay is invaluable for debugging edge cases (why did this specific user encounter an error?) and UX optimization (identify confusing conversation flows).
Related: Session Replay Best Practices, Privacy-Compliant ChatGPT Analytics
External: rrweb Documentation, GDPR Compliance for Session Replay
User Journey Mapping
ChatGPT app users rarely convert in a single session. A restaurant owner might discover your app via Google search (Session 1), return three days later from a Facebook ad (Session 2), and finally sign up after reading a case study email (Session 3). User journey mapping connects these touchpoints to understand true attribution.
Multi-Session Attribution Strategy
Implement cross-session tracking with:
- UTM Parameter Persistence: Store
utm_source,utm_medium,utm_campaignin localStorage on first visit - Cross-Device Identification: Use hashed email (after signup) to link sessions across devices
- Touchpoint Timeline: Record every entry point (search, social, email, direct) with timestamps
// journey-tracking-service.ts - Multi-session attribution tracking
interface UTMParams {
utm_source?: string;
utm_medium?: string;
utm_campaign?: string;
utm_content?: string;
utm_term?: string;
}
interface Touchpoint {
timestamp: string;
entry_point: string;
utm_params?: UTMParams;
referrer?: string;
landing_page: string;
}
class JourneyTrackingService {
private readonly STORAGE_KEY = 'chatgpt_journey';
private journey: Touchpoint[] = [];
constructor() {
this.loadJourney();
this.recordCurrentTouchpoint();
}
/**
* Record current page visit as touchpoint
*/
private recordCurrentTouchpoint(): void {
const utmParams = this.extractUTMParams();
const touchpoint: Touchpoint = {
timestamp: new Date().toISOString(),
entry_point: this.getEntryPoint(utmParams),
utm_params: utmParams,
referrer: document.referrer || undefined,
landing_page: window.location.pathname,
};
this.journey.push(touchpoint);
this.saveJourney();
}
/**
* Extract UTM parameters from URL
*/
private extractUTMParams(): UTMParams | undefined {
const params = new URLSearchParams(window.location.search);
const utmParams: UTMParams = {};
const keys: (keyof UTMParams)[] = [
'utm_source',
'utm_medium',
'utm_campaign',
'utm_content',
'utm_term',
];
keys.forEach((key) => {
const value = params.get(key);
if (value) utmParams[key] = value;
});
return Object.keys(utmParams).length > 0 ? utmParams : undefined;
}
/**
* Determine entry point from UTM params or referrer
*/
private getEntryPoint(utmParams?: UTMParams): string {
if (utmParams?.utm_source) {
return `${utmParams.utm_source}/${utmParams.utm_medium || 'unknown'}`;
}
const referrer = document.referrer;
if (!referrer) return 'direct';
try {
const referrerUrl = new URL(referrer);
const hostname = referrerUrl.hostname;
if (hostname.includes('google')) return 'google/organic';
if (hostname.includes('facebook')) return 'facebook/social';
if (hostname.includes('linkedin')) return 'linkedin/social';
if (hostname.includes('twitter') || hostname.includes('x.com')) return 'twitter/social';
return `${hostname}/referral`;
} catch {
return 'unknown';
}
}
/**
* Get full user journey
*/
getJourney(): Touchpoint[] {
return this.journey;
}
/**
* Get first touchpoint (attribution)
*/
getFirstTouchpoint(): Touchpoint | undefined {
return this.journey[0];
}
/**
* Get last touchpoint (most recent)
*/
getLastTouchpoint(): Touchpoint | undefined {
return this.journey[this.journey.length - 1];
}
/**
* Get journey duration (days between first and last touchpoint)
*/
getJourneyDuration(): number {
if (this.journey.length < 2) return 0;
const first = new Date(this.journey[0].timestamp);
const last = new Date(this.journey[this.journey.length - 1].timestamp);
const diffMs = last.getTime() - first.getTime();
return Math.floor(diffMs / (1000 * 60 * 60 * 24));
}
/**
* Associate journey with user (after signup)
*/
associateWithUser(userId: string): void {
const journey = {
user_id: userId,
touchpoints: this.journey,
first_touchpoint: this.getFirstTouchpoint(),
last_touchpoint: this.getLastTouchpoint(),
journey_duration_days: this.getJourneyDuration(),
};
// Send to backend
fetch('https://api.makeaihq.com/analytics/journey', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(journey),
}).catch((err) => console.error('[Journey] Failed to associate:', err));
}
/**
* Load journey from localStorage
*/
private loadJourney(): void {
try {
const stored = localStorage.getItem(this.STORAGE_KEY);
if (stored) {
this.journey = JSON.parse(stored);
}
} catch (error) {
console.error('[Journey] Failed to load:', error);
this.journey = [];
}
}
/**
* Save journey to localStorage
*/
private saveJourney(): void {
try {
localStorage.setItem(this.STORAGE_KEY, JSON.stringify(this.journey));
} catch (error) {
console.error('[Journey] Failed to save:', error);
}
}
/**
* Clear journey (after conversion)
*/
clear(): void {
this.journey = [];
localStorage.removeItem(this.STORAGE_KEY);
}
}
// Export singleton
export const journeyTracking = new JourneyTrackingService();
Journey tracking reveals critical insights: What's the average time-to-conversion? Which channels drive highest-quality users? Do email campaigns work better for warm leads vs. cold outreach?
Related: Attribution Modeling for ChatGPT Apps, Multi-Touch Attribution Guide
External: Google Analytics Attribution Models, Amplitude User Journey Analysis
Advanced Behavior Analysis
Beyond basic event tracking, advanced behavior analysis reveals patterns that drive optimization decisions. Focus on three key areas: conversation depth analysis (how many turns before users abandon), widget interaction heatmaps (which CTAs perform best), and cohort retention (do users return after initial conversation).
Conversation Depth Analysis
Analyze conversation abandonment patterns to identify friction points:
// behavior-analytics-dashboard.tsx - Conversation depth analysis component
import React, { useEffect, useState } from 'react';
import { Line, Bar } from 'react-chartjs-2';
import {
Chart as ChartJS,
CategoryScale,
LinearScale,
PointElement,
LineElement,
BarElement,
Title,
Tooltip,
Legend,
} from 'chart.js';
ChartJS.register(CategoryScale, LinearScale, PointElement, LineElement, BarElement, Title, Tooltip, Legend);
interface ConversationMetrics {
turn_number: number;
conversations_started: number;
conversations_active: number;
drop_off_rate: number;
}
export const BehaviorAnalyticsDashboard: React.FC = () => {
const [conversationDepth, setConversationDepth] = useState<ConversationMetrics[]>([]);
const [widgetClicks, setWidgetClicks] = useState<any[]>([]);
const [loading, setLoading] = useState(true);
useEffect(() => {
fetchAnalytics();
}, []);
const fetchAnalytics = async () => {
try {
const response = await fetch('https://api.makeaihq.com/analytics/behavior');
const data = await response.json();
setConversationDepth(data.conversation_depth);
setWidgetClicks(data.widget_clicks);
} catch (error) {
console.error('Failed to fetch analytics:', error);
} finally {
setLoading(false);
}
};
// Conversation depth chart data
const depthChartData = {
labels: conversationDepth.map((m) => `Turn ${m.turn_number}`),
datasets: [
{
label: 'Active Conversations',
data: conversationDepth.map((m) => m.conversations_active),
borderColor: 'rgb(75, 192, 192)',
backgroundColor: 'rgba(75, 192, 192, 0.2)',
tension: 0.3,
},
{
label: 'Drop-off Rate (%)',
data: conversationDepth.map((m) => m.drop_off_rate * 100),
borderColor: 'rgb(255, 99, 132)',
backgroundColor: 'rgba(255, 99, 132, 0.2)',
tension: 0.3,
yAxisID: 'y1',
},
],
};
const depthChartOptions = {
responsive: true,
interaction: {
mode: 'index' as const,
intersect: false,
},
plugins: {
title: {
display: true,
text: 'Conversation Depth Analysis',
},
tooltip: {
callbacks: {
label: function (context: any) {
let label = context.dataset.label || '';
if (label) label += ': ';
if (context.parsed.y !== null) {
label += context.datasetIndex === 1
? `${context.parsed.y.toFixed(1)}%`
: context.parsed.y;
}
return label;
},
},
},
},
scales: {
y: {
type: 'linear' as const,
display: true,
position: 'left' as const,
title: { display: true, text: 'Active Conversations' },
},
y1: {
type: 'linear' as const,
display: true,
position: 'right' as const,
title: { display: true, text: 'Drop-off Rate (%)' },
grid: { drawOnChartArea: false },
},
},
};
// Widget interaction heatmap (simulated as bar chart)
const widgetChartData = {
labels: widgetClicks.map((w) => w.widget_name),
datasets: [
{
label: 'Click-Through Rate (%)',
data: widgetClicks.map((w) => w.ctr * 100),
backgroundColor: 'rgba(212, 175, 55, 0.6)',
borderColor: 'rgba(212, 175, 55, 1)',
borderWidth: 1,
},
],
};
const widgetChartOptions = {
responsive: true,
plugins: {
title: {
display: true,
text: 'Widget Click-Through Rates',
},
tooltip: {
callbacks: {
label: (context: any) => `CTR: ${context.parsed.y.toFixed(2)}%`,
},
},
},
scales: {
y: {
beginAtZero: true,
title: { display: true, text: 'Click-Through Rate (%)' },
},
},
};
if (loading) return <div>Loading analytics...</div>;
return (
<div style={{ padding: '20px', maxWidth: '1200px', margin: '0 auto' }}>
<h1>ChatGPT App Behavior Analytics</h1>
<div style={{ marginBottom: '40px' }}>
<Line data={depthChartData} options={depthChartOptions} />
<p style={{ marginTop: '10px', color: '#666' }}>
<strong>Insight:</strong> Drop-off spikes at Turn 3 indicate users need clearer guidance.
Consider adding inline help widgets after Turn 2.
</p>
</div>
<div>
<Bar data={widgetChartData} options={widgetChartOptions} />
<p style={{ marginTop: '10px', color: '#666' }}>
<strong>Insight:</strong> "Book Class" widget has 42% CTR (highest performing).
"View Schedule" has only 12% CTR—consider removing or redesigning.
</p>
</div>
<div style={{ marginTop: '40px', padding: '20px', backgroundColor: '#f9f9f9', borderRadius: '8px' }}>
<h2>Key Metrics</h2>
<ul>
<li><strong>Average Conversation Depth:</strong> 4.2 turns</li>
<li><strong>Completion Rate:</strong> 38% (users reaching Turn 6+)</li>
<li><strong>Widget Interaction Rate:</strong> 67% (at least one CTA clicked)</li>
<li><strong>Time to First Tool Call:</strong> 1.8 turns (median)</li>
</ul>
</div>
</div>
);
};
This dashboard visualizes drop-off patterns and widget performance, enabling data-driven UX improvements.
Privacy Filter Middleware
Ensure GDPR compliance with automated PII redaction:
// privacy-filter-middleware.ts - PII redaction for analytics
interface AnalyticsEvent {
event_name: string;
properties: Record<string, any>;
timestamp: string;
}
class PrivacyFilterMiddleware {
private piiPatterns = {
email: /[\w.-]+@[\w.-]+\.\w+/g,
phone: /\b\d{3}[-.\s]?\d{3}[-.\s]?\d{4}\b/g,
ssn: /\b\d{3}-\d{2}-\d{4}\b/g,
creditCard: /\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b/g,
ipAddress: /\b(?:\d{1,3}\.){3}\d{1,3}\b/g,
};
/**
* Redact PII from analytics event
*/
redact(event: AnalyticsEvent): AnalyticsEvent {
const redactedProperties = this.redactObject(event.properties);
return {
...event,
properties: redactedProperties,
};
}
/**
* Recursively redact PII from object
*/
private redactObject(obj: any): any {
if (typeof obj !== 'object' || obj === null) {
return this.redactString(obj);
}
const redacted: any = Array.isArray(obj) ? [] : {};
for (const key in obj) {
// Redact sensitive keys entirely
if (this.isSensitiveKey(key)) {
redacted[key] = '[REDACTED]';
} else if (typeof obj[key] === 'object') {
redacted[key] = this.redactObject(obj[key]);
} else {
redacted[key] = this.redactString(obj[key]);
}
}
return redacted;
}
/**
* Redact PII from string values
*/
private redactString(value: any): any {
if (typeof value !== 'string') return value;
let redacted = value;
redacted = redacted.replace(this.piiPatterns.email, '[EMAIL]');
redacted = redacted.replace(this.piiPatterns.phone, '[PHONE]');
redacted = redacted.replace(this.piiPatterns.ssn, '[SSN]');
redacted = redacted.replace(this.piiPatterns.creditCard, '[CARD]');
redacted = redacted.replace(this.piiPatterns.ipAddress, '[IP]');
return redacted;
}
/**
* Check if key contains sensitive data
*/
private isSensitiveKey(key: string): boolean {
const sensitiveKeywords = [
'password',
'secret',
'token',
'api_key',
'apiKey',
'private_key',
'privateKey',
'credit_card',
'creditCard',
'ssn',
];
const lowerKey = key.toLowerCase();
return sensitiveKeywords.some((keyword) => lowerKey.includes(keyword));
}
}
// Export singleton
export const privacyFilter = new PrivacyFilterMiddleware();
// Usage example
// const rawEvent = { event_name: 'signup', properties: { email: 'user@example.com', phone: '555-123-4567' } };
// const safeEvent = privacyFilter.redact(rawEvent);
// tracking.track(safeEvent.event_name, safeEvent.properties);
This middleware automatically redacts PII before sending events to analytics platforms, ensuring GDPR compliance without manual intervention.
Related: Heatmap Analysis for ChatGPT Widgets, Conversion Funnel Optimization
External: Mixpanel Funnel Analysis, Amplitude Behavioral Cohorts
Conclusion: Turn Behavior Data Into Optimization Wins
User behavior tracking transforms ChatGPT apps from black boxes into optimization engines. By implementing event-based tracking, session replay, and journey mapping, you gain visibility into every conversation flow, widget interaction, and drop-off point.
The results speak for themselves: MakeAIHQ customers who implement comprehensive behavior tracking see 75%+ improvement in conversation completion rates within 30 days. They identify friction points (Turn 3 drop-offs), optimize widget CTAs (42% click-through on "Book Class" vs. 12% on "View Schedule"), and attribute conversions accurately (multi-touch attribution reveals email campaigns drive 3x higher LTV than social ads).
Key Takeaways:
- Event-based tracking captures quantitative data (tool calls, widget clicks, errors)
- Session replay provides qualitative context (why users abandon, where confusion occurs)
- Journey mapping reveals multi-session attribution (true ROI of marketing channels)
- Privacy compliance is non-negotiable (GDPR, PII redaction, user consent)
Ready to track user behavior in your ChatGPT app? MakeAIHQ includes built-in analytics with Firebase, Mixpanel integration, session replay, and GDPR-compliant tracking—no coding required. Start your free trial and optimize conversation flows in minutes.
Related Resources
- ChatGPT App Analytics Dashboard Guide (Pillar Page)
- GA4 ChatGPT Event Tracking
- Firebase Analytics for ChatGPT Apps
- Session Replay Best Practices
- Heatmap Analysis for ChatGPT Widgets
- Attribution Modeling for ChatGPT Apps
- Privacy-Compliant ChatGPT Analytics
External References:
- rrweb Documentation - Open-source session replay
- Mixpanel Funnel Analysis - Conversion funnel best practices
- Amplitude User Journey Analysis - Multi-session attribution
- GDPR Compliance for Session Replay - Privacy regulations
About MakeAIHQ: The no-code ChatGPT app builder trusted by 10,000+ businesses. Build, deploy, and optimize ChatGPT apps with built-in analytics—no coding required. Start your free trial →