App Store Analytics for ChatGPT Apps: Complete Implementation Guide
Understanding how users discover, download, and engage with your ChatGPT app in the App Store is critical for sustainable growth. App Store analytics provides the data-driven insights needed to optimize every aspect of your app's performance—from discovery to retention to revenue.
In this comprehensive guide, you'll learn how to implement production-ready analytics infrastructure for ChatGPT apps. We'll cover everything from App Store Connect API authentication to building real-time performance dashboards, implementing attribution models, and deploying predictive analytics.
Why App Store Analytics Matter for ChatGPT Apps
The ChatGPT App Store launched with over 800 million weekly users, creating unprecedented competition for visibility. Without robust analytics, you're flying blind:
- Discovery optimization: Identify which keywords drive impressions and downloads
- Conversion tracking: Measure the effectiveness of your app listing assets
- User behavior analysis: Understand how users engage with your app features
- Revenue attribution: Track which marketing channels generate paying customers
- Predictive insights: Forecast growth trends and identify churn risks before they escalate
According to Apple's App Store metrics best practices, apps that actively monitor and optimize based on analytics data see 3-5x higher growth rates than those that don't.
The MakeAIHQ ChatGPT App Analytics Guide provides the strategic framework for leveraging these insights to dominate your category.
Analytics Infrastructure Setup
Before collecting metrics, you need robust infrastructure that handles authentication, data extraction, warehousing, and real-time processing.
App Store Connect API Authentication
The App Store Connect API uses JWT-based authentication with private keys. Here's a production-ready authentication client:
// app-store-auth.ts - App Store Connect API Authentication
import * as jwt from 'jsonwebtoken';
import * as fs from 'fs';
import axios from 'axios';
interface AppStoreCredentials {
issuerId: string;
keyId: string;
privateKeyPath: string;
vendorNumber: string;
}
export class AppStoreConnectAuth {
private credentials: AppStoreCredentials;
private token: string | null = null;
private tokenExpiry: Date | null = null;
constructor(credentials: AppStoreCredentials) {
this.credentials = credentials;
}
/**
* Generate JWT token for App Store Connect API
* Tokens expire after 20 minutes per Apple's requirements
*/
private generateToken(): string {
const privateKey = fs.readFileSync(this.credentials.privateKeyPath, 'utf8');
const now = Math.floor(Date.now() / 1000);
const expiration = now + (20 * 60); // 20 minutes
const token = jwt.sign(
{
iss: this.credentials.issuerId,
exp: expiration,
aud: 'appstoreconnect-v1'
},
privateKey,
{
algorithm: 'ES256',
header: {
alg: 'ES256',
kid: this.credentials.keyId,
typ: 'JWT'
}
}
);
this.tokenExpiry = new Date(expiration * 1000);
return token;
}
/**
* Get valid authentication token, regenerating if expired
*/
public getToken(): string {
const now = new Date();
if (!this.token || !this.tokenExpiry || now >= this.tokenExpiry) {
this.token = this.generateToken();
}
return this.token;
}
/**
* Make authenticated request to App Store Connect API
*/
public async request(endpoint: string, params: any = {}): Promise<any> {
const token = this.getToken();
try {
const response = await axios.get(
`https://api.appstoreconnect.apple.com/v1${endpoint}`,
{
headers: {
'Authorization': `Bearer ${token}`,
'Content-Type': 'application/json'
},
params
}
);
return response.data;
} catch (error: any) {
if (error.response?.status === 401) {
// Token expired, regenerate and retry
this.token = null;
return this.request(endpoint, params);
}
throw error;
}
}
/**
* Fetch app metadata by bundle ID
*/
public async getApp(bundleId: string): Promise<any> {
const data = await this.request('/apps', {
'filter[bundleId]': bundleId
});
return data.data[0];
}
/**
* Fetch analytics reports for specific metrics
*/
public async getAnalytics(
appId: string,
measures: string[],
frequency: 'DAY' | 'WEEK' | 'MONTH',
startDate: string,
endDate: string
): Promise<any> {
return this.request('/analyticsReportRequests', {
'filter[app]': appId,
'filter[reportType]': 'APP_STORE_ENGAGEMENT',
'filter[reportSubType]': 'SUMMARY',
'filter[frequency]': frequency,
measures: measures.join(','),
'filter[startTime]': startDate,
'filter[endTime]': endDate
});
}
/**
* Get sales and trends data via Reporter API
*/
public async getSalesData(
reportType: 'SALES' | 'SUBSCRIPTION',
reportDate: string
): Promise<any> {
const token = this.getToken();
try {
const response = await axios.post(
'https://reportingitc-reporter.apple.com/reportservice/sales/v1',
{
userid: this.credentials.issuerId,
version: '1.0',
mode: 'Normal',
queryInput: JSON.stringify({
vendorNumber: this.credentials.vendorNumber,
reportType,
reportSubType: 'SUMMARY',
dateType: 'Daily',
date: reportDate
})
},
{
headers: {
'Authorization': `Bearer ${token}`,
'Content-Type': 'application/json'
}
}
);
return response.data;
} catch (error: any) {
console.error('Sales data fetch error:', error.message);
throw error;
}
}
}
// Usage example
const auth = new AppStoreConnectAuth({
issuerId: process.env.APP_STORE_ISSUER_ID!,
keyId: process.env.APP_STORE_KEY_ID!,
privateKeyPath: './AuthKey_XXXXXXXXXX.p8',
vendorNumber: process.env.APP_STORE_VENDOR_NUMBER!
});
export default auth;
For more on integrating App Store analytics with your broader ChatGPT app analytics strategy, see our pillar guide.
Data Extraction Frequency
Determine optimal extraction frequency based on your app's scale:
- Hourly: High-traffic apps (10K+ daily users), real-time campaigns
- Daily: Most ChatGPT apps, standard monitoring
- Weekly: Low-traffic apps, trend analysis
Core Metrics Collection
Implement a metrics aggregator that collects key performance indicators from multiple sources and normalizes them into a consistent schema.
// metrics-aggregator.ts - Multi-Source Metrics Collection
import { AppStoreConnectAuth } from './app-store-auth';
import { BigQuery } from '@google-cloud/bigquery';
interface MetricSnapshot {
timestamp: Date;
appId: string;
impressions: number;
productPageViews: number;
downloads: number;
redownloads: number;
updates: number;
sessions: number;
activeDevices: number;
crashes: number;
revenue: number;
subscriptions: number;
conversionRate: number;
retentionDay1: number;
retentionDay7: number;
retentionDay30: number;
}
export class MetricsAggregator {
private auth: AppStoreConnectAuth;
private bigquery: BigQuery;
constructor(auth: AppStoreConnectAuth, projectId: string) {
this.auth = auth;
this.bigquery = new BigQuery({ projectId });
}
/**
* Fetch all core metrics for a date range
*/
public async collectMetrics(
appId: string,
bundleId: string,
startDate: string,
endDate: string
): Promise<MetricSnapshot[]> {
// Fetch engagement metrics
const engagement = await this.auth.getAnalytics(
appId,
[
'impressions',
'pageViewCount',
'units',
'sessions',
'activeDevices',
'crashes'
],
'DAY',
startDate,
endDate
);
// Fetch revenue metrics
const sales = await this.auth.getSalesData('SALES', endDate);
const subscriptions = await this.auth.getSalesData('SUBSCRIPTION', endDate);
// Fetch retention metrics
const retention = await this.auth.getAnalytics(
appId,
['retention'],
'DAY',
startDate,
endDate
);
// Normalize and combine data
const snapshots: MetricSnapshot[] = this.normalizeMetrics(
engagement,
sales,
subscriptions,
retention,
appId
);
return snapshots;
}
/**
* Normalize metrics from different API responses
*/
private normalizeMetrics(
engagement: any,
sales: any,
subscriptions: any,
retention: any,
appId: string
): MetricSnapshot[] {
const snapshots: MetricSnapshot[] = [];
// Process engagement data
engagement.data?.forEach((record: any) => {
const timestamp = new Date(record.attributes.date);
const snapshot: MetricSnapshot = {
timestamp,
appId,
impressions: record.attributes.impressions || 0,
productPageViews: record.attributes.pageViewCount || 0,
downloads: record.attributes.units || 0,
redownloads: record.attributes.redownloads || 0,
updates: record.attributes.updates || 0,
sessions: record.attributes.sessions || 0,
activeDevices: record.attributes.activeDevices || 0,
crashes: record.attributes.crashes || 0,
revenue: 0, // Updated below
subscriptions: 0, // Updated below
conversionRate: this.calculateConversionRate(
record.attributes.pageViewCount,
record.attributes.units
),
retentionDay1: 0, // Updated below
retentionDay7: 0,
retentionDay30: 0
};
// Merge sales data
const salesRecord = this.findRecordByDate(sales, timestamp);
if (salesRecord) {
snapshot.revenue = parseFloat(salesRecord.customerPrice || 0);
}
// Merge subscription data
const subRecord = this.findRecordByDate(subscriptions, timestamp);
if (subRecord) {
snapshot.subscriptions = parseInt(subRecord.activeSubscriptions || 0);
}
// Merge retention data
const retRecord = this.findRecordByDate(retention.data, timestamp);
if (retRecord) {
snapshot.retentionDay1 = retRecord.attributes.retentionDay1 || 0;
snapshot.retentionDay7 = retRecord.attributes.retentionDay7 || 0;
snapshot.retentionDay30 = retRecord.attributes.retentionDay30 || 0;
}
snapshots.push(snapshot);
});
return snapshots;
}
/**
* Calculate conversion rate from page views to downloads
*/
private calculateConversionRate(views: number, downloads: number): number {
if (views === 0) return 0;
return (downloads / views) * 100;
}
/**
* Find data record matching specific date
*/
private findRecordByDate(data: any[], date: Date): any {
return data.find((record: any) => {
const recordDate = new Date(record.date || record.attributes?.date);
return recordDate.getTime() === date.getTime();
});
}
/**
* Save metrics to BigQuery for long-term storage
*/
public async saveMetrics(
snapshots: MetricSnapshot[],
dataset: string,
table: string
): Promise<void> {
await this.bigquery
.dataset(dataset)
.table(table)
.insert(snapshots);
console.log(`Saved ${snapshots.length} metric snapshots to BigQuery`);
}
}
Learn how to combine App Store metrics with in-app analytics in our App Discovery Optimization guide.
BigQuery Data Warehouse
Store analytics data in BigQuery for scalable querying and machine learning:
// bigquery-loader.ts - BigQuery Schema and Loader
import { BigQuery } from '@google-cloud/bigquery';
export class BigQueryLoader {
private bigquery: BigQuery;
private datasetId: string;
constructor(projectId: string, datasetId: string) {
this.bigquery = new BigQuery({ projectId });
this.datasetId = datasetId;
}
/**
* Create analytics dataset and tables
*/
public async initializeSchema(): Promise<void> {
// Create dataset
await this.bigquery.createDataset(this.datasetId, {
location: 'US'
});
// Create metrics table
const metricsSchema = [
{ name: 'timestamp', type: 'TIMESTAMP', mode: 'REQUIRED' },
{ name: 'appId', type: 'STRING', mode: 'REQUIRED' },
{ name: 'impressions', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'productPageViews', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'downloads', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'redownloads', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'updates', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'sessions', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'activeDevices', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'crashes', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'revenue', type: 'FLOAT', mode: 'NULLABLE' },
{ name: 'subscriptions', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'conversionRate', type: 'FLOAT', mode: 'NULLABLE' },
{ name: 'retentionDay1', type: 'FLOAT', mode: 'NULLABLE' },
{ name: 'retentionDay7', type: 'FLOAT', mode: 'NULLABLE' },
{ name: 'retentionDay30', type: 'FLOAT', mode: 'NULLABLE' }
];
await this.bigquery
.dataset(this.datasetId)
.createTable('app_metrics', {
schema: metricsSchema,
timePartitioning: {
type: 'DAY',
field: 'timestamp'
},
clustering: {
fields: ['appId']
}
});
// Create attribution table
const attributionSchema = [
{ name: 'timestamp', type: 'TIMESTAMP', mode: 'REQUIRED' },
{ name: 'appId', type: 'STRING', mode: 'REQUIRED' },
{ name: 'source', type: 'STRING', mode: 'REQUIRED' },
{ name: 'medium', type: 'STRING', mode: 'NULLABLE' },
{ name: 'campaign', type: 'STRING', mode: 'NULLABLE' },
{ name: 'downloads', type: 'INTEGER', mode: 'NULLABLE' },
{ name: 'revenue', type: 'FLOAT', mode: 'NULLABLE' },
{ name: 'cost', type: 'FLOAT', mode: 'NULLABLE' },
{ name: 'roas', type: 'FLOAT', mode: 'NULLABLE' }
];
await this.bigquery
.dataset(this.datasetId)
.createTable('app_attribution', {
schema: attributionSchema,
timePartitioning: {
type: 'DAY',
field: 'timestamp'
},
clustering: {
fields: ['appId', 'source']
}
});
console.log('BigQuery schema initialized');
}
/**
* Query metrics with aggregation
*/
public async queryMetrics(
appId: string,
startDate: string,
endDate: string,
aggregation: 'DAY' | 'WEEK' | 'MONTH' = 'DAY'
): Promise<any[]> {
const query = `
SELECT
TIMESTAMP_TRUNC(timestamp, ${aggregation}) as period,
SUM(impressions) as impressions,
SUM(productPageViews) as productPageViews,
SUM(downloads) as downloads,
SUM(sessions) as sessions,
SUM(activeDevices) as activeDevices,
SUM(crashes) as crashes,
SUM(revenue) as revenue,
AVG(conversionRate) as avgConversionRate,
AVG(retentionDay1) as avgRetentionDay1,
AVG(retentionDay7) as avgRetentionDay7,
AVG(retentionDay30) as avgRetentionDay30
FROM \`${this.datasetId}.app_metrics\`
WHERE appId = @appId
AND timestamp BETWEEN @startDate AND @endDate
GROUP BY period
ORDER BY period
`;
const [rows] = await this.bigquery.query({
query,
params: { appId, startDate, endDate }
});
return rows;
}
}
Performance Dashboards
Build real-time dashboards that visualize key metrics and enable rapid decision-making.
// dashboard-builder.tsx - Real-Time Performance Dashboard
import React, { useEffect, useState } from 'react';
import {
LineChart,
Line,
BarChart,
Bar,
XAxis,
YAxis,
CartesianGrid,
Tooltip,
Legend,
ResponsiveContainer
} from 'recharts';
import { BigQueryLoader } from './bigquery-loader';
interface DashboardProps {
appId: string;
projectId: string;
datasetId: string;
}
interface MetricData {
period: string;
impressions: number;
downloads: number;
conversionRate: number;
revenue: number;
retentionDay30: number;
}
export const PerformanceDashboard: React.FC<DashboardProps> = ({
appId,
projectId,
datasetId
}) => {
const [data, setData] = useState<MetricData[]>([]);
const [loading, setLoading] = useState(true);
const [timeRange, setTimeRange] = useState<'7d' | '30d' | '90d'>('30d');
useEffect(() => {
loadMetrics();
}, [appId, timeRange]);
const loadMetrics = async () => {
setLoading(true);
const loader = new BigQueryLoader(projectId, datasetId);
const days = timeRange === '7d' ? 7 : timeRange === '30d' ? 30 : 90;
const endDate = new Date();
const startDate = new Date();
startDate.setDate(startDate.getDate() - days);
const metrics = await loader.queryMetrics(
appId,
startDate.toISOString(),
endDate.toISOString(),
'DAY'
);
setData(metrics);
setLoading(false);
};
const totalImpressions = data.reduce((sum, d) => sum + d.impressions, 0);
const totalDownloads = data.reduce((sum, d) => sum + d.downloads, 0);
const totalRevenue = data.reduce((sum, d) => sum + d.revenue, 0);
const avgConversion = data.length > 0
? data.reduce((sum, d) => sum + d.conversionRate, 0) / data.length
: 0;
return (
<div className="dashboard-container">
<div className="dashboard-header">
<h1>App Store Performance</h1>
<div className="time-range-selector">
<button
onClick={() => setTimeRange('7d')}
className={timeRange === '7d' ? 'active' : ''}
>
7 Days
</button>
<button
onClick={() => setTimeRange('30d')}
className={timeRange === '30d' ? 'active' : ''}
>
30 Days
</button>
<button
onClick={() => setTimeRange('90d')}
className={timeRange === '90d' ? 'active' : ''}
>
90 Days
</button>
</div>
</div>
{loading ? (
<div className="loading">Loading metrics...</div>
) : (
<>
{/* KPI Summary Cards */}
<div className="kpi-grid">
<div className="kpi-card">
<h3>Total Impressions</h3>
<div className="kpi-value">{totalImpressions.toLocaleString()}</div>
</div>
<div className="kpi-card">
<h3>Total Downloads</h3>
<div className="kpi-value">{totalDownloads.toLocaleString()}</div>
</div>
<div className="kpi-card">
<h3>Avg Conversion Rate</h3>
<div className="kpi-value">{avgConversion.toFixed(2)}%</div>
</div>
<div className="kpi-card">
<h3>Total Revenue</h3>
<div className="kpi-value">${totalRevenue.toLocaleString()}</div>
</div>
</div>
{/* Downloads Trend Chart */}
<div className="chart-container">
<h2>Downloads Trend</h2>
<ResponsiveContainer width="100%" height={300}>
<LineChart data={data}>
<CartesianGrid strokeDasharray="3 3" />
<XAxis dataKey="period" />
<YAxis />
<Tooltip />
<Legend />
<Line
type="monotone"
dataKey="downloads"
stroke="#8884d8"
strokeWidth={2}
/>
</LineChart>
</ResponsiveContainer>
</div>
{/* Conversion Funnel */}
<div className="chart-container">
<h2>Conversion Funnel</h2>
<ResponsiveContainer width="100%" height={300}>
<BarChart data={data}>
<CartesianGrid strokeDasharray="3 3" />
<XAxis dataKey="period" />
<YAxis />
<Tooltip />
<Legend />
<Bar dataKey="impressions" fill="#8884d8" />
<Bar dataKey="downloads" fill="#82ca9d" />
</BarChart>
</ResponsiveContainer>
</div>
{/* Retention Chart */}
<div className="chart-container">
<h2>30-Day Retention</h2>
<ResponsiveContainer width="100%" height={300}>
<LineChart data={data}>
<CartesianGrid strokeDasharray="3 3" />
<XAxis dataKey="period" />
<YAxis />
<Tooltip />
<Legend />
<Line
type="monotone"
dataKey="retentionDay30"
stroke="#ff7300"
strokeWidth={2}
/>
</LineChart>
</ResponsiveContainer>
</div>
</>
)}
</div>
);
};
For advanced dashboard features, see our ASO Analytics Implementation guide.
Trend Analysis
Detect significant changes in metrics using statistical analysis:
// trend-analyzer.ts - Statistical Trend Detection
export class TrendAnalyzer {
/**
* Calculate moving average for smoothing
*/
public movingAverage(data: number[], window: number): number[] {
const result: number[] = [];
for (let i = 0; i < data.length; i++) {
if (i < window - 1) {
result.push(data[i]);
} else {
const sum = data.slice(i - window + 1, i + 1).reduce((a, b) => a + b, 0);
result.push(sum / window);
}
}
return result;
}
/**
* Detect significant trend changes using percentage change
*/
public detectTrendChanges(
data: number[],
threshold: number = 0.15
): Array<{ index: number; change: number }> {
const changes: Array<{ index: number; change: number }> = [];
for (let i = 1; i < data.length; i++) {
const previous = data[i - 1];
const current = data[i];
if (previous === 0) continue;
const percentChange = (current - previous) / previous;
if (Math.abs(percentChange) >= threshold) {
changes.push({
index: i,
change: percentChange
});
}
}
return changes;
}
/**
* Calculate growth rate between two periods
*/
public calculateGrowthRate(
startValue: number,
endValue: number,
periods: number
): number {
if (startValue === 0) return 0;
return Math.pow(endValue / startValue, 1 / periods) - 1;
}
/**
* Forecast future values using linear regression
*/
public linearForecast(data: number[], periods: number): number[] {
const n = data.length;
const xSum = (n * (n - 1)) / 2;
const ySum = data.reduce((a, b) => a + b, 0);
let xySum = 0;
let xSquareSum = 0;
for (let i = 0; i < n; i++) {
xySum += i * data[i];
xSquareSum += i * i;
}
const slope = (n * xySum - xSum * ySum) / (n * xSquareSum - xSum * xSum);
const intercept = (ySum - slope * xSum) / n;
const forecast: number[] = [];
for (let i = n; i < n + periods; i++) {
forecast.push(slope * i + intercept);
}
return forecast;
}
}
Anomaly Detection
Identify unusual patterns that require immediate attention:
// anomaly-detector.ts - Real-Time Anomaly Detection
export class AnomalyDetector {
/**
* Detect anomalies using standard deviation method
*/
public detectAnomalies(
data: number[],
threshold: number = 2
): Array<{ index: number; value: number; zscore: number }> {
const mean = data.reduce((a, b) => a + b, 0) / data.length;
const variance = data.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / data.length;
const stdDev = Math.sqrt(variance);
const anomalies: Array<{ index: number; value: number; zscore: number }> = [];
data.forEach((value, index) => {
const zscore = (value - mean) / stdDev;
if (Math.abs(zscore) > threshold) {
anomalies.push({ index, value, zscore });
}
});
return anomalies;
}
/**
* Detect seasonality patterns
*/
public detectSeasonality(data: number[], period: number): boolean {
if (data.length < period * 2) return false;
const autocorrelation = this.calculateAutocorrelation(data, period);
return autocorrelation > 0.5;
}
/**
* Calculate autocorrelation for lag
*/
private calculateAutocorrelation(data: number[], lag: number): number {
const mean = data.reduce((a, b) => a + b, 0) / data.length;
let numerator = 0;
let denominator = 0;
for (let i = 0; i < data.length - lag; i++) {
numerator += (data[i] - mean) * (data[i + lag] - mean);
}
for (let i = 0; i < data.length; i++) {
denominator += Math.pow(data[i] - mean, 2);
}
return numerator / denominator;
}
}
Attribution Analytics
Track which marketing channels drive the most valuable users using UTM parameters and attribution models.
// utm-tracker.ts - Campaign Attribution Tracker
import { BigQuery } from '@google-cloud/bigquery';
interface UTMParams {
source: string;
medium?: string;
campaign?: string;
term?: string;
content?: string;
}
export class UTMTracker {
private bigquery: BigQuery;
private datasetId: string;
constructor(projectId: string, datasetId: string) {
this.bigquery = new BigQuery({ projectId });
this.datasetId = datasetId;
}
/**
* Parse UTM parameters from URL
*/
public parseUTM(url: string): UTMParams {
const urlObj = new URL(url);
return {
source: urlObj.searchParams.get('utm_source') || 'direct',
medium: urlObj.searchParams.get('utm_medium') || undefined,
campaign: urlObj.searchParams.get('utm_campaign') || undefined,
term: urlObj.searchParams.get('utm_term') || undefined,
content: urlObj.searchParams.get('utm_content') || undefined
};
}
/**
* Track attribution event
*/
public async trackAttribution(
appId: string,
userId: string,
utm: UTMParams,
eventType: 'impression' | 'download' | 'purchase',
value?: number
): Promise<void> {
const row = {
timestamp: new Date().toISOString(),
appId,
userId,
source: utm.source,
medium: utm.medium,
campaign: utm.campaign,
term: utm.term,
content: utm.content,
eventType,
value: value || 0
};
await this.bigquery
.dataset(this.datasetId)
.table('attribution_events')
.insert([row]);
}
/**
* Get attribution report by source
*/
public async getAttributionReport(
appId: string,
startDate: string,
endDate: string
): Promise<any[]> {
const query = `
SELECT
source,
medium,
campaign,
COUNT(DISTINCT CASE WHEN eventType = 'impression' THEN userId END) as impressions,
COUNT(DISTINCT CASE WHEN eventType = 'download' THEN userId END) as downloads,
COUNT(DISTINCT CASE WHEN eventType = 'purchase' THEN userId END) as purchases,
SUM(CASE WHEN eventType = 'purchase' THEN value ELSE 0 END) as revenue
FROM \`${this.datasetId}.attribution_events\`
WHERE appId = @appId
AND timestamp BETWEEN @startDate AND @endDate
GROUP BY source, medium, campaign
ORDER BY revenue DESC
`;
const [rows] = await this.bigquery.query({
query,
params: { appId, startDate, endDate }
});
return rows;
}
}
Learn more about attribution modeling in our ChatGPT App Marketing guide.
Campaign ROI Calculator
Calculate return on ad spend (ROAS) for each marketing channel:
// campaign-roi-calculator.ts - ROAS Analysis
export class CampaignROICalculator {
/**
* Calculate ROAS for a campaign
*/
public calculateROAS(revenue: number, cost: number): number {
if (cost === 0) return 0;
return revenue / cost;
}
/**
* Calculate customer acquisition cost (CAC)
*/
public calculateCAC(cost: number, customers: number): number {
if (customers === 0) return 0;
return cost / customers;
}
/**
* Calculate lifetime value to CAC ratio
*/
public calculateLTVCACRatio(ltv: number, cac: number): number {
if (cac === 0) return 0;
return ltv / cac;
}
/**
* Generate ROI report for all campaigns
*/
public generateROIReport(campaigns: any[]): any[] {
return campaigns.map(campaign => ({
...campaign,
roas: this.calculateROAS(campaign.revenue, campaign.cost),
cac: this.calculateCAC(campaign.cost, campaign.customers),
profitMargin: ((campaign.revenue - campaign.cost) / campaign.revenue) * 100
}));
}
}
Multi-Touch Attribution Model
Implement advanced attribution that credits multiple touchpoints:
// attribution-model.ts - Multi-Touch Attribution
export class AttributionModel {
/**
* Linear attribution: Equal credit to all touchpoints
*/
public linearAttribution(touchpoints: string[], value: number): Map<string, number> {
const attribution = new Map<string, number>();
const creditPerTouchpoint = value / touchpoints.length;
touchpoints.forEach(touchpoint => {
attribution.set(
touchpoint,
(attribution.get(touchpoint) || 0) + creditPerTouchpoint
);
});
return attribution;
}
/**
* Time decay attribution: Recent touchpoints get more credit
*/
public timeDecayAttribution(
touchpoints: Array<{ channel: string; timestamp: Date }>,
value: number,
halfLife: number = 7
): Map<string, number> {
const attribution = new Map<string, number>();
const conversionTime = Date.now();
let totalWeight = 0;
const weights = touchpoints.map(tp => {
const daysAgo = (conversionTime - tp.timestamp.getTime()) / (1000 * 60 * 60 * 24);
const weight = Math.pow(0.5, daysAgo / halfLife);
totalWeight += weight;
return { channel: tp.channel, weight };
});
weights.forEach(({ channel, weight }) => {
const credit = (weight / totalWeight) * value;
attribution.set(channel, (attribution.get(channel) || 0) + credit);
});
return attribution;
}
}
For complete attribution implementation, see our comprehensive guide on implementing advanced analytics for ChatGPT apps.
Predictive Analytics
Use machine learning to forecast future performance and identify churn risks.
# growth-forecaster.py - ML-Based Growth Prediction
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from prophet import Prophet
class GrowthForecaster:
def __init__(self, historical_data: pd.DataFrame):
self.data = historical_data
self.models = {}
def prepare_data(self):
"""Prepare time series data for modeling"""
self.data['date'] = pd.to_datetime(self.data['date'])
self.data = self.data.sort_values('date')
self.data['day_index'] = range(len(self.data))
return self.data
def train_linear_model(self, target_col: str):
"""Train simple linear regression"""
X = self.data[['day_index']].values
y = self.data[target_col].values
model = LinearRegression()
model.fit(X, y)
self.models[f'{target_col}_linear'] = model
return model
def train_prophet_model(self, target_col: str):
"""Train Facebook Prophet model for seasonality"""
df = self.data[['date', target_col]].rename(
columns={'date': 'ds', target_col: 'y'}
)
model = Prophet(
yearly_seasonality=True,
weekly_seasonality=True,
daily_seasonality=False
)
model.fit(df)
self.models[f'{target_col}_prophet'] = model
return model
def forecast(self, target_col: str, periods: int):
"""Generate forecast for next N periods"""
# Linear forecast
linear_model = self.models.get(f'{target_col}_linear')
if linear_model:
last_index = self.data['day_index'].max()
future_indices = np.array([[last_index + i + 1] for i in range(periods)])
linear_forecast = linear_model.predict(future_indices)
# Prophet forecast
prophet_model = self.models.get(f'{target_col}_prophet')
if prophet_model:
future = prophet_model.make_future_dataframe(periods=periods)
prophet_forecast = prophet_model.predict(future)
return {
'linear': linear_forecast.tolist(),
'prophet': prophet_forecast['yhat'].tail(periods).tolist()
}
Churn Prediction
Identify users at risk of churning before they leave:
# churn-predictor.py - ML-Based Churn Prediction
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
class ChurnPredictor:
def __init__(self):
self.model = GradientBoostingClassifier(
n_estimators=100,
learning_rate=0.1,
max_depth=5
)
def prepare_features(self, user_data: pd.DataFrame) -> pd.DataFrame:
"""Engineer features for churn prediction"""
features = pd.DataFrame()
# Usage frequency features
features['days_since_last_session'] = user_data['days_since_last_session']
features['avg_sessions_per_week'] = user_data['avg_sessions_per_week']
features['session_length_avg'] = user_data['session_length_avg']
# Engagement features
features['feature_usage_count'] = user_data['feature_usage_count']
features['days_since_signup'] = user_data['days_since_signup']
features['total_sessions'] = user_data['total_sessions']
# Monetization features
features['is_paying'] = user_data['is_paying'].astype(int)
features['lifetime_revenue'] = user_data['lifetime_revenue']
return features
def train(self, user_data: pd.DataFrame, churn_labels: pd.Series):
"""Train churn prediction model"""
X = self.prepare_features(user_data)
y = churn_labels
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
self.model.fit(X_train, y_train)
accuracy = self.model.score(X_test, y_test)
print(f'Churn prediction accuracy: {accuracy:.2%}')
return accuracy
def predict_churn_probability(self, user_data: pd.DataFrame) -> np.ndarray:
"""Predict churn probability for users"""
X = self.prepare_features(user_data)
return self.model.predict_proba(X)[:, 1]
For more machine learning applications, explore mobile analytics best practices.
Production Implementation Checklist
Before deploying your analytics infrastructure to production:
- Authentication: App Store Connect API credentials secured in environment variables
- Data Pipeline: Automated daily/hourly metrics collection running
- Data Warehouse: BigQuery tables partitioned and clustered for performance
- Dashboards: Real-time performance dashboards accessible to stakeholders
- Alerting: Anomaly detection alerts configured for critical metrics
- Attribution: UTM tracking implemented across all marketing campaigns
- Predictive Models: Growth forecasting and churn prediction models trained
- Documentation: Analytics schema and query patterns documented
- Access Control: Role-based access to analytics data enforced
- Cost Monitoring: BigQuery query costs monitored and optimized
For complete deployment guidance, see our ChatGPT App Analytics Guide.
Conclusion: Data-Driven ChatGPT App Growth
App Store analytics transforms your ChatGPT app from a passive listing into a data-driven growth engine. With the infrastructure and code examples in this guide, you can:
- Monitor performance in real-time with automated metrics collection
- Optimize discovery using attribution analytics and conversion tracking
- Predict trends with machine learning forecasting models
- Prevent churn by identifying at-risk users before they leave
The ChatGPT App Store is highly competitive—only apps that leverage analytics to continuously optimize will achieve sustainable growth.
Ready to build your analytics infrastructure? Start your free trial with MakeAIHQ and get production-ready analytics templates, BigQuery schemas, and dashboard components for your ChatGPT app.
Related Resources:
- Complete ChatGPT App Analytics Strategy
- App Discovery Optimization Guide
- ASO Analytics Implementation
- ChatGPT App Marketing Automation
Last updated: December 2026