Connect AppsFlyer to ChatGPT

Mobile Growth Portfolio Health Check

Using the Catchr MCP, pull AppsFlyer data for [List of Client App/Account IDs] over [Period, e.g., the last 7 days] and compare it with [Comparison Period, e.g., the previous 7 days]. For each client, report Total Cost, Installs, Average eCPI, Revenue, ROI, Loyal Users Rate, and Uninstalls Rate, then evaluate the results against [Client-Specific KPI Targets]. Label every account as healthy, watch, or critical, rank the accounts needing attention, identify the largest measurable driver by Media Source or Campaign, and finish with the three actions the consultant should prioritize today.

Client-Ready Mobile Attribution Review

Using the Catchr MCP, analyze AppsFlyer performance for [Client App/Account ID] over [Reporting Period] versus [Previous Period]. Break down Impressions, Clicks, CTR, Installs, Average eCPI, Revenue, ROI, Sessions, and Loyal Users by Media Source, Campaign, Country Code, and Platform. Produce a client-ready review with an executive summary, the strongest and weakest acquisition contributors, material period-over-period changes, and three recommendations supported by the data. Use plain English, quantify every claim, and clearly separate observed relationships from unproven causes.

Cross-Client Attribution Risk Scanner

Using the Catchr MCP, audit AppsFlyer attribution signals across [List of Client App/Account IDs] for [Period]. Analyze Is Primary Attribution, Conversion Type, Is Retargeting, Retargeting Conversion Type, Media Source, Contributor 1 Media Source, Contributor 2 Media Source, Contributor 3 Media Source, Rejected Reason, and Rejected Reason Value. Flag accounts with sudden changes, unusually high rejected-event concentrations, or inconsistent attribution patterns versus [Baseline Period or Threshold]. Rank risks by business impact, cite the affected campaigns and values, classify each case as configuration, traffic-quality, or attribution-window investigation, and give the account owner one concrete validation step.

Daily Mobile Client Priority Queue

Using the Catchr MCP, pull month-to-date AppsFlyer data for [List of Client App/Account IDs] and compare each client with [Monthly Cost, Install, eCPI, Revenue, or ROI Target] and [Previous-Month-to-Date Period]. Report Total Cost, Installs, Average eCPI, Revenue, ROI, Loyal Users Rate, and Uninstalls Rate, estimate pacing against the supplied targets, and rank the clients the freelancer should address today. For every off-track client, identify the largest measurable driver by Media Source or Campaign and provide one specific next action plus the metric that will confirm recovery.

Monthly App Performance Story

Using the Catchr MCP, create a client-ready AppsFlyer report for [Client App/Account ID] covering [Current Month] versus [Previous Month]. Summarize Impressions, Clicks, CTR, Total Cost, Installs, Conversion Rate, Average eCPI, Revenue, ROI, Sessions, Loyal Users, and Uniinstalls, then explain the most important changes by Media Source, Campaign, Country Code, and Platform. Structure the output as an executive summary, what worked, what underperformed, evidence-based explanations, and next-month priorities. Keep it concise, define mobile-attribution terminology, and do not infer causes that the data cannot prove.

Campaign and Creative Efficiency Finder

Using the Catchr MCP, analyze AppsFlyer data for [Client App/Account ID] over [Period, e.g., the last 28 days] at daily granularity by Campaign, Adset, and Ad. Track Impressions, Clicks, CTR, Cost, Installs, Average eCPI, AF Purchase (Event Counter), AF Purchase (Sales), Revenue, and ROI. Apply [Minimum Spend, Impression, or Install Volume], identify assets whose click-through, install efficiency, or purchase value has materially improved or deteriorated versus [Baseline Period], and rank them by business impact. Recommend whether to scale, maintain, refresh, or pause each asset, with the values supporting the decision.

Profitable Mobile Commerce Acquisition Plan

Using the Catchr MCP, pull AppsFlyer data for [E-commerce App/Account ID] over [Period] and compare it with [Previous Period]. Evaluate Total Cost, Installs, Average eCPI, AF Purchase (Event Counter), AF Purchase (Unique Users), AF Purchase (Sales), Revenue, ARPU, and ROI by Media Source and Campaign. Apply [Target ROI], [Maximum eCPI], and [Minimum Install or Purchase Volume], classify sources as scale, maintain, test, or reduce, and propose a prioritized budget reallocation within [Allowed Budget Change]. Show the figures and expected success metric behind every recommendation.

In-App Shopping Funnel Diagnostic

Using the Catchr MCP, analyze the mobile commerce funnel for [E-commerce App/Account ID] over [Period]. Use Installs, AF Complete Registration (Unique Users), AF Login (Unique Users), AF Add to Wishlist (Unique Users), and AF Purchase (Unique Users), with the corresponding Event Counter fields where useful. Calculate the conversion rate between each stage, compare it with [Previous Period or Target Rates], and break the weakest stage down by Media Source, Campaign, Platform, Country Code, and App Version. Identify the highest-impact leakage point and recommend three specific product, lifecycle, or acquisition tests with a measurable success threshold.

Retention and Re-Engagement Revenue Watch

Using the Catchr MCP, review post-install value for [E-commerce App/Account ID] over [Period] versus [Comparison Period]. Track Sessions, Loyal Users, Loyal Users Rate, Activity Average DAU, Activity Average MAU, Activity Average DAU/MAU Rate, Uniinstalls, Uninstalls Rate, Activity Revenue, and Revenue. Compare acquisition and retargeting segments using Is Retargeting, Retargeting Conversion Type, Media Source, Campaign, and Country Code. Flag segments where installs or spend are growing while engagement, retention, or revenue quality is weakening, quantify the change, and provide a prioritized retention or re-engagement action plan.

Mobile Measurement Anomaly Monitor

Using the Catchr MCP, retrieve daily AppsFlyer data for [App/Account ID or All Connected Apps] over [Period, e.g., the last 90 days]. Monitor Impressions, Clicks, Installs, Sessions, Revenue, AF Complete Registration (Event Counter), AF Purchase (Event Counter), Loyal Users, and Uniinstalls against a [Trailing Window, e.g., 28 days]. Flag deviations beyond [Threshold, e.g., 2 standard deviations or 30%], then break each anomaly down by App Name, Media Source, Campaign, Platform, App Version, SDK Version, and Event Source. Classify each finding as likely instrumentation, extraction, attribution, or genuine business movement, show the evidence, and specify the next validation query.

Attribution Path Consistency Audit

Using the Catchr MCP, audit AppsFlyer attribution consistency for [App/Account ID or All Connected Apps] over [Period]. Analyze Is Primary Attribution, Media Source, Campaign, Contributor 1 Media Source, Contributor 2 Media Source, Contributor 3 Media Source, Conversion Type, Attribution Lookback, Is Retargeting, Retargeting Conversion Type, and Reengagement Window. Identify duplicate-looking paths, missing primary attribution, abrupt source-mix shifts, and conversion patterns that conflict with the configured windows, applying [Materiality Threshold]. Return a reproducible issue table with first observed date, affected app and source, magnitude, likely explanation, confidence level, and exact remediation or follow-up check.

Consent and SDK Coverage Audit

Using the Catchr MCP, assess measurement coverage for [App/Account ID or All Connected Apps] over [Period] using Ad User Data Enabled, Ad Personalization Enabled, GDPR Applies, Is Lat, Platform, OS Version, App Version, SDK Version, Media Source, Installs, Sessions, and AF Purchase (Event Counter). Compare signal availability and key event rates across platform, OS, app-version, and SDK-version cohorts, and flag sudden breaks, sparse consent fields, or versions where installs continue but sessions or purchase events fall outside [Expected Range]. Quantify affected volume, avoid exposing device-level identifiers, and deliver a prioritized instrumentation checklist with owners [Analytics, Engineering, or Marketing] and a validation metric for each fix.

Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Connect AppsFlyer to ChatGPT

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How to connect AppsFlyer to ChatGPT ?

Authorize the AppsFlyer account in Catchr

Add the Catchr MCP from official ChatGPT store

Choose, adapt, and run a prompt.

A simple client question should not trigger an hour of data prep.

Pull another CSV

Rebuild the context

Summarize it by hand

Ask the account. Get to the explanation.

Prepare data before every question.

Connect once. Keep asking.

Twelve AppsFlyer prompts for analyzing real account data.

AppsFlyer to ChatGPT FAQs

What AppsFlyer data can ChatGPT analyze through Catchr?

How granular can AppsFlyer analysis be in ChatGPT?

Can ChatGPT connect acquisition activity to downstream outcomes in AppsFlyer?

Can ChatGPT detect attribution or data-quality issues in AppsFlyer?

What types of prompts work best for AppsFlyer analysis?

Can I combine AppsFlyer with other data sources in ChatGPT?

Can I analyze multiple apps or attribution accounts together?

What do I need to connect AppsFlyer to ChatGPT with Catchr?

How recent is the AppsFlyer data, and how much history can I analyze?

Can ChatGPT change anything in AppsFlyer through Catchr?

How much does it cost to connect marketing data to ChatGPT with Catchr?

‍Can I try the ChatGPT integration before choosing a plan?

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