Connect Adjust to ChatGPT

Multi-Account Mobile Growth Health Check

Using the Catchr MCP, pull Adjust data for [List of Client Account/App IDs] over [Period, e.g., the last 7 days] and compare it with [Comparison Period, e.g., the previous 7 days]. For each account or app, report Ad Spend, Installs, ECPI, Revenue, ROAS (All Revenue), and Rejected Install Rate. Apply [Client-Specific KPI Targets or Thresholds], label each account as healthy, watch, or critical, rank the accounts that need attention, and explain the main driver of every warning in one sentence. Finish with the three actions the consultant should take first today.

Client-Ready Weekly Acquisition Briefing

Using the Catchr MCP, analyze Adjust performance for [Client Account/App ID] over [Reporting Period] versus [Previous Period]. Break down Ad Spend, Clicks, Installs, Click Conversion Rate, ECPI, Revenue, and ROAS (All Revenue) by Network (Attribution), Campaign (Attribution), and Country. Create a concise client-ready briefing with an executive summary, the three strongest contributors, the three weakest contributors, material period-over-period changes, and specific recommendations tied to the data. Use plain English and state when the available data does not support a causal conclusion.

Cross-Client Cost and Attribution Reconciliation

Using the Catchr MCP, review Adjust data for [List of Client Account/App IDs] over [Period]. Compare Ad Spend (Network) with Ad Spend (Attribution), and Network Installs with Installs, using Ad Spend Diff and Network Installs Diff where available. Rank accounts by the largest absolute and percentage gaps, then break material discrepancies down by [Network, Campaign, or Country]. For each flagged account, classify the finding as likely reporting latency, configuration inconsistency, or requiring investigation; cite the supporting values and provide a concrete validation step for the account owner.

Daily Client Priority Queue

Using the Catchr MCP, pull month-to-date Adjust data for [List of Client Account/App IDs] and compare each client with [Monthly Ad Spend, Install, ECPI, Revenue, or ROAS Target] and [Previous-Month-to-Date Period]. Report Ad Spend, Installs, ECPI, Revenue, and ROAS (All Revenue), estimate pacing against the supplied targets, and rank the clients the freelancer should work on today. For every off-track client, identify the largest measurable driver by Network (Attribution) or Campaign (Attribution) and give one specific next action.

Monthly Mobile Performance Story

Using the Catchr MCP, create a client-ready Adjust report for [Client Account/App ID] covering [Current Month] versus [Previous Month]. Summarize Ad Spend, Impressions, Clicks, CTR, Installs, ECPI, Revenue, ROAS (All Revenue), Sessions, and Uninstalls, then explain the most important changes by Network (Attribution), Campaign (Attribution), and Country. Structure the output as an executive summary, what worked, what underperformed, likely explanations supported by the data, and next-month recommendations. Keep the language concise and define any technical term the client may not know.

Creative Performance Decline Finder

Using the Catchr MCP, analyze Adjust data for [Client Account/App ID] over [Period, e.g., the last 28 days] at daily granularity by Creative (Attribution) and Campaign (Attribution). Track Attribution Impressions, Clicks (Attribution), CTR, Ad Spend (Attribution), Installs, ECPI, Revenue, and ROAS (All Revenue). Flag creatives with [Minimum Spend or Impression Volume] whose CTR, install efficiency, or ROAS has materially deteriorated versus [Baseline, e.g., the preceding 14 days]. Rank the findings by business impact and recommend whether to refresh, reduce, or keep each creative, while avoiding conclusions when volume is insufficient.

Profitable App Acquisition Optimizer

Using the Catchr MCP, pull Adjust data for [E-commerce App/Account ID] over [Period] and compare it with [Previous Period]. Evaluate Ad Spend, Installs, ECPI, Revenue Events, Revenue, Gross Profit, and ROAS (All Revenue) by Network (Attribution) and Campaign (Attribution). Against [Target ROAS], [Maximum ECPI], and [Minimum Spend or Install Volume], identify which sources are ready to scale, which should be monitored, and which should be reduced. Recommend a prioritized budget reallocation within [Total Budget or Allowed Change Percentage] and show the data behind every recommendation.

Market and Device Expansion Map

Using the Catchr MCP, analyze Adjust performance for [E-commerce App/Account ID] over [Period] by Country, Device Type, and OS Name. Compare Paid Installs, ECPI, Revenue, Revenue Events, Sessions, and ROAS (All Revenue), applying [Minimum Install Volume] so low-volume segments are not overinterpreted. Rank the best expansion opportunities and the weakest segments, quantify each segment's contribution to total installs and revenue, and propose three testable actions for [Target Markets or Devices] with a clear success metric for each test.

Post-Install Engagement and Revenue Watch

Using the Catchr MCP, track Adjust performance for [E-commerce App/Account ID] over [Period] versus [Comparison Period]. Analyze Installs, Sessions, Average DAUs, Average WAUs, Average MAUs, Uninstalls, Renewals, Cancels, Revenue Events, and Revenue by [App, Country, or Network (Attribution)]. Flag segments where acquisition is growing but engagement or monetization is weakening, quantify the change, and separate scale effects from genuine rate deterioration where possible. End with a prioritized list of product, lifecycle, or acquisition checks for the e-commerce team.

Daily Adjust Data Anomaly Monitor

Using the Catchr MCP, retrieve daily Adjust data for [Account/App ID or All Connected Apps] over [Period, e.g., the last 90 days]. For Ad Spend, Impressions, Clicks, Installs, Revenue, Revenue Events, Sessions, Uninstalls, and ROAS (All Revenue), compare each day with a [Trailing Window, e.g., 28-day] baseline and flag deviations beyond [Threshold, e.g., 2 standard deviations or 30%]. Break each anomaly down by App, Network (Attribution), Campaign (Attribution), and Country to locate its source. Classify it as a likely data-quality issue or a plausible business change, show the evidence, and specify the next validation query.

Network-to-Attribution Consistency Audit

Using the Catchr MCP, audit Adjust reporting consistency for [Account/App ID or All Connected Apps] over [Period]. Compare Network Impressions with Attribution Impressions, Clicks (Network) with Clicks (Attribution), Ad Spend (Network) with Ad Spend (Attribution), and Network Installs with Installs by Date, App, and Network (Attribution). Calculate absolute and percentage gaps, apply [Materiality Threshold], and identify persistent, sudden, or isolated discrepancies. Return a reproducible issue table with affected dimensions, first observed date, magnitude, likely data-layer explanation, confidence level, and the exact follow-up check required.

Privacy and Rejection Signal Audit

Using the Catchr MCP, analyze Adjust privacy and traffic-quality signals for [Account/App ID or All Connected Apps] over [Period]. Track ATT Consent Rate, ATT Status Authorized, ATT Status Denied, Limit Ad Tracking Install Rate, Rejected Installs, Rejected Install Rate, and available rejection-reason metrics such as Rejected Installs Click Injection and Rejected Installs Invalid Signature. Break results down by Date, App, OS Name, Country, and Network (Attribution), flag statistically or operationally material shifts against [Baseline Period or Thresholds], and distinguish privacy-mix changes from suspicious traffic patterns. Produce a concise audit table and prioritized instrumentation or source-quality checks.

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

Connect Adjust to ChatGPT

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How to connect Adjust to ChatGPT ?

Authorize the Adjust 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 Adjust prompts for analyzing real account data.

Adjust to ChatGPT FAQs

What Adjust data can ChatGPT analyze through Catchr?

How granular can Adjust analysis be in ChatGPT?

Can ChatGPT connect acquisition activity to downstream outcomes in Adjust?

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

What types of prompts work best for Adjust analysis?

Can I combine Adjust with other data sources in ChatGPT?

Can I analyze multiple apps or attribution accounts together?

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

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

Can ChatGPT change anything in Adjust 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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