Connect Branch to ChatGPT

Multi-Account Mobile Attribution Health Check

Using the Catchr MCP, pull Branch data for [List of Client App/Account IDs] over [Period, e.g., the last 7 days] and compare it with [Comparison Period]. For each client, report Impression, Click, Install, Open, Commerce Event, Cost, and the shares of events marked Attributed and Deep Linked. Calculate click-through rate, click-to-install rate, and cost per install from the available fields, apply [Client-Specific Targets or Alert Thresholds], and label each account healthy, watch, or critical. Rank the clients that need attention today, identify the campaign, channel, or advertising partner driving each issue, and give the consultant one concrete next action per flagged account.

Client-Ready Branch Campaign Briefing

Using the Catchr MCP, analyze Branch data for [Client App/Account ID] over [Reporting Period] versus [Previous Period]. Break down Impression, Click, Install, Open, Commerce Event, and Cost by Last Attributed Touch - Advertising Partner Name, Last Attributed Touch - Channel, Last Attributed Touch - Campaign, and Last Attributed Touch - Creative Name. Calculate click-through rate, click-to-install rate, cost per install, and commerce events per install, then prepare a concise client-ready briefing covering what improved, what declined, the three strongest and weakest contributors, and prioritized recommendations. Use plain English, cite the supporting values, and do not claim causality when the data only shows correlation.

Cross-Client Deep Link Issue Radar

Using the Catchr MCP, review Branch data for [List of Client App/Account IDs] over [Period] and compare the rate and volume of events marked Deep Linked with [Baseline Period or Expected Deep-Link Rate]. Segment the results by Last Attributed Touch - Campaign, Last Attributed Touch - Channel, Last Attributed Touch - Creative Name, OS, Platform, App Version, and Country. Flag sudden drops, client accounts with unusually low deep-link rates, or segments where Click remains stable while Install, Open, or Commerce Event falls. Rank findings by business impact, distinguish likely routing or instrumentation issues from campaign-performance changes, and provide one specific validation step for each client owner.

Daily Branch Client Priority Queue

Using the Catchr MCP, pull month-to-date Branch data for [List of Client App/Account IDs] and compare each client with [Monthly Install, Commerce Event, Cost, or Efficiency Target] and [Previous-Month-to-Date Period]. Report Impression, Click, Install, Open, Commerce Event, Cost, and the share of events marked Deep Linked, then calculate pacing, click-to-install rate, and cost per install. Rank the clients the freelancer should address today, identify the largest measurable driver by Last Attributed Touch - Campaign, Last Attributed Touch - Channel, or Last Attributed Touch - Advertising Partner Name, and give one specific next action for every off-track client.

Monthly Mobile Growth Client Story

Using the Catchr MCP, create a client-ready Branch report for [Client App/Account ID] covering [Current Period] versus [Previous Period]. Summarize Impression, Click, Install, Open, Commerce Event, Reinstall, Cost, Attributed, and Deep Linked, and calculate click-through rate, click-to-install rate, cost per install, and commerce events per install. Explain the most important changes by Last Attributed Touch - Campaign, Last Attributed Touch - Creative Name, Last Attributed Touch - Advertising Partner Name, Country, and OS. Structure the output as an executive summary, what worked, what underperformed, evidence-based explanations, and next-period actions in plain English.

Campaign and Creative Decline Finder

Using the Catchr MCP, analyze daily Branch data for [Client App/Account ID] over [Period, e.g., the last 28 days] by Last Attributed Touch - Campaign and Last Attributed Touch - Creative Name. Track Impression, Click, Install, Open, Commerce Event, and Cost, and calculate click-through rate, click-to-install rate, cost per install, and commerce events per install. Flag campaigns or creatives above [Minimum Impression, Click, or Cost Volume] whose acquisition or downstream performance has deteriorated versus [Baseline, e.g., the preceding 14 days]. Rank findings by estimated business impact and recommend whether to keep, investigate, refresh, or reduce each item, with the supporting values.

Mobile Commerce Acquisition Funnel

Using the Catchr MCP, pull Branch data for [E-commerce App/Account ID] over [Period] versus [Comparison Period]. Build a campaign-level funnel using Impression, Click, Install, Open, and Commerce Event, broken down by Last Attributed Touch - Advertising Partner Name, Last Attributed Touch - Campaign, and Last Attributed Touch - Creative Name. Calculate the conversion rate between each consecutive stage and cost per install and cost per commerce event from Cost. Apply [Minimum Volume] and [Target Cost or Conversion Rates], identify the largest leaks and the campaigns ready to scale or reduce, and recommend a prioritized budget and optimization plan backed by the data.

Deep-Link Commerce Journey Optimizer

Using the Catchr MCP, analyze Branch events for [E-commerce App/Account ID] over [Period] and compare events marked Deep Linked with events not marked Deep Linked. Report Click, Install, Open, Commerce Event, Web Session Start, and Web-to-App Auto Redirect by Last Attributed Touch - Campaign, Last Attributed Touch - Feature, Last Attributed Touch - Marketing Title, and Product Categories. Calculate commerce events per click and per open for each segment, apply [Minimum Event Volume], and identify journeys where deep linking is associated with stronger or weaker downstream commerce activity. Recommend three testable changes to links, landing journeys, or campaign messaging, each with a measurable success criterion.

Market and App Experience Growth Map

Using the Catchr MCP, evaluate Branch performance for [E-commerce App/Account ID] over [Period] by Country, Region, OS, Platform, App Store, and App Version. Compare Install, Open, Commerce Event, Reinstall, Cost, and the shares of events marked Attributed and Deep Linked; calculate commerce events per install and cost per commerce event. Use [Minimum Install or Commerce Event Volume] to avoid overinterpreting small samples, rank the best expansion opportunities and weakest app experiences, and propose three actions across acquisition, localization, or app experience with an owner and success metric for each.

Branch Event Pipeline Anomaly Monitor

Using the Catchr MCP, retrieve daily Branch data for [App/Account ID or All Connected Apps] over [Period, e.g., the last 90 days]. For Impression, Click, Install, Open, Commerce Event, Custom Event, Web Session Start, and Cost, 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 anomalies down by Branch App ID, Name, Customer Event Alias, Origin, Platform, OS, App Version, and Environment. Classify each finding as a likely tracking or pipeline issue versus a plausible business change, show the evidence, and specify the next validation query.

Attribution Lag and Touchpoint Audit

Using the Catchr MCP, audit Branch attribution for [App/Account ID or All Connected Apps] over [Period]. Analyze the share of events marked Attributed and the distributions of Days from Last Attributed Touch to Event and Days from Last CTA View to Event by Last Attributed Touch Type, Last Attributed Touch - Advertising Partner Name, Last Attributed Touch - Channel, Last Attributed Touch - Campaign, and Name. Compare [Current Period] with [Baseline Period], flag abrupt shifts, long-tail changes, and partners or campaigns with unusually high unattributed volumes, and return a reproducible issue table with magnitude, first observed date, likely explanation, confidence level, and exact follow-up check.

Deep-Link and Event Taxonomy Quality Audit

Using the Catchr MCP, inspect Branch data for [App/Account ID or All Connected Apps] over [Period] using Timestamp, Name, Customer Event Alias, Origin, Deep Linked, Attributed, First Event for User, Environment, App Version, OS, and Platform. Flag missing or inconsistent event names and aliases, unexpected production-versus-test environment mixes, sudden changes in deep-linked or attributed shares, and app-version or platform segments where Click remains present but Install, Open, or Commerce Event disappears. Quantify every issue, identify when and where it began, separate taxonomy problems from likely link-routing or SDK instrumentation problems, and provide a prioritized remediation checklist with a verification query for each item.

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

Connect Branch to ChatGPT

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How to connect Branch to ChatGPT ?

Authorize the Branch 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 Branch prompts for analyzing real account data.

Branch to ChatGPT FAQs

What Branch data can ChatGPT analyze through Catchr?

How granular can Branch analysis be in ChatGPT?

Can ChatGPT connect acquisition activity to downstream outcomes in Branch?

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

What types of prompts work best for Branch analysis?

Can I combine Branch with other data sources in ChatGPT?

Can I analyze multiple apps or attribution accounts together?

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

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

Can ChatGPT change anything in Branch 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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