Connect Google Analytics to ChatGPT

Multi-Client Analytics Morning Pulse

Using the Catchr MCP, pull Google Analytics data for [List of Client Property/Account IDs] for [Current Period, e.g., yesterday or month to date] and [Comparison Period]. For each client, review Active users, Sessions, Engagement rate, Conversions, Ecommerce purchases, Purchase revenue, and Total revenue where relevant to that client's [Primary Business Objective and KPI Targets]. Label each property Healthy, Watch, or Critical, quantify the largest change, identify the responsible Session default channel group, Session source / medium, Campaign, or Page path and screen class when the data supports it, and return a ranked consultant action list with one evidence-based next step per affected client.

Client-Ready Acquisition and Content Review

Using the Catchr MCP, analyze [Client Property/Account ID] over [Reporting Period] versus [Comparison Period]. Break down Active users, New users, Sessions, Engaged sessions, Engagement rate, Conversions, Session conversion rate, Ecommerce purchases, and Purchase revenue by Session default channel group, Session source / medium, Session campaign, and Landing page + query string. Create a client-ready report that highlights the strongest and weakest acquisition paths, explains which channels and landing experiences drove the change, separates measured evidence from hypotheses, and recommends three prioritized actions with a KPI to monitor for each.

Cross-Property Web and App Anomaly Investigator

Using the Catchr MCP, analyze daily Google Analytics data for [List of Client Property/Account IDs] over [Analysis Period, e.g., the last 30 days] against [Baseline Period]. Detect material spikes, drops, or discontinuities in Active users, Sessions, Engagement rate, Event count, Conversions, Ecommerce purchases, and Total revenue. Trace each anomaly by Stream name, Platform / device category, Country, Session source / medium, and Page path and screen class where available; quantify its timing and business impact, classify it as likely traffic mix, user-experience, campaign, or tracking related, and provide the exact validation query and client-facing action to take next.

Daily Client Analytics Priority Queue

Using the Catchr MCP, pull month-to-date Google Analytics performance for [List of Client Property/Account IDs] and compare each property with [Client-Specific Targets] and [Previous-Month-to-Date Period]. Use the KPI appropriate to each client, such as Active users, Sessions, Engagement rate, Conversions, Session conversion rate, Ecommerce purchases, or Purchase revenue. Calculate progress against the share of the month elapsed, rank clients by urgency and business impact, locate the largest measurable driver by Session source / medium, Session campaign, or Page path and screen class, and give the freelancer one concrete action and follow-up metric for every off-track client.

Monthly Website and App Performance Story

Using the Catchr MCP, create a client-ready Google Analytics report for [Client Property/Account ID] covering [Reporting Month] versus [Previous Month]. Summarize Active users, New users, Sessions, Engagement rate, Average session duration, Conversions, Session conversion rate, Ecommerce purchases, Purchase revenue, and Total revenue, then explain the most important changes by Session default channel group, Session source / medium, Stream name, and Page title and screen name. Structure the output as an executive summary, what worked, what declined, the evidence behind each conclusion, any limitation in the available data, and three plain-English priorities for [Next Month].

Landing Page and Screen Action Backlog

Using the Catchr MCP, analyze [Client Property/Account ID] over [Period] at Landing page + query string and Page path and screen class level. Compare Sessions, Active users, Engagement rate, Bounce rate, Views per session, Scrolled users, Conversions, and Session conversion rate across [Device Categories or Streams] and against [Comparison Period]. Require [Minimum Session Volume], rank pages or app screens with high traffic but weak engagement or conversion, distinguish acquisition-mix problems from on-page or in-app experience signals, and produce a prioritized optimization backlog with evidence, recommended change, and the KPI to recheck after [Test Window].

E-commerce Funnel Leak Finder

Using the Catchr MCP, pull Google Analytics data for [Store Property/Account ID] over [Period] and [Comparison Period]. Analyze Items viewed, Items added to cart, Items checked out, Items purchased, Ecommerce purchases, Purchase revenue, Cart-to-view rate, and Purchase-to-view rate by Item category, Item name, and Platform / device category. Calculate the step conversion rates where needed, identify the funnel stage and product segments responsible for the largest lost opportunity, apply [Minimum Item View or Purchase Volume], and recommend the highest-priority merchandising, checkout, or tracking action with its expected validation metric.

Profitable Acquisition Channel Optimizer

Using the Catchr MCP, analyze [Store Property/Account ID] for [Current Period] versus [Comparison Period] by Session default channel group, Session source / medium, Session campaign, and Landing page + query string. Compare Ads cost, Ads clicks, Cost per conversion, Sessions, Conversions, Ecommerce purchases, Purchase revenue, and Total revenue where populated. Against [Target Cost per Conversion or Revenue Efficiency Target] and [Minimum Volume Threshold], rank channels and campaigns to scale, hold, or investigate, quantify the revenue and conversion contribution of each, and propose a prioritized acquisition plan while flagging any source whose cost data is unavailable or incomplete.

Product and Promotion Opportunity Map

Using the Catchr MCP, pull Google Analytics product data for [Store Property/Account ID] over [Period]. Compare Item-list view events, Item-list click events, Item list click through rate, Item view events, Items added to cart, Items purchased, Item revenue, and Item promotion click through rate by Item list name, Item name, Item category, and Item promotion name where populated. Apply [Minimum Exposure Threshold], identify high-interest products that fail to convert, hidden products with strong purchase efficiency, and promotions that attract clicks without revenue, then return a ranked set of placement, promotion, and product-page tests with one success metric per test.

Event and Commerce Tracking Integrity Audit

Using the Catchr MCP, audit Google Analytics data for [Property/Account ID or All Connected Properties] over [Period] by Normalized Date, Stream name, Event name, and Platform / device category. Review Event count, Conversions, Ecommerce purchases, Transactions, Transaction ID, Purchase revenue, Add to carts, and Checkouts. Flag missing dates, abrupt event-volume breaks, conversion events with no supporting event activity, purchase revenue without transactions, transactions without identifiers, duplicate Transaction ID values, and implausible funnel relationships. Return a reproducible exception table with the failed rule, observed values, severity, whether the finding is conclusive or suspicious, and the exact instrumentation or data-layer check required next.

Daily Analytics Outlier Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of daily Google Analytics data for [Property/Account ID or All Connected Properties], grouped by Normalized Date, Stream name, Session default channel group, and Platform / device category. For Active users, Sessions, Engagement rate, Event count, Conversions, Ecommerce purchases, Purchase revenue, and Total revenue, compare each day with its trailing [Baseline Window, e.g., 28 days] and flag changes beyond [Threshold, e.g., 2 standard deviations or 30%]. Apply [Minimum Volume Threshold], localize every outlier, classify it as likely data quality, traffic-mix, release, campaign, or genuine user-behavior change, and state the evidence plus the next validation query.

Acquisition Attribution and UTM Quality Audit

Using the Catchr MCP, audit acquisition reporting for [Property/Account ID or All Connected Properties] over [Period] using Session source, Session medium, Session source / medium, Session campaign, Session default channel group, Source / medium, Campaign, Ads clicks, Ads cost, Sessions, Conversions, and Purchase revenue. Quantify traffic, conversion, and revenue assigned to blank, '(not set)', direct, inconsistent, or unexpectedly fragmented source, medium, and campaign values; compare session-scoped and conversion-scoped acquisition patterns for material mismatches; and flag sustained ad cost with missing or implausibly low downstream sessions or conversions. Return a prioritized issue table with affected values, magnitude, likely tagging or attribution cause, confidence level, and the exact UTM, linking, or tracking validation step.

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

Connect Google Analytics to ChatGPT

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How to connect Google Analytics to ChatGPT ?

Authorize the Google Analytics 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 Google Analytics prompts for analyzing real account data.

Google Analytics to ChatGPT FAQs

What Google Analytics data can ChatGPT analyze through Catchr?

How granular can Google Analytics analysis be in ChatGPT?

Can ChatGPT explain traffic, engagement, or conversion changes in Google Analytics?

Can ChatGPT identify funnel and measurement issues in Google Analytics?

What types of prompts work best for Google Analytics analysis?

Can I combine Google Analytics with other data sources in ChatGPT?

Can I analyze multiple accounts, properties, sites, or apps together?

What do I need to connect Google Analytics to ChatGPT with Catchr?

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

Can ChatGPT change anything in Google Analytics 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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