Connect Facebook Ads (Meta Ads) to ChatGPT

Daily Portfolio Health Check

Using the Catchr MCP, pull today's Facebook Ads data for [List of Client Account Names/IDs] and compare it with [Baseline Period, e.g., the previous 7-day daily average]. For each account, review Spend, Impressions, Clicks, CTR (all), CPC (all), CPM, Purchases, and Cost per website purchase; use [Client KPI Targets] where provided. Label every account Healthy, Watch, or Critical, identify the metric and campaign responsible for each Watch or Critical status, and return a ranked morning action list with one evidence-based diagnosis and one concrete next step per affected client.

Objective-Aligned Client Dashboard

Using the Catchr MCP, pull Facebook Ads performance for [Client Account Name/ID] over [Reporting Period] and [Comparison Period]. Use Campaign Objective and Campaign Name to group campaigns by business goal, then select the relevant available metrics: Website purchase ROAS, Purchases, Purchases Values, and Cost per website purchase for sales; Leads and Cost per website lead for lead generation; or Clicks, CTR (all), CPC (all), Impressions, Reach, and CPM for traffic or awareness. Build a client-ready dashboard with KPI totals, trends, top and bottom campaigns, and a concise narrative covering wins, risks, likely drivers supported by the data, and the three actions to take next.

Cross-Account Anomaly Investigator

Using the Catchr MCP, analyze Facebook Ads data for [List of Client Account Names/IDs] over [Analysis Period, e.g., the last 14 days] against [Baseline Period, e.g., the preceding 28 days]. Detect sudden changes in Spend, CPM, CTR (all), CPC (all), Frequency, Purchases, Cost per website purchase, and Website purchase ROAS; also compare Spend with Campaign Daily Budget or Campaign Budget Lifetime where available to identify pacing risks. Trace every anomaly to Account Name, Campaign Name, Adset Name, and Ad Name where the data supports it, quantify the deviation, distinguish confirmed evidence from hypotheses, and recommend a specific action such as budget reallocation, creative refresh, targeting review, or tracking validation.

Daily Client Objective Check-Up

Using the Catchr MCP, pull month-to-date Facebook Ads performance for [List of Client Account Names/IDs] and compare each account with [Monthly Spend and KPI Targets]. Use the target relevant to each client, such as Purchases, Website purchase ROAS, Cost per website purchase, Leads, Cost per website lead, Clicks, or CTR (all), and calculate progress versus the share of the month elapsed. Rank clients by urgency, flag overpacing and underdelivery, identify the campaign causing the largest gap, and tell me which client to prioritize today with one concrete action and the metric that should improve.

Monthly Client Performance Story

Using the Catchr MCP, pull Facebook Ads data for [Client Account Name/ID] for [Reporting Month] and [Previous Month]. Summarize Spend, Impressions, Clicks, CTR (all), CPC (all), CPM, Purchases, Purchases Values, Cost per website purchase, and Website purchase ROAS, then break the most important changes down by Campaign Name. Write a concise client-ready report in plain English that explains what improved, what declined, which data points support each conclusion, what remains uncertain, and the three priorities for [Next Month].

Creative Fatigue Action Queue

Using the Catchr MCP, analyze active ads for [Client Account Name/ID] over [Analysis Period, e.g., the last 21 days], grouped by Ad Name and Ad Created At. Track Frequency, CTR (all), CPM, Spend, Purchases, Cost per website purchase, Quality Ranking, Engagement Rate Ranking, and Conversion Rate Ranking where populated, comparing the first and most recent [Trend Window]. Flag ads with rising Frequency and weakening response or conversion efficiency, require [Minimum Spend or Impressions] before classifying fatigue, and return a ranked refresh queue with the evidence, likely issue, recommended creative change, and follow-up KPI for each ad.

ROAS and Revenue Pulse

Using the Catchr MCP, pull Website purchase ROAS, Purchases Values, Purchases, Spend, and Cost per website purchase for [Account Name/ID] over [Current Period] and [Comparison Period]. Confirm whether ROAS and purchase value are improving, stable, or declining against [ROAS Target], quantify every change, and break the result down by Campaign Name and Adset Name to show which segments are driving it. Finish with a prioritized recommendation for scaling, holding, or reducing spend, including the supporting metric and any data-volume caveat.

Daily Campaign Issue Detector

Using the Catchr MCP, pull today's Facebook Ads results for every campaign with Campaign Effective Status equal to active on [Account Name/ID], and compare them with [Recent Baseline, e.g., the previous 7 comparable days]. Use Campaign Objective to evaluate each campaign with the appropriate available metrics, including Spend, Impressions, Clicks, CTR (all), CPC (all), CPM, Purchases, Cost per website purchase, Website purchase ROAS, Leads, or Cost per website lead. Flag campaigns that are off target or materially outside their baseline, explain the exact evidence, and provide one immediate action plus the metric to recheck after [Review Window].

Conversion Funnel Leak Dashboard

Using the Catchr MCP, pull Views Content, Adds To Cart, Initiates Checkout, Purchases, Purchases Values, Spend, and Website purchase ROAS for [Account Name/ID] over [Period], with [Comparison Period] as a benchmark. Calculate View Content-to-Add to Cart, Add to Cart-to-Initiate Checkout, Initiate Checkout-to-Purchase, and overall View Content-to-Purchase conversion rates. Build a visual funnel dashboard showing volumes, step conversion rates, period-over-period changes, and the campaign responsible for the largest leak; then recommend the highest-priority funnel action while clearly noting that Facebook-attributed event counts may not represent unique users.

Tracking and Attribution Integrity Audit

Using the Catchr MCP, audit Facebook Ads data for [Account Name/ID or All Connected Accounts] over [Period]. At Account Name, Campaign Name, and Date Start level, review Spend, Purchases, Purchases Values, Website purchase ROAS, Conversions within 1 day of click, Conversions within 1 day of view, Conversions within 7 days of click, Conversions within 28 days of click, and their corresponding available conversion-value fields. Flag missing dates or identifiers, conversion gaps despite sustained spend, impossible negative values, abrupt breaks in event volume, and attribution-window relationships that fail expected monotonic checks. Return an exception table with observed values, failed rule, severity, whether the signal is conclusive or only suspicious, and the next validation step.

Daily Performance Outlier Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of Facebook Ads data for [Account Name/ID or All Connected Accounts], grouped by Date Start, Account Name, and Campaign Name. For Spend, CPM, CTR (all), CPC (all), Frequency, Purchases, Cost per website purchase, and Website purchase ROAS, compare each daily value with its trailing [Baseline Window, e.g., 30-day] mean and standard deviation and flag deviations beyond [Z-score Threshold, e.g., 2]. Require [Minimum Volume Threshold] before evaluating rate metrics, classify each outlier as likely data quality, spend or mix shift, creative saturation, or genuine performance change, and output the evidence plus the recommended investigation.

Market-Level Portfolio Dashboard

Using the Catchr MCP, pull Facebook Ads data for [Market/Vertical List of Account Names/IDs] over [Reporting Period] and [Comparison Period]. Aggregate Spend, Impressions, Clicks, Purchases, Purchases Values, Website purchase ROAS, and Cost per website purchase across the market, using weighted calculations for rate and cost metrics rather than averaging account-level rates. Build a market dashboard with trends, contribution by account, and concentration risk; if performance declined, quantify which accounts and campaigns explain the change, separate volume effects from efficiency effects, and provide a prioritized validation and remediation plan.

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

Connect Facebook Ads to ChatGPT

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How to connect Facebook Ads to ChatGPT ?

Authorize the Facebook Ads 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 Facebook Ads prompts for analyzing real account data.

Facebook Ads to ChatGPT FAQs

What Facebook Ads data can ChatGPT analyze through Catchr?

How granular can Facebook Ads analysis be in ChatGPT?

Can ChatGPT find efficiency and budget opportunities in Facebook Ads?

Can ChatGPT diagnose targeting, creative, or conversion issues in Facebook Ads?

What types of prompts work best for Facebook Ads analysis?

Can I combine Facebook Ads with other data sources in ChatGPT?

Can I analyze multiple accounts or campaigns together?

What do I need to connect Facebook Ads to ChatGPT with Catchr?

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

Is Catchr update Facebook Ads data ?

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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