Connect Microsoft (Bing) Ads to ChatGPT

Cross-Account Search and Shopping Triage

Using the Catchr MCP, pull Microsoft Ads data for [List of Client Account Names/IDs] over [Current Period, e.g., today or the last 7 days] and compare it with [Comparable Baseline Period]. At Account Name, Campaign Name, and Campaign Type level, review Spend, Impressions, Clicks, Ctr, CPC, Conversions, Cost Per Conversion, Revenue, and Return On Ad Spend against [Client-Specific KPI Targets]. For affected Search campaigns, inspect Search Query, Keyword, Delivered Match Type, Impression Share Percent, Impression Lost To Budget Percent, and Impression Lost To Rank Agg Percent; for Shopping campaigns, inspect Merchant Product Id and Product Group. Label every account Healthy, Watch, or Critical, explain the measurable driver, and return a ranked morning action queue with one concrete next step per client.

Client-Ready Microsoft Ads Weekly Brief

Using the Catchr MCP, analyze Microsoft Ads performance for [Client Account Name/ID] over [Reporting Period] versus [Comparison Period]. Break down Spend, Impressions, Clicks, Ctr, CPC, Conversions, Conversion Rate, Cost Per Conversion, Revenue, and Return On Ad Spend by Campaign Name, Campaign Type, Device Type, and Network. Add a Search section using Search Query, Keyword, Quality Score, and Top Impression Share Percent, and a Shopping section using Merchant Product Id, Product Group, Product Category1, and Product Type1 where populated. Produce a client-ready brief with an executive summary, wins, risks, confirmed drivers, data limitations, and three prioritized actions for [Next Period].

Portfolio Budget Reallocation Planner

Using the Catchr MCP, evaluate [List of Client Account Names/IDs] over [Analysis Period] and [Comparison Period] to identify where Microsoft Ads budget should be protected, reduced, or expanded. Rank Account Name, Campaign Name, and Campaign Type combinations by Spend, Conversions, Cost Per Conversion, Revenue, and Return On Ad Spend, applying [Minimum Spend or Conversion Threshold] and each client's [Target CPA or ROAS]. Trace Search waste to Search Query, Keyword, Bid Match Type, and Quality Score, and Shopping opportunities to Merchant Product Id, Product Group, Benchmark Bid, and Benchmark Ctr. Return a client-by-client reallocation plan with the amount or percentage to move, supporting evidence, expected KPI impact, and a clear distinction between observed facts and hypotheses.

Morning Client Priority Queue

Using the Catchr MCP, pull month-to-date Microsoft Ads data for [List of Client Account Names/IDs] and compare each client with [Monthly Budget and KPI Targets], [Previous-Month-to-Date Period], and the share of the month elapsed. Use Month To Date Spend, Monthly Budget, Conversions, Cost Per Conversion, Revenue, and Return On Ad Spend, then trace the largest gap to Campaign Name and Campaign Type. For Search, inspect Impression Lost To Budget Percent versus Impression Lost To Rank Agg Percent; for Shopping, inspect Merchant Product Id and Product Group. Rank the clients the freelancer should work on today and give one concise issue, one evidence-backed action, and one follow-up KPI for every off-track account.

Monthly Microsoft Ads Client Story

Using the Catchr MCP, create a client-ready Microsoft Ads report for [Client Account Name/ID] covering [Current Month] versus [Previous Month]. Summarize Spend, Impressions, Clicks, Ctr, CPC, Conversions, Conversion Rate, Cost Per Conversion, Revenue, and Return On Ad Spend, then explain the largest changes by Campaign Name, Campaign Type, Device Type, and Network. Include one Search insight using Search Query, Keyword, or Quality Score and one Shopping insight using Merchant Product Id, Product Group, or Product Category1 when those campaign types are present. Write in plain English with an executive summary, what worked, what underperformed, the evidence behind each conclusion, unresolved questions, and three priorities for [Next Month].

Two-Hour Optimization Sprint

Using the Catchr MCP, inspect Microsoft Ads data for [Client Account Name/ID] over [Analysis Period] and build a prioritized work plan that fits [Available Time, e.g., two hours]. For Search, evaluate Search Query, Keyword, Delivered Match Type, Quality Score, Landing Page Experience, Impression Lost To Budget Percent, and Impression Lost To Rank Agg Percent; for Shopping, evaluate Merchant Product Id, Product Group, Benchmark Bid, and Benchmark Ctr. Score opportunities using Spend, Clicks, Conversions, Cost Per Conversion, Revenue, and Return On Ad Spend with [Minimum Data Threshold], then return tasks ordered by expected value with the exact action, supporting evidence, estimated effort, client-safe rationale, and metric to monitor.

Shopping Product Profitability Matrix

Using the Catchr MCP, pull Microsoft Shopping campaign data for [E-commerce Account Name/ID] over [Period] and [Comparison Period], grouped by Merchant Product Id, Product Group, Product Category1, Product Type1, Country Of Sale, and Campaign Name. Evaluate Spend, Impressions, Clicks, Ctr, CPC, Conversions, Cost Per Conversion, Revenue, and Return On Ad Spend against [Target ROAS or CPA] and [Minimum Data Threshold]. Classify each product or group as Scale, Maintain, Fix, or Exclude, quantify the period-over-period change, identify products consuming spend without sufficient return, and provide a prioritized merchandising, bid, or campaign action for the top [Number] opportunities.

Search Query Growth and Waste Miner

Using the Catchr MCP, analyze Microsoft Ads Search performance for [E-commerce Account Name/ID] over [Period], grouped by Campaign Name, Ad Group Name, Search Query, Keyword, Bid Match Type, and Delivered Match Type. Use Spend, Impressions, Clicks, Ctr, CPC, Conversions, Cost Per Conversion, Revenue, Return On Ad Spend, Quality Score, and Landing Page Experience, with [Minimum Click or Spend Threshold] and [Target CPA or ROAS]. Return three evidence-based lists: queries to add as keywords, queries to review as Negative Keywords, and high-intent themes for new ad groups or Shopping feed alignment. Show the supporting metrics and proposed match type for every action, and label low-volume findings as tests rather than conclusions.

Search and Shopping Revenue Guardrail

Using the Catchr MCP, pull daily Microsoft Ads results for [E-commerce Account Name/ID] over [Current Period] and [Comparison Period], split by Campaign Type and Campaign Name. Track Spend, Conversions, Conversion Rate, Cost Per Conversion, Revenue, and Return On Ad Spend against [Daily or Monthly Revenue Target]. For Search, add Impression Share Percent, Impression Lost To Budget Percent, and Impression Lost To Rank Agg Percent; for Shopping, drill into Merchant Product Id and Product Group. Identify whether any revenue decline comes from lost demand, auction visibility, traffic efficiency, conversion efficiency, or a specific product group, then recommend one immediate action and the metric to recheck after [Validation Window].

Conversion and Revenue Integrity Audit

Using the Catchr MCP, audit Microsoft Ads conversion reporting for [Account Name/ID or All Connected Accounts] over [Period] at Normalized Date, Account Id, Campaign Id, Campaign Name, Goal, and Goal Type level. Compare Conversions with All Conversions, Conversion Rate with All Conversion Rate, Cost Per Conversion with All Cost Per Conversion, Revenue with All Revenue, and Return On Ad Spend with All Return On Ad Spend; review View Through Conversions separately. Flag missing dates or identifiers, Spend with prolonged zero conversions, conversions with zero revenue where value is expected, impossible negative values, and abrupt breaks in goal-level coverage. Return an exception table with the failed rule, observed values, severity, whether the evidence is conclusive or suspicious, and the next validation step outside Catchr when required.

Ninety-Day Performance Outlier Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of daily Microsoft Ads data for [Account Name/ID or All Connected Accounts], grouped by Normalized Date, Account Name, Campaign Name, Campaign Type, Device Type, and Network. For Spend, Impressions, Clicks, Conversions, and Revenue, compare each day with its trailing [Baseline Window, e.g., 30-day] mean and standard deviation; derive Ctr, CPC, Conversion Rate, Cost Per Conversion, and Return On Ad Spend from summed base values when aggregating. Flag deviations beyond [Z-Score Threshold, e.g., 2] only after [Minimum Volume Threshold] is met, trace each outlier to Search Query, Keyword, Merchant Product Id, or Product Group where possible, and classify it as likely data quality, mix shift, auction pressure, or genuine performance change with a reproducible next check.

Search and Shopping Rollup Reconciliation

Using the Catchr MCP, validate Microsoft Ads reporting consistency for [Account Name/ID or All Connected Accounts] over [Period]. Build independent rollups of Spend, Impressions, Clicks, Conversions, and Revenue by Account Id, Campaign Id, Campaign Name, and Campaign Type, then reconcile them separately against Device Type and Network breakdowns; do not sum overlapping segmented extracts together. For Search campaigns, test coverage and key completeness for Search Query, Keyword Id, and Delivered Match Type; for Shopping campaigns, test Merchant Product Id, Product Group, Product Category1, and Product Type1 coverage. Report absolute and percentage discrepancies beyond [Tolerance], duplicate or missing identifiers, null-heavy fields, and a remediation plan that preserves raw extracts and documents every assumption.

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

Connect Microsoft Ads to ChatGPT

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How to connect Microsoft Ads to ChatGPT ?

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

Microsoft Ads to ChatGPT FAQs

What Microsoft Ads data can ChatGPT analyze through Catchr?

How granular can Microsoft Ads analysis be in ChatGPT?

Can ChatGPT find efficiency and budget opportunities in Microsoft Ads?

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

What types of prompts work best for Microsoft Ads analysis?

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

Can I analyze multiple accounts or campaigns together?

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

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

Can ChatGPT change anything in Microsoft Ads 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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