Connect Outbrain to ChatGPT

Multi-Account Native Campaign Health Scan

Using the Catchr MCP, pull Outbrain data for [List of Client Account Names/IDs] over [Current Period] and compare it with [Baseline Period]. For each account, review Spend, Impressions, Clicks, CTR, ECPC, Conversions, CPA, ROAS, Campaign On Air, Campaign Enabled, and Campaign On Air Reason against [Client KPI Targets]. Label each account Healthy, Watch, or Critical, rank the exceptions by budget exposure and performance gap, identify the Campaign Name driving each issue, and give the consultant one evidence-based next action per affected client.

Client-Ready Publisher and Content Review

Using the Catchr MCP, analyze Outbrain performance for [Client Account Name/ID] over [Reporting Period] versus [Comparison Period], broken down by Campaign Name, Publisher Name, Promoted Content Title, Promoted Content URL, and Creative Format. Compare Spend, Impressions, Clicks, CTR, ECPC, Total Conversions, Total CPA, Total Conversion Value, and Total ROAS, applying [Minimum Spend or Impression Threshold] before ranking results. Produce a plain-English client brief that highlights the publisher-content combinations creating efficient conversions, the combinations wasting spend, and three prioritized actions to scale, refresh, or pause activity.

Portfolio Budget Pacing and Delivery Alerts

Using the Catchr MCP, review Outbrain campaigns across [List of Client Account Names/IDs] as of [Review Date] and over [Performance Period]. Use Campaign Name, Campaign On Air, Campaign On Air Reason, Campaign Enabled, Budget Amount, Budget Currency, Budget Start Date, Budget End Date, Budget Pacing, Budget Type, Budget Shared, Spend, and Conversions. Flag campaigns that are off air unexpectedly, disabled despite planned activity, at risk of overpacing or underdelivery against [Planned Spend Curve], or approaching their end date with insufficient delivery. Return a client-by-client alert table with severity, supporting values, and the exact operational check or optimization to perform next.

Morning Client Priority Queue

Using the Catchr MCP, pull Outbrain performance for [List of Client Account Names/IDs] for [Today, Yesterday, or Month to Date] and compare it with [Relevant Baseline]. For each client, check Spend, Impressions, Clicks, CTR, ECPC, Total Conversions, Total CPA, Total ROAS, Campaign On Air, and Campaign On Air Reason against [Client Targets]. Rank clients by urgency, explain the largest confirmed issue in one sentence, identify the Campaign Name responsible where available, and give the freelancer the first task to complete today plus the KPI to recheck after [Review Window].

Monthly Native Advertising Client Story

Using the Catchr MCP, pull Outbrain data for [Client Account Name/ID] for [Reporting Month] and [Previous Month]. Summarize Spend, Impressions, Clicks, CTR, ECPC, Total Conversions, Total CPA, Total Conversion Value, and Total ROAS, then explain the most important changes by Campaign Name, Publisher Name, and Promoted Content Title. Write a concise, client-ready report in plain English covering what improved, what declined, which data points support each conclusion, what remains uncertain because of low volume, and the three priorities for [Next Month].

Video Drop-Off and CTA Test Plan

Using the Catchr MCP, analyze Outbrain video performance for [Client Account Name/ID] over [Period], grouped by Campaign Name, Promoted Content Title, Publisher Name, Platform, and Creative Format. Use Video Plays, Video Reached First Quarter, Video Reached Second Quarter, Video Reached Third Quarter, Video Reached Completion, Video Average View Duration, Clicks on Video, Video Active Completions, Video Active Completions Percentage, Spend, and Total Conversions. Calculate stage-to-stage retention where denominators are available, identify the creatives and publishers with the sharpest drop-off, and deliver a prioritized client action plan for hooks, message sequencing, CTA timing, format, and distribution.

Native Commerce Profitability Pulse

Using the Catchr MCP, pull Outbrain results for [E-commerce Account Name/ID] over [Current Period] and [Comparison Period]. Review Spend, Clicks, ECPC, Total Conversions, Total CPA, Total Conversion Value, and Total ROAS against [Target CPA] and [Target ROAS], then break the change down by Campaign Name and Conversion Name. Quantify whether revenue efficiency is improving, stable, or declining, identify the campaigns contributing most to the movement, and recommend where to increase, hold, or reduce budget while noting any segment below [Minimum Conversion Threshold].

Shopper-Acquisition Publisher Map

Using the Catchr MCP, analyze Outbrain traffic for [E-commerce Account Name/ID] over [Period], segmented by Publisher Name, Platform, Platform OS, Country Name, and Region Name. Compare Spend, Impressions, Clicks, CTR, ECPC, Total Conversions, Total CPA, Total Conversion Value, and Total ROAS across segments, using weighted totals and [Minimum Volume Threshold] before judging efficiency. Build a ranked map of publisher-device-market combinations that acquire valuable shoppers efficiently versus those consuming spend without sufficient conversion value, and recommend specific exclusions, bid or budget reallocations, or landing-page checks with the data supporting each action.

Native Headline and Landing-Page Winner Finder

Using the Catchr MCP, compare Outbrain promoted content for [E-commerce Account Name/ID] over [Test Period] using Promoted Content ID, Promoted Content Title, Promoted Content URL, Promoted Content Section Name, Creative Format, Promoted Content Enabled, Impressions, Clicks, CTR, Spend, ECPC, Total Conversions, Total CPA, Total Conversion Value, and Total ROAS. Apply [Minimum Impressions and Conversions] before declaring winners, separate scalable winners, promising tests, and underperformers, and identify repeatable title, section, format, and destination-page patterns. Recommend which content to scale, refresh, disable, or retest and propose three concrete native advertising experiments.

Extraction and Metric Integrity Audit

Using the Catchr MCP, audit Outbrain data for [Account Name/ID or All Connected Accounts] over [Period] at Date, Campaign ID, Promoted Content ID, and Publisher ID level. Use Extracted Date, Spend, Impressions, Clicks, CTR, ECPC, Conversions, Total Conversions, Total Conversion Value, and Total ROAS to detect stale extracts, missing dates or identifiers, duplicate dimensional records, daily coverage gaps, negative values, Clicks greater than Impressions, and rate or efficiency metrics that do not reconcile with their component fields within [Tolerance]. Return an exception table with account, date, entity IDs, failed rule, observed values, severity, and recommended validation step.

Click-Through vs View-Through Attribution Reconciliation

Using the Catchr MCP, reconcile Outbrain conversion reporting for [Account Name/ID or All Connected Accounts] over [Period], grouped by Date, Campaign Name, and Conversion Name. Compare Conversions, Total Click Value, ROAS, and CPA with View Conversions, Total View Value, View Conversion Rate, Total Conversions, Total Conversion Value, Total CPA, and Total ROAS. Test whether total conversion counts and values reconcile with click-through plus view-through components within [Tolerance], flag missing or impossible relationships, quantify the Spend and conversion value affected, and distinguish likely attribution-configuration issues from extraction or transformation defects.

Daily Native Performance Outlier Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of daily Outbrain data for [Account Name/ID or List of Accounts], segmented by Campaign Name, Publisher Name, Promoted Content Title, Platform, and Country Name. Monitor Spend, Impressions, Clicks, CTR, ECPC, Total Conversions, Total CPA, Total Conversion Value, and Total ROAS; compare each day with its trailing [Baseline Window, e.g., 28-day] mean and standard deviation, and flag deviations beyond [Outlier Threshold, e.g., 2 standard deviations] after applying [Minimum Volume Threshold]. Classify each outlier as likely data quality, delivery interruption, publisher or content mix shift, tracking change, or genuine performance movement, and provide the supporting cross-metric evidence and next investigation.

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

Connect Outbrain to ChatGPT

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How to connect Outbrain to ChatGPT ?

Authorize the Outbrain 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 Outbrain prompts for analyzing real account data.

Outbrain to ChatGPT FAQs

What Outbrain data can ChatGPT analyze through Catchr?

How granular can Outbrain analysis be in ChatGPT?

Can ChatGPT find efficiency and budget opportunities in Outbrain?

Can ChatGPT diagnose targeting, creative, or conversion issues in Outbrain?

What types of prompts work best for Outbrain analysis?

Can I combine Outbrain with other data sources in ChatGPT?

Can I analyze multiple accounts or campaigns together?

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

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

Can ChatGPT change anything in Outbrain 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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