Connect StackAdapt to ChatGPT

Multi-Client Programmatic Pulse

Using the Catchr MCP, pull StackAdapt data for [List of Client Account Names/IDs] over [Period] and compare it with [Comparison Period]. For each client, summarize Cost, Impressions, Unique Impressions, Clicks, CTR, eCPM, Conversions, eCPA, and ROAS, then break material changes down by Advertiser Name, Campaign Group Name, Campaign Name, Channel Name, and Channel SubType Name. Judge performance against [Client KPI Targets], label each account On Track, Watch, or At Risk, rank the accounts that need attention today, and give one evidence-based next action for every Watch or At Risk account.

Client Channel Performance Debrief

Using the Catchr MCP, analyze StackAdapt performance for [Client Account Name/ID] over [Period] versus [Comparison Period]. Compare native, display, video, audio, and any other available programmatic formats through Channel Name and Channel SubType Name, using Cost, Impressions, Clicks, CTR, eCPM, Engagements, Engagement Rate, Conversions, eCPA, and ROAS; add Video Starts, Video Completions, Video Completion Rate, Audio Starts, Audio Completions, and Audio Completion Rate only where the channel supports them. Produce a client-ready debrief explaining which channels drove the result, which are underperforming, and three prioritized actions for the next period.

Portfolio Frequency and Efficiency Watchlist

Using the Catchr MCP, review active StackAdapt campaigns across [List of Client Account Names/IDs] over [Period]. Use Campaign State and Campaign Status to identify active delivery, then compare Frequency with Frequency Cap Limit and Frequency Cap Expiry alongside Cost, CTR, CVR, eCPA, Conversion Revenue, and ROAS by Advertiser Name and Campaign Name. Flag campaigns with rising frequency and deteriorating response or conversion efficiency against [Thresholds or Historical Baseline], estimate the Cost exposed to each issue, and return a severity-ranked watchlist with the evidence, a client-safe explanation, and a specific action such as reviewing the cap, refreshing messaging, adjusting targeting, or reallocating spend.

Morning Client Priority Queue

Using the Catchr MCP, pull StackAdapt performance for [List of Client Account Names/IDs] for [Period, e.g., month to date] and compare it with [Comparison Period] and [Client KPI Targets]. For each client, report Cost, Impressions, Clicks, CTR, Conversions, eCPA, ROAS, Frequency, and Campaign Status, then identify the Campaign Name and Channel Name driving the largest negative change. Rank clients by urgency based on performance deterioration and Cost exposure, and give me one clear task to complete today for each of the top three accounts.

Monthly Programmatic Client Story

Using the Catchr MCP, create a client-ready StackAdapt report for [Client Account Name/ID] covering [Reporting Period] versus [Previous Period]. Summarize Cost, Impressions, Unique Impressions, Clicks, CTR, eCPM, Frequency, Conversions, CVR, eCPA, Conversion Revenue, and ROAS, and explain the most important movements by Campaign Group Name, Campaign Name, Channel Name, and Channel SubType Name. Write in plain English with an executive summary, what worked, what did not, the evidence behind each conclusion, and three practical priorities for [Next Period]; clearly separate observed facts from likely explanations.

Video and Audio Completion Fix List

Using the Catchr MCP, inspect StackAdapt video and audio campaigns for [Client Account Name/ID] over [Period], filtered through Channel Name and Channel SubType Name. For video, compare Video Starts, Video Q1 Playbacks, Video Q2 Playbacks, Video Q3 Playbacks, Video Completions, Video Completion Rate, View Rate, eCPCV, Cost, and Conversions; for audio, compare Audio Starts, Audio Q1 Playbacks, Audio Q2 Playbacks, Audio Q3 Playbacks, Audio Completions, Audio Completion Rate, Cost, and Conversions. Apply [Minimum Start or Cost Threshold], locate the largest playback drop-offs by Campaign Name, and produce a prioritized fix list with the issue, supporting metrics, recommended test, and success criterion.

Programmatic Commerce Efficiency Guardrail

Using the Catchr MCP, pull StackAdapt results for [Account Name/ID] over [Period] and compare them with [Comparison Period] and [ROAS or eCPA Target]. Report Cost, Conversions, Conversion Revenue, Revenue, ROAS, eCPA, CVR, Click Conversions, Impression Conversions, Click Conversion Rate, and Impression Conversion Rate by Campaign Name, Channel Name, and Channel SubType Name. Identify the campaigns and formats responsible for the largest improvement or decline, distinguish click-led from impression-led attributed outcomes, and recommend where to protect, reduce, or reallocate spend without presenting attributed revenue as incremental revenue.

Native Display and Video Mix Optimizer

Using the Catchr MCP, evaluate the StackAdapt format mix for [Account Name/ID] over [Period]. Group campaigns by Channel Name and Channel SubType Name to compare native, display, video, and other available formats using Cost, Impressions, Unique Impressions, Clicks, CTR, eCPM, Engagements, Engagement Rate, Conversions, eCPA, Conversion Revenue, and ROAS; for video, also include Video Starts, Video Completions, Video Completion Rate, View Rate, and Views. Apply [Minimum Cost or Impression Threshold], rank formats by the KPI in [Business Objective], and propose a concrete budget reallocation and testing plan with a success metric for each recommendation.

Campaign Saturation Revenue Leak Detector

Using the Catchr MCP, analyze daily StackAdapt trends for [Account Name/ID] over [Period] by Campaign Name, Channel Name, and Channel SubType Name. Track Frequency, Unique Impressions, Impressions, CTR, Engagement Rate, CVR, eCPA, Conversion Revenue, and ROAS, and compare each campaign's recent [Recent Window] with its earlier [Baseline Window]. Flag campaigns where reach is saturating or Frequency is rising while engagement or commercial efficiency declines, quantify the Cost and Conversion Revenue associated with the deterioration, and recommend whether to adjust frequency caps, refresh campaign messaging, change the channel mix, or continue monitoring.

Campaign Metric Integrity Audit

Using the Catchr MCP, audit StackAdapt data for [Account Name/ID or All Connected Accounts] over [Period] at Date, Advertiser ID, Campaign Group ID, and Campaign ID level. Check for missing identifiers or dates, duplicate entity-date records, negative Cost, Impressions, Clicks, Conversions, or Revenue, Unique Impressions greater than Impressions, rates outside their valid range, and unexplained inconsistencies among Conversions, Click Conversions, Impression Conversions, Cookie Conversions, and Secondary Conversions. Do not assume attribution components are additive when their definitions overlap; instead, flag the exact relationship observed and return an exception table with affected entity, date, failed rule, values, severity, and validation step.

Daily Programmatic Outlier Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of daily StackAdapt data for [Account Name/ID or All Connected Accounts], segmented by Advertiser Name, Campaign Name, Channel Name, and Channel SubType Name. For Cost, Impressions, Unique Impressions, Clicks, CTR, eCPM, Frequency, Conversions, eCPA, Conversion Revenue, ROAS, Video Completion Rate, and Audio Completion Rate, compare each day with its trailing [Baseline Window, e.g., 30-day] mean and standard deviation. Flag deviations beyond [Threshold, e.g., 2 standard deviations], classify each as likely data-quality, mix-shift, delivery, or genuine performance change using cross-metric evidence, and output the date, entity, affected metrics, severity, and recommended investigation.

Extraction Freshness and Coverage Monitor

Using the Catchr MCP, assess StackAdapt pipeline freshness and coverage for [Account Name/ID or All Connected Accounts] over [Period] using Extracted Date, Date, Advertiser ID, Campaign Group ID, Campaign ID, Campaign State, Campaign Status, Channel Name, Cost, Impressions, Clicks, Conversions, Video Starts, and Audio Starts. Detect stale extraction timestamps, missing dates, unexpected null identifiers, duplicate records, flatlined metrics, and active campaigns with no recent observations; evaluate video and audio metric completeness only for their relevant channels. Quantify the scope of every issue, distinguish plausible zero-delivery periods from probable pipeline failures, and produce a remediation-ready table with owner placeholder [Data Owner], evidence, priority, and next validation step.

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

Connect StackAdapt to ChatGPT

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How to connect StackAdapt to ChatGPT ?

Authorize the StackAdapt 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 StackAdapt prompts for analyzing real account data.

StackAdapt to ChatGPT FAQs

What StackAdapt data can ChatGPT analyze through Catchr?

How granular can StackAdapt analysis be in ChatGPT?

Can ChatGPT find efficiency and budget opportunities in StackAdapt?

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

What types of prompts work best for StackAdapt analysis?

Can I combine StackAdapt with other data sources in ChatGPT?

Can I analyze multiple accounts or campaigns together?

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

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

Can ChatGPT change anything in StackAdapt 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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