Connect AdsWizz to ChatGPT

Multi-Client Audio Campaign Pulse

Using the Catchr MCP, pull AdsWizz data for [List of Client Account Names/IDs] over [Period] and compare it with [Comparison Period]. Build a portfolio health table using Attribution Total Spend, Audio Impressions, Reach Audio Unique Users, CTR, LTR/VTR, Total Conversions, CPA, Conversion Value, and Return on Ad Spend. Evaluate each account against [Client KPI Targets], rank the accounts that require attention, and drill down by Advertiser, Campaign, and Ad for every material gap. End with one evidence-based action and one client-ready explanation per flagged account, and clearly mark unavailable metrics instead of estimating them.

Client Audio Performance Debrief

Using the Catchr MCP, analyze AdsWizz performance for [Account Name/ID] over [Period] versus [Comparison Period]. Report Audio Impressions, Total Reach - Unique Users, LTR/VTR, Clicks, CTR, Total Conversions, Listen-Through Conversions, Click-Through Conversions, View-Through Conversions, CPA, Conversion Value, and Return on Ad Spend. Break the largest changes down by Campaign, Ad, Ad format, Publisher, and Device Type, quantify which segments drove the movement, and produce a client-ready debrief with an executive summary, three key findings, and prioritized next actions. Separate observed facts from interpretations.

Cross-Account Inventory Quality Audit

Using the Catchr MCP, audit AdsWizz inventory for [List of Client Account Names/IDs] over [Period]. Break results down by Publisher, Inventory Type, Publisher Transparency, IAB Categories, Geo - Country, and Device Type, using Audio Impressions, Total Reach - Unique Users, Media eCPM, Fill Rate, LTR/VTR, CTR, Total Conversions, CPA, and Return on Ad Spend. Apply [Minimum Volume Threshold] and compare each segment with its account baseline to flag meaningful quality or efficiency risks. For every finding, identify the affected account and Campaign, show the supporting metrics, and recommend a specific review, exclusion, bid adjustment, or controlled retest.

Daily Client Priority Queue

Using the Catchr MCP, pull month-to-date AdsWizz data for [List of Client Account Names/IDs] and compare each account with [Client KPI Targets] and [Expected Pacing]. Use Attribution Total Spend, Audio Impressions, Total Reach - Unique Users, LTR/VTR, CTR, Total Conversions, CPA, Conversion Value, and Return on Ad Spend. Rank clients by urgency, explain in one sentence why each is on track or at risk, and identify the Campaign or Ad driving every material gap. Finish with a short, prioritized action list the freelancer can execute today.

Monthly Audio Campaign Story

Using the Catchr MCP, create a client-ready AdsWizz report for [Account Name/ID] covering [Period] versus [Comparison Period]. Explain changes in Attribution Total Spend, Audio Impressions, Total Reach - Unique Users, Media eCPM, LTR/VTR, Clicks, CTR, Total Conversions, Listen-Through Conversions, CPA, Conversion Value, and Return on Ad Spend. Attribute the most important movements to Campaign, Ad, Publisher, Geo - Country, or Device Type and quantify their contribution. Write in plain English with an executive summary, what worked, what did not, what the data suggests happened, and three practical next steps.

Creative Listening Drop-Off Check

Using the Catchr MCP, analyze daily AdsWizz listening signals for every Ad on [Account Name/ID] over [Period]. Use Ad format, Ad Type, Ad Duration, Quartile 0, Quartile 25, Quartile 50, Quartile 75, Quartile 100, LTR/VTR, CTR, Total Conversions, and CPA. Apply [Minimum Volume Threshold], compare recent results with each ad's earlier performance and the account median, and flag material listening drop-off or weakening outcomes. For every flagged ad, show the trend evidence, state the likely issue cautiously, and recommend whether to refresh, shorten, test another format, or continue monitoring.

Audio Commerce Efficiency Guardrail

Using the Catchr MCP, pull AdsWizz data for [Account Name/ID] over [Period] and compare it with [Comparison Period] and [ROAS or CPA Target]. Summarize Attribution Total Spend, Audio Impressions, Total Reach - Unique Users, Total Conversions, Conversion Value, Conversion Rate, CPA, and Return on Ad Spend. Break any deterioration down by Campaign, Ad, Ad format, Device Type, and Geo - Country, quantify which segments contributed most, and recommend where to protect, reduce, or reallocate investment. Flag missing Conversion Value or attribution data rather than inferring commercial results.

Listen-Through Sales Impact

Using the Catchr MCP, analyze conversion paths for [Account Name/ID] over [Period] using Listen-Through Conversions, Listen-Through Conversion Rate, Click-Through Conversions, Click-Through Conversion Rate, View-Through Conversions, View-Through Conversion Rate, Same Device Conversions, Cross Device Conversions, Total Conversions, Conversion Value, CPA, and Return on Ad Spend. Compare the paths by Campaign, Ad, Publisher, and Device Type, then identify which combinations generate the strongest attributed outcomes at meaningful scale based on [Minimum Conversion Threshold]. State the attribution limitations clearly and propose three tests to improve profitable reach.

Audio Creative Completion Scorecard

Using the Catchr MCP, evaluate every AdsWizz creative on [Account Name/ID] over [Period]. Break results down by Ad, Ad format, Ad Type, and Ad Duration, using Quartile 0, Quartile 25, Quartile 50, Quartile 75, Quartile 100, LTR/VTR, Total Reach - Unique Users, Clicks, CTR, Total Conversions, CPA, and Return on Ad Spend. Apply [Minimum Impression or Quartile 0 Threshold], rank creatives by listening depth and commercial outcome, and flag ads with strong initial delivery but weak Quartile 100 or weak conversion efficiency. Return a keep, refresh, test, or pause recommendation for each material creative with the metrics that support it.

Attribution Integrity Reconciliation

Using the Catchr MCP, reconcile AdsWizz attribution for [Account Name/ID or List of Accounts] over [Period] by Date and Campaign. Compare Total Conversions with Listen-Through Conversions, Click-Through Conversions, and View-Through Conversions; also inspect Unique Conversions, Same Device Conversions, Cross Device Conversions, Conversion Value, Attribution Total Spend, CPA, and Return on Ad Spend. Test null coverage, internal consistency, and abrupt discontinuities against [Data Quality Thresholds]. Flag affected dates and entities, classify each issue as a likely attribution, conversion-value, or extraction risk, and provide the exact validation check to run next without inventing missing data.

Audio Delivery Outlier Monitor

Using the Catchr MCP, pull daily AdsWizz data for [Account Name/ID or All Connected Accounts] over [Period, e.g., the last 90 days]. For Audio Impressions, Total Reach - Unique Users, Media eCPM, Fill Rate, LTR/VTR, Clicks, CTR, Attribution Total Spend, Total Conversions, CPA, and Return on Ad Spend, compare each day with its trailing [Window, e.g., 30-day] mean and standard deviation. Flag deviations beyond [Threshold, e.g., 2 standard deviations], then drill down by Advertiser, Campaign, Ad, Publisher, and Inventory Type. Separate probable data-quality incidents from plausible delivery or performance events, state the evidence, and output a severity-ranked alert table.

Inventory and Consent Data Quality Audit

Using the Catchr MCP, audit AdsWizz delivery coverage for [Account Name/ID or List of Accounts] over [Period]. Break data down by Date, Publisher, Inventory Type, Publisher Transparency, Content Transparency, Listener Consent Value, Listener Consent Source, Device Type, and Geo - Country; inspect Inventory, Audio Impressions, Display Impressions, Total Impressions, Fill Rate, Total Reach - Unique Users, and Total Revenue. Measure null, unknown, and unmapped rates for each dimension, detect abrupt mix changes using [Data Quality Thresholds], and identify the accounts, campaigns, publishers, and dates affected. Return a BI-ready issue table with severity, evidence, likely pipeline stage, and recommended validation step.

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

Connect AdsWizz to ChatGPT

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How to connect AdsWizz to ChatGPT ?

Authorize the AdsWizz 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 AdsWizz prompts for analyzing real account data.

AdsWizz to ChatGPT FAQs

What AdsWizz data can ChatGPT analyze through Catchr?

How granular can AdsWizz analysis be in ChatGPT?

Can ChatGPT find efficiency and budget opportunities in AdsWizz?

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

What types of prompts work best for AdsWizz analysis?

Can I combine AdsWizz with other data sources in ChatGPT?

Can I analyze multiple accounts or campaigns together?

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

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

Can ChatGPT change anything in AdsWizz 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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