Connect Amazon DSP to ChatGPT

Multi-Advertiser Delivery Command Center

Using the Catchr MCP, pull Amazon DSP data for [List of Client Accounts or Advertisers] over [Period] and compare it with [Comparison Period]. For each Advertiser Name, summarize Order Budget, Total Cost, Line Item Delivery Rate, Impressions, Reach, Frequency Average, Click Through Rate, Viewability Rate, Purchases, Sales, and Roas. Compare delivery and outcomes with [Client Targets or Historical Baseline], label each advertiser Healthy, Watch, or Critical, identify the specific orders or Line Item Id values driving every Watch or Critical result, and rank the accounts the consultant should review first with one evidence-based action per account.

Client-Ready DSP Performance Brief

Using the Catchr MCP, analyze Amazon DSP performance for [Client Account Name/ID] over [Reporting Period] versus [Previous Period]. Break down Total Cost, Impressions, Clicks, Click Through Rate, Cost Per Thousand Impressions, Viewability Rate, Reach, Frequency Average, Combined Purchases, Combined Product Sales, and Combined ROAS by Order Name and Line Item Id. Explain which orders improved or declined, separate reach and engagement changes from purchase and sales changes, quantify the main drivers, and produce a concise client-ready brief with an executive summary, three wins, three risks, and a prioritized recommendation for each risk.

Cross-Client Media Quality Watchlist

Using the Catchr MCP, review Amazon DSP media quality for [List of Client Accounts] over [Period]. Analyze Viewability Rate, Measurable Impressions, Viewable Impressions, Invalid Impression Rate, Invalid Impressions, Invalid Click Through Rate, Frequency Average, Total Cost, and Cost Per Thousand Impressions by Advertiser Name, Supply Source, Site, Device Name, Creative Type, and Creative Size. Compare results with [Quality Thresholds] and [Previous Period], flag expensive or deteriorating placements and audience-overexposure patterns, and return a prioritized watchlist containing the client, affected dimension, evidence, likely business impact, and a specific action such as excluding a site, shifting supply, changing format, or adjusting frequency.

Morning Client Pacing Queue

Using the Catchr MCP, pull Amazon DSP data for [List of Client Accounts] for [Current Month to Date or Recent Period]. For each client, compare Order Budget and Line Item Budget with Total Cost, Line Item Delivery Rate, Impressions, Click Through Rate, Viewability Rate, Purchases, and Roas, using Order Start Date, Order End Date, Line Item Start Date, and Line Item End Date to assess pacing. Flag overspend, underspend, stalled delivery, weak media quality, or declining outcomes against [Client Targets or Historical Baseline], then give me a ranked morning work queue with the account, affected Order Name or Line Item Id, urgency, evidence, and the first action I should take today.

Monthly Amazon DSP Client Report

Using the Catchr MCP, pull Amazon DSP performance for [Client Account Name/ID] for [Reporting Period] and [Previous Period]. Summarize Total Cost, Impressions, Reach, Frequency Average, Clicks, Click Through Rate, Cost Per Thousand Impressions, Viewability Rate, Detail Page Views, Add To Cart, Purchases, Sales, and Roas, then break the most important changes down by Order Name and Line Item Id. Write a client-ready report in plain English that explains what improved, what declined, which data supports each conclusion, how click-through and view-through purchases contributed, and the three priorities for [Next Period] without presenting correlation as proven causation.

Creative Format Optimization Backlog

Using the Catchr MCP, analyze Amazon DSP creative performance for [Client Account Name/ID] over [Period] by Creative Ad Id, Creative Type, Creative Size, and Device Name. Compare Impressions, Click Through Rate, Cost Per Thousand Impressions, Viewability Rate, Frequency Average, Video Ad Start, Video Ad Complete, Video Ad Completion Rate, Detail Page Views, Purchases, and Roas, applying [Minimum Impression Threshold] before ranking results. Identify strong formats, likely fatigue patterns, and weak device-format combinations, then create a prioritized optimization backlog with the evidence, expected impact, effort level, specific change to test, success metric, and review date for every task.

Amazon Commerce Funnel Leak Finder

Using the Catchr MCP, pull Amazon DSP data for [Account Name/ID] over [Period] and [Comparison Period]. Build the path from Impressions and Clicks to Detail Page Views, Add To Cart, Purchases, Units Sold, Sales, and Roas, using Detail Page View Rate, Add To Cart Rate, Purchase Rate, and the closest catalog fields available for each stage. Break results down by Order Name, Line Item Id, and Asin or Featured Asin, quantify period-over-period changes and conversion losses between stages, identify the products and line items responsible for the largest leaks, and recommend the three highest-impact budget, targeting, product-page, or creative actions to test next.

New-to-Brand Growth Scorecard

Using the Catchr MCP, evaluate customer acquisition for [Account Name/ID] over [Period] against [Comparison Period or Target]. Report Total Cost, Total New To Brand Purchases, Total New To Brand Purchases Percentage, Total New To Brand Purchase Rate, Total New To Brand Sales, and Total New To Brand ROAS by Order Name, Line Item Id, Product Category, and Asin. Contrast these metrics with Total Purchases, Total Sales, and Total ROAS to distinguish acquisition from overall revenue efficiency, rank the line items that add the most new customers at sustainable returns, flag spend that mainly reaches existing buyers, and recommend where to scale, hold, or reduce investment.

ASIN Profitability and Halo Analyzer

Using the Catchr MCP, analyze Amazon DSP performance for [Account Name/ID] over [Period] by Asin, Parent Asin, Featured Asin, Product Name, and Product Category. Compare Total Cost with Purchases, Purchases Brand Halo, Sales, Sales Brand Halo, Combined Product Sales, Combined Purchases, and Combined ROAS; where relevant, separate Purchases Clicks from Purchases Views. Rank products by efficient direct and halo contribution, identify ASINs that consume spend without generating sufficient sales or downstream product interest, state whether each conclusion is driven by click-through or view-through activity, and produce a clear scale, maintain, test, or pause recommendation for every material ASIN.

Amazon DSP Data Integrity Audit

Using the Catchr MCP, audit Amazon DSP records for [Account Name/ID or All Connected Accounts] over [Period]. Check completeness and uniqueness of Date, Advertiser Id, Order Id, Line Item Id, Creative Ad Id, and Asin where applicable; validate that clicks do not exceed Impressions, Viewable Impressions do not exceed Measurable Impressions, Purchases Clicks plus Purchases Views reconcile with Purchases when the reporting grain permits it, Total Cost is non-negative, and Order Start Date, Order End Date, Line Item Start Date, and Line Item End Date are logically ordered. Return an exception table with account, record identifiers, failed rule, observed values, severity, and recommended remediation or source-system check.

Click-View Attribution Reconciliation

Using the Catchr MCP, reconcile Amazon DSP conversion reporting for [Account Name/ID] over [Period] by Date, Order Id, Line Item Id, Conversion Type, and Asin. Compare Purchases with Purchases Clicks and Purchases Views, Combined Purchases with Combined Purchases Clicks and Combined Purchases Views, and Total Purchases with Total Purchases Clicks and Total Purchases Views; pair those volumes with Sales, Combined Product Sales, Roas, and Combined ROAS. Calculate reconciliation gaps only where the metrics share a compatible grain, flag missing components, abrupt changes in click-view mix, or totals that cannot be explained by available components, and deliver a validation table that distinguishes likely attribution-setting changes from extraction or aggregation issues.

Daily Cost and Delivery Outlier Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of Amazon DSP data for [Account Name/ID or All Connected Accounts], grouped by Date, Advertiser Id, Order Id, and Line Item Id. Monitor Total Cost, Impressions, Cost Per Thousand Impressions, Click Through Rate, Viewability Rate, Invalid Impression Rate, Frequency Average, Line Item Delivery Rate, Purchases, and Roas. Compare each daily value with its trailing [Baseline Window, e.g., 30-day] mean and standard deviation, flag deviations beyond [Z-score Threshold] plus zero or missing-value runs, and classify each anomaly as likely data-quality, pacing, inventory-mix, audience-saturation, or genuine performance change with the evidence and next validation query required.

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

Connect Amazon DSP to ChatGPT

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How to connect Amazon DSP to ChatGPT ?

Authorize the Amazon DSP 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 Amazon DSP prompts for analyzing real account data.

Amazon DSP to ChatGPT FAQs

What Amazon DSP data can ChatGPT analyze through Catchr?

How granular can Amazon DSP analysis be in ChatGPT?

Can ChatGPT find efficiency and budget opportunities in Amazon DSP?

Can ChatGPT diagnose targeting, creative, or conversion issues in Amazon DSP?

What types of prompts work best for Amazon DSP analysis?

Can I combine Amazon DSP with other data sources in ChatGPT?

Can I analyze multiple accounts or campaigns together?

What do I need to connect Amazon DSP to ChatGPT with Catchr?

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

Can ChatGPT change anything in Amazon DSP 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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