Connect Awin to ChatGPT

Multi-Client Affiliate Health Check

Using the Catchr MCP, pull Awin data for [List of Client Account Names/IDs] over [Period] and compare it with [Comparison Period]. For each Account Name and Advertiser Name, report Clicks, Total Transactions, Total Value, Total Commission, Confirmed Transactions, Pending Transactions, Declined Transactions, Confirmed Value, Pending Value, and Declined Value. Calculate transaction rate as Total Transactions divided by Clicks and the confirmed, pending, and declined shares of Total Transactions, compare results with [Client Targets or Historical Baseline], label each account Healthy, Watch, or Critical, and return a ranked consultant action list with the affected Campaign or Campaign Publisher Name, quantified issue, and next step.

Client-Ready Publisher Performance Brief

Using the Catchr MCP, analyze Awin performance for [Client Account Name/ID] over [Reporting Period] versus [Previous Period]. Break down Clicks, Total Transaction, Sale Amount, Commission Amount, Transaction Approved, Transaction pending, Transaction declined, Sale Amount Approved, Sale Amount Pending, Sale Amount Declined, and Commission Amount by Campaign Publisher Name and Campaign. Calculate transaction rate, approval rate, decline rate, and commission-to-sale ratio, identify the publishers and campaigns driving each material change, and write a client-ready brief with an executive summary, three wins, three risks, and one evidence-based action for each risk.

Cross-Account Affiliate Risk and Opportunity Queue

Using the Catchr MCP, review Awin data for [List of Client Accounts] over [Period] against [Comparison Period or Targets]. By Account Name, Advertiser Name, Campaign Publisher Name, and Campaign, detect sharp changes in Clicks, Total Transactions, Total Value, Total Commission, Confirmed Transactions, Pending Transactions, Declined Transactions, and Transaction Lapse Time. Quantify which publishers create the largest confirmed-value gains, pending backlogs, decline-rate increases, or commission-efficiency deterioration, then produce a single prioritized queue showing client, publisher or campaign, opportunity or risk, supporting values, business impact, urgency, and the specific action the consultant should take.

Morning Client Affiliate Priority List

Using the Catchr MCP, pull Awin data for [List of Client Account Names/IDs] for [Current Month to Date or Recent Period] and compare it with [Previous Equivalent Period and Client Targets]. For every client, summarize Clicks, Total Transactions, Total Value, Total Commission, Confirmed Transactions, Pending Transactions, Declined Transactions, and Transaction Lapse Time; calculate transaction, confirmation, pending, and decline rates. Identify the Campaign Publisher Name or Campaign responsible for each material variance, then give me a ranked morning work list with client, urgency, evidence, target gap, and the first action I should take today.

Monthly Awin Client Report

Using the Catchr MCP, pull Awin performance for [Client Account Name/ID] for [Reporting Period] and [Previous Period]. Summarize Clicks, Total Transactions, Total Value, Total Commission, Confirmed Transactions, Confirmed Value, Pending Transactions, Pending Value, Declined Transactions, and Declined Value, and calculate transaction rate, approval rate, decline rate, average transaction value, and commission-to-value ratio. Break the most important changes down by Campaign Publisher Name and Campaign, then write a client-ready report in plain English covering results, what improved, what declined, the evidence behind each conclusion, unresolved risks, and three priorities for [Next Period].

Publisher Optimization Backlog

Using the Catchr MCP, analyze Awin data for [Client Account Name/ID] over [Period] by Campaign Publisher Name, Campaign, Creative Name, and Creative Tag Name. Compare Impressions, Clicks, Total Transaction, Sale Amount, Commission Amount, Transaction Approved, Transaction pending, and Transaction declined with [Previous Period or Targets], applying [Minimum Volume Threshold] before ranking results. Identify scalable publisher-creative combinations, high-click low-transaction combinations, deteriorating approval rates, and underused strong creatives, then create a prioritized optimization backlog with evidence, expected impact, effort, exact test or publisher follow-up, success metric, and review date.

Publisher Profitability and Scale Matrix

Using the Catchr MCP, pull Awin data for [Account Name/ID] over [Period] and [Comparison Period]. For each Campaign Publisher Name and Campaign, report Clicks, Total Transaction, Sale Amount, Commission Amount, Transaction Approved, Transaction pending, Transaction declined, and Sale Amount Approved. Calculate transactions per click, approved transactions per click, approval rate, decline rate, average approved order value, and commission-to-approved-sale ratio. Apply [Minimum Click or Transaction Threshold], rank publishers by confirmed commercial contribution and efficiency, and assign each publisher a Scale, Maintain, Investigate, or Reduce recommendation with the data and concrete next action behind it.

Voucher-Code Revenue Quality Analysis

Using the Catchr MCP, analyze Awin transactions for [Account Name/ID] over [Period] by Transaction Voucher Code Used, Transaction Voucher Code, Campaign Publisher Name, and Transaction Campaign. Compare Transaction Sale Amount, Transaction Commission Amount, Commission Status, Transaction Type, and transaction counts based on Transaction ID for voucher and non-voucher orders, using [Comparison Period or Benchmark] where available. Calculate average order value, commission-to-sale ratio, approval share, and decline share for each segment, flag voucher codes or publishers associated with weak revenue quality or unusually high commission cost, and recommend which codes or partnerships to expand, review, or restrict without presenting correlation as proof of incrementality.

Declined-Sales Leakage Detector

Using the Catchr MCP, review Awin performance for [Account Name/ID] over [Period] and compare it with [Previous Period]. Analyze Declined Transactions, Declined Value, Declined Commission, Transaction declined, Sale Amount Declined, Commission Amount Declined, Transaction Decline Reason, Transaction Amend Reason, Campaign Publisher Name, Campaign, and Order Ref. Calculate decline rates by publisher, campaign, and reason, quantify the sales value and commission affected, distinguish isolated orders from recurring patterns, and deliver a prioritized leakage table with likely operational cause, evidence, validation step, owner, and recommended action for each material issue.

Awin Transaction Integrity Audit

Using the Catchr MCP, audit Awin transaction data for [Account Name/ID or All Connected Accounts] over [Period]. At the finest compatible grain, test uniqueness and completeness of Transaction ID, Order Ref, Advertiser ID, Publisher ID, Transaction Date And Time, Commission Status, Transaction Sale Amount, Transaction Commission Amount, and their currency fields. Flag duplicate Transaction IDs or Order Refs, missing identifiers or statuses, negative or implausible amounts, transaction dates preceding Click Date And Time, currency mismatches, and amended transactions without Transaction Amend Reason. Return an exception table with account, transaction identifiers, failed rule, observed values, severity, and recommended remediation.

Affiliate Status Reconciliation

Using the Catchr MCP, reconcile Awin aggregates for [Account Name/ID or All Connected Accounts] over [Period] by Date, Advertiser Name, Campaign Publisher Name, and Campaign where the reporting grain is compatible. Compare Total Transaction with Transaction Approved, Transaction pending, Transaction declined, and Transaction Bonus; compare Sale Amount with Sale Amount Approved, Sale Amount Pending, Sale Amount Declined, and Sale Amount Bonus; and compare Commission Amount with Commission Amount Approved, Commission Amount Pending, Commission Amount Declined, and Commission Amount Bonus. Calculate reconciliation gaps, flag missing components or unexplained differences beyond [Tolerance], and provide a validation table separating likely status-timing effects from extraction, aggregation, or currency issues.

Daily Affiliate Data Anomaly Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of Awin data for [Account Name/ID or All Connected Accounts], grouped by Date, Account ID, Advertiser ID, Campaign Publisher Id, and Campaign. Monitor Clicks, Total Transaction, Sale Amount, Commission Amount, Transaction Approved, Transaction pending, Transaction declined, and Transaction Lapse Time. Compare each daily value and derived transaction, approval, pending, decline, and commission-to-sale rates with its trailing [Baseline Window, e.g., 30-day] mean and standard deviation, flag deviations beyond [Z-score Threshold] plus missing or zero-value runs, and classify each anomaly as likely data-quality, status-lag, publisher-mix, tracking, or genuine performance change with supporting evidence and the next validation query.

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

Connect Awin to ChatGPT

How to connect Awin to ChatGPT

Authorize the Awin account in Catchr

Install the Catchr app in ChatGPT

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.

See it in ChatGPT

Twelve Awin prompts for analyzing real account data.

Multi-Client Affiliate Health Check

Client-Ready Publisher Performance Brief

Cross-Account Affiliate Risk and Opportunity Queue

Multi-Client Affiliate Health Check

Awin to ChatGPT pricing

Starter

Growth

Scale

Awin to ChatGPT: FAQ

What Awin data can ChatGPT analyze through Catchr?

How granular can Awin analysis be in ChatGPT?

Can ChatGPT identify the most valuable publishers in Awin?

Can ChatGPT find commission, approval, or transaction leakage in Awin?

What types of prompts work best for Awin analysis?

Can I combine Awin with other data sources in ChatGPT?

Can I analyze multiple advertiser accounts or affiliate programs together?

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

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

Can ChatGPT change anything in Awin through Catchr?

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