MethodMarketingPaid advertisingIntermediate

Review ad campaign performance

A diagnostic of campaign data with what is working, what is wasting money and the next five actions.

This is a practical, copy-paste process for weekly or monthly reporting.. Use it when you want a clear structure, then add your own details and the linked evidence.

Published by Deepstory Research. Reviewed Sep 2026.

How to use this method

1. Copy or downloadCopy the prompt below, or download the method file and attach it to your chat.
2. Paste into any AIChatGPT, Claude, Gemini or Copilot. Replace each [PLACEHOLDER] with your details, or let the AI ask you.
3. Add evidenceAttach the evidence files listed on this page so the AI works from sourced numbers, not guesses.

The prompt

Paste it as-is. The AI will ask for anything still in [BRACKETS].

Download .md

# Review ad campaign performance
Method by Deepstory Research: deepstoryresearch.com/method/campaign-performance-review

You are a performance-marketing lead doing a weekly account review. Follow the method below step by step. Goal: A diagnostic of campaign data with what is working, what is wasting money and the next five actions.

## My inputs
- Campaign data export: [CAMPAIGN_DATA_EXPORT] (example: attach CSV)
- Targets: [TARGETS] (example: CPA ₹600, ROAS 3x)
- Period: [PERIOD] (example: last 30 days vs previous 30)

Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source.

## Steps to follow
1. Summarise spend, conversions, CPA, ROAS vs target.
2. Break down by campaign, ad group/ad set, device, geography.
3. Identify wasted spend and winners.
4. Diagnose causes (tracking, auction, creative, landing page, seasonality).
5. Recommend 5 prioritised actions with expected impact.

## Deliver the output in this structure
- Headline results
- Breakdown tables
- Waste and winners
- Diagnosis
- Action list

## Quality rules
- Check tracking integrity before concluding performance changed.
- Compare like-for-like periods.
- Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION.
- If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED].
- End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.

What you need to give the AI

Input	Placeholder	Example
Campaign data export	[CAMPAIGN_DATA_EXPORT]	attach CSV
Targets	[TARGETS]	CPA ₹600, ROAS 3x
Period	[PERIOD]	last 30 days vs previous 30

Use it when

Weekly or monthly reporting.
CPA suddenly jumped.

What you get

Headline results
Breakdown tables
Waste and winners
Diagnosis
Action list

The procedure, step by step

Read this before you run it, so you can check the AI actually followed the method.

1
Summarise spend, conversions, CPA, ROAS vs target.
2
Break down by campaign, ad group/ad set, device, geography.
3
Identify wasted spend and winners.
4
Diagnose causes (tracking, auction, creative, landing page, seasonality).
5
Recommend 5 prioritised actions with expected impact.

Quality checks built into the prompt

• Check tracking integrity before concluding performance changed.
• Compare like-for-like periods.
• Every number labelled FACT, ESTIMATE or ASSUMPTION; claims outside attached evidence tagged [UNVERIFIED].

Evidence to use with this method

Methods tell the AI how to think. Evidence gives it sourced numbers to think with. Download a context file and attach it with the prompt.

EvidenceRetail

E-Commerce Share of US Retail

Online sales as a share of total US retail, and the growth gap that keeps pushing that share up.

Open evidence Download context file

EvidenceFinance

UPI vs Visa & Mastercard: India’s Payments Rewiring

Ten years of UPI measured against the US card networks, against cash, and against the merchant-discount-rate question reopened by the Taxation and Other Laws (Amendment) Bill, 2026.

Open evidence Download context file

EvidenceFinance

Six-Economy Macro Baseline

A common-country, common-period baseline for real growth, inflation, modeled unemployment and trade exposure across the United States, China, India, Germany, the United Kingdom and Japan.

Open evidence Download context file

Run next

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