---
type: method
slug: campaign-performance-review
title: "Review ad campaign performance"
category: "Marketing"
subcategory: "Paid advertising"
level: intermediate
source: https://deepstoryresearch.com/method/campaign-performance-review
publisher: Deepstory Research
---

> **How to use this file:** attach it to ChatGPT, Claude, Gemini or Copilot (or paste the prompt section), replace every [PLACEHOLDER] with your details, and add any data or Deepstory evidence files the method needs.

## When to use this method
- Weekly or monthly reporting.
- CPA suddenly jumped.

## Prompt

# 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.

## Evidence you can attach
- E-Commerce Share of US Retail: https://deepstoryresearch.com/data/us-ecommerce-share (AI context file: https://deepstoryresearch.com/context/us-ecommerce-share.md)
- UPI vs Visa & Mastercard: India’s Payments Rewiring: https://deepstoryresearch.com/data/upi-vs-cards (AI context file: https://deepstoryresearch.com/context/upi-vs-cards.md)
- Six-Economy Macro Baseline: https://deepstoryresearch.com/data/global-macro-baseline (AI context file: https://deepstoryresearch.com/context/global-macro-baseline.md)

---
Deepstory methods are process guidance for AI tools. Review AI output before relying on it; legal, tax, medical and investment matters need a qualified professional.