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Bruno Ashford @brunoashfordSubscribeModule 8 · Lesson 8.5 · 12 min
Campaign and Competitor Analysis
Read your ad and campaign numbers in plain English and compare your offer with competitors.
Use ← → to move. The marker highlights text and the pencil draws on the slide; navigation locks while you annotate. Undo removes the last mark, Esc exits.
1Export the data you already have
Export the last 60 or 90 days from your ad platforms (for example Google Ads and Meta Ads) and your CRM leads with their source. Save them in data/campaigns/. CSV is fine. You do not need a special connector to start.
2Ask for the business answer, not the dashboard
Clicks and impressions are not the goal. Ask about cost per lead and, if you can match leads to sales in the CRM, cost per customer.
Use the data-analyst subagent on the files in data/campaigns/. For each campaign, calculate spend, leads, cost per lead, and, by matching lead source in the CRM export, customers won and cost per customer. Explain in plain English which campaigns to scale, which to fix and which to pause, and why. Flag any data you could not match.3Look at what competitors say and offer
Use the web research routine from Module 6 to see competitors' public offers, landing pages and ads. Platforms like Meta publish public ad libraries where you can see ads other businesses are running.
Research the public ads and landing pages of my top 3 competitors (listed in CLAUDE.md). For each: main offer, price if shown, main promise, call to action, and what makes it different from ours. Cite links. Then suggest 3 ad angles we are not using that fit our strengths.4Decide one change per week
Ask for the single change with the biggest expected impact: move budget, fix a landing page, test a new angle. One change a week, measured, beats ten changes you cannot evaluate.
5Keep a running history
Save each analysis to a dated file. Next month, ask the analyst to compare against the previous file. Over a quarter you build a real picture of what works for your business, not for businesses in general.
Many owners spend thousands a month on ads and read the results once a quarter, usually in a report from someone else. They know how much they spent. They do not know which campaign actually brought customers.
In this playbook your data analyst reads the exports, translates the numbers into business language, and connects them to what competitors are doing. You go from 'I think the ads are working' to 'this campaign brings customers at 42 dollars each and that one at 190 dollars, so we move the budget'.
Key points
- Ask for cost per lead and cost per customer, not clicks.
- Exports are enough to start; connectors can come later.
- Competitor research shows the angles you are missing.
- One measured change per week beats ten guesses.
Avoid
- Judging campaigns by clicks and impressions.
- Making budget decisions from numbers the analyst could not match to sales.
- Copying a competitor's offer instead of finding your own angle.
An online store selling outdoor gear spent around 6,000 dollars a month across Google and Meta. The analysis showed one Meta campaign generating cheap clicks but almost no purchases, while a branded search campaign converted at five times the rate. The competitor review found two rivals leading with free returns, which the store also offered but never mentioned. They moved budget, added 'free returns' to every ad, and tracked the change weekly.