How To Use AI To Analyze Google Ads Performance And Write Reports

By Corey Frankosky · Last Updated October 18, 2026

Most Google Ads reports tell you what happened and stop there. Clicks went up, cost per conversion went down, here is a chart. What business owners actually want to know is why, and what to do next. That is the part that takes time to write. In this post I will show you how to use AI to analyze Google Ads performance, explain the changes in plain English, and write a monthly report that someone will actually read.

1Why AI fits Google Ads reporting

Reporting has two parts. The first is analysis: comparing periods, finding what moved, and tracing it back to a cause. The second is writing: turning that into something a business owner can read in two minutes.

AI is good at both, with one big condition. It can only analyze the data you give it, and it does not know what happened in your business that month. If you ran a promotion, lost a technician, or had a slow week because of a storm, the AI has no idea unless you tell it.

So the process is simple. You supply the data and the context. The AI does the comparing and the first draft of the writing. You check the numbers and make the decisions.

Question to Answer:

How long does your current monthly Google Ads report take to put together?

2Decide what you are measuring first

Before you ask AI to analyze anything, decide which numbers matter. If you skip this, you will get a report that comments on every metric in the account, including the ones that do not matter.

For most local service businesses, the short list is:

• Conversions, meaning real calls and form leads, not page views.
• Cost per conversion, compared to what a customer is worth to you.
• Total spend, compared to budget.
• Conversion rate, to show whether the landing page is doing its job.
• Search impression share, to show how much demand you are missing.

My guide to Google Ads KPIs and 9 key Google Ads metrics to track go further on choosing these. Keep in mind that none of this works if conversion tracking is broken. If you are not sure your calls and forms are tracked correctly, fix that before you analyze anything.

Question to Answer:

What are the three numbers that tell you whether your Google Ads are working?

3Export the right data

Export two periods so the AI can compare them, usually this month and last month. If your business is seasonal, add the same month last year.

• Campaign report with spend, clicks, conversions, cost per conversion, conversion rate, and search impression share.
• Conversions by conversion action, so you can see calls versus forms.
• Device and location reports if you suspect a change there.
• Change history for the month, so the AI can connect results to what you changed.
• Search terms report if you want waste included. I cover that in how to use AI to analyze your search terms report.

Download each as a CSV and remove the title and totals rows so each file is a clean table. Check your AI tool's data settings first. I cover that in building a custom AI marketing assistant.

Question to Answer:

Which comparison period gives you a fair picture for your business, last month or last year?

4Give the AI context it cannot see

This is the step most people skip, and it is the step that makes the report useful. Write a short note covering what the data cannot show:

• Your goals: target cost per lead, monthly lead goal, budget.
• What you changed: new campaigns, bid strategy changes, budget changes, new landing pages.
• What happened in the business: promotions, holidays, weather, staffing, a phone system outage.
• What happened offline: how many leads turned into booked jobs, if you know.

For example, "Cost per lead went up 30%" sounds bad. "Cost per lead went up 30% because we paused the campaign for a week while the office was closed, and the restart took a few days to settle" is a completely different story. Only you can supply that second half.

Question to Answer:

What happened in your business this month that does not show up in Google Ads?

5The analysis prompt

Upload your files and your context note, then ask for analysis before you ask for a report. Keeping these separate gives you a chance to check the analysis first.

Asking the AI to separate known causes from guesses is important. Without it, AI tools tend to present a guess with the same confidence as a fact.

Question to Answer:

Are you asking for analysis and the report in one step, or two?

6Check the numbers

Never send an AI written report without checking the numbers. AI tools are much more accurate when they run calculations on the file, but they can still read the wrong column, double count a totals row, or mix up two periods.

Here is a quick check that takes five minutes:

1. Compare total spend and total conversions in the AI's table to the totals in Google Ads for the same dates.
2. Spot check one campaign by hand.
3. Recalculate one percentage change yourself.

If those match, the rest is very likely right. If they do not, find out why before going any further. A wrong number in a report damages trust much more than a late report does.

Question to Answer:

Do your AI's total spend and conversions match the totals in Google Ads exactly?

7Write the report

Once the analysis checks out, ask the AI to turn it into a report. The format I recommend has three parts, and the whole thing should fit on one page:

• What happened. Three to five sentences with the main numbers compared to last period and to goal.
• Why it happened. The causes, with guesses labeled as guesses.
• What we are doing next. Two to four specific actions for next month.

Tell the AI who the reader is. A report for a business owner should skip jargon like "impression share lost to rank" and say "our ads were not showing for some searches because our bids were too low." A report for a marketing manager can keep the terms.

If you want the numbers in a live dashboard alongside the written summary, my Google Ads ROI dashboard setup guide shows how to build one.

Question to Answer:

Could the person reading your report explain the results to someone else after one read?

8Keep the decisions with a person

AI will happily recommend changes. Raise this budget, pause that campaign, switch bid strategies. Some of those suggestions will be good. Some will ignore things it cannot know, like your profit margin on each service, how busy your crews are, or the fact that a campaign drives calls that show up as booked jobs offline.

Treat AI recommendations as a list of questions to look into, not a list of changes to make. One of the biggest mistakes I see is someone making five changes at once because an AI report suggested them. Then nobody can tell which change helped and which one hurt.

Make one or two changes at a time, write them down, and include them in next month's context note. That gives next month's analysis a clean cause and effect to look at.

Question to Answer:

Which one change will you make this month, and how will you know if it worked?

9Build a monthly routine

The real payoff comes from doing this the same way every month. Save your analysis prompt and report format in a ChatGPT or Claude project, so each month you just upload new files and a new context note.

A typical routine looks like this: export on the first business day of the month, write your context note, run the analysis, check the numbers, generate the report, and send it. Once it is set up, the whole process takes under an hour.

Every quarter, step back and look at the bigger picture. Is the structure still right? Are you tracking the right conversions? That is where an outside review helps. Our Google Ads audit looks at the whole account, not just last month's numbers, and you can use the Google Ads audit checklist to run your own.

Question to Answer:

What day each month will you run your Google Ads report?