When running Google Ads campaigns (feel free to change for Facebook Ads, LinkedIn Ads, Microsoft Advertising…), you always need to know how much you’ll spend by the end of the month. Ideally, this information is automatically pushed to you on a regular basis so you don’t have to regularly go to a spreadsheet to get the number or even calculate it manually. In this article, we’ll look at solving this problem with Make because Google will simply not give you anything like this.

So for example, today is 2023-03-05 so four full days of March 2023 are passed and you need to project the cost for the remaining 27 March days. If your recent average daily spend has been $687 per day then the total estimated cost by the end of March will be: cost for the four fully passed days in March + (27 remaining days x $687).

Let’s say March spend has been $2800 so far so the total estimated cost for March as of 2023-03-05 is: $2800 + (27 x $687) = $21349 => you want to get this updated number every day in morning via email so you can e.g. compare it against your monthly budget and possibly take an action if you are running too hot.

Manually calculating the estimated cost by the end of the month is a relatively easy task as long as you have all the inputs. The trouble is that:

1. You definitely don’t want to calculate this manually every day.
2. Getting the inputs from Google Ads can be a little painful.
3. If you push your Google Ads data to a spreadsheet, you still need to figure out how to push the information to you so you don’t have to open the spreadsheet every day.

So to solve all three points, I’m using this Make scenario:

Every single morning at 10 am, I’m getting this email:

I don’t have to calculate anything and the pacing is pushed to me. Let’s look at what the email tells me:

1. In the subject, I see whether the account was spending money yesterday (Spending Yest? = Yes)
2. I also see what’s the current estimated spend for the month ($21459)
3. I also see the account name and ID (blanked out for the purposes of the article)
4. In the body, I see days passed in the current month, days to go, days in month.
5. I see an explanation of how my average for projections is calculated.
6. I see basic performance stats for the This month.
7. I see Pacing Table which gives me projections based on the different calculation approaches.
8. And finally, I also see the Daily table showing the last 2 months of data.

Interested? Grab the Make blueprint here:

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How to configure the blueprint?

1. Sign up for Make. If you run the report just for one account daily, even the Free account should be enough.
2. Create a new scenario in Make and then import the downloaded JSON into the scenario.
   
3. Open the first Google Ads module and set up the connection to your Google Ads account – if the account you want to work with is in your MCC or you want to process multiple accounts from one MCC at once, use your MCC account ID to login. If the account is not in a MCC, use the standard account ID.
   
4. Select the created connection in the second Google Ads module.
5. Create a connection for the Email module so your email can go out.
6. Configure the first module by entering the first account id and emails which you should get the regular email. You can enter more than one email – just use comma as separator. You can also enter a test recipient. You can also enter more accounts for which you want to calculate the pacing – just keep adding more {…} collections and follow the existing syntax. I’ve already prepared configs for two accounts in the blueprint.
   
7. Decide whether you want to run a test in the second module – keep “true” for testing. Edit to anything else when you don’t want to run tests anymore. I strongly recommend to run at least one test by pressing the RUN ONCE button in Make.
   
8. Schedule the scenario to run as often as you’d like.
9. Relax and wait for the pacing email to come.

Conclusion

Beautiful isn’t it? Instead of having to visit client’s Google Ads account regularly to make sure we are not overspending, I now just wait for the information to come to me. Alonside the calculated pacing, I’m also getting basic account performance info as a bonus.

Of course, this report could be improved – with some modifications. you can run a similar scenario which could be working with the data from all your advertising networks but in that case, the data would have to be stored in a centralized database first (e.g. in BigQuery) which is doable but not trivial.

Do you need something fancier than this?
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