How to Build a Google Ads Daily Pacing Script

By Sam Jacobs - Wednesday September 9, 2026 Share (Twitter) WhatsApp Summarize ChatGPT Perplexity Grok Google AI Every morning, before anything else, someone on the GrowRoom paid media team would open a budget pacing spreadsheet and review our clients’ budget spent month-to-date vs the target.

Next, they’d cross-reference it against the respective Google Ads accounts, and (if the account was not on target), review what campaigns were causing the account to misspend, and investigate why. Then they’d develop an internal report notifying the team of which account and campaigns were not on target, why they weren’t, and what we should do about it.

Multiply that by every account in the portfolio, every single day, and you can see the problem. It wasn’t a hard task, but a necessary one. Keeping client spend on track across a large portfolio of Google Ads accounts is a core part of running paid media.

For a long time, that meant doing it manually… until we started automating it with a script that was built and refined over several iterations.

Where This Started Our performance marketing and media agency, GrowRoom, manages paid media for a range of clients, and as our team and client list grew, I started noticing how much account manager time was being swallowed by manual, repetitive checks – I’d guess most agency owners know this problem. Budget pacing was one of the biggest offenders.

The process pulled data from Google Ads into a tracking sheet, and someone had to read it, interpret it, validate it against the live account, and write up what it meant for that account. It was a manual data reviewing task, done by team members who were capable of much more useful things, and as the client list grew, so did the hours it consumed.

The task took around half an hour every morning; 5 days a week, 20 days a month. That’s 10 hours of manual time spent on a task that needed to be done, but I was sure that this process could be automated and save manual account manager time. The Problem The pacing sheet itself wasn’t the problem.

Every morning, it told us exactly how each account was spending against the target. Once we knew an account was over or under pace, someone still had to dig into why, and that was the problem.

An account manager had to spend time reviewing campaigns, working out what was driving the drift, then writing up a report so the rest of the team knew what was going on and what to do about it.

Spend drifts for all kinds of reasons – demand shifts, auction competition, a CPC creeping up over a few days – and figuring out which one applied to a given account, on a given day, took some real digging. Multiply that across every account in the portfolio, and it added up to hours of manual review every day, on top of the pacing check itself.

It went beyond the top line number too. The team had no quick way to see which campaigns or ad groups were burning budget inefficiently, or which ones were performing well enough to deserve more of it.

Every day, that meant: Reading the pacing data and flagging off-track accounts Reviewing campaign and ad group performance to understand why Writing up a report on what was happening and where to push or pull budget Making the necessary changes Three of those four steps were purely data analysis and reporting.

Only the last one was actual implementation work. The Solution The pacing sheet told us what was happening, but the script needed to explain why.

The fix was a Google Ads script, run at MCC level, that did what the budget pacing sheet already did; reads monthly spend, compare against target, and classifies as on pace, overspending, or underspending, suggest avg. daily spend remaining, and then takes it one step further, completing the investigation work the account manager would do manually.

The script looks at the last 30 days of campaign and ad group data, and suggests based on performance where the budget should shift, i. e. , toward efficient converters, or away from campaigns, ad groups or keywords which were underperforming.

The script will review performance at a keyword level, identifying if certain terms are performing above or below a pre-established threshold, as well as review auction insights – identifying if key changes within the competitive landscape are likely the cause of performance shifts, and flag that as part of the report.

The full report lands in a shared inbox every morning at 9am. The goal was to strip the job down to just two things: QA the report, then act on it. Data validation, performance review, and report-writing all happen automatically now.

The Implementation We built the script iteratively, tested in the Google Ads script editor, and refined through real execution logs at each stage. A few things about the setup are important to point out here: A Google Sheet acts as the sole place budgets live, with three