Google Ads

The challenge

A small local budget, a booking action that had to be tracked properly or the whole campaign was guesswork, and a short season. There was no room for a learning phase funded by wasted clicks.

What we did

• Set up the booking conversion first and confirmed it fired on completion, not on page view
• Kept the account to search intent tied to booking rather than to people reading about golf
• Cut terms that attracted browsers and held the average cost per click at CA$0.32
• Reviewed the search terms report weekly through the season

The challenge

More than 450 campaigns cannot be optimised by hand. The work is deciding what to pause, what to scale and what to leave alone, then doing it consistently every week rather than in bursts.

What we did

• Grouped campaigns by performance instead of by brand so decisions applied to sets
• Paused what did not convert and moved that budget to what did
• Reviewed the search terms report weekly rather than monthly
• Held the top ad group at a 21.81% click-through rate

The challenge

Average cost per click ran between US$4.01 and US$5.53. At those prices a loose keyword list burns the budget in days, and the wrong audience produces enquiries that never qualify.

What we did

• Split final expense and whole life into separate campaigns so the message matched the search
• Kept keyword lists tight and negative lists tighter
• Wrote ad copy to the age and intent of each audience rather than to the product
• Monitored cost per lead daily because one broad term can absorb a day of budget

The challenge

The team managed several Google Ads accounts and made optimisation decisions by reading platform reports manually. That is slow, easy to get wrong, and wasted spend can sit unnoticed for weeks before anyone catches it. They wanted a system that reviewed performance every day and told them what to act on first, without handing control of the account to an automation.

What we did

• Set up Google Cloud project and Google Ads API access
• Built a secure pipeline pulling campaign, keyword, conversion and cost data
• Structured raw data into decision-ready metrics: CPA, conversion rate and wasted spend
• Built an LLM agent that reviews the data and writes prioritised recommendations
• Defined the optimisation logic for when to pause, scale or change match type
• Kept the agent in advisory mode so the team approves every change

The challenge

The account leaned on broad, expensive search terms, and conversion tracking was misconfigured across GTM and GA4. Reporting was unreliable, so budget decisions were being made in the dark.

What we did

• Rebuilt the account structure around real buying intent
• Fixed conversion tracking end to end on GTM and GA4
• Mapped keywords to intent and cut the wasted spend
• Moved budget into Performance Max and scaled what worked