Four accounts. All with
the dashboards attached.

This is the only case study here with real revenue

The client runs ServiceTitan, so every lead can be followed through to a closed deal and an invoice. The revenue figures above are not modelled, not proxied from average job value, and not a conversion value we assigned in Google Ads. They are what the business actually billed, matched back to the channel that produced the lead.

That distinction matters more than the numbers. Any agency can report conversions. Reporting the revenue those conversions became, from the client's own job-management system, is a different claim.

What closed-loop tracking revealed — and it was counterintuitive

Local Services Ads produced the cheapest leads in the account: $86 each, against $157 on Search. On cost per lead alone, LSA was the obvious place to move budget.

It was the worst buy in the account. Those cheap leads closed at 11.0% against Search's 31.6% — roughly a third of the rate. Revenue per lead came in at $359 against $1,075, so a Search lead was worth three times as much despite costing 82% more. LSA returned 4.19×; Search returned 6.83×.

Had that $47,429 of Local Services budget performed at Search efficiency, it would have produced about $324,000 rather than $198,927. Optimising toward cheap leads would have actively cost this client money, and there is no way to see that without revenue tracked to the close.

The efficiency shift in Google Ads

Comparing two matched 236-day windows: spend fell 20%, conversions rose 35%, and cost per conversion fell 41% from $255 to $151. All four figures in the account screenshot reconcile exactly against each other.

The mechanism was the click price. Average CPC went from $34.45 to $18.76 — down 46%. Worth being straight about the trade-off: conversion rate slipped slightly, from 13.50% to 12.43%, so the cheaper clicks converted a little less well. The click price fell faster than the conversion rate did, which is why the cost per conversion still dropped by more than 40%.

Where the caution belongs

Bing shows the highest close rate in the table at 57.1% and a 6.03× return — on 14 leads and 8 deals. That is far too small a sample to act on, and it is quoted here only because it is in the data, not because it means anything yet.

We are reporting a lower number than the dashboard does

The client's reporting dashboard shows a blended return of 9.50× across all channels. We are not using that figure, because $102,030 of the $261,340 it counts came from Google Business Profile and organic search — channels with zero ad spend.

Crediting paid advertising with revenue it did not buy would make the number roughly 64% larger and entirely misleading. On the $27,510 that was actually spent, the return was 5.79×. That is the number on this page.

The interesting part: they paid more per click, not less

Comparing two matched 236-day windows, cost per conversion fell 47%, from $229 to $122 — while spend rose 82%. Scaling and improving efficiency at the same time is the hardest combination to pull off, because the usual way to grow spend is to buy cheaper, worse traffic.

The mechanism here was the reverse. Average cost per click went up 44%, from $8.95 to $12.93. What moved was the conversion rate: 3.90% to 10.57%, nearly tripling. The account stopped buying cheap clicks and started buying the right ones.

All four figures in the Google Ads screenshot reconcile exactly against each other, in both the current and the prior window.

What the free channels tell you anyway

Google Business Profile produced 181 leads — more than either paid channel — at no media cost. Organic produced another 48. That is worth knowing for two reasons: it means the paid channels are not the only thing working, and it means the blended figures in any all-channel dashboard need unpicking before they mean anything.

The client

Demanding, and reasonably so. They wanted results from day one and were benchmarking against their pre-COVID numbers — a bar set in a different market, with different competition and different costs. For the first stretch we were fielding complaints roughly every week.

What we did not do

We did not chase the complaint. Loosening match types and buying cheaper clicks would have made the lead count jump and the cost per lead fall on paper, and it would have filled their phones with people who were never going to book. Instead the account was rebuilt around one question: what does a real first-time customer look like, and how do we buy more of exactly that?

How the leads are counted — this is the part that matters

Every lead is tracked in CallRail. A conversion is only counted for a first-time caller or a form submission. Repeat customers and abandoned calls are excluded, and only calls a human has marked as qualified get imported back into Google Ads as conversions.

That is why the numbers below do not agree, and why they should not: CallRail logged 539 contacts from Google Ads over the period — 480 calls and 59 forms — while Google Ads counts 467 conversions. Thirteen percent of the raw contact volume is deliberately not counted. Most reporting works the other way.

Where it stands

Cost per lead has halved. The weekly complaints stopped. The account now runs on a mix of Performance Max and Search, with 89% of all leads arriving as phone calls — which is what a bee removal business actually looks like, and why the whole account is built around the phone rather than the form.

What is next

Offline conversion imports are being implemented now, so the account will optimise against booked revenue rather than lead count. That is the step that changes the question from "what did a lead cost?" to "what did a customer earn?", and it is where this account goes from working to compounding.

The situation

A Portuguese port house with real heritage at home and effectively zero brand awareness in the United States, entering the market recently on about $2,300 a month across two campaigns. No US search history to bid against, no existing customer base to remarket to, and a product most American buyers think of once a year.

Why this category is harder than it looks

Alcohol is restricted content on Google. Under Google's alcohol policy, ads for the online sale of alcohol may only run in approved locations, targeting anyone below the legal drinking age is prohibited, and several formats are simply unavailable — dynamic search ads, Gmail ads, Discovery ads, app install ads and others are all off the table. On top of the platform rules sit the American state-by-state direct-to-consumer shipping laws, which decide where a bottle can legally be sent at all.

The practical consequence is that a lot of the usual growth levers do not exist here. What is left is search intent, shopping feed quality, and knowing exactly which geography can actually receive an order.

Read the dates before the numbers

The earlier window runs 3 November to 31 March. It contains Christmas — and port is a gifting product whose year is weighted heavily toward the festive season. The later window, 1 April to 27 August, is the off-season.

So the comparison is not flattering to the newer period; it is the opposite. Revenue more than doubled in port's quietest five months, measured against a period containing its busiest. A like-for-like seasonal comparison would show a wider gap, not a narrower one.

Where it stands

The account crossed 1.0×. At 0.64× every dollar of ad spend returned 64 cents and paid traffic was subsidised by the rest of the business. At 1.49× it pays for itself and contributes. That is the threshold that matters far more than the percentage improvement either side of it.