More demand. Less wasted spend.

A multi-location garage services company, storage and organization systems, across four major U.S. metropolitan markets, engaged January 2026. Roughly $250K in H1 media managed across Google and Meta.

• Google Ads leads, all four markets +67% 274 → 457, at 26% lower cost per conversion · last 90 days vs prior 90
• Average Meta cost per lead, controlled test -45% Up to 61% in one market · March 2026 phased rollout
• Leads marked qualified in CallRail 5 / 2,739 Across six months, all sources, flagship brand · Jan–Jun 2026

An established multi-location advertiser spending six figures a quarter with call tracking already in place. The trouble sat one layer up.

• 01 Five separate websites and five separate ad accounts, one per market, which split organic authority across five domains and gave each ad account a data pool too small to optimise
• 02 Reporting ran through manually keyed spreadsheets maintained by an outside fractional-CMO layer instead of conversions flowing into the CRM, adding approval steps without adding data
• 03 Performance was benchmarked against 2021, a lockdown-era peak for home-improvement demand, and judged week by week, a window too short for any ad platform to demonstrate anything

They could not tell what their previous marketing companies had actually been doing, and the numbers those companies reported did not survive contact. Prior campaigns were built around keywords with no connection to buying intent, and the conversion goals counted duplicates, so the reported cost per lead looked far lower than the true one. The business was steering off instruments that read wrong.

Once we cleaned the lead flow, deduplicated conversion counting, and rebuilt tracking so every source reported honestly, the marketing measurement problem was solved. What that exposed was the layer behind it: a sales operation and company structure that never picked the leads up. 2,739 leads reached the client's own CallRail in six months; 5 were marked qualified, and three systems (the ad platform, the CRM, and call tracking) each held a different lead count with no reconciliation between them. The constraint was never lead volume. It was everything after the lead.

The company's CRM had built-in call tracking. It had never been used. The prior marketing operation, run under the same fractional-CMO layer, marketed itself as specialist in that exact platform, and the platform's most basic measurement feature sat switched off for the entire relationship. When we cleaned the phone tracking and the attribution, years of broken automations surfaced at once, and the fix was blamed for the mess it revealed. That is the pattern: when measurement finally works, it exposes everything built on top of the old numbers, and the messenger takes the hit.

Nothing on the accounts was touched for the first two weeks. Then five moves ran in sequence so every later result could be attributed to a specific decision, not to the sum.

Merged five market-siloed websites into one multi-location site, pooling five domains' worth of fragmented organic authority into a single domain that could actually rank.

Same logic on Google Ads: unified the market-siloed account structure so the platform sees the whole business's conversion data, not five starved slices of it.

As the accounts stabilised, we built exact-match conversion actions across Google, Meta, and Google Business Profile, one definition of a lead, counted once, everywhere. This is what made every number on this page comparable.

With clean measurement in place, we ran a phased Meta experiment: 12 days on lookalike audiences alone as the control, then first-party customer data layered in, so any change in cost per lead could be attributed to the data, not to luck.

Did not raise total budget to chase volume. Did not adopt the weekly evaluation cadence or the 2021 baseline. Did not take over the client's internal lead qualification, which sat outside the ad accounts and turned out to be the binding constraint.

• 01 Consolidate the web
• 02 Consolidate the ads
• 03 Standardise conversions
• 04 Then test, controlled
• 05 Did not do

The account the way an auditor reads it: every stage from search to conversion, last 90 days against the prior 90, all four markets, enabled and paused campaigns included so nothing is cherry-picked.

Reading the chain: more people searched, we captured a slightly larger share, bought clicks 7% cheaper at a higher click-through rate, and converted them 26% more efficiently into 67% more leads. Cost rose 23% by design, to fund the added volume. Every controllable metric improved. The one zero on the board is the number only the client's own systems could supply, and it never arrived.

Leads up 35% to 123% in every market. Cost per conversion fell in three of four. In Metropolitan 4 spend nearly doubled to capture a 135% surge in search demand, and cost per conversion held within 5% while leads rose 89%. Buying 89% more leads at roughly flat unit cost during a demand spike is a win, and it is disclosed as the trade it was.

Preview outline below; each ships in the same rhythm as the sections above, using the numbers already verified on Dimi's mockup.

• 08 The Meta customer-data test. Four per-metro phased tables (Phase 1 lookalike vs Phase 2 + customer data)
• 09 Cross-market summary. Average CPL $51.08 → $27.70, -45%
• 10 Organic and local. Rankings, traffic, non-brand share, +62% clicks, ~2.8× est. traffic value
• 11 Local search. 48% of all leads from Business Profile / Maps at zero media cost
• 12 The result. 10-row before/after table with “Loop open” on the two revenue rows
• 13 What went sideways. Meta Q2 decline + the manual reporting layer
• 14 What the client acknowledged. On the data, and on the revenue column
• 15 Limitations and where this transfers
• 16 Why this matters for you
• 17 How to trust these numbers. Four-point evidence design