Three home service companies. Three structural problems hiding in plain sight. Here's exactly what we found — and the before/after numbers that prove it.
“We were spending more than ever and somehow making less. Turns out two of our vendors were basically billing us to confuse us. The diagnostic paid for itself in the first month.”— Owner, Foundation Repair Company — Midwest
A mid-sized foundation repair company had been running paid media for three years. Lead volume was steady, but margins were quietly shrinking. Monthly ad spend had crept up to $22,000 — distributed across Google Search, Local Service Ads, two lead aggregators, and a direct mail vendor.
After a full revenue diagnostic, we mapped every lead source against booked jobs — not just form fills. Two channels (one lead aggregator and the direct mail campaign) were consuming $9,400/month but producing fewer than 8% of booked jobs. The close rate drop was explained by lead quality degradation from the aggregator — unqualified leads flooding the CSR team.
Eliminated the lead aggregator contract ($4,200/mo) immediately
Paused direct mail ($5,200/mo) and redirected into Google LSA
Built a lead scoring protocol for the CSR team
Established true attribution tracking cost from click to closed contract
Implemented a 48-hour follow-up automation sequence for unconverted leads
| Metric | Before | After | Change |
|---|---|---|---|
| Monthly Ad Spend | $22,000 | $12,600 | |
| Cost Per Booked Job | ~$520 | ~$322 | |
| Close Rate | 28% | 41% | |
| Active Channels | 5 | 3 | |
| Net Margin | Baseline | +14 pts |
Results are representative. Individual outcomes vary based on company size, market conditions, and existing infrastructure.
“The problem wasn't our closers — it was that we were treating a $14,000 insurance job the same as a $1,200 repair quote. Once we separated the pipelines, everything changed.”— Owner, Roofing Contractor — Southeast
A Southeast roofing contractor with a strong local reputation was generating solid lead volume — roughly 280 estimates per month. On paper, the business looked healthy. In reality, they were closing only 19% of estimates, well below the regional benchmark of 31–35%. Leadership assumed it was a sales team issue. It wasn't.
The revenue map revealed three distinct lead categories funneled into one homogenous pipeline. Insurance claim leads — the highest-value segment — were receiving the same 6-day follow-up cadence as low-intent retail leads. Storm aggregator leads had a 9% close rate and were consuming 38% of sales team bandwidth. Meanwhile, organic and referral leads — closing at 47% — had no dedicated nurture pathway.
Segmented the pipeline into three tracks: insurance claims, organic/referral, and paid acquisition
Built a 4-hour response SLA specifically for insurance claim leads
Reduced storm aggregator spend by 60% — redirected to Google LSA
Created a referral acceleration program to increase organic lead volume
Implemented a 90-day nurture sequence for unconverted estimates
| Metric | Before | After | Change |
|---|---|---|---|
| Monthly Estimates | 280 | 280 | |
| Close Rate | 19% | 31% | |
| Avg Lead Response Time | 6.2 days | 4 hours | |
| Aggregator Budget Share | 38% | 15% | |
| Annualized Revenue Impact | Baseline | +$1.4M |
Results are representative. Individual outcomes vary based on company size, market conditions, and existing infrastructure.
“We were effectively paying $20K a month for noise. Once we cut the two worst channels, everything became clear.”— Marketing Director, Restoration Company — Multi-Market
A restoration company operating across three markets was spending aggressively on digital marketing — $34,000/month across six channels. ROAS hovered around 1.4×, barely covering the cost of the campaigns. Campaign managers were optimizing for clicks — not closed revenue.
Full revenue mapping revealed two channels (a display retargeting network and a local sponsorship hybrid) were collectively spending $20,400/month and contributing 11% of booked jobs. Meanwhile, Google Search in Market 2 — running at a $380 cost-per-booked-job with a 62% upsell rate — was receiving only 12% of the total budget.
Defunded Channel 4 and Channel 6 entirely — freed $20,400/month
Tripled Google Search investment in Market 2
Rebuilt optimization goals around cost-per-booked-job, not cost-per-click
Developed market-specific creative for each of the three markets
Built a revenue attribution dashboard connecting spend to closed contract value
| Metric | Before | After | Change |
|---|---|---|---|
| Monthly Ad Spend | $34,000 | $34,000 | |
| Blended ROAS | 1.4× | 3.8× | |
| Active Channels | 6 | 4 | |
| Budget on Top Channel | 12% | 36% | |
| CPJ on Best Channel | $380 | $141 |
Results are representative. Individual outcomes vary based on company size, market conditions, and existing infrastructure.
Every one of these companies thought they had a lead problem. They didn't. Book a Revenue Diagnostic and find out what's actually happening in your marketing ecosystem.
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