Leads Coming In. Budget Walking Out the Door.
The account wasn't broken on the surface — leads were coming in. But when we looked at the search term report, a large portion of spend was going to queries that had nothing to do with buying, selling, or renting property through an agent. Informational searches, competitor brand queries, job listings, and DIY intent were all triggering ads and burning budget.
The deeper issue was structural. Brand and non-brand terms were in the same ad groups, broad match was the dominant match type, and ad copy didn't match the specific value proposition on the landing page. The result: high spend, mediocre lead volume, and CPL well above the threshold needed to make the channel profitable.
Six Structural Problems Found in the Audit
The audit uncovered six separate issues — each one individually reducing lead volume or inflating cost. Together they created an account that was underperforming its own potential by a wide margin.
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We didn't tinker with bids and wait for improvement. We rebuilt the account structure from scratch — then optimized bidding on top of a clean foundation. Here's exactly what was done and why:
Campaign Architecture: Brand / Non-Brand / Competitor Separation
Moved all branded terms (agency name variations) into a dedicated low-bid branded campaign with a tight budget cap. Non-brand acquisition keywords went into separate campaigns by property type and intent. Competitor terms received their own campaign with specific messaging. This gave independent budget control, bidding strategies, and clean performance reporting for each intent tier.
Match Type Overhaul: Broad Replaced with Phrase and Exact
Audited every keyword in the account and rebuilt keyword lists using phrase match and exact match. Broad match retained only for specific research-phase keywords with strict negative exclusions applied. This immediately tightened which queries triggered ads — reducing irrelevant clicks without reducing reach for high-intent searches.
Negative Keyword Audit: 340 Exclusions Added
Reviewed 90 days of search term data and categorized all irrelevant query types: rental intent, job listings, educational/DIY content, competitor-name-only queries, news/price-check terms, and geographic mismatches. Built 340 targeted negative keywords across account, campaign, and ad group levels. Weekly search term reviews were scheduled to maintain list quality going forward.
Ad Copy to Landing Page Alignment (Message Match)
Rewrote ad headlines and descriptions to directly reflect the specific value proposition on each landing page. Where ad copy promised "Free Home Valuation," the landing page led with a Free Home Valuation headline and form — not a generic homepage. This alignment improved Quality Score, reduced CPC, and increased the probability that clicking users converted because the experience matched their expectation.
Mobile Landing Page Optimization: Form Above the Fold
Moved the lead capture form to above the fold on the mobile version of the primary landing page. Simplified the form to four fields: name, phone, property type, and timeline. Added a click-to-call button at the top of the page as a parallel conversion path. Mobile conversion rate improved significantly from this single change, validating the hypothesis that the form placement was a primary bottleneck.
Conversion Tracking Segmentation: Lead Quality Visibility
Separated conversion tracking into two distinct conversion actions: qualified form submissions (primary, used for bidding) and phone click-throughs (secondary, tracked but not used for smart bidding). This allowed the algorithm to optimize for the conversion type most correlated with actual qualified leads rather than treating all clicks equally. It also gave the team visibility into which campaigns and keywords drove high-quality vs. low-quality inquiries.
More Leads. Lower Cost. Better Quality.
All improvements delivered within 30 days — the structural changes took effect immediately, and the bidding algorithm recalibrated quickly because it was now working with clean, accurate conversion data. The CPL reduction and conversion increase both came from the same source: budget concentrated on the highest-intent queries rather than spread across broad-match waste.
| Metric | Before | After | Change |
|---|---|---|---|
| Monthly Conversions | Baseline | +103% | ↑ +103% |
| Cost Per Lead | Baseline | ↓21% | ↑ ↓21% |
| Negative Keywords | <40 terms | 340+ terms | ↑ Built |
| Irrelevant Click Share | High | Minimal | ↑ Eliminated |
| Brand Traffic Isolation | Mixed in | Separated | ↑ Cleaned |
| Mobile Form Placement | Below fold | Above fold | ↑ Fixed |
| Lead Quality Visibility | None | Full segmentation | ↑ Implemented |
| Timeline to Results | — | 30 Days | ↑ Fast |
The account's performance problem wasn't budget — it was relevance. By forcing every dollar to compete only for queries from people actively looking to buy or sell property, the account naturally produced more conversions at lower cost. Structure determines performance ceiling; bidding just optimizes within it.
Three Campaigns. Three Distinct Intent Tiers.
After restructuring, the account ran three clearly separated campaigns, each with its own budget, bid strategy, and keyword set — delivering clean data and independent optimization for each intent type:
Is Your Google Ads Account Wasting Budget the Same Way?
This case study is directly relevant if your account shows any of these signs: