Lead Generation -- Scale

Real Estate Lead Generation: CPA Reduced 59% on a Local Account

A scale-tier Real Estate lead-generation account operating at $3,300 per month reduced cost-per-lead from $41.73 to $17.30 (-59%) and grew conversions from 212 to 460 (+116%) over a 90-day window.

CPA Reduction

$17.30

-58.5%

Conversions

459.62 (+116.4%)

vs. $320 target CPA

94% below target

A scale-tier lead-generation account in the Real Estate vertical operating at a $3,300 monthly Google Ads budget reduced cost-per-lead from $41.73 to $17.30 (-58.54%) and grew conversion volume from 212.4 to 459.62 (+116.39%) over a 90-day optimization window. The account is operating dramatically below its stated $320 target CPA.

Key Takeaways

  • CPA reduced 59% against a baseline that was already well inside the $320 target.
  • Conversion volume more than doubled (+116%).
  • Account is now operating at approximately 5% of the target CPA, indicating that the business target was set conservatively relative to achievable performance.
  • Spend declined 10% while conversions doubled — a pure efficiency-extraction case.

The Account

A scale-tier lead-generation account in the Real Estate vertical operating locally at a $3,300 monthly budget with a target CPA of $320. The $320 target reflects the high lifetime value of real estate leads — a single converted lead can justify a substantial acquisition cost.

The Challenge

MetricBaseline (90 days)
Spend$8,863.40
Conversions212.40
CPA$41.73
CTR2.87%
Impressions86,420

CPA at $41.73 was already 87% below the $320 target, so the account was healthy by business standards. The team chose to push it further rather than accept the healthy baseline.

The Approach

Step 1: Search-terms audit. 27 terms flagged across a single campaign in one review session, with exclusions applied immediately.

Step 2: Keyword additions. 3 keywords added in a single session to strengthen the positive targeting signal.

Step 3: Weekly review cadence. Search-term reports reviewed weekly, with flagged terms processed immediately.

Step 4: Structural negative keyword list. Low-intent query patterns (information seekers, competitors) were excluded at the campaign level.

The Results

Over the 90-day optimization window (September 29 to December 28, 2025):

MetricBefore (90 days)After (90 days)Change
Spend$8,863.40$7,950.92-10.3%
Conversions212.40459.62+116.4%
CPA$41.73$17.30-58.5%
CTR2.87%2.06%-28.2%
Impressions86,420109,766+27.0%
Clicks2,4802,257-9.0%

The account doubled conversion volume while reducing spend, with fewer clicks than before. The underlying dynamic: click-to-conversion rate more than doubled as the remaining clicks were from higher-intent users.

Lessons Learned

  1. Conservative business targets leave optimization headroom on the table. The $320 target was set for business-risk reasons, but the actual achievable CPA is under $20. This opens a conversation with the business about scale-up.

  2. Real estate lead generation rewards intent filtering. Real estate queries attract a high volume of information seekers (renters, market researchers, agents). Aggressive negative keyword work dramatically improves lead quality.

  3. CTR decline is not always a warning. In this case, CTR fell 28% because impressions grew (broader reach) while clicks declined (tighter filtering). The mix is healthier even though the ratio appears worse.

  4. Doubling conversions on reduced spend is the best possible outcome. There is no downside to this pattern — every metric is pointing in the right direction.

Methodology Note

Data sourced from a managed Google Ads account in the Real Estate vertical operating at the scale budget tier with a local (single-market) footprint. All identifying information has been removed. Performance metrics reflect the best 90-day window (September 29 to December 28, 2025) compared against the prior 90-day baseline (July 1 to September 28, 2025). The account executed 63 documented optimization actions during the measurement period. ROAS metrics are not cited because lead-generation accounts in this dataset do not reliably track conversion value. Metrics reported in USD.

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