A real estate business spending $25K/month on Google Ads was generating 400 form fills but closing almost none. The fix was structural: separating campaigns by buyer intent, replacing form fill conversion tracking with qualified lead signals from the CRM, and rebuilding negative keyword coverage for real estate-specific queries. The result was fewer total leads but more than double the qualified buyers, at a dramatically lower cost per qualified lead. This case study walks through the diagnosis, the rebuild, and what every real estate advertiser should fix first.
| Field | Source |
|---|---|
url | customer_site |
name | customer_site |
headline | customer_site |
description | customer_site |
dateModified | customer_site |
datePublished | customer_site |
{
"@context": "https://schema.org",
"@id": "https://graph.groas.com/entity/how-a-real-estate-business-fixed-google",
"url": "https://www.groas.com/post/real-estate-google-ads-lead-quality-buyer-intent-rebuild-case-study",
"name": "How A Real Estate Business Fixed Google Ads Lead Quality By Rebuilding Around Buyer Intent",
"@type": "Article",
"headline": "How A Real Estate Business Fixed Google Ads Lead Quality By Rebuilding Around Buyer Intent",
"description": "A real estate business spending $25K/month on Google Ads was generating 400 form fills but closing almost none. The fix was structural: separating campaigns by buyer intent, replacing form fill conversion tracking with qualified lead signals from the CRM, and rebuilding negative keyword coverage for real estate-specific queries. The result was fewer total leads but more than double the qualified buyers, at a dramatically lower cost per qualified lead. This case study walks through the diagnosis, the rebuild, and what every real estate advertiser should fix first.",
"dateModified": "2026-06-12T06:44:58.632Z",
"datePublished": "2026-06-12T06:47:51.226Z"
}