From Five Signals to Fifty
How Bozzuto unlocked Google's AI with better data, and cut its cost per lead by 62%
94%
increase in conversion rate
62%
reduction in cost per lead
19%
increase in click-through rate
About Company
Bozzuto is one of the US's most established residential property management companies, overseeing more than 300 properties and earning a reputation for creating communities where people genuinely want to live.
Company size
Enterprise
Industry
Real Estate
Brainlabs featured services
SEO
Paid Social
Data Strategy
Paid Search
See what Brainlabs can do for your team
Speak to a BrainlabberGoogle Ads was generating leads. Just not the right ones.
For Bozzuto, Google Ads was a staple of lead generation across their portfolio of more than 300 properties. But beneath the numbers, something wasn't working. Traditional tracking methods could only capture surface-level interactions, which meant Smart Bidding models were optimising on incomplete data. Leasing teams were being flooded with unqualified leads from people casually browsing rather than seriously considering a move. The system was generating activity, but it couldn't distinguish between a curious click and a genuine renter.
The root cause was a data gap. With only five conversion signals feeding into Google's AI, the algorithm had no meaningful way to separate high-intent behaviour from low-intent noise. It was optimizing for what it could see, and what it could see wasn't enough.
Key friction points:
- Smart Bidding models were optimizing on only five conversion signals, missing the vast majority of meaningful renter behaviour
- Leasing teams were handling high volumes of unqualified leads with no way to filter them upstream
- Without granular conversion data, budget was being allocated based on guesswork rather than intent
"The delivery, insights, and recommendations were unparalleled, and we truly appreciate it. The ability to feed real leasing behavior into Google Ads has completely changed how we allocate spend and target renters. Let's keep this positive momentum going, we have much to accomplish together.”
Xiyao Yang
VP of Digital Marketing
From five signals to fifty, rebuilt from the ground up
1. Built a custom measurement framework using GA4 and Server-Side GTM
After identifying the data gap as the core problem, the team integrated GA4 with Server-Side Google Tag Manager to build a completely bespoke tracking infrastructure across all 300+ properties. This replaced unreliable client-side tracking with a more accurate, consistent data layer and reduced inconsistencies in event tracking at scale. Critically, it expanded conversion tracking from five signals to fifty, with custom event mapping covering every meaningful touchpoint in the renter's decision journey from first visit through to lease inquiry.
2. Fed richer data into Smart Bidding to transform how Google's AI operated
With the new infrastructure in place, real-time conversion data began flowing into Google Ads for the first time. The transformation in how Smart Bidding operated was significant. Models that had previously functioned as simple auction-based optimisers evolved into intelligent, behaviour-driven decision-makers. Google's AI now knew when to push spend aggressively on high-intent users and when to pull back on low-intent traffic, ensuring maximum efficiency across every campaign. By closing the data gap, the AI evolved into a genuine lead-generation tool, ensuring that high-intent users were not just targeted but converted at a level that hadn't been possible before.
3. Continuously refined audience segmentation and bidding as the data matured
Performance monitoring wasn't a final phase. It ran throughout the entire process. By continuously refining audience segmentation and bidding strategies as the system accumulated richer behavioural data, the team kept the model agile across all 300+ properties. The gains didn't plateau after launch. They compounded.
After identifying the data gap as the core problem, the team integrated GA4 with Server-Side Google Tag Manager to build a completely bespoke tracking infrastructure across all 300+ properties. This replaced unreliable client-side tracking with a more accurate, consistent data layer and reduced inconsistencies in event tracking at scale. Critically, it expanded conversion tracking from five signals to fifty, with custom event mapping covering every meaningful touchpoint in the renter's decision journey from first visit through to lease inquiry.
2. Fed richer data into Smart Bidding to transform how Google's AI operated
With the new infrastructure in place, real-time conversion data began flowing into Google Ads for the first time. The transformation in how Smart Bidding operated was significant. Models that had previously functioned as simple auction-based optimisers evolved into intelligent, behaviour-driven decision-makers. Google's AI now knew when to push spend aggressively on high-intent users and when to pull back on low-intent traffic, ensuring maximum efficiency across every campaign. By closing the data gap, the AI evolved into a genuine lead-generation tool, ensuring that high-intent users were not just targeted but converted at a level that hadn't been possible before.
3. Continuously refined audience segmentation and bidding as the data matured
Performance monitoring wasn't a final phase. It ran throughout the entire process. By continuously refining audience segmentation and bidding strategies as the system accumulated richer behavioural data, the team kept the model agile across all 300+ properties. The gains didn't plateau after launch. They compounded.
Fifty signals later, the results made the case for rebuilding
Closing the data gap didn't just improve campaign efficiency. It changed what the campaigns were capable of. Google's AI, now working with a complete picture of renter behaviour rather than a partial one, could allocate budget with a level of precision that simply wasn't possible before.
- 94% increase in conversion rate
- 62% reduction in cost per lead
- 19% increase in click-through rate
94%
increase in conversion rate
62%
reduction in cost per lead
19%
increase in click-through rate
