Woodrow on 30th
>7% click-through rate with full attribution to tour bookings
We rebuilt the measurement infrastructure and paid search system for Woodrow on 30th, a Baltimore student housing property facing a fixed academic-year leasing deadline with units to fill. Their previous advertising efforts lacked reliable conversion tracking, making it impossible to connect marketing spend to actual leasing outcomes—no visibility into which campaigns, keywords, or creative generated tour requests, applications, or leases. Our 4-week engagement delivered a complete attribution system: comprehensive conversion tracking implementation connecting ad interactions to tour bookings, high-intent Google Ads targeting students actively searching for housing near campus, efficient campaign structure optimized for qualified engagement rather than volume, and full-funnel attribution connecting spend directly to leasing interest. The results transformed their marketing effectiveness: 7%+ click-through rates confirming targeting precision, 100% conversion tracking coverage enabling data-driven optimization, and direct attribution of tour bookings to specific campaigns and keywords. For the first time, Woodrow on 30th could confidently scale advertising—knowing exactly which spend generated leasing outcomes—and hit their occupancy targets before the academic year deadline.
4 weeks · Paid search · Conversion tracking · Attribution
Results
Verified in analytics data
Click-through rate
>7%
Conversion tracking
100%
Budget efficiency
Optimized
Starting Point
Where They Started
Woodrow on 30th is a student housing property serving the Johns Hopkins University community in Baltimore. Located in Charles Village near campus, it offers furnished apartments specifically designed for graduate students—larger units than undergraduate housing, quiet study environments, and proximity to academic buildings. The property had strong fundamentals: quality units, reasonable pricing, good location. But it faced a constraint that defines student housing: the academic calendar. The leasing cycle for fall occupancy begins months in advance. Students secure housing in spring for August move-in. Missing this window means vacant units for an entire academic year—revenue loss that cannot be recovered until the next cycle. With a fixed deadline approaching, Woodrow had units to lease and limited time to lease them. The complication: their previous paid advertising had no reliable conversion tracking. They were spending on Google Ads and social media, but had no visibility into which campaigns generated actual leasing interest. Clicks were counted, but tours weren't tracked. Keywords were bid on, but no one knew which ones led to applications. This created several critical problems. First, optimization was impossible—without knowing what worked, they couldn't allocate budget effectively. Second, scaling was risky—increasing spend without attribution clarity meant potentially pouring money into ineffective channels. Third, timing pressure intensified every decision—the deadline was fixed, and every week of uncertainty meant missed opportunities to reach prospective tenants. Fourth, student housing search behavior is intense but brief—prospects actively search for a few weeks, then decide. Missing them during active search meant losing them entirely. The objective was establishing attributable tour bookings before the academic year began. Not just impressions. Not just clicks. Measurable tour requests that could be traced to specific campaigns, keywords, and creative—enabling confident optimization and scaling during the critical leasing window.
Primary constraints
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Fixed leasing deadline tied to the academic calendar
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No reliable conversion tracking on prior campaigns
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Ad spend could not be tied to tour bookings
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No confidence in scaling or adjusting spend
System Built
What We Built
01
Measurement foundation
We began with comprehensive conversion tracking implementation—the foundation for everything that followed. First, we audited existing Google Ads and Analytics setups, identifying gaps where tracking was broken, misconfigured, or missing entirely. We implemented enhanced conversion tracking: inquiry form submissions (prospects requesting information), tour scheduling (the critical conversion event), and application starts (indicating serious intent). Each conversion was tagged with campaign, ad group, keyword, and creative identifiers—creating complete attribution visibility. We configured Google Tag Manager to centralize tracking deployment, enabling rapid adjustments without code changes. Phone tracking was implemented for prospects who called rather than submitted forms. UTM parameters were standardized across all campaigns. The result was complete funnel visibility: we could trace a tour booking back through every touchpoint—search query, ad clicked, landing page visited, time on site, pages viewed, form submitted. For the first time, Woodrow had data instead of guesses.
02
High-intent targeting
With tracking established, we rebuilt campaigns around high-intent targeting. Student housing search behavior is specific—we needed to intercept prospects during active research, not passive browsing. Keyword strategy focused on terms indicating immediate housing need: 'student housing baltimore,' 'johns hopkins housing,' 'graduate student apartments baltimore,' 'furnished apartments near johns hopkins,' 'charles village apartments.' We excluded terms indicating research phase only ('average rent baltimore,' 'johns hopkins campus map') and undergraduate-focused searches ('dorms,' 'residence halls'). Geographic targeting was precise: radius around campus with bid adjustments for closest proximity. Ad copy spoke directly to graduate student concerns—quiet study space, furnished units, graduate community, proximity to specific academic buildings. Landing pages aligned with search intent: housing searches saw unit listings and availability, Johns Hopkins searches saw graduate-specific benefits, location searches saw neighborhood amenities. Campaign structure separated high-intent (bidding aggressively) from exploratory (lower bids, testing), enabling budget allocation based on demonstrated conversion likelihood.
03
Efficiency structure
Budget allocation was designed for efficiency, not volume. We implemented target CPA (cost per acquisition) bidding once sufficient conversion data accumulated, allowing Google's algorithms to optimize for tour bookings within defined cost parameters. Daily budgets were monitored and adjusted based on performance—shifting spend from underperforming ad groups to winners within 24-48 hours. Bid strategies varied by campaign type: manual CPC for high-intent exact match terms we wanted to dominate, automated bidding for broader discovery campaigns. Negative keywords were added aggressively—terms generating clicks but not tours were excluded to prevent budget waste. Ad scheduling optimized for when prospective students actually searched (evening hours, weekends) with bid modifiers. Device bid adjustments prioritized mobile (70%+ of student housing searches) while maintaining desktop presence. The 7%+ click-through rate validated our approach—ads were highly relevant to searchers, attracting qualified clicks rather than curious browsers. Every dollar was accountable: if it didn't contribute to tour bookings, it was reallocated or eliminated.
04
Attribution and optimization
The attribution system transformed how Woodrow approached paid advertising. Weekly optimization reviews identified winning patterns: which keywords generated tours at acceptable cost, which ad creative resonated (headlines mentioning 'Graduate Students' consistently outperformed generic 'Student Housing'), which landing pages converted best (unit detail pages beat general information pages). We implemented systematic A/B testing: ad copy variations, landing page layouts, call-to-action wording, form field requirements. Losers were paused within days; winners received increased budget allocation. The conversion data revealed insights beyond campaign optimization—seasonal search trends, competitive positioning (when competitors increased bids), and prospect research behavior (which unit features generated most interest). Most importantly, attribution created confidence. When a tour was scheduled, we knew exactly which search term, ad, and landing page produced it. This enabled decisive scaling: as the deadline approached, we could increase spend confidently, knowing which campaigns would deliver results. Woodrow hit their occupancy targets with time to spare—the first time their advertising had been truly accountable to leasing outcomes.
Proof and Details
The Complete Picture
Click-through rate
>7%
CTR exceeded 7 percent while maintaining efficient CPC, confirming targeting accuracy.
Conversion tracking
100%
All inquiries and tour requests tied directly to ads and keywords.
Targeting precision
High intent
Focused on location- and intent-specific student housing searches.
Attribution clarity
Full funnel
Ad spend connected directly to tour bookings.
Campaign velocity
Fast launch
Campaign deployed within days to meet academic-year deadlines.
What This Means
This model applies to businesses facing deadline pressure without attribution clarity. When measurement is established first, paid spend can be directed toward real outcomes rather than activity.
If you are spending without attribution, request an audit.
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