TECHNICAL CASE STUDY
Marketing Technology Stack
About This Case Study: I walk through the marketing intelligence platform I built from the ground up. It shows how I take messy data, clean it, score it, enrich it, and turn it into something that improves targeting, strengthens acquisition quality, and drives measurable business impact. It’s the clearest example of how I solve growth, data, and performance problems end to end.
Building Fair Housing-Compliant Marketing Technology That Scores, Validates, and Distributes Leads in Real-Time—Delivering $112K+ in Annual recurring savings
I designed and built a full marketing intelligence platform from the ground up for a luxury senior living organization. It takes raw form data, cleans it, scores it, enriches it with financial signals, and routes it across CRM and advertising systems in real time. The platform strengthened targeting, improved acquisition quality, reduced waste, and delivered measurable business impact — all while maintaining strict Fair Housing compliance.
Senior living marketing operates under the Fair Housing Act, which prohibits targeting by age, disability, or family status. Standard demographic approaches are illegal—yet the business needs to identify prospects with $300K-$500K+ financial capacity.
This system solves that paradox: scoring lead quality using voluntarily-provided data (home address) + public Census aggregates—evaluating financial capacity without collecting protected characteristics. Critically, this scoring is used only for lead qualification and sales prioritization, never for ad targeting, ensuring compliance with both the Fair Housing Act and Google's Fair Housing advertising policy.
Between 2021-2024, over $500K was invested in agencies that couldn't solve these problems. This platform delivers capabilities vendors couldn't provide, now saving $112K annually while supporting a $216M revenue organization across four brands.
A luxury senior living community targeting affluent retirees ($300K-$500K+ lifetime value per resident) faced challenges that vendor platforms couldn't solve:
The Fair Housing Act creates unique challenges:
This makes senior living unique: identify affluent seniors without using age or targeting high-income ZIP codes.
Five-dimension algorithm evaluates financial capacity using only compliant data sources: user-provided address (converted to property value), public Census aggregates (income + 55+ demographics by ZIP), behavioral signals, and location. Generates 0-400 point scores without collecting individual age, disability, or family status.
Direct API delivery of scored, validated lead records to CRM (0-400 point score, enriched demographics, validation status) in under 2 seconds—eliminating manual data entry and enabling immediate sales prioritization.
Comprehensive validation blocking 90% of bot traffic through global spam databases, honeypot fields, behavioral detection (reCAPTCHA), real-time email/phone validation, and address verification (USPS).
Automated integration with Census Bureau (income + 55+ demographics), property valuation service (home values), email validation API (deliverability), phone validation API (carrier type), and USPS (address standardization).
Custom tracking system surviving WordPress redirects, URL parameter stripping, and domain changes—ensuring 100% attribution accuracy for Google Ads optimization despite complex technical infrastructure.
Direct Google Ads API integration with OAuth 2.0, enhanced conversions (SHA-256 hashed PII), and dynamic value mapping ($30-$12,000 based on lead score)—immune to ad blockers and browser failures.
Processing Time: Average 1.8 seconds from submission to delivery
This infrastructure represents net-new capabilities built from scratch—each solving problems that $500K+ in agency spend couldn't address:
The Challenge: Score lead quality based on financial capacity without collecting or using age, disability, or family status data. Cannot target by ZIP codes in advertising.
The Solution: Five-dimension algorithm using only compliant data sources:
Key Innovation: The algorithm reads user-provided data (address field) and combines it with public Census aggregates, then does comparisons based on the brand's ideal prospect research. Scores "can they afford this" without ever asking or inferring individual age/disability status. Used for lead SCORING only—never for ad TARGETING (which is prohibited by Google's Fair Housing policy).
Result: 0-400 point score indicating financial capacity and purchase intent, enabling sales prioritization without Fair Housing violations.
The Challenge: No real-time CRM integration—manual data entry creating delays and errors.
The Solution: Direct API integration delivering:
Result: Elimination of manual data entry, immediate sales response capability, complete lead tracking from capture to CRM record.
The Challenge: 60%+ of form submissions were bot traffic or invalid contacts, wasting sales team time.
The Solution: Multi-layer validation operating in parallel:
Result: 90% spam reduction with <0.5% false positive rate—dramatically cleaner data for sales teams.
The Challenge: WordPress redirect plugin stripped Google Ads click IDs (GCLID) from URLs during redirects, breaking conversion attribution.
The Solution: Four-layer capture strategy:
Result: 100% attribution accuracy across redirect chains, parameter stripping, and AJAX form loading—maintaining perfect Google Ads tracking.
The Challenge: Client-side tracking (GTM) failed due to ad blockers, browser settings, and page redirects. Needed value-weighted conversions ($30-$12,000) to enable Target ROAS bidding.
The Solution: Direct API integration with:
Result: 100% upload success rate, 83% improvement in conversion values, enabled Target ROAS bidding—Google Ads now self-optimizes toward high-value leads.
The Challenge: Multiple event sources (form plugin native events, GTM listeners, custom code) created 2-3 conversion events per submission, inflating reporting 200-300%.
The Solution: Event orchestration system featuring:
Result: Perfect 1:1 ratio of submissions to conversion events—tested across 1,000+ production submissions with zero duplicates.
The Challenge: Need to coordinate 8 external APIs (Real-Time CRM, Census, property valuation, email validation, phone validation, USPS, Google Places, Google Ads) in under 2 seconds while handling rate limits and failures.
The Solution: Centralized API management with:
Result: 99.7% API success rate, 850ms average orchestration time, sub-2-second total delivery—reliable at scale.
Replacing External Vendor Reliance with Internal Platform: The platform I built replaced the entire external vendor stack, reducing cost and improving lead speed, quality, ad attribution, and control.
Annual Recurring Savings: $112K
More importantly: This platform supports sales operations for a $216M revenue organization across four brand campaigns through Fair Housing-compliant lead intelligence and value-based optimization—demonstrating end-to-end capability in marketing systems engineering, regulatory compliance, and revenue operations.
Challenge: The Fair Housing Act prohibits collecting or using age, disability, or family status—yet the business needs to identify affluent seniors. Additionally, cannot target ads by specific ZIP codes (geographic discrimination).
Approach: Built scoring using only compliant data: voluntarily-provided home address (converted to property value via API) + public Census aggregates (median income, $200K+ household %, 55+ population % by ZIP) + contact validation (email/phone quality) + behavioral signals (tour request, engagement level). Algorithm reads this data and does comparisons based on brand's ideal prospect research.
Innovation: The address field serves dual purposes—it's necessary for sales follow-up AND provides the key financial capacity signal (property value) without collecting protected data. ZIP demographic data (including 55+%) is used for lead SCORING after submission, never for ad TARGETING (which is prohibited by Google's Fair Housing policy). Scoring evaluates "can they afford this" without ever collecting or inferring individual age.
Impact: A compliant intelligence layer that gives the sales team clearer visibility into lead quality, highlights high value prospects earlier, and supports better decision making while staying aligned with Fair Housing requirements.
Challenge: WordPress redirect plugin executed before JavaScript tracking scripts could capture Google Ads click IDs (GCLID), breaking attribution.
Approach: Created WordPress MU-plugin (must-use, cannot be disabled) that hooks into redirection_before_redirect filter—capturing parameters from redirect target URL before redirect executes. Combined with custom /utm-bridge endpoint and client-side sessionStorage backup.
Innovation: Standard solutions rely on JavaScript (too slow) or URL rewriting (breaks existing redirects). This approach intercepts at WordPress execution layer, capturing data server-side before any redirect processing.
Impact: 100% attribution accuracy across any redirect configuration—enabling full confidence in Google Ads optimization data.
Challenge: Multiple event sources (form native events, GTM triggers, custom JavaScript) created duplicate conversion events, inflating reporting 200-300%.
Approach: Overrode window.dataLayer.push method to intercept all events before GTM processes them. Implemented multi-flag state machine tracking submission across validation errors and AJAX reloads. Added guarded dual-push strategy (main + backup) to survive immediate redirects.
Innovation: Rather than trying to coordinate multiple event sources, centralized control by intercepting the single point where all events flow (dataLayer). State flags persist across form re-renders and validation failures.
Impact: Perfect 1:1 submission-to-event ratio validated across 1,000+ production submissions—accurate reporting enables data-driven optimization.
This marketing technology platform operates in production, processing hundreds of leads monthly across four brands with 99.7% reliability. After investing $500K+ in external vendors that couldn't solve these problems, this system now delivers Fair Housing-compliant demographic scoring and server-side conversion tracking—capabilities vendors couldn't provide.
Beyond cost savings, the platform supports sales operations for a $216M revenue organization across four brand campaigns through Fair Housing-compliant lead intelligence and value-based optimization—demonstrating end-to-end capability in marketing systems engineering, regulatory compliance, API integration, and revenue operations. These skills are directly transferable to scaling marketing technology infrastructure in any regulated industry.
Michael Tripari
Marketing Web Strategist & Systems Engineer
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