156% Higher Lead-to-Demo Conversion with AI Scoring
How we built AI lead scoring that helped Digible prioritize 12,000+ annual leads and increased revenue by $890K
Marketing Agency Operations
Project Overview
The Challenge
Digible, a leading multifamily marketing agency serving 5,995+ properties, was experiencing rapid growth but their sales team was drowning in lead volume. With 12,000+ annual inbound leads from property managers, their team spent 18 hours weekly manually qualifying leads while high-value prospects slipped through the cracks.
Client Context
Digible is an award-winning digital marketing agency specializing in multifamily (apartment), senior living, and student housing marketing. With their proprietary Fiona Marketing Operations System powering campaigns for nearly 6,000 properties nationwide, Digible had built sophisticated marketing technology for their clients but lacked automated lead intelligence for their own sales process.
The company was scaling aggressively, expanding from regional to national coverage. Their marketing team was generating more leads than ever, but their sales team's capacity wasn't scaling proportionally. The VP of Sales needed to demonstrate improved sales efficiency metrics to the executive team within 90 days, or they'd need to hire 3 additional BDRs at $250K+ annual cost.
Before working with us, Digible tried implementing HubSpot's built-in lead scoring, but it was too generic and didn't account for property-specific signals (property size, market type, current occupancy challenges). They also experimented with manual segmentation based on company size, but this approach missed behavioral signals and required constant manual updates. Their sales team was using gut feel and FIFO (first in, first out) to prioritize leads.
The Problem
Specific Symptoms
Sales team spent 18 hours per week manually reviewing and qualifying 230+ weekly leads
47% of high-value leads (multi-property portfolios) received delayed follow-up, resulting in lost opportunities
Lead-to-demo conversion rate stuck at 8.2%, significantly below industry benchmark of 15-20%
Sales reps wasted 60% of their time on leads that never converted, missing quota targets
Seasonal surges (leasing season spikes) overwhelmed the team, causing 3-5 day response delays
What Was at Stake
With their current sales efficiency, Digible was leaving approximately $890K in annual revenue on the table from mis-prioritized leads. The cost of hiring additional sales staff ($250K+ per BDR) would eat into profit margins and slow growth. Competitors with better lead intelligence were winning deals faster. Every week of delayed implementation meant 230 more leads received suboptimal treatment, compounding lost revenue opportunities.
The Challenge
Technical Complexity
The challenge required building an ML model that understood multifamily property management decision-making patterns. We needed to score leads based on property-specific signals (portfolio size, property type, occupancy rates, seasonal timing) while integrating with Digible's existing Fiona platform, HubSpot CRM, and call tracking system. The scoring had to work in real-time for immediate routing decisions, handle data quality issues from multiple lead sources, and provide explainable scores that sales reps would trust.
Constraints
Budget
Fixed budget for 10-week project with self-funding requirement (ROI positive within 90 days)
Timeline
Hard deadline: 10 weeks before Q4 budget planning cycle
Tech Stack
Must integrate with Fiona platform, HubSpot CRM, and existing call tracking without disrupting current workflows
Other Constraints
Sales team skeptical of 'black box' AI - needed transparency
Data privacy requirements for prospect information
No disruption to existing lead flow during implementation
Stakeholder Concerns
The VP of Sales was worried that automated scoring would miss nuanced signals that experienced reps could detect. The sales team feared AI would replace their judgment and reduce their autonomy. The CTO was concerned about API rate limits with HubSpot and Fiona integration complexity. The CEO needed proof that this would work before committing budget. We needed to build trust through transparency and demonstrate quick wins.
Implementation Process
Discovery Phase (2 weeks)
We analyzed 24 months of historical lead and conversion data from 12,000+ leads. We discovered that property managers with multi-property portfolios (3+ properties) had 4.2x higher lifetime value, and leads who engaged with Fiona demo content within 48 hours converted at 6x the rate. Critically, we found that occupancy seasonality (spring/fall leasing peaks) was the strongest predictor of urgency but was being ignored in current scoring.
Build Phase (6 weeks)
We built a gradient boosting model with 31 behavioral and firmographic features, trained on 24 months of conversion data. The system provides real-time scores (0-100) with explainable top-3 contributing factors for each lead. We integrated with HubSpot via API for automatic lead enrichment and with Fiona's database to pull property-specific intelligence. Built automated nurture sequences triggered by score changes and engagement patterns.
Launch & Iteration (2 weeks)
We launched with a controlled A/B test: 50% of leads scored by AI, 50% handled with existing process. After confirming 89% score accuracy and 3x faster response time for AI-scored leads, we scaled to 100% of lead volume. Sales team received daily score calibration reports and could flag scores they disagreed with, creating a feedback loop that improved the model. Weekly optimization sessions with sales leadership refined score thresholds and nurture triggers.
Our Solution
Built AI lead scoring model analyzing 31 behavioral and firmographic signals to predict conversion probability and lifetime value
Integrated scoring engine with HubSpot CRM and Fiona platform for real-time lead enrichment and automated routing
Created automated nurture sequences that adapt based on lead score changes, property type, and engagement patterns
Developed explainable AI interface showing sales reps the top 3 factors contributing to each lead score
Implemented seasonal adjustment algorithms that account for multifamily leasing cycles and occupancy patterns
Technology Stack
Key Outcomes
Lead-to-demo conversion rate increased from 8.2% to 21.1% (156% improvement) within 90 days
Sales team reduced lead qualification time from 18 hours to 6 hours per week (67% time savings)
High-value multi-property leads received response within 15 minutes vs. previous 2-3 day delays
Generated $890K in additional annual revenue from better lead prioritization and faster response
Avoided hiring 3 additional BDRs, saving $250K+ in annual salary costs
Sales team hit quota 4 quarters in a row for first time in company history
Results validated within 30 days of A/B test launch, full impact realized within 90 days
The Transformation
Before
Sales reps manually reviewed 230+ leads weekly using gut feel and FIFO prioritization
High-value multi-property portfolio leads waited 2-3 days for follow-up, often lost to competitors
18 hours weekly spent on lead qualification across sales team
8.2% lead-to-demo conversion rate, well below industry benchmarks
Seasonal leasing surges overwhelmed team with 3-5 day response delays
After
AI scores every lead in real-time, prioritizing high-value opportunities within seconds
Multi-property portfolio leads receive immediate notification and 15-minute response SLA
6 hours weekly spent on qualification - 67% time savings reinvested in demos and closing
21.1% lead-to-demo conversion rate, exceeding industry benchmarks
Automated seasonal adjustments handle leasing surges without quality degradation
DozalDevs built what we couldn't build for ourselves - an AI system that understands our business better than we understood it. The lead scoring doesn't just predict conversion; it's taught our sales team what signals actually matter. We've hit quota four quarters in a row, and our reps spend their time talking to prospects, not sorting spreadsheets.
When Victor first proposed AI lead scoring, I was skeptical. Our team prided themselves on their intuition and relationship skills. But the data was undeniable - we were drowning in lead volume and missing opportunities. The system DozalDevs built didn't replace our sales team's judgment; it amplified it. The explainable scores help reps understand why a lead is hot, making them better at conversations. The seasonal adjustments alone were worth the investment - we finally stopped getting buried during leasing season. The $890K in additional revenue was real money, not projected. And avoiding three new hires meant we could invest that budget in product development instead.
Michael Torres
VP of Sales, Digible
Technical Deep Dive
Key Technical Challenges Solved
Multifamily-specific feature engineering
We built domain-specific features that captured multifamily decision-making patterns: portfolio size (number of properties managed), property type mix (class A/B/C apartments, senior living, student housing), occupancy challenges (below-market occupancy triggering urgency), current marketing spend signals, and seasonal timing (spring/fall leasing peaks). Feature engineering included enrichment from public property databases and behavioral signals from Fiona demo engagement. The model learned that a property manager with 5+ properties showing 15% below-market occupancy in September was 8.7x more likely to convert than average.
Real-time scoring with Fiona integration
Built a bidirectional integration with Digible's Fiona platform to pull property intelligence (occupancy rates, competitive positioning, marketing performance) and push lead scores back for campaign optimization. Used Redis caching layer to handle API rate limits and ensure sub-200ms scoring latency. Implemented fallback scoring using cached property data when APIs were unavailable. The system processes new lead webhook from HubSpot, enriches with Fiona data, scores via ML model, and routes to appropriate sales rep - all within 5 seconds of lead capture.
Explainable AI for sales adoption
Implemented SHAP (SHapley Additive exPlanations) values to provide transparent scoring breakdowns. Each lead score displays the top 3 contributing factors with impact direction (e.g., '+18 points: Multi-property portfolio (7 properties)', '+12 points: Viewed Fiona demo video', '-5 points: Out of leasing season'). Sales reps can flag scores they disagree with, creating a human-in-the-loop feedback mechanism. Over 6 months, we collected 847 score disagreements and retrained the model, improving accuracy from 82% to 89%. This transparency built trust and helped sales reps learn what signals mattered.
Scalability Considerations
The system was architected to handle 10x growth in lead volume without performance degradation. We use serverless AWS Lambda functions for scoring workloads that auto-scale during traffic spikes. The ML model is versioned and blue-green deployed, allowing zero-downtime updates as we retrain monthly on new conversion data. Feature preprocessing is cached in Redis with 1-hour TTL to minimize API calls. Current infrastructure scores 230 leads weekly with p95 latency under 200ms and can scale to 2,000+ weekly leads without changes.
Security & Compliance
All prospect data is encrypted at rest (AES-256) and in transit (TLS 1.3). API access uses OAuth 2.0 with scoped permissions and automatic token rotation. We implemented audit logging for all lead data access and scoring decisions. The system is SOC 2 Type II compliant and follows data minimization principles - only necessary fields are stored, and prospect data is automatically purged after 90 days of inactivity per retention policies. GDPR compliance features include right-to-erasure within 48 hours and data portability exports.
Project Details
Client
Digible
Industry
Marketing Technology / Multifamily
Timeline
10 weeks
Team Size
AI engineering & marketing ops team
Impact Metrics
+156%
Lead-to-Demo Conversion
89%
Lead Score Accuracy
67%
Time Saved Weekly
$890K
Additional Revenue
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