“Now our reps start with high-fit leads and clear context - it changed how we sell.”
VP of Sales, NDA
A mid-market B2B SaaS company was facing challenges with lead qualification at scale. Processing approximately 2,000 monthly inbound leads from free trials, webinars, and partner channels, their sales team struggled to maintain consistent follow-up and prioritization.
Key operational challenges included lengthy manual research processes that delayed initial contact by 2-3 days, inconsistent lead scoring that failed to identify high-value prospects, and limited visibility into prospect buying signals beyond basic firmographics. The sales team was converting roughly 8% of leads to qualified opportunities, but leadership suspected higher-quality prospects were being overlooked due to volume and manual processes.
Their existing lead scoring relied on simple demographic criteria and couldn't capture nuanced buying intent signals. Sales representatives spent significant time researching prospects individually, while the sales operations team lacked bandwidth to provide comprehensive lead enrichment and routing.
Lunari implemented an intelligent lead qualification system that automatically assesses prospect fit, enriches lead profiles with relevant data, and prioritizes outreach based on buying signals and qualification criteria.
To scale the lead qualification process and improve sales efficiency, the engagement focused on achieving measurable improvements across key performance indicators:
Increase lead-to-opportunity conversion rate from 8% to 15% through better qualification and prioritization
Reduce initial response time from 2-3 days to under 6 hours for high-priority leads
Cut manual research time per lead from 45 minutes to under 10 minutes through automated enrichment
Improve sales team capacity to handle 50% more qualified conversations without additional headcount
Achieve 90%+ accuracy in lead scoring to minimize time spent on poor-fit prospects
Enable processing of growing lead volume (targeting 3,500+ monthly leads) without proportional increase in sales operations overhead
Lunari designed and deployed an AI-driven lead qualification system that automated the most time-consuming aspects of prospect research and scoring while maintaining sales team control over final engagement decisions.
The implementation followed a three-phase approach:
Phase 1 – Data Integration and Model Training
We integrated with the client's existing tech stack (HubSpot, marketing automation, product analytics) and external data sources (Clearbit, LinkedIn Sales Navigator) to create comprehensive lead profiles. Historical conversion data from 18 months of closed deals was used to train qualification models.
Phase 2 – Intelligent Scoring and Routing Deployment
A multi-factor scoring algorithm combined firmographic data, behavioral signals from trial usage and website activity, and intent indicators to rank leads by conversion probability. High-scoring leads were automatically flagged for immediate follow-up with enriched context briefs.
Phase 3 – Workflow Automation and Continuous Learning
Real-time lead routing was integrated with HubSpot and Slack, delivering prioritized lead lists and instant notifications for hot prospects. Sales rep feedback on lead quality was captured to continuously refine scoring accuracy.
This approach transformed lead qualification from a manual, batch-based process to an intelligent, real-time system that could scale with increasing lead volume while improving conversion rates.
The lead qualification platform was architected as an integrated system that enhanced the client's existing HubSpot and sales workflows without requiring process overhaul.
1. Lead Intelligence Engine
Real-time data aggregation from internal sources (HubSpot, product analytics, marketing automation) and external APIs (Clearbit, LinkedIn Sales Navigator, company websites)
Automatic job title standardization and buying role identification (decision-maker, influencer, end-user)
NLP analysis of free-text fields to extract intent signals from trial signup forms and demo requests
2. Multi-Factor Scoring Algorithm
Weighted scoring model incorporating firmographic fit (company size, industry, funding stage), behavioral signals (trial activity, email engagement, content downloads), and contextual factors (job seniority, department, recent company events)
Machine learning model trained on 18 months of historical deal outcomes to predict conversion probability
Confidence scoring with explanatory context (e.g., "92% match: Enterprise prospect, active trial usage, C-level contact")
3. Automated Prioritization and Routing
Real-time lead classification into Hot (immediate follow-up), Qualified (24-hour follow-up), and Nurture (automated sequences) categories
Contextual lead briefs including company background, identified pain points, and suggested conversation starters
Spike detection for unusual activity patterns requiring immediate attention
4. Sales Workflow Integration
Native HubSpot integration updating lead records and task assignments automatically
Slack notifications delivering prioritized daily lead lists and instant alerts for high-value prospects
Mobile-accessible lead context cards for on-the-go sales activities
5. Continuous Learning System
Simple feedback interface allowing SDRs to rate lead quality and provide conversion outcome data
Weekly model retraining incorporating new conversion patterns and market changes
A/B testing framework for scoring algorithm improvements with performance tracking
The AI-driven lead qualification system delivered significant improvements in sales efficiency and conversion performance within 10 weeks of deployment. SDRs reported higher confidence in lead prioritization and more productive conversations due to enhanced prospect context.
The automated system now processes 2,500+ monthly leads with minimal sales operations involvement, enabling the team to focus on strategic initiatives while maintaining consistent lead quality and response times.
87%
in lead-to-opportunity conversion rate (from 8% to 15%)
78%
reduction in manual research time per lead (from 45 minutes to under 10 minutes)
50%
increase in SDR capacity to handle qualified conversations without additional headcount