AI Lead Scoring.
Shipped Fast.
Intelligent scoring models that analyze behavior patterns, engagement data, and firmographic signals to predict which leads convert.
What's Included
Behavioral Scoring Model
Score based on website visits, page views, email opens, content downloads, and engagement patterns
Demographic/Firmographic Scoring
Score based on company size, industry, role, location, and budget indicators
Predictive Conversion Model
AI predicts probability of conversion based on historical patterns from your closed deals
CRM Integration
Scores pushed to your CRM in real-time. Sales team sees lead scores alongside contact details
Model Training & Optimization
Continuous model improvement as more deals close, increasing prediction accuracy over time
Trusted by businesses across 12+ industries
The Problem
The Problem
Your sales team treats every lead equally. The founder who fills out a contact form gets the same follow-up as the student doing research for a class project. Your best salespeople waste time on leads that were never going to buy.
Manual lead scoring (assigning points based on job title, company size) is better than nothing but misses the behavioral patterns that actually predict purchase intent. A CEO who visited your pricing page three times this week is hotter than a CEO who downloaded a whitepaper six months ago.
AI lead scoring analyzes hundreds of behavioral and demographic signals to predict which leads are most likely to convert. Your sales team focuses on the top 20% of leads that generate 80% of revenue.
How ai lead scoring transforms your operations.
AI lead scoring uses machine learning to predict which leads are most likely to convert into customers. Unlike rule-based scoring (assign 10 points for visiting pricing page, 5 for downloading ebook), AI scoring identifies complex patterns across dozens of signals that humans would never detect manually.
The model learns from your historical data: which leads converted, what behavior did they exhibit before converting, what demographic characteristics did they share? Over time, the model becomes increasingly accurate at predicting conversion probability for new leads.
For businesses generating 50+ leads per month, AI lead scoring typically improves sales efficiency by 30-50%. Sales teams spend less time on unqualified leads and more time on prospects with genuine purchase intent. The model also identifies leads that need more nurturing versus those ready for a sales call.
Why We Are Different
Wonkrew vs the typicalai lead scoring experience.
“Most AI projects fail because they start with the technology, not the problem. We build AI that solves real business problems, not science experiments.”
Satish Rajendran
Founder, Wonkrew
22+ years in tech and marketing. Former Cognizant. 500+ projects delivered.
How We Work
How we build ai lead scoring.
From requirements to production. Enterprise quality, startup speed.
Data Analysis
Analyze your historical deals: which leads converted, what signals preceded conversion, what demographic patterns exist. Identify the features that predict purchase intent.
Model Development
Build the scoring model using your data. Train on closed-won and closed-lost deals. Validate accuracy with holdout data. Define score thresholds for routing.
CRM Integration
Integrate scoring model with your CRM. Real-time scoring as leads engage. Automated routing rules based on score thresholds. Dashboard for score distribution.
Monitor & Retrain
Track prediction accuracy against actual conversions. Retrain model quarterly with new deal data. Scoring accuracy improves with every closed deal.
Transparency
How we report ai lead scoring results.
No black box. No jargon. Every month you get a clear picture of what we did, what moved, and what we are doing next.
Sprint Report
Features delivered, performance metrics, user feedback. Every sprint visible.
Business Impact
User adoption, efficiency gains, ROI tracking. Tech tied to outcomes.
Review Call
Monthly call to review progress and plan next phase.
Industries We Serve
Across every vertical.
Manufacturing
Kassa, Simta
F&B
Cake Square, Chocomans
EdTech
Stride, Everwin
Healthcare
Kinesis, Cansaa
Sports
Football Plus
Hospitality
Sparsa Resorts
Legal
Lincoln, Pravda
E-Commerce
Velonae, Beanies
AI Lead Scoring results thatmoved the needle.
Kassa ABS Doors
Lead scoring for 4,500+ Meta Ads leads. Scored by engagement, location, and inquiry type. High-score leads routed to senior sales. Lower scores entered automated nurture.
Leads Scored
Routing
Stride Edutech
Lead scoring for prospective students across ACCA and CMA programs. Behavioral signals (content viewed, pages visited, email engagement) predicted enrollment probability.
Enrollment Growth
Scoring
Agam Concert
Lead scoring for ticket purchase probability based on ad engagement, website behavior, and demographic signals. High-intent leads received targeted offers.
Tickets Sold
ROAS
Healthcare Clinic
Scoring patient inquiries by treatment interest, engagement level, and booking intent. Priority routing for high-intent patients to reduce appointment-to-treatment drop-off.
Routing
Drop-Off
Tools & Technology
The tools we work with.
ML & Data
CRM Integration
Analytics
FAQ
Common questions about ai lead scoring.
Minimum 200-500 historical deals (both won and lost) for initial model training. More data = better accuracy. If you have less, we start with rule-based scoring and transition to AI as data accumulates.
With sufficient historical data, AI scoring models typically achieve 70-85% accuracy in predicting conversion probability. Accuracy improves as more deals close and the model retrains on new data.
Yes. We integrate with GoHighLevel, HubSpot, Zoho, Salesforce, and any CRM with an API. Scores update in real-time as leads engage with your content and website.
HubSpot scoring uses rules you define manually (visit pricing page = 10 points). AI scoring discovers the patterns from your actual conversion data, including complex multi-signal patterns that humans would never identify.
Development starts at Rs.2,00,000 ($2,500 USD) for model building, CRM integration, and initial training. Ongoing costs are minimal since the model runs on your infrastructure.
Quarterly retraining with new deal data keeps the model accurate. We automate this process so it runs without manual intervention. After 12 months, the model has learned from enough data to be highly predictive.
Yes. The model can include lead source as a feature. If Google Ads leads convert differently from Meta Ads leads, the scoring model accounts for that automatically.
Major changes require model retraining with new data. We set up monitoring that detects when model accuracy drops (indicating market changes) and triggers retraining.
From Our Blog
AI Lead Scoring insights.
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