Predictive Lead Scoring with AI
Making AI lead-scoring an efficient marketing and sales process
AI development
Martech
2 months (+ 2 months of refinement)
AI
About the project
A B2B SaaS company wanted to improve their lead qualification process. Their marketing and sales teams were overwhelmed with the amount of inbound leads, but lacked a way to prioritize the ones most likely to convert. They partnered with us to build an AI-powered predictive lead scoring engine that integrates with their existing CRM and marketing automation tools.
Client’s goal
Prioritize sales outreach using AI-based lead scores
Reduce manual qualification work
Increase lead-to-deal conversion rates
Integrate smoothly into existing workflows
We were responsible for
Data pipeline setup
Model development
Integration with CRM and automation tools
Support and maintenance
Team
Project manager
Backend engineer
Full-stack engineer
QA engineer
High-level integration flow
Here’s how the AI lead scoring engine integrates into the client’s ecosystem.
Inbound leads
CRM + Marketing automation
AI predictive lead scoring engine
Lead score and insights
Sales team prioritization and outreach
Key features
AI-powered lead scoring engine
Each lead gets a score between 0–100, indicating likelihood to convert, and gets classified into categories like Hot, Warm, Cold, based on scoring thresholds.
Data collection and processing pipeline
An automated pipeline that pulls lead interaction data from multiple sources, normalizes it, and stores it in a centralized database for the AI model to process.
Lead score dashboard
Visual lead funnel showing how many leads fall into each score range, with lead source, campaign, region, and rep filters.
CRM integration layer
Pushes scores and categories into CRM custom fields and automatically assigns leads to sales reps or triggers workflows based on score.
The development process
AI-Powered claims documentation automation.
Discovery
We conducted stakeholder interviews, audited the current CRM and data sources, and defined lead scoring KPIs
Data pipeline setup
We extracted, cleaned, and structured historical lead data (behavioral and demographic) from the client CRM.
Model development
Our team built and trained a machine learning model (classification) to predict lead conversion probability.
Integration
When the model tuning was complete, we developed APIs to push lead scores into CRM and created dashboards for sales teams.
Deployment and monitoring
We deployed the model via cloud services and monitored its performance for monthly retraining and fine-tuning.
Support and handoff
Finally, we prepared the necessary documentation and training for internal teams and provided post-launch refinement.
Tech stack
The tech stack we used to build the solution.
Development challenges and solutions
How our team dealt with a range of development challenges.
Lack of historical conversion labels
Challenge: The AI model needs labeled historical data to learn. The client didn’t track this consistently.
Solution: We reconstructed conversion labels from existing sales pipeline stages and used proxy signals like demo bookings or product sign-ups when actual conversion data was sparse.
Sales team resistance to “black box” AI
Challenge: Salespeople were skeptical of trusting an AI score without understanding why a lead was hot or cold.
Solution: We implemented model explainability (SHAP values) and added a "Why This Score?" panel in the dashboard to facilitate the adoption.
CRM integration constraints and limitations
Challenge: The CRM’s API doesn’t allow for easy real-time or bulk field updates. This made integration harder than expected.
Solution: We used a middleware microservice to manage API calls intelligently and queue updates. Also, we employed Zapier for non-critical automations.
Result
When we finished the initial integration, we spent 2 months refining the model so that it can provide the best possible results. Here are the results we helped our client achieve:
13% increase in lead-to-deal conversion rate
25% faster average sales cycle
The sales reps use the lead score daily
