About the Client
A B2B professional services firm with a long sales cycle and a high-value consultative offering needed better visibility into which leads were likely to close — and how to close them faster.
The Challenge
The client’s CRM was full of data… but not insight.
They wanted to answer three big questions:
- Which contacts are most likely to convert?
- What behaviors predict success?
- How can we shorten the time it takes to close deals?
Their team was relying on gut instinct, ad hoc follow-up, and generalized engagement metrics — with no clear way to prioritize leads or refine strategy.
The Solution
We designed a custom analytics project combining:
- Machine learning-based lead scoring (XGBoost model, 90.5% AUC)
- Time-to-event (survival) analysis to model deal velocity
- Ensemble modeling to identify hidden drivers of faster closes
- CRM-ready scores, tiers, and confidence bands — prepared for seamless rollout
Each lead received a dynamic score and tier based on real-world conversion probability. We also uncovered the key behavioral signals that truly matter.
Key Findings
- Lifecycle Stage was the strongest predictor of both conversion and speed to close.
- Contact frequency (how often sales followed up) mattered more than job title.
- Engagement scores helped qualification — but didn’t directly accelerate deals.
- Lead scoring sharply separated lead quality:
- 0% conversion in Low tier
- 76.8% conversion in High tier
Deliverables Ready for Implementation
- ➡️ Prioritization clarity: Sales team now knows who to call first
- ➡️ Faster funnel: Clear playbook for accelerating lifecycle stages
- ➡️ Lead scoring system: Ready to integrate into CRM workflows
- ➡️ Executive insight: A data-backed roadmap for future sales strategy
Could Your Business Use This Level of Clarity?
This kind of work is perfect for:
- B2B sales teams with long or complex funnels
- SMBs using HubSpot, Zoho, Salesforce, or similar CRMs
- Founders and operators ready to turn raw data into action
