What data does lead scoring actually need?

Enough closed-won and closed-lost history to learn from — typically several hundred outcomes at minimum. Companies with thin history are better served by an explicit rules-based score that the sales team agrees with, which can be replaced by a model later.

Will sales trust an AI score?

Only if it explains itself and they helped define what good looks like. Scores delivered without reasons get ignored. The adoption work is agreeing the definition of a qualified lead before any model is trained.

How often should a scoring model be retrained?

Whenever your mix of buyers shifts — a new market, a new price point, a new channel. In practice, quarterly review with retraining as needed beats a fixed schedule, because model drift follows business change rather than the calendar.

Back to Projects

AI Lead Scoring

Sales Automation

CRM

Lead Routing

AI Lead Scoring & Routing

Deployed an AI system that scores and routes every inbound lead the moment it arrives — lifting qualified-lead conversion 18% and cutting manual triage 60%.

Overview

A B2B client was drowning in unqualified inbound and letting good leads go cold. I scoped and deployed an AI lead-scoring engine that reads CRM and marketing signals in real time, ranks each lead by likelihood to close, and routes it to the right rep automatically. Sales stopped chasing dead ends and focused on the leads that convert — wired into their existing stack, no in-house data team required.

Key Results

+18% qualified-lead conversion

Leads scored and routed in real time

60% less manual lead triage

99.9% uptime in production

Technologies Used

AI Scoring Models

CRM Integration

Real-Time Data

Workflow Automation

Project Category

ai agents

Related work

ai agents

Want results like these in your business?

I work with growing companies as a Fractional Chief AI Officer — owning AI strategy, vendor decisions, and the outcomes. See the documented results or explore every way to work together.

Explore Fractional Chief AI Officer

FAQ

Questions about AI Lead Scoring & Routing

What data does lead scoring actually need?: Enough closed-won and closed-lost history to learn from — typically several hundred outcomes at minimum. Companies with thin history are better served by an explicit rules-based score that the sales team agrees with, which can be replaced by a model later.
Will sales trust an AI score?: Only if it explains itself and they helped define what good looks like. Scores delivered without reasons get ignored. The adoption work is agreeing the definition of a qualified lead before any model is trained.
How often should a scoring model be retrained?: Whenever your mix of buyers shifts — a new market, a new price point, a new channel. In practice, quarterly review with retraining as needed beats a fixed schedule, because model drift follows business change rather than the calendar.