Lead Qualification Bot

A B2B SaaS company generating 500+ inbound leads monthly uses static lead scoring: job title gets 10 points, company size 15 points, whitepaper download 20 points. This treats all VP titles equally, ignores buying committee dynamics, and scores competitors the same as prospects.

6sense demonstrated that intent signals across buying committees identify in-market accounts. Drift proved conversational AI can engage visitors and book meetings in real time. Predictive scoring increases sales acceptance rates by up to 35% versus rules-based scoring (Forrester 2024).

The result of bad scoring: sales wastes time on unqualified leads, qualified prospects go cold waiting, and marketing cannot measure true lead quality.

This agent replaces static scoring with a predictive model trained on your closed-won and closed-lost data. It evaluates leads across firmographic fit (company size, industry, tech stack), behavioral signals (pages viewed, content consumed, engagement frequency), and third-party intent signals where available.

When a high-scoring lead engages — visits pricing, returns for a third session, submits a demo request — the agent engages conversationally, asks qualifying questions aligned with your sales methodology (BANT, MEDDIC, or custom), captures budget and timeline, and books a meeting on the rep's calendar.

Sales receives a complete lead dossier: qualification answers, behavioral timeline, firmographic profile, and fit score with transparent feature explanations.