Intelligent Lead Scoring for B2B SaaS
Qualification time cut by 60%. Close rate nearly doubled from 8% to 19%.
Client details anonymized under NDA. The work, approach, and results shown here are real. Contact me for references.
Faster Qualification
Conversion (from 8%)
Time to Contact
Leads per Rep/Week
The Challenge
What they were dealing with
A growing B2B SaaS company had over 500 inbound leads per month flowing in from the website, demo requests, webinars, and partner referrals. The sales team manually qualified every single one. Scoring was inconsistent across reps, and the team spent 60% of their time on prospects that never converted. Meanwhile, the best leads sat in queue behind tire kickers.
No unified view of lead signals across six different acquisition channels
High value leads sitting in queue behind prospects who were never going to buy
Reps cherry picking leads based on gut feel instead of actual data
Marketing had no way to optimize campaigns because there was no scoring feedback loop
Before
8%
Conversion Rate
3 days
Time to Contact
40 max
Leads per Rep/Week
60%
Rep Time on Low Quality
The Approach
How I solved it
The root problem was not scoring. It was signal fragmentation. Leads interacted across six channels but the scoring only looked at form data. I built a pipeline that ingests website behavior (pages visited, time on site, return visits), CRM history, firmographic data (company size, industry, tech stack via enrichment), and content engagement (webinar attendance, documents downloaded).
The scoring model weights recency and buying intent signals heavily. Someone who visited the pricing page three times in a week scores higher than a VP who downloaded a whitepaper six months ago. Auto routing ensures the highest scored leads hit the top rep's queue within minutes, not days.
The marketing team got something they never had before: a closed loop feedback system showing exactly which campaigns produce leads that actually convert, not just leads that fill out forms.
Signal Mapping
Audited all six lead sources and identified 23 distinct intent signals across the full customer journey.
Enrichment Pipeline
Built automated firmographic enrichment using Clearbit plus custom scrapers for tech stack detection on every incoming lead.
Scoring Engine
ML model trained on 18 months of historical CRM data comparing closed won versus closed lost deals to weight each signal.
Routing and Alerts
Threshold based auto routing to sales pods with Slack notifications for hot leads and a dashboard for marketing attribution.
The Results
What changed
60%
Faster Qualification
19%
Conversion (from 8%)
12min
Time to Contact
110
Leads per Rep/Week
“Our reps used to spend Monday mornings sorting through a spreadsheet of leads. Now they open Slack and the best leads are already there, scored and enriched. Close rate nearly doubled and morale is noticeably better.”
Rachel Kim
VP Revenue Ops, B2B SaaS
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