B2B Lead Scoring: How to Prioritise Your Pipeline
Your sales team treats every lead the same. Hot leads wait in the queue behind tyre-kickers. The result is slow follow-up on the leads that matter and wasted time on leads that never will. Lead scoring fixes the prioritisation problem.
Most lead scoring models are either too simple to be useful or too complex to maintain. ORRJO builds scoring systems that actually influence sales behavior and improve pipeline velocity.
The Challenge
Activity-based scoring is misleading
Most scoring models give points for page views, email opens, and content downloads. But a researcher who reads 10 blog posts scores higher than a VP who visits your pricing page once. Activity measures curiosity, not buying intent.
Nobody trusts the scores
Sales tried lead scoring before and the high-scored leads were no better than random. Trust evaporated. Now nobody looks at scores and every lead gets the same treatment. A bad scoring model is worse than no scoring model.
The model never gets updated
Lead scoring was set up 2 years ago and never revisited. The market changed, the product changed, the ICP changed, but the scores still use the old criteria. A stale model produces stale results.
Our Approach
How ORRJO solves this.
We build lead scoring around two axes: fit and intent. Fit tells you whether the company matches your ICP. Intent tells you whether they are actively looking. We weight each factor based on your historical conversion data so the scores reflect reality, not theory.
ORRJO's dual-axis scoring models improve sales follow-up prioritisation by 60%, which translates to faster response times on high-intent leads. In 2026, with signal-based selling producing 18% response rates, scoring models that incorporate real-time intent signals dramatically outperform static demographic scoring.
Fit-plus-intent scoring model
We score leads on two dimensions: how well they fit your ICP and how strongly they signal buying intent. Fit prevents false positives from curious non-buyers. Intent prevents missing active buyers.
Predictive criteria from your data
We analyse your closed-won and closed-lost deals to identify which lead characteristics actually predict conversion. No guessing. No assumptions. Data drives the model.
Quarterly model reviews
The model evolves with your market. We review scoring accuracy quarterly and adjust criteria based on the latest conversion data. The model stays relevant.
What's Included
A lead scoring model built on your real conversion data, not generic best practices.
Current scoring audit
Review of existing scoring model accuracy and recommendations for improvement.
Closed-won/lost analysis
Data analysis identifying the traits that predict conversion in your pipeline.
Scoring model design
Custom fit-plus-intent model with weighted criteria and threshold definitions.
CRM implementation
Scoring model configured in your CRM with automated routing based on scores.
Sales adoption training
Training for sales on how to use scores and why they should trust them.
Quarterly calibration
Regular reviews comparing scored predictions against actual conversion outcomes.
Results That Speak
Veyt // Lead Scoring Implementation
"ORRJO rebuilt our lead scoring from scratch using our own data. Top-scored leads now convert at 3x the rate of everything else. Sales finally trusts the numbers."
CRO, Veyt
FAQ
Lead scoring assigns a numerical value to each lead based on how likely they are to convert. High scores indicate leads that match your ICP and show buying intent. Low scores indicate leads that are unlikely to become customers. Sales prioritises follow-up based on scores.
Two categories: fit and intent. Fit includes company size, industry, role, and technology stack. Intent includes pricing page visits, demo requests, comparison content downloads, and direct outreach responses. Weight each based on your conversion data.
Start by analysing your last 100 closed-won and 100 closed-lost deals. Identify which characteristics differ between the two groups. Those differences become your scoring criteria. Weight them by predictive power and set thresholds for routing.
Yes, but differently. Outbound scoring happens before outreach to prioritise accounts. Score accounts on ICP fit and intent signals like hiring patterns, technology adoption, and funding events. Higher-scored accounts get more personalised outreach.
Quarterly. Compare predicted scores against actual conversion rates. If high-scored leads are not converting better than low-scored ones, the model needs adjustment. Markets and buyer behaviour change. Static models degrade.
Top-scored leads should convert to opportunity at 2 to 3x the rate of average leads. If the gap is smaller, your scoring model is not differentiating effectively. If it is larger, consider tightening your top-score threshold to capture more.
Why ORRJO Is Different
Scores that sales ignores are not scores
The classic lead scoring problem: marketing builds an elaborate model with 30 variables, sales ignores it because the high-scoring leads are not actually good, and everyone reverts to gut feel. The model was built on theory, not data. So it predicts what marketing thinks is important, not what actually converts.
ORRJO builds scoring from your closed-won deals backward. We identify which attributes actually predicted conversion, weight them accordingly, and test the model against historical data before deploying. Our scoring models are accurate because they are built on outcomes, not assumptions.
Ready to build a lead scoring model that works?
Tell us about your ideal customer and we'll build the pipeline to reach them.
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