Predictive lead scoring
AI models that automatically score leads based on conversion probability, trained on your historical data.
Detailed explanation
Traditional lead scoring uses manual rules (CEO = 20 points, viewed pricing = 15 points, etc). Predictive lead scoring uses machine learning to find patterns in thousands of deals: which combination of attributes (job title, company size, behavior, timing) lead to conversion? The model learns from your won and lost deals and predicts for new leads their probability of converting. This is more accurate than manual scoring because ML finds subtle patterns that humans miss. Tools like HubSpot, Salesforce Einstein, or 6sense have predictive scoring. The advantage is that your SDRs automatically focus on leads most likely to convert, which dramatically increases efficiency.
Synonyms
Examples
A lead has medium job title (VP = 15 pts) and small company (50 employees = 10 pts). Manual score: 25/100 = low priority. Lead gets high score and SDR calls immediately → closed deal.
Measure the effect using data from your own process.
When to use this?
Implement predictive lead scoring once you have 500+ historical leads (AI needs data to learn). Use it alongside manual scoring first, validate accuracy, then switch fully. Review quarterly if the model still fits - your ICP can evolve.
Sources & further reading
Your Company OS approach
Your Company OS implements predictive lead scoring in your CRM and ensures it's actually used. We first ensure clean data (garbage in = garbage out), train the model on your won/lost deals, and integrate scores into your SDR workflow. Crucial: we make it actionable - high-score leads go directly to SDR queue with context on why they scored high. We also monitor model performance and retrain quarterly with new data.
Want to know more about Predictive lead scoring?
Want to know how to put Predictive lead scoring to work in your organisation? Get in touch with Your Company OS.