AI LeadScoring
Help salespeople call the right leads first, using a score learned from your own past wins and losses, with the reasons visible.
AI Lead Scoring: what the work involves
When enquiries arrive from ads, forms, WhatsApp and referrals, a small sales team treats them all alike or follows gut feel. Hot leads wait behind tyre-kickers, and good prospects go cold while someone chases a student looking for free advice. Static point systems, such as ten points for a director title, are guesses that nobody revisits.
We study your CRM history of leads that became customers and those that did not. Features come from what was known at the time of enquiry: source, company size, industry, location, message content, response speed, pages visited, and engagement with first replies. We train an interpretable model, usually logistic regression or gradient boosting, and validate it on newer leads than it learned from. A language model can read the free-text enquiry for urgency and fit. The score appears in the CRM with the top reasons and a simple band, for example call today. Scores are one input for the rep, and we track whether high-scored leads truly convert better.
Core features
Learned from your outcomes
The score reflects which of your past leads became customers, not a generic template of what a good lead looks like.
Enquiry text understanding
A language model reads the message for need, urgency and fit, adding signals a form field cannot capture.
Visible reasons
Each score lists its main contributing factors, so reps understand it and managers can challenge it.
Bands and routing
Leads fall into simple bands that trigger actions, such as immediate call, nurture sequence or low priority.
CRM-native display
Scores and reasons show on the lead record in HubSpot, Zoho or your CRM and update as new activity arrives.
Conversion tracking
We compare conversion rates across bands over time, to confirm the score deserves the trust it gets.
What we get right before launch
Self-fulfilling prophecy
Leads the team ignores because of a low score never get a chance to convert, so the model seems right. We reserve a share of low-band leads for normal follow-up to keep the learning honest.
Biased historical data
If past reps favoured certain regions or company types, the model copies that. We check score patterns across segments, remove proxies for protected traits and revisit rules with managers.
Too few closed deals
Small teams may have only dozens of won deals, which is not enough for stable modelling. In that case we begin with transparent rules and gather data, rather than present a fragile model.
Tools and technology
- Python
- XGBoost
- scikit-learn
- OpenAI
- FastAPI
- HubSpot API
- Zoho CRM API
- PostgreSQL
Common questions, answered
How many past deals do we need?
As a rough guide, a few hundred leads with a decent number of wins. With less, we recommend simple, transparent rules and recording outcomes carefully, then moving to a learned model once data supports it.
Will reps trust a score?
Trust comes from visible reasons, a pilot with a small group, and checks against real outcomes. We encourage reps to override and record why, which improves the model over time.
Does it replace human qualification?
No. It sets the order of attention. A conversation still decides whether a lead is a fit, and low-scored leads remain visible and workable.
Can it score leads from WhatsApp?
Yes, if conversations are logged into your CRM or a connected store. Message content and response speed become inputs, subject to consent and the privacy limits we set.
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Ready to start your AI Lead Scoring project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.