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Audience intelligence

Predictive Lead Intelligence & Scoring

Machine learning models that assess customer acquisition potential, conversion probability, and expected commercial value.

Lead scoring in most CRMs is a list of points added for a job title or a downloaded whitepaper, set up once and rarely revisited. Predictive Lead Intelligence & Scoring replaces it with models trained on which leads actually converted, how long they took and what they were worth, so the score reflects evidence rather than a rule someone wrote three years ago.

Where it helps

  • Sales teams working leads in order of arrival because every lead looks the same
  • Marketing-qualified leads that sales does not trust or follow up
  • Account prioritization done by instinct across a large territory
  • Forecasts that count every open lead the same way regardless of its chance of closing

What we deliver

  • Conversion probability. The likelihood that each lead or account becomes a customer, based on behaviour, firmographics and history.
  • Expected value. Predicted deal size and lifetime value, so a lead’s priority reflects what it is worth, not just whether it will close.
  • Fit and intent signals. Which leads resemble the customers who stay and grow, and which are showing buying behaviour now, with the reasons shown to the rep.
  • CRM delivery. Scores, reasons and recommended next actions written into your CRM and routing rules, refreshed as new activity arrives.

How an engagement runs

  1. Assess. We review your lead history, the data captured at each stage and the way sales and marketing define a qualified lead today.
  2. Build. We train the models, test them on past quarters and compare their ranking with the one your team used at the time.
  3. Integrate. Scores and reasons appear in the CRM views reps already use, and feed the attribution and revenue views for marketing.
  4. Run. We monitor accuracy as the market and your offer change, retrain on a schedule and report on conversion by score band.

What changes for your team

  • Reps start the day with the leads most likely to close, and a reason for each
  • Marketing and sales agree on what a good lead looks like, because the definition comes from outcomes
  • Pipeline forecasts weight each lead by its real chance of closing

Start a project

Make the next decision with the data you already have.

A 30-minute call about your sources, your channels and the decision you want to make. No slides, no sandbox data.