Business challenges
01
Sales teams call every lead with equal priority
No scoring means reps spend as much time on cold prospects as hot ones. Conversion rates stay flat regardless of headcount increases because the prioritisation problem is never solved.
02
Churn discovered after the decision is made
Customer health is assessed manually and quarterly. By the time a customer is flagged as at-risk, the decision to leave has already been made internally. Intervention is too late.
03
Revenue forecasts built on gut feel
Pipeline reviews rely on rep intuition and manager optimism. Forecast accuracy sits below 70%. Finance cannot plan and leadership cannot commit reliably to the board.
04
Models built on dirty or insufficient data
Predictive models trained on unclean CRM data produce unreliable scores that reps learn to ignore. The model is blamed when the real problem is the data quality it was trained on.
What is included
Predictive lead scoring model trained on your historical conversion data -- closed won, closed lost, and qualifying activity. Scores updated in real-time as new activity is recorded. Surfaced as a native CRM field reps can sort, filter, and act on.
Deliverables
How it works
Data assessment
Evaluate historical data quality, volume, and completeness -- the foundation that determines model accuracy before a single algorithm is chosen.
Feature engineering
Identify and transform the CRM fields, activity signals, and behavioural data most predictive of your target outcome.
Model build & training
Build and train the predictive model on your historical data with cross-validation to prevent overfitting and benchmark against holdout data.
CRM integration
Surface model outputs as native CRM fields -- lead scores, churn risk flags, forecast probabilities -- where your team already works every day.
Calibration & handover
Calibrate model thresholds against your business rules, measure baseline accuracy, and hand over with documentation and a monthly maintenance schedule.
Success stories
Lead scoring increased our qualified pipeline by 40% without increasing headcount. Reps stopped spending time on cold leads and started spending it on the ones the model identified as high-probability. The change in how they prioritise their week was visible within 2 weeks of go-live.
The churn prediction model identified 23 at-risk accounts we had zero visibility on through manual health reviews. We intervened and saved 14 of them. The revenue retained in those 14 accounts was more than the cost of the model to build.
Healthcare
180% increase in marketing-influenced pipeline
B2B Healthcare Company
Professional Services
300% increase in qualified inbound
CRM Consultancy (60 staff)
Platforms we use for this service
Free resource
Data requirements, model selection framework, feature engineering guide, and CRM integration patterns for predictive lead scoring projects.
From our team
FAQ
Ready to start?
We respond within 1 business day with an honest assessment -- no commitment required.
Client results
