Revenue
Track likely bookings and sales value against pipeline coverage, win rates and billing timing.
Pipeline coverage
Predictive analytics UK / AI forecasting services
Predictive analytics that show what is likely next.
If your forecast still relies on last year plus instinct, early signals are being missed. Granular Metrics Ltd. builds practical models for revenue, demand, churn, cash flow, risk and operational performance.
East Hertfordshire delivery for UK organisations.
Cash flow outlook / 90 days
Validated view
£184k
likely closing balance
Current
30 days
60 days
90 days
Range includes debtor days and pipeline conversion.
Model routes
Practical forecasts for decisions that cannot wait.
Each model connects to a specific operating choice. You see the expected range, the drivers behind it and the signal that needs attention.
Demand
Plan stock and staffing around seasonal demand, order patterns and service capacity.
Units required
Churn
Spot retention risk through usage changes, renewal timing and account activity.
Retention risk
Cash flow
Model receipts and outgoings using debtor days, payment behaviour and committed spend.
Cash runway
Risk
Rank emerging exposure by severity, frequency and the evidence behind each alert.
Risk exposure
Operations
Anticipate service-level pressure before missed targets appear in a monthly report.
Service level
Driver-based planning
Scenario planning for uncertainty.
Compare conservative, likely and stretched outcomes before a change reaches the ledger. Adjust a driver such as stock availability or pipeline conversion, then see how the range moves.
Decisions need context.
Pipeline conversion
26%
Conservative
Likely
Stretched
Cash balance in 90 days£184k
Expected receipts£96k–£121k
Action triggerReview debtor days
Machine learning analytics
Anomaly detection that flags what reports miss.
Unexpected variance, unusual ordering patterns and service-level changes appear beside the KPI they affect. The alert includes a useful starting point, so a manager can check the cause quickly.
Early signals matter.
08:40 / routine scan
Order value moved outside its normal weekday range. The model compares recent behaviour with the established baseline.
09:10 / attention needed
Service level fell across one delivery route. The alert links the change to a capacity gap for review.
11:25 / action recorded
Operations team adjusted the allocation plan. The event stays in the monitoring history for validation.
Before model use
Clear data checks before you trust a forecast.
Useful predictions depend on usable history. Granular Metrics Ltd. checks the source data first, records exceptions and tests the model against information it has not seen.
Evidence comes first.
History and completeness
We check the available time period, missing records, duplicated rows and changes in how KPIs were measured.
Exceptions and data ownership
Known one-off events are logged. Owners can explain unusual periods before they influence the forecast.
Holdout validation
A section of historical data stays outside model training. Its results show how the approach performs on unseen periods.
Monitoring and retraining
Accuracy, drift and alert quality are reviewed as conditions change. Retraining follows evidence, not a calendar guess.
Responsible AI in the UK
Forecasts support judgement. They do not replace it.
Models should be explainable enough for the people accountable for the decision. We include human review, transparent drivers and a clear record of model limitations.
Personal data is handled with UK GDPR and Data Protection Act considerations in mind.
Model logic, confidence ranges and known blind spots are documented in plain English.
Regular review keeps predictions connected to current business conditions.
Start with one forecast problem
Book a forecasting review.
Bring one KPI, your current forecasting approach and the data used today. We will identify a sensible first model and the checks it needs.
Call +44 7141 945987 or email [email protected]