Solution
Predictive Analytics
Move from reporting what happened to anticipating what is likely to happen next, with forecasts and risk signals delivered where your teams make decisions.
The problem
Decisions made looking in the rear-view mirror
Most organisations have plenty of reports about the past. Far fewer have reliable signals about the future: which customers may leave, how much stock will be needed or when capacity will run short.
Without those signals, teams react late, buffers are set by guesswork and opportunities to intervene are missed.
Causes and impact
Why it happens and what it costs
Data spread across systems
Relevant signals are not combined in one place.
Spreadsheet forecasting
Manual models are hard to maintain and rarely updated.
No path to action
Insights stay in reports instead of triggering decisions.
Operational impact
- Excess stock or stock-outs
- Avoidable customer churn
- Under- or over-staffing
- Late responses to emerging risks
The solution
Predictions embedded in everyday decisions
We build forecasting and scoring models on your historical data, validate them carefully and deliver predictions into dashboards, CRM records or operational workflows. Each prediction comes with context about its main drivers and uncertainty.
Models are monitored and retrained as patterns change, so predictions stay relevant.
Main features
Key features
Forecasting models
Demand, revenue, volume and capacity forecasts with ranges.
Risk and propensity scores
Churn, payment risk or conversion likelihood per record.
Explainable drivers
Key factors behind each prediction shown to users.
Decision dashboards
Predictions alongside actuals and thresholds.
Triggered actions
Alerts or tasks created when risk crosses a threshold.
Model monitoring
Accuracy and drift tracked over time.
High-level workflow
How it works
Illustrative example: subscription business
A subscription service scores each customer’s churn risk weekly. High-risk accounts appear in the success team’s CRM view with the main contributing factors, prompting timely, personalised outreach.
- 1
Data collected
Historical and current data assembled from source systems.
- 2
Model scores
Forecasts and scores generated on schedule or in real time.
- 3
Delivered
Results appear in dashboards and business systems.
- 4
Action taken
Teams act on alerts and record outcomes.
- 5
Model improved
Outcomes feed back into retraining.
Integration requirements
Systems typically involved
- Data warehouse or databases
- CRM and ERP
- BI and dashboard tools
- Workflow and alerting tools
Implementation considerations
Data history
Sufficient, representative history is needed to learn patterns.
Defined actions
Agree in advance what teams will do with each prediction.
Explainability
Sensitive decisions need transparent reasoning.
Ongoing ownership
Someone must own model performance after launch.
Potential benefits
What this solution is designed to improve
Actual results depend on your starting point, data and adoption; we agree how to measure them during discovery.
Earlier intervention
Act on risks before they become problems.
Better planning
Stock, staff and budgets aligned with expected demand.
Focused effort
Teams prioritise the customers or cases that need attention.
Learning organisation
Outcomes continuously improve future predictions.
Explore more
Related solutions and services
Machine Learning Solutions
Custom models for prediction, classification, recommendation and anomaly detection on your data.
Learn more about Machine Learning SolutionsBusiness Dashboards
Real-time dashboards that bring KPIs from many systems into one trusted view.
Learn more about Business DashboardsData Engineering & Analytics
Reliable data pipelines, warehouses and analytics that turn scattered data into decisions.
Learn more about Data Engineering & AnalyticsInventory Intelligence
Demand-led stock visibility, replenishment suggestions and early alerts across locations.
Learn more about Inventory IntelligenceFAQs
Frequently asked questions
Answers to the questions we hear most often. If yours is not covered here, our team is happy to talk it through.
Accuracy depends on your data and the problem. We measure it on historical data before launch and report it transparently rather than promising a figure in advance.
Yes, where the use case requires it. Many business forecasts work well with daily or weekly updates.
What would you like to see coming?
Tell us the decision you want to improve and we will assess what your data can support.
