Clear, owned outputs.
- Use case and baseline definition
- Predictive models with evaluation
- Explanations of key drivers
- Integration into dashboards or systems
- Monitoring and refresh schedule
Predictive analytics uses historical data to estimate what is likely to happen: which customers may leave, which leads are most likely to buy, how much demand to expect next month. We build predictive models and, just as importantly, put their outputs into the dashboards and systems where people make decisions.
The problems Predictive Analytics solves.
Forecasting, churn and lead scoring, and risk models delivered where decisions happen.
Forecasts are based on last year plus a percentage.
Sales teams can't tell which leads to call first.
Customers leave without warning.
Predictions exist but aren't used in day-to-day decisions.
What Predictive Analytics covers.
Forecasting
Demand, revenue, workload and inventory forecasts.
Churn prediction
Identifying customers at risk of leaving.
Lead and propensity scoring
Ranking leads and customers by likelihood to act.
Risk models
Estimating credit, fraud or operational risk.
Explainability
Showing which factors drive each prediction.
Operational delivery
Scores and forecasts pushed into CRM, BI or apps.
Predictions that change a decision.
A forecast or score is only useful if someone acts differently because of it. We design the action alongside the model.
- Demand forecasting
- Forecasts by product and location with ranges, feeding purchasing and staffing plans.
- Churn prediction
- Which customers are likely to leave and why, with retention actions ranked by expected value.
- Lead scoring
- Ranking leads by likelihood to convert so sales time goes to the best opportunities.
- Risk scoring
- Credit, fraud or operational risk scores with explanations a reviewer can check.
- Monitoring
- Tracking accuracy over time and recalibrating when behaviour shifts.
Why it's worth it.
Earlier action
Act before problems or opportunities pass.
Better prioritization
Teams focus on the most likely outcomes.
Understood drivers
Explanations build trust and reveal insight.
When Predictive Analytics is the right move.
- Lead scoring for a B2B sales team.
- Demand forecasting for inventory planning.
- Churn prediction for a subscription business.
Technologies & platforms
- Python
- scikit-learn
- XGBoost
- Prophet
- Databricks
- BigQuery ML
- Power BI
- Salesforce
How Predictive Analytics work runs.
- 01
Frame
The decision or task to improve, the measure of success and the constraints.
- 02
Assess data
Availability, quality, access, privacy and labelling needs.
- 03
Prototype
A small, honest test of whether the approach can work.
- 04
Build and evaluate
Model development with documented evaluation against agreed criteria.
- 05
Deploy and monitor
Integration, monitoring for drift and performance, and retraining plans.
Predictive Analytics questions.
How accurate will predictions be?
That depends on your data and how predictable the outcome is. We measure accuracy against a baseline before deployment and report it honestly.
Can predictions appear in our CRM?
Yes. Scores can be written back to CRM records so sales and service teams see them where they work.
How often are models updated?
On a schedule matched to how quickly your data changes, with monitoring to trigger earlier updates if performance drops.
Talk to us about predictive analytics.
Tell us which decision or process you want to improve and what data you have. We'll reply with whether it's a good fit and how we would test it.