Predictive Analytics
Forecasting and prediction models — demand, churn, risk, and maintenance — built on your data and delivered into the tools your teams already use.
- Data readiness check before any build
- Transparent accuracy metrics
- Predictions delivered inside existing tools
- Monitoring for model drift
- Python
- pandas
- scikit-learn
- XGBoost
- Prophet
- PyTorch
- SQL
- dbt
What predictive analytics covers
Predictive analytics uses historical data to estimate what is likely to happen next: how much stock you will need, which customers are at risk of leaving, which invoices will be paid late, which machines need attention. The value comes from putting those predictions in front of the person who can act on them.
We start by checking whether your data can answer the question, then build, validate, and deploy models with clear accuracy reporting. Predictions are delivered through dashboards, alerts, or directly into your CRM or ERP, and models are monitored and retrained as patterns change.
Where predictive analytics makes the difference
The challenges we hear most often — and how we address each one.
Stock-outs and overstock tie up cash
Demand forecasts help purchasing order the right quantities.
Customers leave without warning
Churn models flag at-risk accounts early enough to act.
Decisions rely on gut feel
Predictions with accuracy metrics support confident decisions.
Reports only explain the past
Forward-looking scores and forecasts show what is likely next.
What we deliver
Key features and capabilities of our predictive analytics work.
Demand forecasting
Sales and inventory forecasts by product, location, and season.
Churn & retention prediction
Identify at-risk customers early enough to act.
Risk & fraud scoring
Score transactions, applications, or accounts for review.
Predictive maintenance
Anticipate equipment failures from sensor and service data.
Lead & opportunity scoring
Prioritise sales effort on the most likely conversions.
Dashboards & alerts
Predictions surfaced where decisions are made.
How the engagement runs
A clear sequence with a visible output at every step — so you always know where the project stands.
- 01
Use-case framing
Define the decision, the prediction, and how success is measured.
- 02
Data audit
Assess sources, quality, history, and gaps.
- 03
Baseline & prototype
Compare a simple baseline with candidate models.
- 04
Validation
Test on held-out data and review results with your team.
- 05
Deployment
API, dashboard, or in-app delivery with access controls.
- 06
Monitoring
Track accuracy and drift; retrain on a schedule.
Technologies & tools
Chosen per project for fit, maintainability, and your existing stack.
- Python
- pandas
- scikit-learn
- XGBoost
- Prophet
- PyTorch
- SQL
- dbt
- Power BI
- Metabase
- AWS SageMaker
- Azure ML
Industry applications
Sectors where we most often apply this service.
Predictive Analytics: common questions
How much data do we need?
It depends on the question. Forecasting typically needs at least one to two years of history to capture seasonality; classification problems need enough examples of each outcome. The data audit answers this before you commit to a build.
How accurate will the predictions be?
We measure accuracy against a simple baseline on data the model has not seen and share those numbers before deployment. We do not promise a figure in advance because it depends on your data.
Do we need a data warehouse first?
Not always. We can work from exports or operational databases for a first model, and recommend a warehouse when it is justified by scale.
Can predictions appear in our CRM or ERP?
Yes. Scores and forecasts can be written back to the systems your teams already use, so nobody has to open another tool.
More from AI Development & Consulting
Ready to talk about predictive analytics?
Tell us what you need. We reply within one business day with questions or a call slot — and a fixed, written proposal after.