Machine learning (ML) finds patterns in historical data and uses them to make predictions. You don't need big-tech budgets – many valuable use cases work with the data SMEs already have.
1. Demand forecasting
Predict sales or required stock per product and week. Reduces stockouts and excess inventory.
2. Customer churn prediction
Identify customers likely to cancel a subscription or stop ordering, so sales can act early.
3. Lead scoring
Rank incoming leads by likelihood to convert, based on past deals.
4. Predictive maintenance
Use machine sensor data to predict failures before they happen and plan maintenance efficiently.
5. Quality inspection
Camera-based models detect defects on production lines faster and more consistently than manual checks.
6. Price optimisation
Suggest prices based on demand, competition and margins – with humans keeping the final decision.
7. Anomaly detection
Spot unusual transactions, sensor readings or system behaviour that may indicate errors or fraud.
8. Recommendations
Suggest relevant products or content to customers in shops and apps.
What you need
- Historical data of reasonable quality (often 1–3 years)
- A clear business question and success metric
- A way to use predictions in daily work (dashboard, ERP, app)
The process
- Define the question and metric
- Explore and prepare the data
- Build and evaluate models
- Compare with a simple baseline
- Integrate into processes
- Monitor performance over time
Start simple
Often a simple model or even good statistics delivers most of the value. Complexity should be added only when it measurably improves results.
We help SMEs identify promising ML use cases and implement them pragmatically.