Anticipate seasonal demand
Orders vary widely with seasons and promotions.
Statistical and machine learning models to address business questions and work on forecasting your key indicators.
- Explore and prepare available data
- Build and evaluate models
- Interpret results with business experts
Concrete use cases.
Spot unusual invoices
Some invoices carry unusual amounts that are noticed too late.
Which signals help us anticipate changes in our key indicators?Appointments
Assess what the data can support
Demand forecasting, customer segmentation and anomaly detection require suitable data. Exploration assesses coverage, missing values and potential bias. It also helps determine whether a model adds value beyond a simple business rule or historical average.
Evaluate before production
Training and evaluation use separate datasets to distinguish memorization from predictive ability. Metrics reflect the cost of errors for your business. Once a model is selected, updates, monitoring and interpretation of its results need to be organized.
Frequently asked questions
How much data do we need?
There is no universal threshold. Useful volume depends on the phenomenon, its variability and the intended model. Quality, historical coverage and representativeness matter as much as quantity.
Can a forecast be guaranteed?
No. A forecast includes uncertainty and depends on the conditions represented in the data. We explain limitations and compare results with a baseline to support informed decisions.