Services · Data Science
Decisions about the future, from historical data.
We answer business questions such as demand forecasting, customer segmentation and price analysis with machine learning models, and connect those models to daily operations.
Why it matters
A model creates value the day it enters the decision process.
Reports answer the question “What happened?”. Data science focuses on “What will happen?” and “What should we do?”: how much of each product will sell, which customer will respond to which campaign, how will a price change affect sales?
Many data science projects fail to deliver results because, even though the model is good, it is never connected to operations. We design the model from day one around the decision process it will serve, and feed its results into your reports and systems.
What you get from this service
- Reliable demand forecasts for inventory and ordering decisions
- Customer groups segmented by behaviour
- Measurement of the real impact of prices and campaigns
- Early detection of unusual situations
- Transparent Power BI reports for monitoring results
What do we do?
We start from the business question and aim to reach the answer with the simplest, most explainable method.
01
Demand and sales forecasting
We develop forecasting models by product, store and period that account for campaign and calendar effects.
02
Customer segmentation
We identify customer groups based on purchasing behaviour and support marketing and campaign decisions.
03
Basket analysis
We reveal products that are bought together and surface product placement and cross-selling opportunities.
04
Price and campaign analysis
We measure the effect of price changes and campaigns on sales.
05
Anomaly detection
We automatically flag unusual movements in sales, inventory or operational data.
06
Model operations
We make sure models run regularly, are monitored, and that their results are delivered to your systems.
How do we proceed?
The output of every step is defined, so you always know what you are receiving.
01
Business question and data
We clarify the question to solve, the success criteria and the data required.
02
Exploratory analysis
We examine patterns in the data and start with a simple baseline model.
03
Model and validation
We develop the model and measure its performance with time-ordered tests.
04
Putting it into operation
We make the model run on a schedule and connect its results to reports and systems.
Technologies we use
- Python
- pandas
- scikit-learn
- LightGBM
- SQL Server
- Power BI
Frequently asked questions
How much data does a data science project need?
It depends on the question. For demand forecasting, at least one to two years of regular sales data is usually helpful. During discovery, we assess whether your existing data is sufficient.
How accurate will the results be?
No model can know the future with certainty. That is why we compare every model with a simple baseline method and report the margin of error transparently.
Will we be able to understand how the model makes decisions?
Yes. We choose explainable methods wherever possible and use reports to show which factors influence the model the most.
Do models become outdated over time?
As market and customer behaviour change, model performance can decline. That is why we monitor performance regularly and retrain the model when needed.
What does your data say about the future?
Share the business question you want to solve, and let’s explore together what data science can do.