Retail sales prediction using Machine Learning

Case Study


T-Mobile Polska is a well known leader in telecommunication services. As many companies that depend on retail, they need to optimize client acquisition through direct sale at physical stores (POS).

T-Mobile sales prediction using machine learning case study - Introduction

To optimize location selection process it's usually best to combine field expert knowledge with data analysis and prediction tools.




  • data science
  • machine learning
  • predictive modeling
  • data engineering


Artificial Intelligence & Machine Learning

The problem

Imagine you have to open dozens of stores in a short period of time, in all major cities in the region. Let’s say you have 2-3 possible locations for each store. That’s over 1000 potential locations, usually with little to no data history.

T-Mobile sales prediction using machine learning case study - Problem

How to find locations with highest sales potential?

To answer this question TEONITE cooperated with T-Mobile business and big data teams.

The Solution

TEONITE applied Data Science approach to collect critical information and lead the analysis process through:

  • data engineering and feature engineering,
  • building ML pipelines for evaluation of multiple predictive models,
  • analyzing core GSM network data (animized behavior of users),
  • analyzing correlation with sales data,
  • visualization and information discovery.
T-Mobile sales prediction using machine learning case study - Solution

The goal was to select the model with the highest success rate in predicting chosen sales KPIs. Our evaluation included various ML models and techniques: eXtreme gradient boosting, LightGBM, random forests and neural networks.

T-Mobile sales prediction using machine learning case study - Solution

As a result, we've delivered predictive models and in-depth report to to help the business optimize the process of location selection.


  • Python Data Science stack: Pandas, NumPy, scikit-learn
  • JupLterLab and Jupyter Notebooks analytical environment
  • Orange toolbox for data mining and data visualization
  • Apache Spark data processing
  • Custom Machine Learning pipeline
  • PostgreSQL database
  • Selected models: Extreme Gradient Boosting (XGB), Neural Networks, ensemble methods

Michał Krauze Head Of New Business and Innovation

The project implemented by TEONITE provided us with valuable information about the data held by T-Mobile. Knowledge in the field of Data Science and Machine Learning, an agile approach and experience in software development enabled the modeling of sales-relevant indicators.

Next up:


Content-based platform for a global, NGOs support environment

Read case study

Planning a new project?

We know how to make it work!

Get a free project estimate