Problem:
One of the leading retail marketers in Poland needed short-term forecasts of the number of shoppers for better store work planning.
Rozwiązanie:
Using modern machine learning algorithms, the models were prepared to forecast traffic in stores 10 and 30 days in advance. The forecasts are provided daily in the form of interactive dashboards and email alerts.
Effects:
- Forecast of traffic in each store (accuracy of about 5% for 30 days ahead)
- Possibility to understand what 4 factors affect the traffic in the stores and how
- Identification of the 3 most important promotions affecting traffic in stores
- Possibility to identify days with increased traffic 30 days in advance
Data sources:
- Transaction systems
- Weather database
- Calendar of promotional campaigns
