After sales
Classification of customers into well-defined after-sales segments, providing the company with a deeper understanding of client behavior across branches.
COMPANY
NORS Group S.A.
RESEARCH CENTRE
LEMA and Universidade do Porto (Faculdade de Ciências)
PRODUCTIVE SECTOR
Service Management
Problem description
Nors Trucks and Buses Portugal RT, one of the Nors Group companies would like to strategically segment after-sales customers across multiple branches to boost service efficiency and parts sales in the heavy machinery industry.
Challenges and goals
The main goal was to implement a customer segmentation approach applicable across all company branches, despite their differing customer volumes and profiles.
A key challenge was adapting the segmentation to the specificities of the business context, where customer behavior varies significantly between regions.
Mathematical and computational methods
The main segmentation technique applied was the RFM (Recency, Frequency, Monetary) model, using a scoring system to classify customers based on their after-sales purchasing behavior.
The model was implemented using Python, with data pre-processing, scoring computation, and customer grouping performed programmatically.
Results and Benefits
The project resulted in the classification of customers into well-defined after-sales segments, providing the company with a deeper understanding of client behavior across branches.
This segmentation serves as a valuable decision-support tool, enabling more targeted marketing campaigns, improved service quality, and a stronger customer relationship management strategy. Ultimately, it contributes to enhanced operational efficiency and increased profitability, by aligning commercial actions with customer needs and potential.
