Internship Customer Segmentation and Profitability Analysis Using Advanced Analytics

Details

Value Reporter – Customer profitability solutions

The Finance Business Advice (FBA) team will benefit using a predictive tool to forecast each customer's profit margin over the next year and to classify customers into segments based on predicted profitability and behaviour. Traditional methods of analysing customer value often look backward, calculating historical profitability or using generic segmentation (e.g., sectors, product usage) that may not reflect future profit potential. Existing segmentation approaches might overlook critical cost factors – for example, customers who frequently use services might appear valuable by revenue but could actually yield lower profit once costs are considered.

Problem statement

The core problem is twofold:

• How can we accurately forecast a customer's profit margin for an upcoming period using historical data and advanced analytics?
• How can we use these forecasts (along with behavioural data) to segment customers in a meaningful way that highlights differences in profitability and supports strategic decision-making?

Objectives

• Develop a predictive model to estimate customer profit margin for the next year.
• Create customer segments based on predicted profitability and behavioral attributes.
• Provide actionable insights to help the company proactively manage high-value and low-margin customers.

Research questions

• To what extent can historical customer data predict a customer's future profit margin?
• Which machine learning techniques are most effective for forecasting customer profit margin in this context?
• How can predicted profitability and customer behavioral attributes be used to segment customers into meaningful groups?
• What are the characteristics of the resulting customer segments in terms of profitability patterns and behaviors?
• How can the outcomes of profit margin forecasting and customer segmentation be applied to drive business value?

Methodology

• Collect and preprocess historical customer, sector, product, organization, performance category, country and financial data.
• Build predictive models (e.g., regression, ensemble methods) for profitability forecasting.
• Apply clustering techniques (e.g., K-Means, Hierarchical Clustering) for segmentation.
• Validate models using metrics like RMSE for prediction and other scoring measures for clustering.

Expected outcomes

• A predictive tool for estimating customer profitability.
• A segmentation framework that highlights differences in profitability and cost structures.
• Strategic recommendations for managing customer relationships.

Responsibilities

• Collect, clean and preprocess historical customer and financial datasets.
• Develop and validate predictive models for customer profit margin forecasting.
• Perform customer segmentation using clustering methods and interpret segment characteristics.
• Communicate findings and provide actionable recommendations to stakeholders.

Requirements

• Currently enrolled at a Dutch university (or EU university for EU passport holders) for the duration of the internship (mandatory).
• Knowledge or coursework in predictive modelling, regression, ensemble methods, clustering (e.g., K-Means, hierarchical clustering) and model validation metrics (e.g., RMSE).
• Experience or familiarity with data collection and preprocessing.
• Analytical mindset and ability to translate model outputs into business insights.

Benefits

• Internship allowance of 700 EUR based on a 36 hours work week.
• Your own work laptop.
• Hybrid working to blend home working for focus and office working for collaboration and co-creation.
• Personal growth and challenging work with endless possibilities.
• An informal working environment with innovative colleagues.

Applications

• Upload your CV and motivation letter via the Apply button.

About our internships

• More than 350 students join the internship program each year. Many former interns move into permanent roles or onto the International Talent Programme (traineeship).