Internship ​Customer Segmentation and Profitability Analysis using Advanced Analytics

The team

Value Reporter – Customer profitability solutions - 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. 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.

Moreover, 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 advance 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 others scorings for clustering.

Expected Outcomes

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

Rewards and benefits

This is a great opportunity to train with highly skilled people who are experts in their field. You’ll do a lot and learn a lot – not only about your specialist area and the bank, but also about yourself and whether this type of environment is right for you.

You’ll also benefit from:

• 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

During the duration of your internship at ING, it is mandatory to be enrolled at a Dutch university (or EU-university for EU passport holders).

Applications

Want to apply directly? Please upload your CV and motivation letter by clicking the ‘Apply’ button.

About our internships

Every year, more than 350 students join our internship program. While there are no guarantees about your future, many of our former interns move into a permanent role or onto our International Talent Programme (traineeship).

Whatever happens, an internship at ING is the ideal opportunity to meet a wide variety of people, to build up your own network, and to learn about many different aspects of banking – put simply, it’s a great start to your career.