Credit Card Marketing
Financial markets are changing at unprecedented rates and investor confidence is challenged every day. This article aims to provide decision-makers in the banking and finance industry with an overview of innovative statistical methods that have the potential to impact the bottom line and improve customer experience.
In this case study example, a bank would like to explore and better understand the demographics and other characteristics associated with whether a customer accepts a credit card offer. We build a classification model using decision trees that will provide insights into why some bank customers accept credit card offers.
Decision trees use a classification or partition as the basis for a predictive model to explore patterns, uncover relationships, screen the most influential factors, and make conclusions about a response. Key words used include: Classification trees, training and validation, misclassification, leaf report and lift curves.
- Situation: As the market research manager, you want to understand if there is a relationship between client factors and accepting credit card offers.
- Task: The problem is that there are multiple customer factors and not all the relationships, distributions and interactions are fully understood.
- Action: You create a simple Partition Analysis then move onto advanced Bootstrap Forest and Boosted Trees methods to find the best model.
- Result: Using these Machine Learning techniques you compare the best models and identify the optimal values to maximize credit card acceptance.
- Next Steps: Your research is shared interactively with your colleagues and informs the next marketing campaign and improves bank profitability.
Credit Card Case Study Library
Want to learn more? Then take a look at these Mastering JMP webinars on Decision Tree Based Methods.
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