The aim of this project is to target customers with a tailored marketing strategy using clusterng. The analysis covers various aspects, including exploratory data analysis (EDA), PCA, clustering using K-Means, and deriving marketing strategies based on the identified customer clusters.
The data comes from Kaggle and it contains the data of 9000 active credit card holders during the last 6 months.
Make sure you have the following packages installed:
- numpy and pandas for reading and manipulations
- plotly_express and plotly.graph_objects for interactive plotting
- scikit-learn for Principal Component Analysis (PCA) and clustering (K-Means)
The final five customer clusters are :
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Cluster 1 (Active Cash Advance Customers) They have a high limit than most and they use it for purchasing stuff. Additionally, they make use of installments and a bit of cash advances.
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Cluster 2 (All-in Active Customers) They have a higher limit than Cluster 1, however they purchase even more frequently and have a higher use of installments.
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Cluster 3 (Cash Advance Customers) They have a lower limit and use a higher proportion of it for cash advances than others.
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Cluster 4 (Dead Customers) They do no do much. They do not buy, they do not take cash advances.
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Cluster 5 (Installment Customers) They prefer safety and therefore to make purchases via installments, and do not take any cash advance.
Here are some findings that helped clustering customers:
To make sure we keep customers in the business we have a marketing strategy set for each one of them.
Cluster 1 (Active Cash Advance Customers):
- How: Provide discounts or rewards for specific purchase categories.
- Why: Maintain the interest.
Cluster 2 (All-in Active Customers):
- How: Introduce exclusive rewards for high-frequency transactions.
- Why: This group has the money and like to spend it.
Cluster 3 (Cash Advance Customers):
- How: Explore other credit card types e.g. low cash advance but low fees and higher tenure.
- Why: Too frequent cash advances are risky for a bank. In the best case, this group of customers would decrease in size and move to the Active Customer group.
Cluster 4 (Dead Customers):
- How: Consider contacting customers with surveys to understand their disengagement reasons.
- Why: This group would need to be understood first before targeting with a marketing technique. Why don't they purchase? Are these people with lower income, are they young?
Cluster 5 (Installment Customers):
- How: Develop partnerships with merchants offering installment-friendly services and potentially that are low in interest.
- Why: It is scalable across other clusters e.g. dead customers.