Why it matters?

Customer segmentation is a basic tool that banks use to group customers based on their similarities. Banks want to understand how these groups change over time. Each customer group should be treated differently based on:

  • Channels: How the customer likes to interact with the bank (tech, phone, in-person).
  • Loyalty: What rewards or experiences will make the customer more loyal.
  • Products: What financial products are best for the customer (savings, investments).
  • Happiness: If the customer is having problems or needs a satisfaction survey.
  • Demographics: What financial needs the customer likely has based on age, job, etc.
  • Risk: If the customer might stop using the bank’s services.

By understanding these customer groups, banks can create better strategies to serve their most important customers.

Data Collection

Banks want to collect as much data as possible about customers to create meaningful groups. The specific data collected depends on the bank’s goals:

  • Increase market share: Understand what attracts new customers.
  • Introduce a new product: Know what customers like and dislike.
  • Increase sales to existing customers: Find ways to sell more to each customer.
  • Keep customers: Identify unhappy customers early to keep their business.
  • Optimize costs: Encourage customers to use cheaper service options.

The approach to segmentation depends on the specific goal.

Demographics

Basic facts like nationality, salary, gender, and age are very important for segmentation. They help show how customers are similar or different.

Products

Looking at what products customers have (savings, investments, loans), how much they use them, and their balances shows their financial needs and engagement.

Transactions

The types, amounts, and locations of transactions show customer spending habits and lifestyles, which is very useful for segmentation.

Channels

The ways customers prefer to interact with the bank (digital, phone, in-person) and how much they use new interaction options show if they are tech-savvy, convenience-seeking, or prefer personal relationships.

Challenges

Some common challenges in customer segmentation are:

Categorical data: Many variables, like demographics and behaviors, are categorical (non-numeric). They may need to be converted to numbers.

Too many variables: Having lots of data variables can make analysis very complex. Special methods can help reduce the number of variables.

Techniques

Some popular techniques for customer segmentation are:

  • K-means clustering
  • Hierarchical clustering
  • Self-organizing maps
  • Latent class analysis

Here’s a simple example of using k-means clustering in Python:

from sklearn.cluster import KMeans

# Assuming X is the feature matrix
kmeans = KMeans(n_clusters=5, random_state=0)
clusters = kmeans.fit_predict(X)

The resulting clusters variable contains the group assignment for each customer. Banks can then study each group’s characteristics, needs, and value. Creating visualizations and running statistical tests on the group profiles is a great next step.

When done well, customer segmentation helps banks improve their strategies and operations to better serve distinct customer groups. It can have a big impact on many areas, from product design to risk management to customer experience. That’s why it remains a key part of analytics in modern banking.