Solving Banking Segmentation Assignments Using Schelling's Model

What is Schelling’s Segregation Model?

Schelling’s Segregation Model, initially used to understand residential segregation, explains how individual preferences can lead to broader societal patterns. In a banking context, the model helps study how customer preferences lead to segmentation among different banking services such as savings, loans, investments, and insurance.

Applying Schelling’s Model to Banking Assignments

Banking segmentation problems based on Schelling’s model revolve around understanding how customers gravitate towards particular financial products. The model allows us to simulate how minor shifts in customer preferences create larger patterns in the financial landscape.

Key Research Questions in Such Assignments

• How do small changes in customer preferences affect the overall distribution of customers across banking services?
• Can banks optimize service offerings to maintain an even distribution and reduce customer dissatisfaction?
• How do factors like income level and previous banking experience influence customer movement?

Steps to Solve a Banking Segmentation Assignment

Step 1: Understanding the Dataset

• Attributes of a Simulated Dataset

Banking segmentation assignments often involve working with a simulated dataset. This dataset generally includes:

• Customer ID: A unique identifier for each customer
• Preferred Service: Banking products such as savings, loans, investments, and insurance
• Income Level: Low, medium, or high income, affecting service preferences
• Previous Experience: Positive, neutral, or negative customer satisfaction ratings
• Initial Data Cleaning and Pre-processing

Before proceeding with analysis, the dataset must undergo preprocessing, which involves:

• Checking for missing or inconsistent data to ensure dataset completeness.
• Validating income distribution to reflect real-world patterns (e.g., 20% high-income, 40% medium-income, 40% low-income).
• Ensuring satisfaction scores fall within the expected range (0.3 to 1.0).

Step 2: Defining the Simulation Environment

• Representing Customers on a 2D Grid
• The banking environment is modeled as a grid where each cell represents a customer.
• Some cells are left empty to allow customer movement.
• Each customer is assigned a starting position based on their banking preference.
• Happiness Threshold
• Customers check their neighbors (adjacent customers in the grid).
• If less than 50% of their neighbors share their service preference, they become unhappy.
• Customers move to a new position if dissatisfied.
• Iterations in the Simulation
• The simulation runs for a set number of iterations (e.g., 100 iterations).
• Unhappy customers relocate until they either find a suitable position or the maximum iterations are reached.

Implementing the Code for Simulation

Step 3: Structuring the Codebase

• Organizing the Code for Clarity

To effectively implement the simulation, the code should be structured into modular functions:

• Data Setup: Initialize customer attributes and place them on the grid.
• Happiness Calculation: Check customer satisfaction based on neighbors.
• Movement Algorithm: Move unhappy customers to vacant positions.
• Simulation Loop: Iterate until stability is reached.
• Key Functions Explained

calculate_happiness(x, y, grid, df, threshold=0.5)

• Checks if a customer is happy based on the percentage of similar neighbors.
• Returns True if satisfied, False otherwise.

move_unhappy_customers(grid, df)

• Identifies unhappy customers and moves them to available empty spots.

run_simulation(grid, df, num_iterations=100)

• Runs the entire simulation, updating the grid and repositioning customers at each step.

Step 4: Visualizing the Results

Using Data Visualization to Interpret Customer Movement

• Scatter plots to show the initial and final distribution of customers.
• Heatmaps to visualize service preference density.
• Bar charts to depict changes in customer distribution over iterations.

Analyzing the Simulation Results

Step 5: Understanding Emerging Segmentation Patterns

• Interpreting the Results
• Clusters will form where customers with similar preferences group together.
• High-income customers are likely to move towards investment services.
• Dissatisfied customers will relocate frequently before stabilizing.
• Evaluating Banking Service Optimization
• If banks want an even distribution, they may need to introduce incentives or adjust product offerings to reduce drastic movements.

Step 6: Presenting and Reporting the Findings

Statistical Measures for Evaluating the Model

• Percentage of happy customers at the end of the simulation.
• Comparison of initial vs. final distributions to measure shifts in segmentation.
• Impact of income levels on banking product preferences.

Conclusion: Best Practices for Solving Such Assignments

When solving customer segmentation assignments based on Schelling’s model, it is essential to:

• Understand the theoretical framework to align simulation outcomes with real-world banking patterns.
• Prepare a well-structured dataset with appropriate attributes and distributions.
• Implement a robust simulation model that efficiently calculates customer happiness and movement.
• Visualize and analyze results using appropriate data representations to derive meaningful insights.
• Interpret findings carefully, making recommendations for banking institutions to optimize their services based on the observed trends.

By following these steps, students can effectively approach and solve such assignments while gaining a deeper understanding of customer segmentation dynamics in banking.