Use Cases

April 23, 2024

Demographics and Product Analysis: A HDFC UseCase

Wanna know how a leading full-service commercial bank like HDFC Bank might be using customer demographic & product fit analysis method to improve financial performance? Read here! :)

You've probably heard about HDFC Bank, right?

It's this giant in the Indian banking world that pretty much has a credit card solution for anything you can think of - from everyday purchases to building a financial safety net for those rainy days.

HDFC Bank, one of India's premier banking institutions, showcases impressive financial health and operational strength in the banking sector.
According to the Reserve Bank of India data for November 2023, HDFC Bank’s cards in circulation stood at nearly 19.51 million as compared to 19.18 million cards in October 2023.

HDFC Bank is followed by SBI Card, ICICI Bank, and Axis Bank, in terms of the number of credit cards issued.

1. Credit Card Issuance: HDFC Bank has issued over 14 million credit cards to customers.

2. Market Share: Holds an estimated 24% share of the Indian credit card market by volume of transactions.

3. Annual Spend: Credit card transactions through HDFC Bank are estimated to exceed INR 1.2 trillion annually.

4. Digital Engagement: Over 85% of credit card transactions are conducted digitally, via online or mobile platforms.

5. Fraud Incidents: Maintains a fraud incident rate significantly below the industry average, at less than 0.02% of transaction volume.

6. Customer Satisfaction: Scores above 80% in customer satisfaction surveys, particularly in categories such as customer service and rewards redemption.

7. Rewards Redemption: Customers redeem rewards points valued at over INR 5 billion each year through the bank's credit card rewards program.

Try this Business Use Case by yourself here👇
Slideshow Demo
Product Fit Analysis Data Story

About Dataset

Let’s Inspect our Credit cards sales Data. The dataset consists of six entries capturing credit card transactions, each delineating key attributes such as customer details, transaction specifics, and financial terms. It includes fields like customer name, advisor handling the transaction, annual fee, commission earned, transaction category (predominantly "CREDIT CARDS"), and transaction dates. Additionally, managerial hierarchies, including national heads, zone heads, and sales managers, are documented alongside location specifics such as city, state, and PIN code. The dataset provides insights into credit card transactions facilitated by advisors, detailing associated fees, commissions, and payment cycles, as well as the specific credit card products and banking institutions involved.

Vehicle Insurance Dataset Fields
Column Name Data Type Unique Values Description
row_id Integer Unique identifier for each row.
lead_code String 15029 Unique code associated with each lead.
customer_name String 11784 Name of the customer.
advisor String Name of the advisor.
advisor_code String 196 Unique code associated with each advisor.
city String 77 Customer's city.
state String 17 Customer's state.
pincode Integer 137 Postal code for the customer's location.
annual_fee Float 29 The annual fee associated with the credit card.
commission Float 34 Commission amount for the sale.
commission_cycle Integer The cycle or period for commission payment.
category String 2 The category of the product, in this case, "CREDIT CARDS".
credit_card_name String 32 Name of the credit card.
bank String 10 The bank issuing the credit card.
national_head String 2 Name of the national head.
zone_head String 4 Name of the zone head.
sales_manager String 20 Name of the sales manager.
dt_payin Date The date when payment was made/received.
dt_payout Date The date when payout (commission) was made.

The Customer Loan Data contains over 15712 rows and 12 of our Columns have String Data type, 3 of our Columns have Integers Data Type, 2 column have Float Data type and 2 of the columns have Date Data Type.

Dataset Here

How HDFC Uses its demographics and product Analytics ?

To start with the analysis of demographics and product analysis, it is necessary to follow the 4 factors of Data Analysis and that are : 

1. Identify the users or stakeholders for the dashboard.

2. Design Empathy Map to define Users' Goals and Challenges or pain points.

3. Identify Metrics or KPIs Matter the Most.

4. Understand the Objectives and Goals.

5. Ask Business Questions

I know it looks a bit overwhelming, that's why in this article, we'll lay the foundation for a top-notch Workforce analysis‍.

Step 1: Identify the users or stakeholders for the analysis

Define the User or stakeholder who will use the customer demographic & product fit data story. In our case, We’ll use a Financial Services Manager. Recognizing their diverse needs, challenges, and priorities becomes the cornerstone for tailoring an effective data story.  

User Persona: Financial Services Manager

  • Assessing loan applications to determine creditworthiness and compliance with lending criteria.
  • Managing loan portfolios to ensure a healthy balance between risk and return.
  • Monitoring loan disbursement and repayment activities to ensure they adhere to agreed terms.
  • Collaborating with sales and finance teams to develop and refine loan products.
  • Providing financial advice and support to clients throughout the loan process.

  • Access to detailed financial data and credit reports for risk assessment.
  • Tools for managing and analyzing loan portfolios.
  • Up-to-date information on market conditions and regulatory changes affecting lending.
  • Systems for tracking loan disbursements, repayments, and performance.

  • Balancing the need to grow the loan portfolio with the imperative to minimize bad debts.
  • Quickly adapting to changes in financial regulations and compliance requirements.
  • Managing relationships with borrowers, including handling late payments and renegotiating terms.
  • Identifying and mitigating risks associated with loan defaults and market fluctuations.

Step 2: Design Empathy Map

To truly connect with the experiences and expectations of a Financial Services Manager, the creation of an empathy map is invaluable. This visual tool allows for a deeper understanding of the emotions, aspirations, and pain points of users. 

By empathizing with their perspectives, we can design a data story that not only meets functional requirements but also resonates with the human elements of their roles.

Step 3: Identify the Key Performance Indicators (KPI’s)

Once an empathy map is ready. As a data analyst, you need to decide on the most important things to keep an eye on, called KPIs (Key Performance Indicators).

The heartbeat of any analysis lies in KPI’s and their Metrics. It's crucial to identify the KPIs that matter most to achieving the defined objectives and by focusing on the most relevant metrics, organizations can gain actionable insights into demographics and product analysis.

Vehicle Insurance Dataset Fields
KPI Formula Description
Credit Card Sales by City Count of lead_code per city Measures the total number of credit cards sold in each city, highlighting regional preferences and product market fit.
Average Annual Fee by State Sum of annual_fee by state / Count of lead_code by state Calculates the average annual fee customers are charged for credit cards in each state, indicating economic capabilities and preferences at a regional level.
Commission Earned per Advisor Sum of commission by advisor Evaluates the total commission earned by each advisor, reflecting on their performance and the product's profitability to them.
Bank-wise Credit Card Sales Count of lead_code per bank Identifies the volume of credit card sales associated with each bank, highlighting bank popularity and the success of their products in the market.
Top Performing Credit Card Category Count of lead_code per category Determines the most popular credit card category (e.g., CREDIT CARDS), showing customer preference for specific card benefits or features.
State-wise Distribution of Commission Sum of commission by state Shows how commission earnings are distributed across states, providing insights into regional market performance and the effectiveness of advisors in those areas.
Credit Card Name Impact on Sales Count of lead_code per credit_card_name Reveals the impact of specific credit card names on sales volume, indicating product popularity and market acceptance of credit card features offered by the bank.

Step 4: Understand the Goals & Objectives of User

Now, on the basis of Empathy Map and KPI’s, we need to define our goals and objectives of the Users. So, that it will align with the data story functionalities to ensure decision making. Here are the Key Objectives & Goals :


Enhance credit card product alignment with customer demographics, ensuring product relevance and appeal. Aim for improved financial performance through strategic advisor engagement and optimized commission structures, leveraging insights from KPI analysis.

  • Boost Credit Card Sales Volume: Aim for a 10% increase by targeting key demographics, informed by sales and demographic KPIs.
  • Balance Financial Efficiency: Improve the annual fee vs. commission ratio by 5%, focusing on optimizing bank-specific strategies.
  • Enhance Advisor Performance: Increase commission efficiency and satisfaction by refining commission cycles, aiming for better advisor retention and motivation.
  • Strengthen Market Fit and Customer Satisfaction: Adjust product offerings based on demographic preferences and feedback, targeting higher retention and satisfaction rates.

Step 5 : Ask Business Questions

Beyond KPI’s, organizations must engage in business-driven inquiry. This involves asking strategic questions that directly align with overarching business objecti

Q1. What is the number of credit cards sold by state and city?
  • Metric: credit cards sold, state ,city
  • Question
    • Credit card sold by state, credit card sold by city
  • Observation: In terms of state, Delhi is being followed by Rajasthan for the number of credit cards sold.

Q2 How the total number of credit cards sold and average annual fee of the bank are correlated? Is there any difference in 2024?
  • Metric: number of credit cards sold, average annual fee
  • Question
    • Number of credit cards sold and average annual fee by banks
  • Observation: HDFC tops the chart with both a high number of credit cards sold and having low average annual fee. SCB bank has the highest average annual fee and also low number of credit cards sold.

Q3.Who are the top performing zone heads and sales managers by commission?
  • Metric: commission
  • Question:
    • Commission by Zone head
    • Commission by Sales Manager
  • Observation: Bharti and Atif lead the chart in terms of commissions as zone heads.

Q4. What are the total commissions by sales managers and advisors?
  • Metric: Commision
  • Question
    • Total Commission by Sales Manager
    • Total Commission by Advisors
  • Observation: Azam tops the list followed by Zeeshan and Bharti, with more than double commissions.


We can infer that while HDFC Bank is offering high commissions and has a significant market share with its HDFC Signature cashback credit card variant, it's not efficiently monetizing its customer base, resulting in a shortfall in revenue compared to commission expenditure. Additionally, Kotak Bank's efficient processing of advisor commissions suggests good operational efficiency in that aspect.


The analysis suggests a significant opportunity to optimize credit card product offerings and advisor incentives based on customer demographics and financial performance metrics. By addressing the misalignment between annual fees and commissions, particularly with certain banks, and leveraging demographic insights for targeted marketing, there's potential to enhance sales volumes and financial efficiency. Moreover, focusing on high-commission products and refining commission cycles can improve advisor motivation and performance, contributing to overall business growth and customer satisfaction.

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