Codebasics Resume Project Challenge – Data Analytics Project
Codebasics Resume Project Challenge – Data Analytics Project
This Project is a Codebasics Resume Project Challenge -8.
I was not able to participate in this challenge, however I still finished the project and recorded a video presentation.
About The Project
Mitron Bank is a legacy financial institution headquartered in Hyderabad. They want to introduce a new line of credit cards, aiming to broaden its product offerings and reach in the financial market.
They provided a sample dataset of 4000 customers across five cities on their online spend and other details.
As a Data Analyst I have to provide insights and ideas based on the given data to the management to make Data Driven decisions.
Live Dashboard
Project
The report has 4 dashboards and 1 recommendation page.
Demographics
This dashboard categorization of cusotmers on the basis of Age group, Location, Occupation, Gender, Marital Status & Income Band.
This report has insights like Average monthly income per customer, customers count in various categories.
Income Utilization
As the name suggests, the monthly income utilization among different categories. Income utilized by males, females, married and unmarried customers etc.
It is important to know the income utilization as it explains where the money needs to be spent every month.
Spending Insights
The speding of monthly income on various categories to learn the spending pattern in order to find the opportunities and target the relevant customers.
The one important insight in this report is the use of platforms for spending the money.
Key Insights
Key insights is a report based on the use of Credit Card.
The Credit Card filter is applied on this report for all the insights like spending on different categories, usage by customers based on Gender and Occupaction etc.
Recommendations
The last page of this report has recommendations for the strategy team to come up with the new products and offers.
Demographics
Insights
| Insights | Values |
|---|---|
|
Total Customers |
4000 |
|
Male |
2597 |
|
Female |
1403 |
|
Married |
3136 |
|
Single |
864 |
|
Total Cities |
5 |
Financial Demographics Insights
| Insights | Values |
|---|---|
|
Average Monthly Income Per Customer |
₹51657 |
|
Highest Average Salary/Income (Business Owners) |
₹70091.2 |
|
Lowest Average Salary/Income (Freelancers) |
₹35058.3 |
|
Highest Average Salary (City) |
₹52344.8 |
|
Lowest Average Salary (City) |
₹51073.3 |
|
Highest Salary Age Group (45+) |
₹60395.5 |
|
Lowest Salary Age Group (21-24) |
₹41238.5 |
Income Utilization
Insights
| Insights | Values |
|---|---|
|
Average Monthly Income Per Customer |
₹51657 |
|
Average Monthly Spend Per Customer |
₹22120.7 |
|
Average Monthly Spend % Per Customer |
42.82% |
|
Average Monthly Spend % By Females |
39.92% |
|
Average Monthly Spend % By Males |
44.39% |
|
Average Spend Per Transaction |
₹614.5 |
|
Highest Income Utilization % (Bills) |
19.76% |
|
Lowest Income Utilization % (Apparel) |
6.41% |
|
Highest Income Utilization % (Age Group 35-45) |
46.72% |
|
Lowest Income Utilization % (Age Group 45+) |
34.70% |
Spending Insights
| Insights | Values |
|---|---|
|
Average Monthly Income Per Customer |
₹51657 |
|
Average Monthly Spend Per Customer |
₹22120.7 (42.82%) |
|
Average Spend Per Transaction |
₹614.5 |
|
Highest Monthly Spend (On Bills) |
₹4371.4 (19.76%) |
|
Lowest Monthly Spend (On Apparel) |
₹1418.2 (6.41%) |
|
Highest Income Spend (By Age Group 35-45) |
₹24959.4 (46.72%) |
|
Lowest Income Spend (By Age Group 45+) |
₹21182.7 (34.70%) |
|
Highest Income Spend (By Occupation – Salaried IT Emplyoees) |
₹31391.1 (51.04%) |
|
Lowest Income Spend (By Occupation – Business Owners) |
₹23281.7 (33.22%) |
|
Payment Method With Highest Spend (Credit Card) |
₹9012.9 (40.74%) |
Key Insights
Insights For Credit Card Usage
| Insights | Values |
|---|---|
|
Average Spend Per Transaction |
₹1001.4 |
|
Average Monthly Spend Per Customer |
₹9012.9 (40% Of Total Spend) |
|
Total Money Spend |
₹216M |
|
Total Spend By Males |
₹144M |
|
Total Money Spend By Females |
₹76M |
|
Total Money Spend By Married Customers |
₹176M |
|
Money Spend By Single Customers |
₹40M |
|
Highest Money Spend On Bills |
₹46,332,586 (21.42%) |
|
Lowest Money Spend On Apparel |
₹13,969,973 (6.46%) |
|
Highest Amount Spend By IT Salaried Employees |
₹101M |
|
Lowest Amount Spend By Government Employees |
₹16M |
|
Highest Spending Month (September) |
₹47,261,526 |
|
Lowest Spending Month (May) |
₹27,765,710 |
Key Findings
With all the insights shared above, I saw some opportunities. These opportunities can be used to create tailored Credit Cards as per the customer base.
1. Top 3 categories where most of the customers spend money are:
- Bills – 19.76%
- Groceries – 16.26
- Electronics – 14.99%
More than 50% of the total monthly spend is spent on these 3 categories.
2. Payment method where the highest amount of monthly income is spent is Credit Card
40.7% of total spend is done using credit card. Which is a good sign. It tells how customers are happy to use the credit cards for most of their spendings.
3. Age group 25-34 spend 46.6% of monthly spend using Credit Card.
4. Average spend per transaction on credit card is ₹1001.4.
5. Salaried IT Employees are the highest income spenders among other working professionals.
They spend 51.04% of their monthly income.
6. September is the highest income spending month.
Some of the key findings are shared in the video presentation. So, please watch the complete video.
NOTE: All the recommendations below are summarized and details are discussed in the video presentation.
Recommendations
Cashback Card
Cashback cards are the most commonly available product in the market, and mostly, it is offered to low-salary band customers.
- Low salary band customers do not want liabilities, so they do not use credit cards often.
- Increase the Interest-free period from existing 45 days to 60 days if possible.
- 5 Years no yearly charge scheme.
- Offer to Age group between 21-24. (They spend least on Travel and Bills)
Discount Card
Discount Cards can be offered to the frequently using users. This is to retain the customers.
- Discount on minimum monthly spend.
- Run Discounts From August till January.
- Special Festival Offers.
- Discounts on Gadgets (As IT sector people tends to spend more using Credit Cards and they earn more.
Discount Card
Diner’s Club Cards are premium products and so they can be offered to specific salary window customers.
These cards shall come up with special access to leisure activities.
EX: Airport Lounges, Five Star Hotels Health and Fitness facilities, Occasional Air ticket upgrades, Free chauffer service to and from Airports etc.
These cards are sometimes kept as to show off and doesn’t really depends on discounts but on luxury services.
Apparel, Entertainment and Food have the lowest average transaction amounts and overall spend. (Choose partners who are recently launching their IPOs)
Travel offers can also be pushed as after the Covid this sector is on its peak again. (High prices of Air Tickets and Indian local travel economy is boosted as well since the launch of new Trains, Airports, Hotels and Infrastructure.
Note: Transaction Ids could have helped in better understanding how many times the credit card is used over other payment methods. Currently all the customers have 216 total transactions and 54 transaction using each platform.
About the Dashboard Design:
As it is visible that I have not used a lot of custom colors in the dashboard. This is because most of the banks use white and blue colors in their branding. I have seen this in India, UAE and UK that these are most commonly used colors. So while working on the report I saw the default colors are good enough to work with and not much of a change is needed.
The challenge link: Codebasics Resume Project Challenge – 8