- About Mitron Bank
- Objective of the Project
- Problem Statement
- Demographic
- Spending Patterns
- Grid View
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.
The objective is to analyze this data and provide actionable, data-driven recommendations to guide Mitron Bank in tailoring the new credit cards to customer needs and market trends.
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Demographic classification: Classify the customers based on available demography such as age group, gender, occupation etc. and provide insights based on them.
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Avg income utilisation %: Find the average income utilisation % of customers (avg_spends/avg_income). This will be your key metric. The higher the average income utilisation %, the more is their likelihood to use credit cards.
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Spending Insights: Where do people spend money the most? Does it have any impact due to occupation, gender, city, age etc.? This can help you to add relevant credit card features for specific target groups.
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Key Customer Segments: By doing above, you should be able to identify and profile key customer segments that are likely to be the highest-value users of the new credit cards. This includes understanding their demographics, spending behaviours, and financial preferences.
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Credit Card Feature Recommendations: Provide recommendations on what key features should be included in the credit card which will improve the likelihood of credit card usage. This should be backed by the insights from data provided and also some secondary research on the internet for this.
For demographic classification, I have conducted a thorough customer demographic analysis using Power BI, and here are the key findings presented in a visually engaging manner:
The dataset includes a large group of 4000 customers, which I'm using as the main focus of my analysis.
• Most of our customers are males, making up 64.93% of the total, showing a slight male-focused demographic.
• However, the significant presence of females (35.08%) demonstrates a diverse customer base.
• The age bracket of 25-35 stands out as the largest segment, comprising 1498 customers. Particularly among males, this group shows a notable presence.
• Additionally, customers aged 35-45 represent a considerable portion (1273), indicating an even distribution between genders.
• Although smaller in size, the 45+ age category remains a significant segment that warrants attention.
• Mumbai stands out for its high customer concentration, boasting 1078 predominantly male customers.
• Additionally, other prominent cities such as Chennai, Bangalore, and Delhi NCR make substantial contributions to our customer base.
• A significant portion of our customer base (1294) comprises salaried IT employees, indicating a strong presence of tech-oriented individuals.
• The array of occupations represented, ranging from freelancers to business owners, offers a chance to customize services to cater to diverse professional requirements.
• The vast majority of our customers, accounting for 78.41%, are married, underscoring the necessity of offering financial solutions tailored to family needs.
• Although unmarried customers constitute a smaller portion, they still make up a significant 21.6% of our customer base.
o uncover crucial insights into customer spending behaviors and grasp the average income allocation across various segments, an exhaustive analytical investigation has been initiated. To ensure a thorough comprehension, a specialized "Spending Patterns" page has been intricately developed using Power BI. This page acts as the focal point for unraveling detailed information, containing a wide array of Key Performance Indicators (KPIs) along with informative charts and graphs.
• The age bracket of 25-34 demonstrates the highest income, expenditure, and utilization rate at 43.66%.
• Following closely is the 35-45 age group, boasting a utilization rate of 46.52%, which stands as the second-highest.
• The most money is spent on bills, totaling $105 million, with an average usage of 46%.
• Other significant spending includes $86 million on groceries and $80 million on electronics, while the least is spent on other miscellaneous items, totaling $16 million.
• Salaried IT professionals lead in both income, amounting to $477 million, and expenditure, totaling $244 million, with a utilization rate of 51.04%. Business owners report an income of $265 million, expenditure of $88 million, and a utilization rate of 33.22%.
• Conversely, government employees demonstrate the lowest utilization rate at 29%.
• Mumbai excels with the greatest income and expenditure, leading to a utilization rate of 51.43%.
• Chennai, Delhi NCR, Bengaluru, and Hyderabad exhibit diverse utilization rates.
• The majority of spending is attributed to credit cards, totaling $216 million, with a utilization rate of 17.45%.
• Alternative payment methods encompass UPI, debit cards, and net banking.
• Male individuals take the forefront in spending, accounting for $357 million, while females contribute $154 million.
• In terms of spending, married individuals lead with a total of $429 million, exceeding unmarried individuals who contribute $102 million.
• September emerges as the highest spending month, accounting for $116M, constituting 21.84% of the total spend.
• Males demonstrate a higher utilization rate of income at 44.39%, in contrast to females who exhibit a rate of 39.92%.
Moreover, an additional page has been specifically designated for a detailed Grid View, facilitating an exhaustive review of all customer data. This structured format allows for a comprehensive examination of individual customer information, thereby supporting refined and tailored analyses.