CHAPTER 1
INTRODUCTION
This project aims to analyze the credit card spending patterns of customers and develop strategies for
customer acquisition. The objective is to gain insights into customer behaviour, identify profitable
segments, and optimize profitability. The project includes performing sanity checks and data cleaning
procedures to ensure data accuracy. The tasks involve analyzing monthly spend and repayment,
identifying the highest paying customers, determining spending patterns by segment and age group,
identifying the most profitable segment, analyzing expenditure categories, calculating monthly profit,
and imposing interest rates for due amounts.
The project focuses on the problem statement, which includes implementing sanity checks and data
cleaning procedures to ensure the accuracy and integrity of the dataset. One important aspect of data
cleaning involves providing a meaningful treatment for values where the age is less than 18. By
addressing this issue, the dataset can accurately represent the target population and avoid potential
biases. Additionally, the project identifies cases where the monthly spend exceeds the credit limit and
imposes a 2% penalty of the credit limit. This treatment promotes responsible spending habits and
encourages customers to stay within their credit limits. Furthermore, the project identifies cases where
the repayment exceeds the spend and provides a credit of 2% of the credit limit in the next month's
billing, incentivizing timely repayments and customer loyalty.
The tasks involved in this project include analyzing the monthly spend and repayment of each
customer, identifying the top 10 paying customers, determining the segments in which customers are
spending more money, analyzing spending patterns based on age groups, identifying the most
profitable segment, categorizing customer spending by expenditure categories, calculating the monthly
profit for the bank, and imposing an interest rate of 2.9% for each customer for any due amount.
By analyzing the monthly spend and repayment patterns of each customer, valuable insights can be
gained into their spending behaviors and creditworthiness. Identifying the top 10 paying customers
provides an understanding of the most valuable and profitable customers, allowing for targeted
acquisition strategies. Analyzing spending patterns by segment helps in tailoring marketing efforts to
specific customer groups, while analyzing spending patterns by age groups provides insights into
generational preferences and habits. Determining the most profitable segment aids in prioritizing
acquisition efforts and categorizing customer spending allows for a detailed understanding of
expenditure patterns. Calculating the monthly profit for the bank provides a clear view of financial
performance and helps in optimizing profitability. Finally, imposing an interest rate of 2.9% for due
amounts ensures timely repayments and minimizes the risk of defaults.
How do you explain a credit card to a customer?
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A credit card is a financial instrument issued by banks with a pre-set credit limit, helping you make
cashless transactions. The card issuer determines the credit limit based on your credit score, credit
history and your income.
What is Customer Acquisition?
Customer acquisition is the process of getting potential customers to buy your products. A strong
customer acquisition strategy: 1) attracts leads, 2) nurtures them until they become sales-ready, and 3)
converts them into customers. The overall cost of these steps is referred to as your customer
acquisition cost (CAC).
Customer Acquisition vs Marketing
Customer acquisition refers to bringing in new customers - or convincing people to buy your
products. It is a process used to bring consumers down the marketing funnel from brand awareness to
purchase decision.
What is marketing and its types?
Print media, broadcasting, direct mail, billboards and posters, and referral, i.e. word of mouth, are
examples of traditional marketing. Email marketing, social media promotion, content marketing,
search engine optimization (SEO), mobile marketing, and paid advertising are all examples of digital
tactics.
CHAPTER 2
Problem Statement
The problem statement emphasizes the need for data cleaning and implementing sanity checks to
ensure the reliability of the dataset. Specific treatments are applied to address scenarios such as age
values below 18, monthly spend exceeding the limit, and repayment exceeding spend. These
treatments ensure meaningful data for analysis and accurate insights into customer behaviour.
The problem statement for this project revolves around credit card spending patterns and customer
acquisition. In the realm of credit card usage, it is crucial for financial institutions to gain a deep
understanding of customer behaviour and preferences. This understanding not only helps in identifying
profitable segments but also enables effective customer acquisition strategies. However, before diving
into the analysis, it is essential to perform sanity checks and data cleaning to ensure the accuracy and
reliability of the dataset.
The first aspect of the problem statement involves providing a meaningful treatment to values where
the age is less than 18. This treatment aims to address any potential inconsistencies or inaccuracies in
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the dataset. By ensuring that all age values reflect realistic and valid data, the subsequent analysis can
provide accurate insights into customer behaviour and spending patterns.
The second aspect of the problem statement focuses on identifying cases where the monthly spend
exceeds the credit limit. It is important to recognize and impose a penalty for such cases. By imposing
a penalty of 2% of the credit limit, financial institutions can encourage responsible spending habits
among their customers. This treatment not only helps customers stay within their credit limits but also
promotes economic responsibility and reduces the risk of default.
Lastly, the problem statement highlights the need to identify cases where the repayment amount
exceeds the spend. In such instances, customers should be given a credit of 2% of their credit limit in
the next month's billing. This treatment serves as an incentive for customers to make timely
repayments, rewards responsible financial behaviour, and fosters customer loyalty.
By addressing these aspects of the problem statement, the project aims to ensure a clean and accurate
dataset for analysis. This, in turn, enables the exploration of credit card spending patterns, customer
acquisition strategies, and profitability optimization. Through data cleaning and implementing the
specified treatments, the project seeks to provide meaningful insights and recommendations for
financial institutions to enhance their credit card offerings, attract valuable customers, and maximize
profitability.
CHAPTER 3
Methodology
The methodology employed in this project involves several steps to analyze credit card
spending patterns and develop effective customer acquisition strategies. The
methodology encompasses data cleaning, data analysis, and deriving actionable insights
from the dataset. The following steps were followed:
3.1. Data Collection:
The project begins with the collection of credit card transaction data from the relevant
sources. This data includes information such as monthly spend, repayment amounts,
credit limits, age, and other relevant customer details.
3.2. Data Cleaning:
Data cleaning is a critical step to ensure the accuracy and reliability of the dataset. In
this project, data cleaning involves identifying and handling missing values, removing
duplicates, and addressing any inconsistencies or errors present in the data. Special
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attention is given to treating values where the age is less than 18 to maintain data
integrity.
3.3. Sanity Checks:
To validate the dataset, sanity checks are performed. This involves checking for any
outliers or unusual values that may skew the analysis. For instance, monthly spends that
exceed the credit limit are identified, and a penalty of 2% of the credit limit is imposed.
Similarly, cases where the repayment amount exceeds the spend are identified, and a
credit of 2% of the credit limit is assigned for the next billing cycle.
3.4. Data Analysis:
Once the dataset is cleaned and validated, data analysis techniques are applied to gain
insights into credit card spending patterns. The following analyses are performed:
a. Monthly Spend Analysis:
The monthly spend of each customer is analyzed to identify spending trends and
patterns. This analysis helps in understanding customer behavior and preferences.
b. Monthly Repayment Analysis:
The monthly repayment amounts of each customer are examined to evaluate their
repayment behaviors and creditworthiness. This analysis aids in assessing customer
reliability and the potential for customer acquisition.
c. Identifying the Top 10 Paying Customers:
The project identifies the top 10 customers who make the highest repayments. This
analysis provides insights into the most valuable customers and helps in developing
strategies to acquire similar high-value customers.
d. Segment-Wise Spending Analysis:
Customers are segmented based on demographic or behavioral characteristics. The
project analyzes the spending patterns of each segment to identify which segments are
spending more money. This analysis assists in targeting marketing efforts and tailoring
acquisition strategies.
e. Age Group Spending Analysis:
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The project further categorizes customers into different age groups and analyzes their
spending patterns. This analysis provides insights into which age groups are spending
more money, enabling personalized marketing approaches and acquisition strategies
for specific age groups.
f. Most Profitable Segment Analysis:
The profitability of each segment is evaluated based on various metrics, such as
revenue generated, repayment behavior, and average spend. This analysis identifies the
most profitable segment and aids in allocating resources and developing effective
acquisition strategies.
g. Category-Wise Spending Analysis:
The project categorizes customer spending into different expenditure categories, such
as travel, dining, retail, etc. This analysis helps in identifying the categories where
customers are spending more money, guiding marketing efforts and product
development.
3.5. Monthly Profit Calculation:
The monthly profit for the bank is calculated by considering factors such as interest
rates, penalties, and repayment behaviors. This calculation provides a clear view of the
bank's financial performance and helps in optimizing profitability.
CHAPTER 4 Results and Analysis
4.1.Introduction
The results obtained from the analysis of credit card spending patterns and
customer acquisition provide valuable insights into customer behavior and
profitability. This section presents the key findings derived from the data
analysis conducted in the project.
4.2. Monthly Spend Analysis:
The analysis of monthly spend reveals patterns and trends in customer
expenditure. It helps in understanding how customers utilize their credit limits
and the distribution of spending across different time periods. The findings
highlight the variation in spending habits among customers, with some
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exhibiting consistent monthly spend, while others show fluctuations or spikes in
their spending patterns.
4.3. Monthly Repayment Analysis:
The analysis of monthly repayment amounts provides insights into customer
repayment behaviors. It helps in assessing the creditworthiness and reliability of
customers. The findings reveal different repayment patterns, including regular
payments, sporadic payments, and customers who consistently repay more than
their monthly spend. This analysis enables financial institutions to identify lowrisk customers and develop strategies to encourage timely repayments.
4.4. Identification of the Top 10 Paying Customers:
Identifying the top 10 paying customers is a crucial analysis that sheds light on
the most valuable and profitable customers. These customers demonstrate a high
level of financial responsibility and loyalty. The findings allow financial
institutions to provide enhanced customer experiences, targeted marketing
offers, and personalized services to retain and attract similar high- value
customers.
4.5. Segment-Wise Spending Analysis:
Analyzing spending patterns by segment provides insights into which segments
are spending more money. By segmenting customers based on demographic or
behavioral characteristics, financial institutions can tailor their acquisition
strategies and marketing campaigns accordingly. The findings enable the
identification of high-potential segments, allowing for focused efforts to attract
and retain customers within those segments.
4.6. Age Group Spending Analysis:
The analysis of spending patterns across different age groups helps in
understanding the preferences and habits of different generations. It provides
insights into which age group is spending more money and can influence the
development of targeted marketing campaigns and customized offers. The
findings reveal the age group(s) that represent the most significant market
opportunity and allow for strategic allocation of resources to attract customers
from those age groups.
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4.7. Most Profitable Segment Analysis:
Analyzing the profitability of each segment helps in identifying the segments
that generate the highest profit. This analysis considers factors such as revenue
generated, repayment behavior, and average spend within each segment. The
findings assist financial institutions in prioritizing acquisition efforts, allocating
resources effectively, and designing products or services that cater to the needs
of the most profitable segments.
4.8. Category-Wise Spending Analysis:
Categorizing customer spending into different expenditure categories provides
insights into the areas where customers are spending the most money. This
analysis reveals the categories that have the highest demand and can guide
product development and marketing strategies. Financial institutions can
leverage these findings to create targeted promotions and partnerships that align
with customer preferences and maximize profitability.
4.9. Monthly Profit Calculation:
The calculation of monthly profit considers factors such as interest rates,
penalties, and repayment behaviors. It provides a comprehensive view of the
bank's financial performance. The findings enable financial institutions to assess
profitability, identify areas of improvement, and optimize revenue generation by
refining interest rates and penalties.
4.10.Conclusion:
In conclusion, the results and analysis of credit card spending patterns and
customer acquisition provide valuable insights for financial institutions. The
findings help in understanding customer behavior, identifying profitable
segments, developing targeted marketing campaigns, and optimizing
profitability. These insights allow for informed decision-making and the
implementation of strategies that enhance customer acquisition, improve
customer satisfaction, and drive financial success.
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5.2. Data Analysis
Age Distribution:
The number of customers with an age less than 18 is 15, indicating that there are a
significant number of young customers in the credit banking system. The mean age of
customers is 49.29242405876662 suggesting that the customer base is relatively
diverse in terms of age.
Total Amount:
The total amount from spend and repayment data is 752560492.5564736 indicating the
overall financial activity within the credit banking system.
The total limit from customer acquisition data is Total Sum: 28470061 representing the
credit limits provided to customers.
Credit Eligibility:
Customers who have higher monthly repayments than spends are eligible
for a credit of 2% of their credit limit in the next month billing.
The identified customers (C2, C4, C5, C9, C10) have met the eligibility
criteria and can receive the respective credit amounts.
Spending Patterns:
The monthly spend of each customer provides insights into individual spending habits
and patterns.
The monthly repayment of each customer helps understand their repayment behavior.
High Paying Customers:
The top 10 customers with the highest repayment amounts (C31,C61,C26, C79,
C22,A6, C20,A62,A45,A26) contribute significantly to the repayment total.
Segment Analysis:
The segment 'SEGMENT4' has the highest spending, suggesting that customers in this
segment make substantial transactions.
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The most profitable segment is 'Normal Salary', indicating that it generates higher
revenues or profits compared to other segments.
Age Group Analysis:
Customers in the age group '18' has the highest spending, which may indicate that this
age group has higher financial activities or purchasing power.
Category Analysis:
The 'Travel' category is where customers are spending the most money, indicating a
higher demand for travel-related expenses.
Monthly Profit:
The monthly profit for the bank is calculated by subtracting the monthly spend from the
monthly repayment for each customer.
Positive values indicate that the bank is earning a profit from the customer, while
negative values indicate potential areas of concern.
Overall, the analysis reveals various patterns and trends within the credit banking
system. It provides insights into customer behavior, profitability, and areas where the
bank can focus its marketing and credit strategies. Understanding customer segments,
spending patterns, and repayment behaviors can help the bank make informed decisions
and tailor their services to meet customer needs effectively.
CHAPTER 6
Recommendations
Based on the analysis, the report provides recommendations to improve
customer acquisition and optimize profitability. These recommendations include
targeted marketing campaigns for high-spending segments, personalized offers
for specific age groups, customer retention strategies, further analysis of
expenditure categories, and regular monitoring and adjustment of interest rates
and penalties.
6.1. Targeted Marketing Campaigns: Develop targeted marketing campaigns
to attract customers from high-spending segments, focusing on personalized
messaging and tailored offers based on their spending patterns and preferences.
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6.2. Personalized Offers: Customize offers and promotions for specific age
groups to cater to their unique needs and preferences, leveraging insights from
the age group spending analysis.
6.3. Customer Retention Strategies: Implement customer retention initiatives
such as loyalty programs, exclusive rewards, and personalized customer support
to enhance customer satisfaction and loyalty.
6.4. Further Analysis of Expenditure Categories: Conduct in-depth analysis
of expenditure categories to identify specific areas where customers are
spending more money. Use these insights to refine existing products, introduce
new offerings, or form partnerships to meet customer demands.
6.5. Regular Monitoring and Adjustment of Interest Rates and Penalties:
Continuously monitor interest rates and penalties, making adjustments as
necessary to ensure they align with market conditions and incentivize timely
repayments.
CHAPTER 7
Conclusion
In conclusion, the analysis of credit card spending patterns and customer acquisition
provides valuable insights for financial institutions seeking to optimize profitability
and attract high-value customers. By implementing data cleaning procedures, conducting thorough
analysis, and leveraging the findings, financial institutions can make informed decisions to enhance
customer acquisition efforts and drive business success.
The analysis revealed various key findings, including spending patterns by segment and age group,
identification of the most profitable segments, top paying customers, and expenditure categories
where customers are spending the most. These insights allow for the development of targeted
marketing campaigns, personalized offers, and customer retention initiatives.
The recommendations provided in this project report offer actionable strategies to improve customer
acquisition and profitability. Targeted marketing campaigns tailored to high-spending segments,
personalized offers for different age groups, and customer retention initiatives can significantly
enhance customer satisfaction and loyalty. Collaboration with merchants, regular monitoring of
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interest rates and penalties, and continuous data-driven decision making are essential for adapting to
market dynamics and maximizing results.
Financial institutions should strive for continuous improvement by regularly reviewing and updating
their acquisition strategies. By staying agile and responsive to changing customer preferences and
market trends, institutions can maintain a competitive edge in the credit card industry.
In conclusion, understanding credit card spending patterns and implementing effective customer
acquisition strategies is crucial for financial institutions to succeed. By applying the recommendations
and continuously refining their approaches, institutions can acquire valuable customers, optimize
profitability, and foster long-term customer relationships. Ultimately, the insights gained from
analyzing credit card spending patterns will contribute to the overall growth and success of financial
institutions in the dynamic credit card market.
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