Bank Customer Segmentation

Most banks have a large customer base - with different characteristics in terms of age, income, values, lifestyle, and more. Customer segmentation is the process of dividing a customer dataset into specific groups based on shared traits.

According to a report from Ernst & Young, “A more granular understanding of consumers is no longer a nice-to-have item, but a strategic and competitive imperative for banking providers. Customer understanding should be a living, breathing part of everyday business, with insights underpinning the full range of banking operations.

About this Dataset

This dataset consists of 1 Million+ transaction by over 800K customers for a bank in India. The data contains information such as - customer age (DOB), location, gender, account balance at the time of the transaction, transaction details, transaction amount, etc.

Interesting Analysis Ideas

The dataset can be used for different analysis, example -

1. Perform Clustering / Segmentation on the dataset and identify popular customer groups along with their definitions/rules
2. Perform Location-wise analysis to identify regional trends in India
3. Perform transaction-related analysis to identify interesting trends that can be used by a bank to improve / optimi their user experiences
4. Customer Recency, Frequency, Monetary analysis
5. Network analysis or Graph analysis of customer data.

1) Data introduction • Banking dataset is a dataset created to identify existing customers who are more likely to sign up for long-term deposits and focus marketing activities on these customers.

2) Data utilization (1)Banking data has characteristics that: • Predict whether a customer subscribes to a term deposit with yes or no based on 13 columns of data such as age, occupation, and marriage. (2)Banking data can be used to: • Customer Segmentation: Data sets segment customers based on their likelihood of signing up for term deposits, enabling more personalized and relevant communication strategies. • Financial Planning: Financial institutions can use insights from this data set to predict future demand for term deposits and support strategic planning and resource allocation.

Between 2013 and 2024, the value of client assets at JPMorgan Chase increased overall in each of the segments considered. The private banking assets of JPMorgan Chase increased from 977 billion U.S. dollars in 2013 to 2,974 billion U.S. dollars in 2024, which was by far the largest segment of the bank.

The Commercial Bank Customer Loyalty Solutions market is experiencing robust growth, driven by increasing competition within the banking sector and a rising demand for personalized customer experiences. Banks are increasingly recognizing the importance of customer retention and loyalty programs as a key differentiator in attracting and retaining profitable customers. The market is projected to witness a substantial expansion over the forecast period (2025-2033), with a Compound Annual Growth Rate (CAGR) exceeding 10%, fueled by technological advancements such as AI-powered personalization, big data analytics for improved customer segmentation, and the growing adoption of omnichannel loyalty programs. Key players like FIS Corporate, Maritz, IBM, and TIBCO Software are heavily investing in developing innovative solutions to cater to this growing demand. The market's segmentation is likely diverse, encompassing solutions tailored to different banking segments (retail, commercial, private), deployment models (cloud-based, on-premise), and functional capabilities (points-based programs, tiered rewards, personalized offers). The restraints on market growth primarily stem from high implementation costs associated with loyalty program development and maintenance, along with the need for integration with existing banking systems. Data privacy concerns and the challenge of effectively measuring return on investment (ROI) for loyalty programs also pose significant challenges. However, increasing regulatory compliance requirements concerning data security are simultaneously driving demand for robust and secure loyalty solutions, mitigating some of these restraints. The market is expected to witness a shift towards cloud-based solutions due to their scalability, cost-effectiveness, and ease of integration. Furthermore, the incorporation of advanced analytics and personalized recommendations is expected to enhance customer engagement and drive higher program participation rates. Regional variations in adoption rates are likely driven by factors such as digital maturity, regulatory frameworks, and customer behavior patterns.

This dataset provides detailed, chronological transaction records for retail banking customers, including transaction types, amounts, merchant categories, account details, and geolocation data. Designed for advanced analytics, it enables modeling of spending patterns, customer segmentation, and risk assessment for financial institutions. The comprehensive structure supports both behavioral analysis and operational monitoring.

The US retail banking market, a sector characterized by intense competition and evolving customer expectations, is projected to experience steady growth. While the provided data lacks specific market size figures, a reasonable estimation can be made. Given a CAGR of 4% and a base year of 2025, we can infer substantial market value. The growth is driven by factors such as increasing digital adoption among consumers, the rise of fintech innovation pushing traditional banks to adapt, and the persistent demand for personalized financial products and services. This necessitates banks to invest heavily in technology, enhance customer experience through seamless digital platforms, and expand their product offerings to remain competitive. Furthermore, regulatory changes and evolving consumer financial behaviors contribute to market dynamism. Despite robust growth projections, the market faces challenges. These include increasing operational costs, stringent regulatory compliance requirements, and the potential for economic downturns to impact consumer spending and loan demand. The competitive landscape, with established giants like JPMorgan Chase & Co., Bank of America Corp., and Wells Fargo & Co. alongside emerging fintech players, necessitates strategic adaptation and innovation to maintain market share. Successful players will be those who can successfully balance profitability with customer-centric strategies, effectively leveraging technology to improve efficiency and enhance customer experience, while adhering to evolving regulatory frameworks. Segmentation within the market will continue to be vital, with specialized offerings targeting demographics and individual needs. Recent developments include: In May 2021, HSBC announced that it is exiting the retail and small business banking market in the United States, in line with its strategy to refocus on corporate and investment banking in Asia., In November 2020, Wells Fargo announced a new solution to help business customers eliminate paper checks by using one-time virtual card numbers to digitally pay invoices through the WellsOne Virtual Card Payments service.. Key drivers for this market are: Next generation technologies, Optimized physical distribution: Analytics and workforce fluidity; Developing an omnichannel workforce. Potential restraints include: Next generation technologies, Optimized physical distribution: Analytics and workforce fluidity; Developing an omnichannel workforce. Notable trends are: The Spending by Retail Banks for digital banking is increasing in US..

Column Name Data Type Description 1. Account ID String Unique identifier for each bank account (e.g., ACC00001) 2 .Customer Name String Randomly generated realistic customer names (e.g., "Emily Johnson"). 3. Account Type Categorical Type of account: Savings, Current, Fixed Deposit, Recurring Deposit. 4.Branch Categorical Branch location (e.g., New York, Los Angeles, Chicago). 5.Transaction Type Categorical Type: Credit (money deposited) or Debit (money withdrawn). 6.Transaction Amount Numerical (USD) Amount transacted, ranging from $10 to $5,000 (randomly generated). 7.Account Balance Numerical (USD) Current balance in the account, ranging from $100 to $100,000.

🛠️ Use Cases Branch-wise transaction analysis

Account type profitability studies

Customer segmentation for marketing

Currency risk exposure analysis

Balance prediction models

Credit/Debit behavior clustering Currency Categorical Account currency: USD, EUR, GBP, JPY, INR.

1) Data Introduction • The Bank Marketing Data Set contains actual customer response data collected from a telephone marketing campaign conducted by a Portuguese bank. It provides various variables including basic customer information, loan status, communication method and frequency, results of previous campaigns, and economic indicators, all of which can be used to predict a customer’s intention to subscribe to a financial product.

2) Data Utilization (1) Characteristics of the Bank Marketing Data Set: • This dataset comprehensively reflects various customer attributes, marketing campaign details, and economic factors, making it highly applicable for tasks such as financial product recommendation, marketing targeting, and customer behavior analysis.

(2) Applications of the Bank Marketing Data Set: • Marketing Performance Prediction: By leveraging multiple input variables, this dataset can be used to develop machine learning models that predict whether a customer will subscribe to a financial product (term deposit). • Customer Segmentation and Strategy Development: Prediction results can be used to establish customized marketing strategies for each customer or to select effective call lists.

This dataset provides granular credit card transaction records, including customer demographics, card details, merchant information, and transaction metadata. It is ideal for banks and fintechs seeking to analyze spending patterns, segment customers, and model risk, enabling data-driven product design and market research.

Attributes details Customer id: This column is about the id of the customer contacted

age : This column consists of the age of each customer

salary : This column represents monthly salary of the customer

balance : This column represents the cash balance in the bank account of the customer

marital : This column consists of the information about the marital status of each customer.

jobedu : This column consists of the information about the job and education of each customer

default: This column consists of two categorical variables ‘yes’ & ‘no’, where
Yes - represents if the customer has defaulted any loan
no - represents if the customer has not defaulted any loan

housing : This column consists of the two categorical variables ‘yes’ & ‘no’, where
yes - represents if the customer has taken housing loan
no - represents if the customer has not taken the housing loan

loan : This column consists of the two categorical variables ‘yes’ & ‘no’, where
yes - represents if the customer has taken personal loan
no - represents if the customer has not taken the personal loan

contact This column provides the information on the means through which the customer has been contacted either ‘cellular’ , ‘telephone’ and ‘unknown’ represents no information

day day of month on which a particular customer is contacted

month This column provides the detail of month in which the customer is contacted during the campaign

duration This column represents the total call duration of each customer

campaign This column is the number of campaign in which customer is contacted.

pdays This column represents the no of days passed by since the customer has been reached via bank for any of the other products (not term deposit).

Here, the value ‘-1’ represents that the customer has never been reached for any product previous This column represents the no of times the customer has been reached in the previous campaigns or for any of the other products(not term deposit)

poutcome This column represents the outcome of the previous reach outs for any of the products(other than term deposits) provided by banks

Unknown - This represents that the customer has not been reached so far

Success - This represents that the previous call was a successful conversion of the customer

Failure - This represents that the customer is not interested in the last product Other - This represents that during the previous call, the customer has not given any definite answer

response This column represents whether the customer has opened the term deposit account or not

Bank Customer Dataset for Personal Loan Prediction This dataset contains demographic, financial, and behavioral data of 5,000 bank customers collected during a marketing campaign aimed at offering personal loans. The primary objective is to predict whether a customer accepted the personal loan offer (personal_loan), making this a supervised binary classification problem.

The dataset includes 14 features such as age, income, credit card usage, education level, mortgage value, and account ownership information. It can be used for machine learning tasks such as classification modeling, feature selection, customer segmentation, and marketing analytics.

1) Data Introduction • The Direct Marketing Campaigns (Bank Marketing) Dataset is a dataset built to predict time deposits (deposit) based on customer characteristics and campaign history in Portuguese banks' phone-based direct marketing campaigns.

2) Data Utilization (1) Direct Marketing Campaigns (Bank Marketing) Dataset has characteristics that: • Consisting of 41,188 rows, individual case data for calls made to customers during each row marketing campaign. • This dataset contains 21 columns (characteristics) that provide detailed information about each phone and attributes related to customers and campaigns. (2) Direct Marketing Campaigns (Bank Marketing) Dataset can be used to: • Marketing Campaign Performance Forecasting and Customer Targeting: Using customer characteristics and historical campaign data, it can be used to predict customers who are likely to sign up for time deposits and to establish effective marketing targeting strategies. • Customer Behavior Analysis and Marketing Strategy Optimization: You can optimize marketing strategies by analyzing campaign response patterns, characteristics by customer group, and correlations with economic indicators, and use them for customer segmentation and customized product suggestions.

In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing strategy often leads to the homogenization of customer demand, making it challenging to distinguish among various products. To address this issue, this paper presents a customer demand learning model based on financial datasets and optimizes the distribution model of bank big data channels through induction to rectify the imbalance in bank customer transaction data. By comparing the prediction models of random forest model and support vector machine (SVM), this paper analyzes the ability of the prediction model based on ensemble learning to significantly enhance the market segmentation of e-commerce banks. The empirical results reveal that the accuracy of random forest model reaches 92%, while the accuracy of SVM model reaches 87%. This indicates that the ensemble learning model has higher accuracy and forecasting ability than the single model. It enables the bank marketing system to implement targeted marketing, effectively maintain the relationship between customers and banks, and significantly improve the success probability of product marketing. Meanwhile, the marketing model based on ensemble learning has achieved a sales growth rate of 20% and improved customer satisfaction by 30%. This demonstrates that the implementation of the ensemble learning model has also significantly elevated the overall marketing level of bank e-commerce services. Therefore, this paper offers valuable academic guidance for bank marketing decision-making and holds important academic and practical significance in predicting bank customer demand and optimizing product marketing strategy.

The Banking Software market is an essential component of the financial services industry, bolstering the operations of banks and financial institutions through sophisticated technological solutions. This market encompasses a wide array of software solutions designed to streamline banking processes, enhance customer

This dataset provides granular financial transaction records enriched with customer segmentation, merchant categorization, and risk scoring. It enables banks and fintechs to analyze spending patterns, detect fraud, and optimize product offerings for targeted customer profiles. The dataset is ideal for risk management, customer analytics, and personalized financial services.

Market Analysis The Banking CRM Software Market is poised to grow significantly, with a projected CAGR of 6.15% during the period 2025-2033. In 2025, the market size was valued at $31.11 billion, and it is estimated to reach $46.01 billion by 2033. The market growth is attributed to the increasing need for banks to enhance customer experience, optimize operations, and personalize marketing campaigns. The adoption of cloud-based CRM solutions is a key driver, as they offer flexibility, scalability, and reduced costs. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) in CRM systems is driving market growth by enhancing customer segmentation and personalization. Key Trends The Banking CRM Software Market is witnessing several key trends. The convergence of CRM and AI is a significant trend, enabling banks to automate tasks, improve decision-making, and deliver a personalized customer journey. Another trend is the integration of CRM systems with core banking platforms, allowing seamless data exchange and a comprehensive view of customer interactions. The rise of mobile CRM solutions is also driving market growth, as it provides bankers with real-time access to customer information and facilitates mobile banking services. Furthermore, the increasing adoption of CRM software by small and medium-sized banks is expanding the market. Key drivers for this market are: Digital transformation Cloud adoption Increased demand for customer analytics Regulatory compliance Growing need for personalized banking experiences. Potential restraints include: Increased need for customer engagement Growing adoption of cloud-based solutions Integration of AI and machine learning Demand for personalized banking experiences Compliance and data security concerns.

This dataset provides detailed logs of checking account overdraft fee events, capturing transaction details, account and customer identifiers, fee amounts, and customer segmentation. It enables financial institutions to identify high-risk customer segments, analyze patterns of overdraft occurrences, and develop targeted strategies to reduce fee events and improve customer financial health.

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Core Banking Software Market Size 2025-2029

The core banking software market size is forecast to increase by USD 56.39 billion, at a CAGR of 30.8% between 2024 and 2029.

The market is witnessing significant growth, driven by the adoption of cloud-based solutions for scalability, cost-effectiveness, and enhanced flexibility. These solutions enable banks to streamline their operations, reduce IT infrastructure costs, and offer personalized services to customers. However, the implementation of cloud-based systems presents challenges, including data security concerns and the need for seamless integration with legacy systems. Another key trend in the market is the modernization of legacy systems to meet the demands of digital banking. Banks are investing in upgrading their core banking platforms to support real-time transactions, omnichannel banking, and advanced analytics. This modernization process can be complex and costly, requiring significant resources and expertise. Despite these challenges, the benefits of upgrading legacy systems, such as improved customer experience and operational efficiency, make it a necessary investment for banks seeking to remain competitive in the digital age.

What will be the Size of the Core Banking Software Market during the forecast period?

Request Free SampleThe market continues to evolve, with various sectors integrating advanced technologies to enhance their operations. Online banking, investment portfolio management, loan management, real-time analytics, and core banking systems are no longer standalone entities but seamlessly integrated components. Financial analysis and business intelligence (BI) provide valuable insights, while digital banking and blockchain technology ensure secure and efficient transactions. User interface (UI) and artificial intelligence (AI) optimize customer experience, and open banking facilitates collaboration between financial institutions. Performance optimization, account opening, and predictive analytics streamline processes, and payment processing is now faster and more secure with API integration and cloud computing. Commercial banking benefits from agile development and risk management, ensuring regulatory compliance and data quality management. Wealth management and investment banking leverage data analytics for informed decisions, while loan origination and fraud detection utilize machine learning (ML) and data encryption for improved accuracy and security. Branch banking and retail banking adapt to the digital age, offering mobile banking and financial reporting, customer segmentation, and account management services. Infrastructure management and system integration ensure seamless operations, enabling financial services to meet the evolving needs of their clients.

How is this Core Banking Software Industry segmented?

The core banking software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudEnd-userBanksFinancial institutionsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The global Core Banking Software (CBS) market continues to evolve, with on-premise deployment remaining a preferred choice for financial institutions. Despite the increasing adoption of cloud-based solutions, regulatory requirements such as GDPR and PCI-DSS necessitate on-premise installations for many organizations. Large financial institutions with complex infrastructure benefit from the enhanced control, security, and customization options provided by on-premise deployment. Data protection and compliance concerns are significant drivers for this choice. The CBS market is characterized by the integration of various functionalities, including deposit management, transaction processing, customer onboarding, wealth management, commercial banking, risk management, data quality management, user experience, loan origination, agile development, fraud detection, mobile banking, investment banking, data analytics, system integration, account management, infrastructure management, financial services, regulatory compliance, banking software, API integration, cloud computing, data security, online banking, investment portfolio management, loan management, real-time analytics, core banking system, financial analysis, business intelligence, digital banking, blockchain technology, user interface, artificial intelligence, open banking, performance optimization, account opening, predictive analytics, payment processing, machine learning, branch banking, retail banking, financial reporting, and customer segmentation. The market's flexibility and d

Market Overview: The global Bank IT Solutions market is projected to grow at a CAGR of XX% from 2025 to 2033, reaching a market size of million USD by 2033. This growth is driven by the increasing adoption of digital technologies in the banking sector to enhance customer experience, streamline operations, and improve risk management. Key market trends include the adoption of cloud-based banking solutions, advancements in artificial intelligence and machine learning, and the increasing focus on data analytics and cybersecurity. The market is highly competitive with established players such as IBM, Oracle, and SAP holding significant market share. Segmentation and Regional Analysis: In terms of segmentation, the market is divided by type (Core Banking System, Credit Management System, Risk and Compliance Management, and Others) and application (State Banks and Private Banks). Regionally, North America and Europe are mature markets, while Asia Pacific is expected to witness the highest growth due to the increasing penetration of smartphones and the rapidly growing banking sector in developing countries. The Middle East and Africa is also expected to experience significant growth, driven by government initiatives to enhance financial inclusion and the adoption of fintech solutions. The report provides detailed analysis of each segment and region, including market size, growth rates, and competitive landscape.