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.

This dataset contains rich, structured information about bank customers, including demographics, account details, product holdings, financial metrics, and segmentation labels. It is ideal for financial institutions seeking to personalize marketing, manage risk, and identify cross-selling opportunities through data-driven customer segmentation and profiling.

Dataset

This dataset was created by Anjolaoluwa Ajayi

Contents

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.

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 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..

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.

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.

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.

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.

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.

The statistic presents the number of customers of Deutsche Bank worldwide from 2012 to 2014, by segment. In 2014, there were approximately **** thousand of Deutsche Asset and Wealth Management division clients. The number of private and business clients amounted to approximately ***** million in that year.

The number of Deutsche Bank branches worldwide in 2016 amounted to *****. This number has been steadily falling since 2010.

This is the replication package for "Beyond the Balance Sheet Model of Banking: Implications for Bank Regulation and Monetary Policy," accepted in 2023 by the Journal of Political Economy.

The US retail banking market, valued at $385.52 billion in 2025, is projected to experience steady growth, exhibiting a Compound Annual Growth Rate (CAGR) of 4.35% from 2025 to 2033. This growth is driven by several key factors. Increased digital adoption among consumers fuels demand for convenient online and mobile banking services, pushing banks to invest heavily in technological upgrades and innovative financial products. The rising prevalence of fintech companies also exerts pressure on traditional banks to enhance their offerings and customer experiences, fostering competition and driving innovation within the sector. Furthermore, a growing emphasis on financial inclusion initiatives and the expansion of underserved markets contribute to the market's expansion. However, challenges exist, including increased regulatory scrutiny, cybersecurity threats, and the ongoing need for banks to adapt to evolving consumer preferences and technological advancements. Competition from both established players and emerging fintech startups necessitates continuous strategic adaptation. The market segmentation reveals a diverse landscape. Private sector banks maintain a significant market share, but public sector and foreign banks also play substantial roles. Within services, personal loans, mortgages, and debit/credit cards represent significant revenue streams, while the "Others" category encompasses growing areas like investment management and wealth management services. Distribution channels continue to evolve, with a shift towards digital platforms complementing traditional branch networks and direct sales. Key players such as Bank of America, JPMorgan Chase, and Wells Fargo maintain strong market positions, employing various competitive strategies including technological investments, expansion into new markets, and mergers and acquisitions. Industry risks include economic downturns, interest rate fluctuations, and potential disruptions from technological innovations. Maintaining profitability while adapting to changing market dynamics is crucial for sustained success within the competitive US retail banking sector.

In 2023, Bank of China's number of corporate online banking customers reached *** million, an ** percent increase compared to the previous year. In contrast, the number of mobile banking customers amounted to almost *** million.

The Commercial Bank Customer Loyalty Solutions market has emerged as a vital component of the financial services industry, as banks strive to retain and engage their customers in an increasingly competitive landscape. These solutions encompass various tools and strategies designed to enhance customer satisfaction an

The Internet-only Bank market has transformed the landscape of financial services by offering consumers an innovative solution to traditional banking. Characterized by the absence of physical branches, these digital banks provide customers with seamless access to banking services via online platforms and mobile appl

The number of users in the 'Digital Banks' segment of the retail & commercial banking market in the United States was modeled to amount to ************* users in 2024. Following a continuous upward trend, the number of users has risen by ************* users since 2017. Between 2024 and 2028, the number of users will rise by ************* users, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Digital Banks.