In today’s rapidly evolving financial landscape, big data analytics has become more than just a buzzword it’s a strategic asset. To understand how data is revolutionizing customer engagement in banking, we sat down with Amit Taneja, author of the recent study “Customer Behavior Analysis in Banking: Leveraging Big Data to Enhance Personalized Services,” published in the International Journal of Innovative Research in Science, Engineering and Technology Q: Can you summarize the main idea behind your research? Amit: Absolutely. The core of the study revolves around how banks are harnessing big data analytics to understand and predict customer behavior more accurately than ever before. We’re talking about leveraging large-scale datasets like transaction histories, demographic data, and digital activity logs to provide highly personalized financial services. Amit: Q: That sounds powerful. What kind of customer data are banks analyzing? Amit: Banks typically analyze three major types of data: Amit: Transaction Histories – This includes data on frequency, volume, and type of financial transactions.Demographic Data – Age, income, gender, occupation, and location all help segment customers.Online Activity Logs – How often a user logs in, what services they interact with, and how long they spend on specific platforms are all monitored to gauge preferences and engagement. Transaction Histories – This includes data on frequency, volume, and type of financial transactions. Transaction Histories Demographic Data – Age, income, gender, occupation, and location all help segment customers. Demographic Data Online Activity Logs – How often a user logs in, what services they interact with, and how long they spend on specific platforms are all monitored to gauge preferences and engagement. Online Activity Logs Q: How do banks make sense of such large and varied data? Amit: Great question. That’s where machine learning comes in. Two key techniques we focus on in the paper are Amit: Clustering (unsupervised learning): This groups customers with similar behaviors. For example, we can identify patterns in spending and segment customers as “transactors,” “revolvers,” or “inactive” users.Segmentation (supervised learning): This uses decision trees or k-nearest neighbor algorithms to classify customers based on known outcomes or characteristics. Clustering (unsupervised learning): This groups customers with similar behaviors. For example, we can identify patterns in spending and segment customers as “transactors,” “revolvers,” or “inactive” users. Clustering (unsupervised learning): Segmentation (supervised learning): This uses decision trees or k-nearest neighbor algorithms to classify customers based on known outcomes or characteristics. Segmentation (supervised learning): Q: What datasets were used in your analysis? Amit: We primarily used: Amit: The Bank Marketing dataset from the UCI Machine Learning Repository, which includes real campaign and demographic data from a Portuguese bank.The Kaggle Customer Segmentation dataset, which, while anonymized, is great for modeling realistic banking behavior using demographic and transaction data. The Bank Marketing dataset from the UCI Machine Learning Repository, which includes real campaign and demographic data from a Portuguese bank. Bank Marketing dataset The Kaggle Customer Segmentation dataset, which, while anonymized, is great for modeling realistic banking behavior using demographic and transaction data. Kaggle Customer Segmentation dataset Q: Any predictive modeling insights you’d like to share? Amit: Definitely. We built several predictive models to forecast outcomes like customer churn or campaign response rates. For example, using the Bank Marketing dataset, Random Forest algorithms performed best with: Amit: Random Forest algorithms 91% accuracy93% precision90% recall 91% accuracy 91% accuracy 93% precision 93% precision 90% recall 90% recall This outperformed other models like Logistic Regression and Decision Trees. Q: Did this approach translate into tangible improvements for banks? Amit: Yes, and the results were impressive. Here are two concrete outcomes: Amit: Customer Segmentation Success: For instance, banks could tailor offerings by understanding segments like high-transaction users versus low-engagement ones.Personalized Recommendations: These led to increased customer satisfaction. In fact, that satisfaction scores rose by about 1 full point (on a 5-point scale) after implementing personalized recommendations. Customer Segmentation Success: For instance, banks could tailor offerings by understanding segments like high-transaction users versus low-engagement ones. Customer Segmentation Success: Personalized Recommendations: These led to increased customer satisfaction. In fact, that satisfaction scores rose by about 1 full point (on a 5-point scale) after implementing personalized recommendations. Personalized Recommendations: Q: And what about customer loyalty and churn? Amit: Big data-driven personalization has been shown to reduce churn dramatically. Churn rates from 2019–2021 shows a consistent drop from 12% to 6% when banks shifted from traditional marketing to big data-driven strategies. Amit: Q: So, what’s the takeaway for banking leaders? Amit: Big data analytics isn’t just a competitive advantage it’s quickly becoming a necessity. By anticipating customer needs, personalizing services, and proactively reducing churn, banks can stay relevant and build lasting customer relationships in a fast-changing digital landscape. Amit: Q: Last question what’s next for this field? Amit: The next frontier is integrating real-time analytics and AI-driven personalization. Imagine a bank that not only knows your spending habits but adapts its offerings to your lifestyle in real time. That’s where we’re headed. Amit:

This story contains new, firsthand information uncovered by the writer.

Between Two Computer Monitors: This story includes an interview between the writer and guest/interviewee. 

How Big Data is Reshaping Customer Experience in Banking

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

$INK: The Solana Memecoin with a Vision for Utility

FinTech: A 2020 Retrospective, and 5 Finance Trends for 2021

$DEFI Token Hits 7 Major Exchanges: A Milestone Achievement

$JTC Network To List On BitMart Exchange

$500k Presale: TG.Casino Passes Milestone with Upcoming Telegram-Powered Platform

$3 Million in Seed Funding for Web3 Founders Announced By Necto Labs

$INK: The Solana Memecoin with a Vision for Utility

FinTech: A 2020 Retrospective, and 5 Finance Trends for 2021

$DEFI Token Hits 7 Major Exchanges: A Milestone Achievement

$JTC Network To List On BitMart Exchange

$500k Presale: TG.Casino Passes Milestone with Upcoming Telegram-Powered Platform

$3 Million in Seed Funding for Web3 Founders Announced By Necto Labs

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps