Thriving in a Data-Driven Age: How Banks are Personalizing Experience (2024)

The evolving financial landscape has witnessed unprecedented growth in the banking sector. Two key drivers behind this success story are timely digitization and a strategic shift towards personalized banking experiences. Banks recognized the importance of customer satisfaction and acquisition, prompting them to embrace personalization as a core value actively. To deliver on this promise of personalization, banks leverage customer data. This data is fed into powerful data analytics tools that generate valuable insights. These insights, in turn, help banks retain existing customers, develop targeted products and services, and attract new clientele.

What is Personalized Banking and How Does it Work?

Personalized banking refers to a set of practices that bank/banks adopt to offer customized services, aligning with individual customer needs and preferences. Banks are aided with distinct types of customer data that enable them to create personalized banking experiences for their clientele across the globe.

The data can be classified into three categories:

1. Transactional: This customer data encompasses a customer’s transactional activity, including deposits, withdrawals, and purchases. By analyzing these transactions, banks can make note of one’s spending habits and understand the reasons behind loan applications.
2. Behavioral: Behavioral data focuses on how and when a customer decides to engage with a bank. This includes the channels they use (mobile app, branch visits, etc.), the reasons for their engagement (transfers, bill payments, etc.), and the types of services they use. By analyzing this data, banks can identify not only appreciated products and services but also those that are highly engaged, allowing them to tailor future offerings.
3. Demographical: Banks collect demographic data such as age, location, gender, income level, and occupation/service type. This data helps them to separate and classify their customers based on similarities or differences. Demographical data empowers banks to understand the unique financial needs of different customer groups so that they can tailor their products and services more effectively.

These three distinct data types are fed into advanced and industry-reliable data analytics tools to gain customer behavior insights and ultimately drive customer-centric decisions.

Data Analytics: The Science Behind Personalized Banking Experiences

Data analytics plays a vital role in the banking sector. It involves the use of various tools and methods to analyze large volumes of data and extract valuable insights from that data. These insights help banks make informed decisions and drive business growth.

With data analytics, banks can gain a deeper understanding of customer behavior, market trends, and facts that guarantee operational efficiency.

Imagine a bank analyzing customer transaction data. By looking at spending habits and income levels, they can segment customers into different groups. One group might be young professionals with a high disposable income but low savings. Based on this insight, the bank could develop a personalized financial management app and offer high-yield savings accounts to incentivize saving. This data-driven approach allows banks to tailor products and services to specific customer needs, encouraging stronger relationships and customer satisfaction.

Learn to Leverage Data Analytics

While data analytics is essential for gaining insights that help banks make strategic decisions for the customer’s benefit, it is equally important to use these insights in areas of utmost importance, specifically the ones directly impacting the customer’s rapport with the bank.

1. By identifying customer’s financial goals: As mentioned earlier, banks leverage various data types (behavioral, transactional, and demographic) to understand the motivations behind a customer’s financial decisions. By analyzing recurring patterns, banks can gain insights into potential financial goals and ultimately assist customers in achieving them.

Imagine a customer who casually inquires about home loan rates during a bank visit and then checks them online later. Based on this combined behavior, banks can reasonably infer that the customer might be considering buying a home.

2. By gauging the customer’s risk tolerance threshold: Banks can leverage data analytics to gauge a customer’s risk tolerance threshold. This analysis considers various factors, including:

• Investment activity: Customers who frequently invest in stocks or engage in active trading might exhibit a higher risk tolerance compared to those who primarily rely on savings accounts.
• Debt management: How a customer handles existing debt can be revealing. Consistent on-time payments suggest a comfort level with managing some financial risk.
• Savings activity: Both the frequency and number of deposits/withdrawals from savings accounts can be indicative of risk tolerance. Customers with a significant buffer between income and spending might be more open to risk.
• Fixed deposit (FD) behavior: Analyzing past instances of early FD withdrawals can suggest a lower risk tolerance, as these withdrawals often incur penalties.
• Spending habits: Customers who consistently spend close to their income limits might have a lower risk tolerance compared to those with a significant buffer.
• Income level: Customers with a higher income may have a larger financial buffer to absorb potential losses, potentially indicating a higher risk tolerance.

3. By pinpointing factors that result in inconvenience: Customer inconvenience is the direct result of operational inefficiency. And data analytics plays a significant role in optimizing operational efficiency in the banking sector. Inefficiencies and lags in processes such as customer onboarding, loan processing, and account management can be easily identified through data analytics.

By analyzing the data, banks can identify the root causes of inefficiencies and implement process improvements to enhance operational efficiency. Moreover, data analytics enables banks to automate manual processes and reduce reliance on paper-based documentation. By digitizing and automating processes, banks can improve accuracy, reduce processing time, and increase overall productivity.

4. By identifying preferred customer channels for banking activities: With the help of data analytics, banks can see which channels (mobile app, online banking, branch visits, ATM usage) customers use most frequently for specific transactions (transfers, bill payments, deposits, investments like SIP). This reveals a preference for certain channels for specific tasks. Time of usage is also available for banks to consider.

Frequent mobile app usage during evenings might suggest a preference for on-the-go banking. Analyzing which features customers utilize most within the mobile app can reveal their preferred digital banking activities. This can encourage the bank to improve certain features and functionalities within the app. Also, the amount of time customers spend on the mobile app compared to other channels indicates their comfort level and potential preference for digital banking.

5. By recognizing the products that are/aren’t required: By leveraging sophisticated data analytics tools, banks can dissect (through sentiment analysis and behavioral analysis) vast amounts of customer data, including purchase history, financial habits, and even browsing behavior. This deep understanding of customer needs and preferences allows them to craft personalized product recommendations that resonate.

For instance, frequent ATM users might benefit from a debit card with wider ATM network access; or young professionals might be interested in credit cards with travel rewards, while families might benefit from life insurance options.

6. By improving risk management and fraud detection: Through data analytics, banks can detect unusual patterns, anomalies, and outliers in transactions, which can indicate potentially fraudulent activities. By monitoring and analyzing transactional data in real-time, banks can identify and prevent fraudulent transactions, protecting both the bank and its customers from financial losses.

In addition to this, data analytics helps banks assess credit risk by analyzing various factors such as credit history, income, and repayment behavior. This helps banks make informed decisions regarding loan approvals, interest rates, and credit limits. As a result, banks can minimize default rates and optimize their loan portfolios.

Benefits of Personalized Banking

Personalized banking goes beyond generic products and services. It is about prioritizing the customer’s needs and wants beyond everything. The good part is that the practice benefits both banks and customers.

Here’s how:

1. Heightened customer satisfaction levels
2. Increased financial literacy
3. Tailored financial experiences
4. Increased revenue opportunities
5. Improved conversion rates
6. Enhanced customer loyalty

The Bottom Line: Win-Win for Banks and Customers

By crafting personalized banking services and experiences for customers, banks achieve a win-win scenario. Customers receive relevant and timely assistance as well as product recommendations, enhancing their banking experience. This translates to increased customer satisfaction, loyalty, and ultimately, revenue growth for the bank. Data analytics empowers banks to move beyond a one-size-fits-all approach, promoting a customer-centric strategy that ensures sustainable success in banking.