Your financial institution takes a risk every time you lend money or extend credit to a client. When you can mitigate the risk, you can make smarter decisions about who you lend money to. Smart banks are using detailed customer analytics to better assess the risk of each customer.
Customer analytics in banking helps you better understand customer behavior and optimize your banking business. And big data, machine learning, and analytics tech like Random Forest algorithms are allowing banks to access customer data in more intricate ways than ever before.
This blog post will explore how forward-thinking banks evaluate the creditworthiness of customers by harnessing the knowledge of customer analytics and machine learning. We’ll discuss the benefits and challenges of adopting these forward-thinking analytics and share best practices for using customer analytics effectively to enhance the customer experience in banking. This is all to help you lend smarter and learn strategies for increasing customer retention and loyalty over their lifetime with your institution.
- How is customer analytics used in banking?
- How banks evaluate customers
- Analyzing customer feedback in banking
- Benefits of customer analytics in banking
- Challenges of implementing customer analytics in banking
- Best practices for using customer analytics in banking
- Future of customer analytics in banking
- Using Idiomatic to analyze banking customer data
How is customer analytics used in banking?
Financial institutions are no strangers to using data-driven insights to gain a better understanding of customers’ behavior to pinpoint potential risks and growth opportunities. Using customer data (such as credit, transaction, and investment history) banks evaluate the risk associated with each lending proposal.
However, in today’s data-intensive world, understanding which analytics provide the best insights and generating those insights accurately can be troublesome and prone to misinterpretation for some lenders. When harnessed and used, this information can help you create personalized marketing strategies, enhance product offerings, and streamline banking operations.
Banks ahead of the curve use machine learning and predictive analytics to anticipate future customer patterns and habits. It all culminates in creating hyper-personalized experiences based on real customer data and sophisticated machine learning predictive models, not generalizations or averages.
How banks evaluate customers
Currently, many banks are using a basic data-driven approach to determine creditworthiness. This involves using data analytics tools that analyze past behavior to predict future actions. These models consider factors such as payment history, outstanding debts, and even details like employment stability. This data can indicate:
- How reliably a customer pays their bills (based on payment history records)
- If the customer is financially overextended (based on debt levels and ratios)
- Stability of continued income to pay back loans (based on employment history).
The use of data analytics in banking allows institutions to make more accurate predictions about potential risks and rewards associated with each individual client—leading to smarter decisions about who they lend money to or offer up-sold services.
While this method of determining credit-worthiness worked in the past, with the volume of data we have available today, we’re missing hugely valuable additional insights from including other information sources to better learn about our customers.
Banks use data analytics to evaluate customers’ creditworthiness by analyzing factors such as payment history, outstanding debts, and employment stability. This helps banks make smarter decisions about lending money or offering other services based on accurate predictions of potential risks and rewards associated with each individual client.
Analyzing customer feedback in banking
In the digital age, customer feedback is more valuable than ever before. It’s a goldmine of insights that can help banks improve their services to better meet customer needs. Let’s face it: banking as an industry (like telecom or airlines) has one of the most loudly negative reputations for customer experience, likely because of the security measures that they must abide by. Listening to what’s frustrating customers is even more important than in other industries since customers are already prone to frustration.
Improving the customer experience in banking is even more crucial to banks improving their relationship with customers. By leveraging customer analytics, banks can analyze feedback to identify trends, patterns, and areas for improvement.
For instance, if multiple customers complain about long wait times on phone support lines or difficulty navigating the online banking platform, these are clear indicators where improvements need to be made. Similarly, positive feedback can highlight what’s working well—whether it’s an efficient mobile app or exceptional in-branch service.
👉 Banks also use AI-driven tools like Idiomatic to collect and analyze this data effectively and quickly. Idiomatic, specifically, categorize feedback into themes using supervised machine learning, making it easier for banks to understand their customers’ experiences and take actionable steps toward improvement.
Customer feedback is a valuable resource for banks to improve their services and stay ahead of the competition. By using customer analytics, banks can identify areas for improvement based on trends and patterns in customer feedback. Tools like Idiomatic can help categorize this data into themes that make it easier for banks to take actionable steps toward improvement.
Benefits of customer analytics in banking
The use of customer analytics in banking offers numerous advantages including enhancing the overall customer experience and improving business performance. Harnessing the power of data through customer analytics enables a more personalized approach to banking—ultimately leading to increased satisfaction levels among clients while also driving profitability for the bank itself.
To explain, here are some key benefits of using customer analytics in banking:
Better decision making
With data-driven insights, banks can make informed decisions about product offerings, marketing strategies, and risk management. Analytics that go beyond basic financial data and metrics can further increase the benefits.
For example, customer feedback and transcripts from customer service calls can help you know your customers on a more detailed level so you can make the changes they actually need, based on more well-rounded data.
The more information you have about your customers, helps you better mitigate risk. For example, instead of running a standard credit check and transaction history, you can use additional sources of information (like surveys, direct deposit history, and employment trends) to determine the likelihood of someone defaulting on a loan.
Improved customer experience
By understanding customers’ behavior and preferences through customer analytics, banks can tailor their services to meet individual needs. Using survey responses and call center recordings, you can also help validate what customers say in other data sources.
For example, by looking at their transaction records, along with call center transcripts, you may learn that a customer often travels abroad and has trouble with declined payments on their debit card. You could then recommend a no-foreign-exchange-fee Visa card to avoid any payment difficulties.
Increased customer loyalty and lifetime value
With an improved customer experience comes increased customer loyalty and longevity. Customers have many choices of financial institutions to bank with, but if you can provide them with a positive banking experience, they’re less likely to quit and move to your competitor.
Increase revenue opportunities
Identifying patterns in customer behavior allows for targeted cross-selling or upselling opportunities. For example, your analytics can determine which customers travel often to offer suitable credit cards, which customers are hyper-aware of savings to offer them a high-interest savings account, or those interested in investing to offer investment accounts.
One Idiomatic client improved their recurring revenue using customer analytics to uncover a problematic pattern in their credit card processing during subscription renewal. Customers were having trouble renewing their subscriptions, often due to an expired card being on file. With this insight, they were able to swifty correct this issue, avoiding churn and dramatically improving ongoing revenue projections.
Improve sales forecasting
You can use customer analytics to build more detailed customer profiles and use machine learning to predict more accurate sales forecasting. You may currently be relying on seasonal trend sales forecasting (ie: you know you sell more mortgages in spring and fall, and people open more savings accounts in September and January). Based on robust banking customer analytics, you can better predict which types or demographics of customers are more likely to bring in more sales in the future.
For example, you can look at your customer data to see that 10% of your customers report being newlywed. Using other data sources analyzed with machine learning, you may notice that within 1-3 years of getting married, couples start having kids and open up RESPs. Or, you may notice those who list their relationship status as common law, buy houses within a year, so you can anticipate many will buy a mortgage from you in that time.
Challenges of implementing customer analytics in banking
Implementing customer analytics in banking comes with its fair share of challenges. One significant hurdle is data privacy and security concerns. Banks handle sensitive financial information that must be protected at all costs. Compliance with regulations like the Gramm-Leach-Bliley Act (GLBA) in the US and Canada’s Privacy Act is crucial to ensure that financial institutions deal with customers’ private information appropriately.
Managing vast amounts of data from various sources, such as transactions, reviews, social media interactions, and customer feedback surveys (like NPS), is another challenge. This requires sophisticated tools for data integration, analysis, and interpretation.
To gain the full advantage of customer analytics, use AI-powered analytics platforms to gather data from all these data sources, including both qualitative and quantitative sources. When you’re dealing with vast amounts of qualitative data, it’s not easy or realistic to have human-led analysis. Instead, machine learning can do it in seconds and provides more detailed predictions than a human can deduce alone.
Idiomatic is best at taking data from multiple sources, categorizing it, and providing the customer experience and analytics insights you need to act on to improve customer satisfaction and business operations. Whether you want to get a more complete picture of an individual customer (for lending or upsell purposes), build more accurate customer profiles, or learn which areas of the customer experience cause the most friction, Idiomatic can help.
Best practices for using customer analytics in banking
Customer analytics can be a game-changer in banking if you implement it the right way. Here are some best practices for your customer analytics strategy:
Integrate data sources
Combine data from various sources to get a complete view of your customers. In banking, common sources of data include transaction history, helpdesk transcripts, surveys, banking app or website usage data, and customer surveys.
Focus on a 360-degree view
It’s not enough to collect and analyze as much data as you can on your customers. To get the most accurate and detailed insights, collect data from the full range of customer experience and demographics including, for example:
- New customers to long-time customers
- Customers who purchase different products
- Quantitative customer data (transactions, login history)
- Qualitative customer data (chat transcripts, emails, chatbot conversations, surveys)
- Customers from different income brackets
Leverage predictive analytics
Predictive analytics can help anticipate future behavior and trends based on historical data. Use machine learning to analyze your current data and trends to help you:
- Categorize your customers to predict customer lifetime value
- Improve upsells
- Predict the likelihood of future product or service purchases for individual customers and your customer base as a whole.
One bank used predictive analytics to predict customer loyalty. This process resulted in a 70% accuracy rating—the ratio of correct predictions— for individual customers.
Create personalized experiences
Did you know that 66% of customers expect you to know what they need and want? This means banks need to truly understand individual customers so they can personalize the banking experience through data-backed, targeted up-sells, or customize the ads and content they see based on their preferences and history.
According to McKinsey, banks are struggling to be successful at personalization. They report that only 28% of banks are using well-rounded and consistent customer data in their AI models, and only 8% are are able to effectively make use of the resulting insights.
With the right data and AI models, you can not only create more personalized experiences, but better test hypotheses and get results in real-time. For example, you may think that customers between the ages of 30-49 who use your mobile banking app desire one feature, while those in the 18-29 age group desire another. Before you invest in this personalization, you can test this hypothesis with your current voice of the customer data to get segmented insights on what different user demographic groups want or have issues with.
Invest in big-data analytics tools
Banks should invest in advanced, big data analytic tools, train their teams on how to use them, and establish clear goals for their customer analytics initiatives. Effective, computer or AI-supported customer data analysis can lead to improved ROI and an optimized customer experience.
One Italian bank reported using analytics to automate their client evaluation process and cut evaluation time by 60%, even after analyzing more than 100 features and data points to determine their clients’ creditworthiness.
Act on machine learning insights
It’s one matter to collect and analyze the customer data, but if you don’t use it you’re wasting your time. If you’re serious about leveraging the power behind customer experience analytics, you need to dedicate yourself to understanding the insights and taking action to fix or improve them.
Future of customer analytics in banking
The future of customer analytics in banking is promising and exciting, with endless opportunities for growth and innovation.
AI and machine learning are key drivers of customer analytics in the future of banking. They enable predictive analytics, allowing banks to anticipate customer needs, offer personalized services, and strengthen their business processes and systems.
Real-time data processing will also be crucial, providing instant insights into customer behavior and helping banks make quick decisions on products or services. Despite AI being seen as the future in many industries, McKinsey reports only 8% of banks are using AI-informed insights to inform their campaigns, as mentioned previously. The same source also reports that only 16% of banks have standard protocols for creating AI tools.
Digital banking platforms will continue to evolve, integrating more seamlessly with other financial tools like budgeting apps or investment platforms for an all-around financial management experience. For example, McKinsey is already seeing 61% of customers interacting with their bank’s digital channels weekly, and 32% prefer mobile banking services over visiting an in-person branch. Banks who leverage customer analytics are seeing tangible results: Today’s banks are already generating 5-15% more revenue from campaigns, and launching campaigns two to four times faster, using insights from customer analytics.
The consensus among banks seems to be that they understand the value of analytics and machine learning as the future of customer experience in banking, but why are they not fully adopting these strategies yet?
Using Idiomatic to analyze banking customer data
Customer analytics in banking can provide valuable insights as you evaluate customer behavior, preferences, and feedback to improve overall customer experience and drive business growth. However, banks are currently underutilizing the power of advanced AI-driven analytics to get the actionable insights they need. Many recognize the value analytics can have, but they fear challenges like overcoming privacy laws and the perceived limitations of technology to support them.
Idiomatic is a robust voice-of-customer software that helps you identify what issues are causing the most customer friction, help you identify roadblocks to company growth or customer retention goals, and get the most value from your customer data sources. By analyzing phone transcripts, email correspondence, social media interactions, and surveys, banks can use Idiomatic to help them learn how to better meet their customers’ needs and expectations.
As the banking industry evolves with new technologies, we expect banks will open their eyes to the immense value these tools have when analyzing customer data shaping the future of the customer experience in banking.
To see the future of banking today, request a demo of Idiomatic to learn how it can provide value for your bank by harnessing the insights behind customer data.