A Pulse on Digitization in Banking Part 2: How Data Analytics Creates Better Customer Experiences
Digitization has given rise to major change in the banking industry. As mobile and online banking usage has skyrocketed, banks grapple with an unprecedented amount of consumer data – and what to do with it. If analyzed properly, this data can help boost the digital customer experience. It can also make things easier on bank employees, enabling them to focus on the more human side of banking.
In our first blog in this series on digitization in banking, we examined how blockchain technology will revolutionize banking. In this blog, we’ll examine how data can best be used to create a better customer and employee experience.
By using big data, banks can segment customers with more granularity than ever before. Instead of grouping customers into broad categories like by age or income, banks can segment their customer base into more targeted groups. These groups can include basic demographics plus information about customer behavior. Customer behavior could include spending habits, relationships with other bank customers, and what products or offers they’ve accepted before.
This kind of data-driven customer segmentation can help banks better target marketing toward customers and support them where needed. With the help of customer data, banks are even able to target customers down to the individual. We’ll speak more on customer personalization a little later on in this blog.
One way banks can leverage customer data is to paint a more accurate picture of a customer’s creditworthiness. Traditionally, creditworthiness in the U.S. is acquired through one of a few credit bureaus. These institutions determine a customer’s credit score using basic indicators. Common indicators include the number of existing loans and credit cards, loan payment history, credit utilization, average account age, and credit inquiries.
But in reality, there is so much more that goes into a person’s actual creditworthiness. Most importantly, a credit score does not take into account income or transactional data. These two factors are critical to a person’s creditworthiness. That’s because they indicate how much capacity a person has to spend (and pay off) and how much a person actually spends.
Let’s take a look at income first. The digital revolution has given rise to new ways of earning money, making our understanding of a person’s income more complex than it used to be. Online shops, drop shipping, food delivery, ridesharing apps, freelance work, and more have become popular ways to diversify income. In fact, more than a third of Americans participate in the gig economy. Data analysis can help banks make sense of inconsistent income streams to get a more accurate picture of a customer’s creditworthiness.
Transactional data, on the other hand, can help banks determine how much a person is spending and how they’re spending. These transactions can impact a customer’s ability to pay back loans and complete credit card payments. A few things to look for in transactional data include buy now pay later accounts, ongoing payments like utility bills or insurance payments, and cryptocurrency investments.
Financial fraud is nothing new, but the rise of online banking has given rise to new fraud methods. In fact, consumers lost a whopping $5.8 billion to fraud in 2021 according to the FTC.
Instead of fraudulently accessing funds from a human teller, bad actors have come up with creative ways to compromise customer identity and access online bank accounts. They can use stolen credentials, phishing, and stolen credit card information to make purchases and access accounts.
Luckily, advanced analytics can help banks predict and detect fraud more easily, making banking safer for consumers. Machine learning algorithms can help banks identify both individual and industry-wide trends in consumer data and spot outliers. This can hopefully catch fraud before it happens or before a customer notices.
A method called fraud risk analytics uses machine learning to apply a risk assessment score to consumer transactions in real time. Depending on the score applied, the algorithm can recommend how to move forward, whether that be accepting or denying the transaction or requiring further authentication.
Personalize Customer Support and Offers
According to a Capco study, almost three in every four consumers surveyed rate personalization as “highly important” to financial service experiences. Data analytics plays a huge role in ensuring that customers receive a personalized experience in digital banking.
It’s important to consider personalization at every stage of the buyer’s journey – from customer acquisition to engagement. Banking analytics has made personalization more possible at every stage.
Here’s how banks could help boost revenue and meet goals by personalizing the customer journey, according to a survey of lending decision-makers by Forrester Consulting and Blend:
- Improving customer acquisition. By meeting potential customers where they are with marketing efforts, banks are more likely to acquire them. Customer data can help identify the right marketing touchpoints.
- Improving cross-selling opportunities. Customer data can help banks understand what products and services customers are most likely to need and/or buy.
- Helping financial institutions better understand and respond to customer needs. Data enables banks to understand where customers could use support so they can respond accordingly.
- Accelerating digital transformation. A bank’s digital transformation journey and a better customer experience go hand-in-hand. Personalization is vital in improving the customer experience.
- Improving customer experience. Personalized experiences show customers that their bank cares about them, understands them, and is able to cater to their unique needs.
- Improving customer retention and loyalty. By delivering a better, more personalized customer experience, banks are more likely to increase customer happiness and loyalty.
Improve the Human Side of Banking
By properly using the wealth of consumer data to increase personalization, improve the customer experience, and optimize tasks, banks free up their employees’ time to work on the more human side of banking. Customers still crave the one-on-one support and interaction they traditionally received from their banks.
However, this human interaction and support can be bolstered by digital support. FAQ pages on the bank website, for one, can help answer simple, impersonal questions. Examples include questions about online banking tools, where to find certain resources, how to complete certain transactions, and more. AI chatbots can also help provide answers to simpler, less personal customer questions. For more personal questions, a bank employee can use customer information to provide more informed, more helpful answers.
Tools like social media and search engines can help banks get a better understanding of customers through customer feedback. Online customer feedback can help banks understand how they’re pleasing current customers and areas of improvement.
Uncover the Trends Driving Digitization in Banking
Now is the time for banks to organize and act upon the wealth of customer data at their fingertips. This data has the power to help banks create better, more personalized, more targeted experiences for their customers. The amount of data banks have will only grow. Now is the time for banks to strategize and find ways to best take advantage of this data.
The growth of customer data analytics in banking is just one of the trends driving digitization in banking.
Digitization in Banking
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