In the last decade, consumer expectations have drastically changed. The advent of digitalization has put personalization and highly-tailored customer experiences at the core of business success. While banks have been utilizing some sort of personalization strategies for years, in the majority of cases, these efforts are insufficient to help them keep up with increasing customer expectations. Conventional customer segmentation practices based on demographic data, age, and other conventional metrics have grown obsolete. This is why many financial organizations now turn to banking software development.
First, with the rapid invasion of innovative fintech companies, it became apparent that consumers are increasingly more inclined to switch to flexible service providers that shape their business models around customer experiences. As a result, some of the most profitable revenue sources for banks have been taken away by these nimble digitally native organizations. Second, this challenge has been compounded by the COVID-19 pandemic, forcing both banks and their customers to rely on digital channels more than ever before.
It may appear surprising that financial institutions have been so reluctant to embark on a digital transformation journey and double down on personalization. However, it’s important to note that banks’ hands have been long tied by the utter inflexibility of their legacy systems, disparate data sources, and increasingly strict data privacy protection laws.
At the same time, the pressing need for change has reached its peak level, leaving financial institutions with no other choice but to embrace service digitization and personalization. In this article, we are going to discuss the meaning of personalization in the banking industry, the most potent personalization strategies, common barriers on the path to their adoption, and the ways of overcoming them.
What is personalized banking?
Personalized banking means tailoring financial service delivery to address specific customer needs in real-time. Banking personalization can be achieved by applying behavioral analytics and data science. The goal of personalized banking is building meaningful, lasting relationships with customers.
Why banks need to bank on personalization
Personalization can be defined as using customer data, behavioral analytics, and data science practices to define customer expectations and satisfy them. In the banking context, personalization is aimed at tailoring financial service delivery to address specific customer needs in near real-time.
For one thing, not harnessing the vast possibilities offered by big data, advanced analytics, innovative CX solutions, and other technologies to advance personalization initiatives is a missed business opportunity for banks. Nowadays, customers expect the same level of engagement and personalization that they experience with customer-centric services outside of the banking industry.
What is more, for decades, price and product quality have been the major competitive differentiator across industries. Business development mostly revolved around introducing new products and adjusting prices to beat the competition. In this new digital age, the ability to provide customers with personalized experiences beyond physical branches and points of sale has become an equally important competitive advantage.
This way, personalization proves the most effective way for banks to deliver on the ever-changing customer expectations, increase loyalty, and drive revenue growth.
Personalized banking can also play an important role in addressing financial exclusion. According to the recent report from FCA, more than 27 million adults in the UK alone have characteristics of low financial resilience. The government now considers the advent of fintech companies as one of the main instruments for tackling financial exclusion, having confirmed the investment of $5 million to establish the Centre for Finance, Innovation and Technology (CFIT). In this context, the personalization and digitization of banking services is poised to play a significant role in broadening access to financial services.
Viable personalized banking strategies
To deliver on increasing customer expectations and stay on top of the competition, banks need to start adapting personalization strategies. Let’s discuss some of the most important personalized banking use cases.
Personalized pricing
According to the 2020 Accenture report, 37% of consumers claim that value for money is the decisive factor for switching their financial service provider. In other words, regardless of how much effort you put into the diversification of banking products or improving customer journeys, many consumers are willing to switch to another bank in the name of less expensive services.
Currently, the majority of banks employ various price optimization methods which center around improving the profitability of a particular product or service. However, this product-centric approach ignores critical price-influencing factors like customers’ immediate needs and customer lifetime value (CLTV). These conventional pricing optimization strategies have proven to be effective in boosting the profitability of specific services but inefficient for driving customer engagement and loyalty.
To tackle this issue, banks can utilize personalized pricing models. In this case, the price is defined based on customer segment preferences and expectations rather than product quality and market trends. Making use of customer big data and advanced analytics, banks can figure out the best service price for a particular customer segment drawing on a customer’s behavior, propensity to purchase, loyalty level, CLTV, and current needs.
Personalized financial advice
Personal finance software providing tailored advice can be another valuable tool for significantly enhancing banking customer relationships and increasing loyalty. The example of digitally native fintech companies proves how personalized financial advice increases the number of bank-customer interactions and drastically improves customer engagement.
Currently, there are a myriad of portfolio management solutions and stock trading apps (including advanced tools leveraging machine learning for stock market investments) helping consumers to improve their financial health. Mint, one of the most popular budgeting apps, can sync user bank accounts, automatically divide expenses into appropriate categories, assess user investment portfolio, set saving goals, send bill payment reminders, and provide many other services.
Mint and thousands of other personal budget apps have tapped a growing demand for tailored budgeting advice. But given that banks have a much more granular insight into customer profiles, a bank-native personalized budgeting solution can become one of the most effective tools for increasing customer engagement.
For example, an embedded analytics system can assess customer transaction history and provide relevant budget management recommendations. In its simplest form, such a system can highlight duplicate services in addition to other amenities that are relatively expensive compared to a customer’s income. Such small improvements can be a good start for introducing personalization. More advanced systems can utilize customer transactions and web data to determine if they can switch to a less expensive mobile service provider. Similarly, personalized budgeting systems can alert customers when they exceed their fuel expense limits or when it’s time to pay credit card bills.
One of the fastest and most effective ways of deploying these personalized budgeting systems is to partner with existing service providers. For example, TransUnion partnered with the aforementioned Mint app to demonstrate to customers how their credit score is determined and suggest ways of improving it. While it may not seem as profitable as developing state-of-the-art budgeting solutions, it’s important to remember the core objective of personalization lies in establishing a long-term emotional connection with the customer, and the aforementioned app fits the bill.
McKinsey recommends banks go as far as assembling dedicated teams that are focused on finding fruitful partnerships with third-party technology providers. It’s also paramount to optimize banking technology infrastructure to enable hassle-free integration with partner technology. This can be done by deploying sand-box environments, ensuring API compatibility, and developing advanced data-sharing solutions.
Personalized product offerings
To realize the full potential of a personalized banking strategy, financial companies also need to deliver customer-tailored service offerings. By analyzing customer behavior across different channels, banks can provide each customer with unique, time-relevant offers.
For example, by assessing new users’ activity on their website and app, banks can personalize calls-to-action based on the steps the prospect customer takes. Similarly, the data about existing customers’ interactions with the bank can be analyzed to determine next-best-action prompts and personalized service offerings. Based on CLTV, purchased services, preferred interaction channels, and tenure, banks can predict customer loyalty scores and reap the personalized banking loyalty benefits.
Banks can also use customers’ location data to offer relevant services at the right time. For example, when a customer enters a shopping mall, their mobile banking app can notify them about partner retailers nearby. With the help of embedded finance, these retailers can provide customers with insurance or a loan without visiting a bank. Similarly, when a customer enters a car dealership, banks can offer relevant insurance services.
Overcoming common personalized banking challenges
Ineffective data governance
As with many other advanced analytics-based initiatives, personalized banking efforts will fail without a robust data governance structure. Unfortunately, many banks still utilize inflexible legacy systems that can’t provide the necessary data infrastructure. This is why deploying personalized banking systems should be a part of the banking digital transformation, which also implies the modernization of a legacy core system.
Given that CBS modernization is often an expensive, cumbersome, and time-consuming initiative, the best solution would be to build a customer data platform on top of the legacy systems. This way, banks can gather data both from the core system and third-party providers and analyze it in a separate, often cloud-based platform. For banks looking to deploy enterprise-wide personalization strategies, robust data infrastructure should be the top priority.
In the context of personalized banking, it seems logical that the more data you collect the better. However, many banks struggle to make sense of lavish amounts of data that their customers generate every second. To understand what exact data organizations can use to advance their personalization efforts, it’s paramount to define customer pain points first. This calls for conducting demographic research and applying behavioral science to understand what exact hurdles customers face when trying to satisfy their banking needs.
Insufficient data privacy
In many ways, personalized banking initiatives are hindered by strict data protection regulations that are growing more stringent. Obviously, no benefits of personalization can be justified if customers’ privacy is compromised, so transparency and consent should become the key secure data management components for banks.
First, given that many advanced personalized banking solutions imply the usage of AI, it’s important to ensure that these systems are bias-free and explainable. Apart from the fact that AI explainability is also a matter of regulatory compliance, it is also important for banks to understand how the system makes certain decisions. Second, regardless of how sophisticated one’s AI system is, it’s imperative for banks to embrace the human-in-the-loop approach, meaning that any decision suggested by AI has to be approved by an employee.
Third, it’s paramount for banks to be transparent about how they collect, store, and analyze customer data, and to allow customers to easily withdraw consent for data collection. Data privacy is crucial for retaining customer trust. In the past years, more consumers have become more thoughtful about the personal data they share and less trusting of their financial providers.
In this case, personalization can become a double-edged sword. If banks don’t pay attention to data privacy, they risk diminishing their customers’ trust even more. This is why it’s imperative for companies not only to ensure that their personalized banking efforts are ethical but to also impress this upon the customers.
As shown in the table below, the majority of customers are unwilling to share some of their more personal data. This means that banks have to find a balance between their needs for customer data and their customers’ willingness to share it.
Conclusion
The banking industry, known to be bound by legacy systems, product silos, and rather conservative business models, has been notoriously slow in adapting to ever-changing customer needs. However, the call for personalized banking services is rather urgent. Fortunately, machine learning tools in banking, cloud, big data, and sprawling ecosystems of digitally native financial service providers have made personalized banking more attainable than ever before.
To enable personalized banking, organizations need to move from a product-centric to a customer-centric business model and start putting more focus on establishing an emotional connection with their customers rather than merely meeting their needs. To achieve personalization at scale, financial institutions need to break the data silos across business, marketing, and IT departments and consolidate this information in a single customer data platform. In many cases, advanced levels of personalization can be achieved only if banks can source data from third-party providers and establish partnerships with innovative fintech companies.
