Banking personalization isn’t a new trend. Instead it’s a re-imagining of the business model that was central to retail banking before the era of mass marketing. This business was once highly personal – loans were made on a handshake, tellers knew almost everyone who came in and so on.
Now advances in technology enable banks and credit unions to know their customers again – their preferences, interests and where they’re at in their life journey. And this technology allows this to happen at scale – something that wasn’t possible in the days when loans were made on a handshake.
It’s coming just in time as banks find themselves being leapfrogged by retailers and tech-savvy companies that put personalization at the center of their business models. Call it what you will – the “Netflix” or ”Amazon effect” – consumers’ expectations have changed how they interact with their banks and other financial institutions to mirror the highly personal, digital experiences they enjoy with these and similar technology giants. Financial institutions have to rise to meet these new customer expectations.
Similar to how tech giants use technology to craft recommendations, banks will be able to use data and analytics to anticipate individual needs, target “segments of one” and build deep relationships that can stand the test of time.
And it will be worth the effort. The Boston Consulting Group estimates that for every $100 billion in assets a bank has, as much as $300 million in revenue growth can be reached through personalized banking experiences, resulting in driving “material competitive advantage for first movers that embrace it over the next five years.”1
What Does Personalized Financial Services Mean?
The underlying premise of personalization is not to just “sell more stuff” but to become trusted advisors – in effect bringing banking full circle. As a recent JD Power study showed, almost 90% of retail banking customers claim they “definitely will” reuse their bank or credit union for another product if they give great financial advice.2 However, it’s difficult to give good financial advice when you don’t know a customer’s goals or priorities – and in an industry survey, only 6% of financial service providers felt confident in their institution’s ability to use data to grow customer relationships.3
Becoming a trusted advisor means going far beyond segmenting and microtargeting or even customizing homepage messages and digitizing the customer journey. Instead it requires hyper-personalization – the ability to use data and analytics to develop a deep understanding of each customer’s needs and orchestrate a set of tailored personal experiences across digital and human channels. Hyper-personalization solutions integrate customer data from multiple sources to create comprehensive profiles that are then used by predictive analytics tools to generate the most relevant recommendations and products.
Examples of Personalization in Banking
Imagine looking for a new home: you point your phone at the house of your dreams and talk directly into your banking app. Your bank tells you not only how much your monthly mortgage repayments would be, but also provides details about local services and taxes by drawing from public information.
Sound farfetched? We’re not there yet but as you can see from the following examples, the industry is on its way. Likewise, we see consumers getting more comfortable handing over their data in return for services that deliver greater personalization. This is critical because the more information a bank has about its customers, the easier it is to provide services that are truly relevant to an individual’s specific needs.
Types of Personalized Banking and Financial Services
Personalization is attained in a number of different ways.
Prescription personalization is essentially segmenting. Customer segmentation is the process of dividing customers into groups based on common characteristics so you can message to each group effectively and with information tailored to their interests. This type of analysis helps banks get to know their customers on a more granular level.
Segmentation reveals information that can be used to inform product messaging (think deepening relationships, cross-selling financial products, and other share of wallet opportunities) and customer service strategies (think financial literacy, financial wellness, etc.). It can also help banks better understand the customer lifespan and predict customer behavior.
Real-time personalization revolves around delivering insights to personalize the customer experience as it is happening. By drawing on real-time data, intelligent financial apps can alert a customer when they are in danger of going over their budget to help them make better decisions now and support their long-term goals.
Machine learning personalization uses algorithms to extract data from large datasets to learn from it and make predictions. For instance, machine learning personalization models can help predict how a borrower may perform with a certain loan or whether a borrower is likely to default.
Benefits of Hyper Personalization
Ultimately, if done well, hyper-personalization in banking can provide a set of “hard” and “soft” benefits for financial institutions including:
- Foster customer loyalty by driving customer advocacy
Customer advocacy is when a customer perceives their financial service provider has their best interest in mind. Advocating for your customer is providing content they find valuable, whether it’s letting them know how their spending compares to their peers, pinpointing specific areas where they can save, or providing advice for their unique situation. By showing customers that you know them, understand their needs, and want to help them reach their goals, you’re taking engagement to a whole a new level.
- Establish trust for greater share of wallet/ higher customer lifetime value
While all industries benefit from being trusted by their customers, banks are in a position to derive the most benefit. If they can become the trusted provider of financial products, services, and advice for the majority of a customer’s financial life, they have much to gain.
- Shift from sales funnel to flywheel model
Banking has traditionally approached sales as a funnel, where you attract, cultivate, and close prospects in a one-way, single-destination fashion. But some banks are beginning to treat sales like a flywheel, where meeting one need perpetuates another because they’re able to tailor opportunities based on a person’s specific needs (at one point in time or for short or long term financial goals), thanks to end-to-end personalization.
- Deliver operational improvements
The sophisticated data aggregation and leading-edge AI and machine learning techniques needed for analytics also enables operations managers to better measure efficiencies, service channels, and customer satisfaction. And automation, already an important part of consumer banking, will become more pervasive, delivering benefits for a bank’s cost structure.
Envestnet | Yodlee's Financial Insights
Hyper-personalized digital experiences aren’t possible without access to consumer financial transactional patterns and trends, and as an industry leader in data aggregation and analytics, Envestnet | Yodlee is uniquely positioned to deliver these insights.
Envestnet | Yodlee Insights Solutions combine actionable insights, peer benchmarking data, personalized views, and critical data needed for segmentation to enable contextual, hyper-relevant customer interactions. Insights Solutions can be delivered through simple APIs and apps to create a strong emotional connection across the customer lifecycle and on all touch points.