In October 2018, Gartner made a bold prediction: by 2030, 80 percent of financial firms will either go out of business or be rendered irrelevant by new competition, changing customer behavior, and advances in technology. This statement makes one thing clear: banks must move quickly to become more relevant to their customers.
Over the past decade, new types of players—such as non-bank financial institutions and fintechs—have disrupted the financial services landscape. The key reason for their success is their ability to anticipate customer needs and deliver the right products and services that traditional providers are too slow to create.
Customer expectations have changed radically in the last five years, thanks to companies like Amazon, Google, Apple, and other technology innovators. Their ability to deliver highly personalized offerings has fueled customers’ desires for the kind of high-touch service that many banks lost in the process of “going digital.”
Technology advances have given firms the ability to operationalize ideas that were out of reach just a few years ago. Artificial intelligence, machine learning, and other new technologies have made it easier to anticipate needs that were never before possible. Yet, many banks have been slower to deploy AI at scale, putting them at a distinct disadvantage.
With these three factors at play, the balance of power has shifted from banks to consumers, who now have more choices than ever when it comes to banking. While the plethora of new options may be good for consumers, it is making growth for retail and commercial banks exceedingly difficult.
In today’s competitive environment, new market players, changing consumer behaviors, and technology advances have created significant downward pressure on banks’ ability to grow, causing higher customer attrition rates and limiting banks’ ability to cross-sell products and services. But banks have a unique opportunity to leverage these trends to win over their customers and obtain new ones in the process, thereby creating top line growth.
A typical global bank loses approximately 18 percent of an average balance to customer attrition. The reason? Banks are failing to offer customers what they truly want: lower rates and fees and better customer service. In fact, 40 percent of customers say they are willing to switch if these issues are not addressed. The key to stemming attrition will be to personalize experiences, giving customers exactly what they want. Without this, banks face losing customers at even higher rates.
Many banks struggle with cross-selling and upselling for one simple reason: they aren’t able to deliver what their customers need, when they need it. The good news is that almost 90 percent of customers own a deposit account with a global retail and commercial bank, so they already have the customers in-house. It’s simply a matter of creating growth within their existing customer base by anticipating customer needs and leveraging insights to determine their interests and wants. In doing so, banks can dramatically improve their cross-sell and upsell rates and increase overall customer engagement.
By better understanding and delivering on customer needs, banks can stem attrition and improve cross-sell and upsell efforts—increasing net interest income by an average of 190 basis points. Identifying where to focus within the business to drive growth may be the easiest piece of the puzzle. What concerns most bank executives today is not the “what” but the “how.”
The balance of power has shifted from banks to consumers, who now have more choices than ever when it comes to banking.
To better understand customers’ needs, habits, and preferences so banks can more successfully engage customers with personalized offerings is not a new idea. However, it is one that is extremely difficult to execute successfully. This notion of Anticipatory Banking is the “how” banks are searching for—a framework and platform that leverages artificial intelligence (AI), machine learning, and behavioral science to better foresee key customer needs and provide relevant information, products, and services that will help them more successfully navigate their finances throughout their lives. More importantly, it offers a way for banks to scale this ability for every customer, across every journey and for every product—optimizing the value they deliver and their ability to grow the business.
This prognostic, data-driven approach to customer service and financial wellness lays the foundation for a tighter relationship between bank and customer—and creates a seamless experience with ease and guidance that become regular occurrences. If banks are looking for ways to deliver Amazon-like customer engagement, Anticipatory Banking offers a way to achieve it.
Anticipatory Banking helps banks improve the success of their cross-sell and upsell efforts by anticipating customer needs and delivering the experiences customers want. Through techniques such as modeling personalized affordability scores to identify demand potential and customer profiling to identify top targets for product recommendations, banks can add as much as 100 basis points to their net interest income.
It also helps them decrease customer churn—culminating in topline revenue growth. Banks can use key pieces of data, such as the composite engagement score per customer and moments in the customer journey that lead to high churn, to thwart attrition. By understanding what motivates customers to leave, banks can use a variety of strategies to mitigate that risk, generating upwards of 90 basis points in net interest income.
Anticipatory Banking leverages AI, machine learning, and behavioral science to help customers improve their financial well-being. It helps banks predict and respond to their customers’ changing financial needs.
Becoming anticipatory means having access to the right blend of first- and third-party data, creating more meaningful customer insights, optimizing the choice of which products or services to offer, and engaging customers through their preferred channels and during the moments that matter most.
Even though banks have vast amounts of historical first-party data, it is important to access additional first-party and third-party data, such as ad impressions, browsing behavior, transactions, and more, to improve the precision of data models and create entirely new ones. By combining sufficient first- and third-party data, banks will be able to identify meaningful behavioral signals which will help them more precisely determine what their customers need at the most relevant moments.
AI also plays a critical role in determining which products and services are more relevant based on customer insights. Banks will be able to personalize existing products and services to meet individual needs and can also create unique offerings tailored for specific customers and situations.
Banks need to build and run hundreds of highly complex, interacting models that will improve insights over time. These insights are geared toward better understanding customer behaviors—needs, wants, habits, professional stages, and more. Using a combination of AI, machine learning, deep learning, and natural language processing, banks can generate the level of understanding needed to effectively anticipate customer needs.
The where, when, and how of connecting each customer or prospect to available offerings is just as important as choosing which ones to present to them. The data can tell us not only what customers want but also where they spend their time online and which messages resonate. Automating customized marketing processes drives engagement, conversion, and loyalty.
For years, banks have been transforming their businesses with varying degrees of success. Customers demand it. Data is available. Technology has advanced to enable it. So, why then are banks struggling? Possibly the biggest hurdle when it comes to transformation has to do with the organizational and operational changes required to make it truly successful. Retooling complex legacy organizations to be more agile and measurable impacts people, process, and technology, yet it is a prerequisite for delivering against individual customer needs automatically and in real time.
The banks who are able to solve for these issues first will be on a clear path to win. But becoming more anticipatory is a journey, not a destination. And while every bank is at some point along the way, very few have yet to make a mark. Some banks may already have a solid data foundation and integrated digital infrastructures. Others may not. Some banks will need to develop new models of customer engagement, while others will be farther along in terms of customer centricity. Most, however, will need to make changes across many areas of the business in order to create the value customers are expecting their banks to provide.
Anticipatory Banking offers a platform for establishing customer and digital centricity at scale—allowing banks to gain competitive advantage in the market, today and tomorrow. It helps elevate customer experiences to rival those of today’s leading consumer brands. Having the right foundation and platform helps banks tackle today’s business challenges, but it also positions them to explore new business opportunities—even across verticals—all while cultivating deeper and more meaningful, mutually beneficial relationships with their customers.
Anticipatory Banking offers a platform for establishing customer and digital centricity at scale.
Senior Director, Management Consulting
Eugenia Perelman is a Senior Director at Publicis Sapient Management Consulting practice and a lead strategist for the financial services industry. Eugenia has over 15 years of consulting experience working with financial services firms to help them transform their businesses to deliver better outcomes for customers, employees, and shareholders. She advises clients on a range of issues including business and digital strategy, customer experience transformation, as well as organizational and operational model changes. Eugenia holds an MBA degree from Cornell University and a bachelor’s degree in Mathematics and Computer Science from the University of Waterloo.
Global Head of AI, Robotics and Data
Rashed Haq is the Global Head of Artificial Intelligence and Data Engineering at Publicis Sapient. He is known for helping global companies transform their businesses and create competitive advantage by defining AI strategies, and developing and deploying AI solutions at scale. Rashed is a frequent speaker at conferences and is a published author of numerous articles, papers, and an upcoming book on enterprise AI transformation. Prior to joining Publicis Sapient, Rashed conducted research in theoretical physics at the Los Alamos National Lab and the Institute for Theoretical Science. Rashed holds graduate degrees in Physics and Mathematics.
To learn more about how we can help you with Anticipatory Banking, visit publicissapient.com/contact.
Publicis Sapient is a digital transformation partner helping established organizations get to their future, digitally-enabled state, both in the way they work and the way they serve their customers. We help unlock value through a startup mindset and modern methods, fusing strategy, consulting, and customer experience with agile engineering and problem-solving creativity. As digital pioneers with 16,000 people and 53 offices around the globe, our experience spanning technology, data sciences, consulting, and customer obsession—combined with our culture of curiosity and relentlessness—enables us to accelerate our clients’ businesses through designing the products and services their customers truly value. Publicis Sapient is the digital business transformation hub of Publicis Groupe. For more information, visit publicissapient.com.
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