The banking industry has always had more consumer data available than most other businesses. The management of transactions and the building of relationships over time has provided insights into behavior that gave a competitive advantage and hurdles to market entry against firms outside the industry.
As consumers embraced digital channels for commerce and communication, banks were among the first companies to take advantage of the new streams of data. Some firms were early movers, employing advanced data analytics, appointing chief data officers, and investing substantial time, effort, and resources in building out infrastructure that enabled data analysis.
Unfortunately, despite the vast amount of data available, the early start and the industry’s formidable resources, most banks are far from realizing big data’s full potential, according to research from the Boston Consulting Group. Some of the reasons for falling short of potential include:
- Competing priorities. Such as addressing regulatory changes in the wake of the financial crisis.
- IT complexity. Because of multilayered systems and siloed data, banks rarely use the full breadth and depth of data at their disposal.
- Lack of coordinated vision. Resulting in suboptimal allocation of human and technical resources and limited interaction and exchange of ideas.
The risk of falling behind in leveraging consumer insights has never been greater since consumer expectations are rising. The majority of these expectations are being set by non-financial competitors.
1. Partnerships Between Banking and Fintech
The vast majority of fintech firms have been established to provide consumers an improved digital experience based on contextual insight and simplified delivery of financial services. By leveraging advanced analytics of consumer data and digital technology, smaller start-ups have been able to build solutions that are superior to those from legacy financial institutions.
The reason why the partnership between legacy banking firms and start-ups makes sense is that, while the fintech firms may have a better engine in many cases, the legacy financial firms have the fuel to make the fintech solutions more successful in the long term. Legacy banks and credit unions have the larger customer bases that fintech firms lack. By combining the data and technology skills of many of the fintechs with the data from the larger legacy firms a win-win is created.
2. Removing Friction from the Customer Journey
Because the consumer does the vast majority of their shopping for a new financial institution using digital channels, it is no longer adequate to wait until the customer or member walks into a branch or decides to purchase a new product online or via a smartphone. Instead, banks and credit unions must engage customers at every stage of their purchase journey, not just because of the immediate opportunities to convert interest to sales, but because two-thirds of the decisions customers make are informed by the quality of their experiences all along their journey.
Digital channels are at the center of this transformation, no longer just representing a cheaper way to interact with customers, but also being critical for executing promotions, stimulating sales, and increasing market share. In order to effectively remove friction along this journey, customer insights need to be leveraged. These insights go far beyond simple demographics, to include channel preferences, lifestage insights and even geolocational information.
3. Making Data Actionable
Having access to data and the ability to process this insight is not enough. Consumers expect their primary financial institution to be able to provide real-time recommendations based on changes in their financial profile. This includes improving the ability to save money, achieve specific financial goals, increase financial knowledge, better budget spending, etc.
According to BCG, data and analytics today bring the ability to combine three elements:
- Vastly bigger volumes of data, including highly detailed data combined from different systems
- Much more insightful models, powered by so-called machine-learning software, which can make data-driven predictions and decisions
- More efficient technology, such as Hadoop software-hardware clusters, which are among the most cost-effective ways to handle massive amounts of both structured and far more complex unstructured data
In other words, what was once the domain of only the ‘big boys’ is now accessible to banks and credit unions of all sizes. The key component missing at many financial organizations is to move data that currently resides in reports and analysis, and use it for the direct benefit of the consumer. “Banks need to stop talking about gathering big data and starting using big data to make a difference for the consumer,” states Beth Merle, VP, Enterprise Solutions at Epsilon. “We need to see the integration and synchronization of data sources, enabling real-time determination of relevant data points for 1) analysis, 2) communication, and 3) decision making – the ‘trifecta’ of big data.”
4. Optichannel Delivery
Whether a financial organization is focused on delivering their products and services using a multichannel strategy (delivery on multiple platforms), omnichannel strategy (delivery through all channels similarly), or ‘optichannel’ experience (delivering solutions using the best (optimum) channel based on the customer’s need and preferred channel), the use of consumer data drives the process. As multiple channels are supported, big data enables an organization to point the consumer to the channel that will provide the best, personalized, experience.
In the future, the integration of processes from the consumer’s perspective is foundational to the optichannel theme. “Rather than looking at channels independently, banking needs to develop and provide financial tools that are integrated in daily life,” states Nicole Sturgill, Principal Executive Advisor for CEB TowerGroup. To accomplish this, organizations will need to integrate both internal and external insights to deliver an experience not unlike the experience delivered by Uber today.
5. Exploring Advanced Technology
One of the most interesting advances in technology is the integration between smart devices. The Internet of Things is defined as a way for devices that are connected to the Internet to communicate and share information with other ‘smart’ devices in real time. By definition, these sensors leverage the capabilities of big data, analytics and even artificial intelligence to anticipate needs, solve problems and improve efficiency. While not fully defined as of yet, there will be significant applications of IoT in financial services.
“By enabling the collection and exchange of information from objects, the IoT has the potential to be as broadly transformational to the financial services industry as the Internet itself,” states Jim Eckenrode, executive director of the Deloitte Center for Financial Services. As with all of the key trends discussed, data and advanced analytics is at the core of these new capabilities.
Why Legacy Financial Firms Struggle
As the volume and depth of the available data grows, analytical models improve, and the talent available to assist in making this data meaningful to the consumer increases, excuses for not leveraging consumer insight become more difficult to justify. Those firms that can apply the data insights first will not only reap immediate financial rewards but will also establish data and analytics capabilities that will be hard for competitors to overcome.
So what are the key challenges being faced by legacy financial firms?
1. Lack of talent. A significant challenge facing the banking industry in their quest to leverage consumer insights for growth is the inability to hire the right talent. As banking organizations compete with all industries for many of the same types of skills, banking is not always viewed as the most exciting career opportunity, especially by millennials. In addition to not being able to hire the right people, some organizations are still structured without data scientists in the marketing organization, where they can be the most effective in building the bridge between data insights and an excellent customer experience.
2. Lack of resources. With so many priorities, and with a focus of reducing costs, some organizations have not allocated enough resources for the purpose of building a strong data analytics initiative. The good news is that, for the first time in a while, there’s not as much money going towards regulatory compliance as in the past. This frees up funds for other initiatives like advanced analytics.
3. Lack of urgency. Despite advanced data analysis being one of the top challenges mentioned in the State of Financial Services Marketing, only the largest regional and national banks (over US$10 billion) ranked improving data and analytics capabilities in their top three priorities (47%), compared to community banks and credit unions (only 8% of these organizations placed data and analytics in their top 3 priorities). In addition, only 36% of organizations were planning to increase their data analytics budgets by more than 10% in 2016, reflecting a surprising lack of commitment to alleviating this challenge.
To compete in a customer-centric economy, banks and credit unions must leverage their information assets. This is definitely not a slam dunk, given the realities of data silos, a dearth of available talent, competing priorities, outdated data/system infrastructure and a surprising lack of urgency despite an industry-wide knowledge of the importance of improved consumer insight.
Despite the challenges, analytics plays a central role in optimizing the delivery of a positive consumer experience in real time across a wide variety of channels. The digital consumer requires this level of experience … the future of banking depends on meeting this challenge.