Multi-channel banking made easier

Most financial institutions (FIs) embrace the idea of data and analytics driven decision-making, yet meet serious challenges when trying to integrate this approach into day-to-day operations. Traditional financial institutions are competing not only amongst each other, but increasingly with various fintech and digital FIs. These newer providers are deploying cutting-edge technology to add value to services offered entirely physically or online. As such, data integration and mining of insights should be core to the banking discipline, as opposed to siloed data analysis that usually results in siloed insights. Poor data quality skews analysis and usually results in poor decision making and flawed business strategy. These isolated insights and poor data quality cost the US economy up to $3.1 trillion, yearly. While FIs have been successfully developing isolated clusters of analytical excellence and accelerating towards their digital-first strategy, most struggle to apply this to their daily operations and capitalize on it by employing advanced analytics.

“Better Service in Banking Begins with Deeper Data Analytics”

Deep Data analysis is when big, unstructured data is analyzed and organized, and unimportant information is stripped away. Thus, Deep Data combines large-scale data collection with the ability to sift for high quality, relevant, and actionable patterns.

Every FI wants to adopt a 'digital first' strategy and keep adding to their digital ecosystem. However, many organizations are perplexed when executing this ‘digital first’ strategy. They understand the need to adopt new technologies for it, but face challenges when prioritizing initiatives. Therefore, a strong foundation for leveraging decisions with data and analytics is pivotal to getting ahead of such challenges.

Focusing on too many initiatives ties up valuable resources and slows innovation. It's essential to know what to prioritize and apply transformative financial resources to where they can innovate and generate the highest net positive impact. Leveraging analytics-based insights to identify, quantify, and prioritize digital initiatives can create additional value for customers and generate operational efficiencies for the FI. A recent Forbes article dubs prioritization as the "new innovation" and emphasizes the need for having a reliable data and analytics infrastructure to identify initiatives to improve customer engagement.

Now that you’ve prioritized what is on your digital roadmap and the resources you’ll need, the next question to ponder is whether your ‘Digital First’ Strategy is built to last?  As use of digital channels accelerated exponentially over the last 18 months, it’s never been more critical to take another look at your digital strategy. Deep Data and analytics can show you where to deepen connections with consumers and businesses, using intuitive solutions that set you apart from the competition today—and in the future.

FIs are rapidly adding new functionality and services, or digitizing legacy offerings to expand their digital ecosystem and stay competitive in the market. However, regulation can often impede innovation and raises the challenge of constantly adding digital services to ensure due diligence and adequate compliance.  

As branches sell products and provide services, these transactions need to comply with regulatory guidelines. Data modeling and analytics can track, categorize, and eventually reduce the number of errors made by branches -- such as missed signatures on new account applications, or insufficient supporting documents for a loan application. Often, a root cause to these issues can be missed, creating inefficiencies and poorly-utilized resources. However, coupling cross-functional teams and dynamic data engagement can flag errors by employees, providing insights into the root cause. These issues can stem from inefficient processes to gaps in staff training, which means that the solutions can be as simple as an enhanced training program or as complex as net-new processes or systems. These errors may appear trivial but, in some cases, can result in severe regulatory or compliance ramifications. It is critical to have skilled resources on the team that can comprehend both branch operations and digital innovations to ensure digitization doesn’t come at the cost of compliance.

A survey conducted by Intuit found that when it comes to financial advice, 44 percent of Millennials are more likely to reach out to their parents, and 33 percent will go to the internet instead of their FI. This makes it critical to safeguard relationships with these digitally native customer segments. Millennials are generating large amounts of data using their smartphones and are actively looking for long-term financial planning – a service typically only offered face-to-face. However, this customer segment has a strong preference for online interaction, and they value good service. They know you collect their data, and they’re willing to share it – but they also want to see you use it to give them a more intuitive and personalized service.

The increasing popularity of social media platforms as a primary mode of communication adds a layer of complexity to delivering a frictionless customer experience. Engaging customers effectively via these new channels requires a continuous effort to gather and analyze “traditional” data along with crumbs of information created by customers through the digital world held in the palm of their hand.

“Internet users generated about 2.5 quintillion bytes of data/day; in 2020, each person generated about 1.7 megabytes/second and 80-90% of all this data is unstructured.”

The ability to interact with customers via different channels presents an opportunity to improve customer experience, increase engagement, and create a seamless transition from digital to physical banking (or vice versa). What’s more, incorporating data and analytics in the common, day-to-day banking operations can enable FIs to revolutionize customer experience and elevate the Return on Experience (ROX), an essential metric of a company's success to measure how well it's delivering against customer expectations.

Using insights derived from analyzing a continuous stream of data, FIs can receive in-depth research about customers to deepen relationships, boost cross-sell, lower acquisition costs, and nurture organic growth. In addition, you can assess this large amount of data through a behavioral analytics lens to develop an online engagement strategy for delighting digital natives and, as demanded, transition them to physical spaces.

In the last few years, almost all FIs have focused their transformation dollars in one way or another in migrating teller-based transactions to various self-service channels, such as ATMs/ITMs, mobile, and online. It is not uncommon for such branch transformation projects to gain insights on migratable transactions through deep analysis of extensive transaction data.

FIs place considerable weight on teller transaction migration to justify investment in modernizing their self-service fleet. While ATMs/ITMs can bridge the divide between physical and digital banking, this kind of approach can have a tunneled scope. The analysis is flawed if it does not consider other reasons as to why the customer heads to the teller rather than self-service. A reliable and constant influx of data can see trends or identify operational gaps preventing the FI from achieving the anticipated level of transaction migration.

A holistic data analysis infrastructure is required to investigate why their customers choose to bank with a teller instead of alternate channels. Customer behavior goes far beyond the unfamiliarity of using an automated machine, or the desire for a personalized conversation. Creating a separation between customer behavior and their transactional activity gives true insight into interactions at teller counters compared to self-service. Additionally, this continuous data gathering and analysis infrastructure creates an excellent mechanism to measure success quantitatively and classify transactions based on their ease of migratability.  

In the last decade, most FIs have incorporated a modestly sophisticated branch analytics infrastructure. However, they lack the expertise, resources, or automation required to convert these analytics or data points into actionable insights in many instances. The ideal branch transformation approach should tackle transaction migration by assessing branch data and refining it into actionable insights for management information. These actionable insights can assist in solving various challenges like selecting the best self-service mix or sites for introducing ITMs or making data-driven decisions using transaction benchmarking.

With this data-driven intelligence, FIs are able to better integrate in-person and digital services and make optimal decisions for their business and customers.