How To Become The Tesla Of Banking
Gone are the days when data was purged from bank systems to free up the performance of bank servers. Today, data is a bank’s most valuable asset that’s not listed on the balance sheet.
Elon Musk, CEO of Tesla Motors has always been known for forward-thinking innovations and pioneering the mega trends in the industry. Data facilitates the forecasting of these trends and keeps Tesla ahead of the competition.
By analyzing external data of the busiest travel routes of drivers, Tesla is constructing Supercharging stations to accommodate customers throughout the country thus improving the owner’s experience.
Through internal data analysis of existing client behavior, Tesla found that most cars are “only used by their owner 5% or 10% of the day.” As a result, Tesla plans to unveil a “shared fleet” where owners can allow their car be rented out while the owner is at work or on vacation.
Through both internal and external data analysis of driver behavior, a growth strategy was implemented around those behaviors and the decision to buy a Tesla becomes easier and more likely for potential customers. As a result, the features and services that have been added make Tesla’s value proposition truly unique.
Data: A Precious Commodity For Financial Institutions
Just as Tesla forecasts customer behavior patterns using data analytics, so too can financial institutions. Instead of automobile buying patterns and driving routes, financial firms can analyze customer spending patterns by demographic, use data to comply with regulatory requirements, and create target-marketing campaigns.
Data analytics must be part of the enterprise-wide growth strategy for financial institutions both on a holistic level and within individual business lines. By gathering and analyzing client behavior from both internal and external sources, institutions can also analyze market trends, assess supply and demand, cut operational costs and perform effective capacity planning.
How Data Analytics Drives Revenue
Through data analysis, institutions gain a better understanding of their client’s behaviors from both internal sources (intranet and content management systems) and external sources (social media, internet, and government business databases). Armed with the data, bank employees can proactively cross-sell products and services to meet clients and prospective clients needs before they walk into a branch.
Data analytics show client behavior including spending and borrowing patterns. By understanding which products clients use and don’t use, marketing strategies and new product initiatives can be built around that client behavior.
Improved Client Retention
With so much attention in banking on growth of new clients, often times existing clients become a second priority. The result is lower client retention rates. Through data analysis at Bonova Advisory, our research has shown that divisions of some institutions lost on average 10% of their existing clients per year. The reason for such high attrition rates was the primary focus of pursuing new opportunities and achieving aggressive sales goals often had the unintended consequence of existing customers being ignored.
A Diversified Revenue Stream
The payoff for implementing a sound data analytics strategy is a diversified revenue stream for the institution. It’s no secret that banks earn revenue from credit products sold to consumers and businesses. However, with the current low yield environment, banks are finding it increasingly difficult to earn a wide enough loan spread to achieve the margins to pay for their fixed costs.
Growing fee income from existing clients is a critical focus for institutions.
The graph shows the revenue split for three of the largest U.S. financial institutions. We can see that roughly half of the revenue of each bank is from fee income.
In other words, the top banks earn as much revenue from fee income on existing customers as they do from credit products and trading income
Having a sound data analytics strategy is imperative to maximizing the revenue earned per client as non-interest income is just as vital to driving revenue as net interest income. And a balanced revenue stream allows institutions to weather periods of persistent low yields in the market.
With an efficient data analytic strategy, institutions can improve client retention rates, diversify their revenue, gain a larger share of the customer’s “wallet”, and lower balance sheet risk as the bank becomes less reliant on credit products to drive revenue. Greater profitability, happier clients, and better retention rates are all achievable through a sound data strategy, and perhaps might even earn the envy of Elon Musk.