By James McHale
Although it is not listed on the balance sheet, one of the most valuable assets for today’s banks is data. The financial industry has begun to realize that designing product and service offerings around the insights derived from data is the best way to compete in an increasingly digital economy. Yet, despite the industry’s recognition of the value in data analytics, a majority of banks struggle to effectively harness the power of their own data.
Like sitting on an oil field without knowing how to extract it, banks have a tendency to gather data without understanding how to best use it.
For banks to differentiate themselves competitively and optimize their financial performance, data analytics must serve as the foundation when developing a marketing strategy. Each day, banks collect and store immense volumes of data, including customer behavioral data like:
- Transaction history
- Product usage
- Bank statements
By analyzing this data, banks can identify opportunities for noticeable improvement across the institution, especially in regards to marketing.
Moreover, designing a solid marketing program and accurately measuring the program’s ROI does not have to be a guessing game. This enables banks to execute on fact-informed strategies and base decisions on actual metrics that are unique to the institution and its customers.
Consolidate and centralize.
Given the amount of data constantly gathered by banks, it is easy to assume that banks can simply begin analyzing data to optimize their performance from the outset. Unfortunately, it is not that easy because data is often stored inefficiently, which limits its power.
With various channels—like mobile and online banking—along with different services such as online bill pay, banks are bombarded with incoming data from multiple systems. This data is typically stored in disparate spreadsheets, making it nearly impossible for anyone at the bank to locate and analyze relevant information.
Additionally, evaluating performance and marketing outcomes based on a fraction of an institution’s data only tells part of the story, providing incomplete results at best and inaccurate results at worst.
Instead, banks must organize and consolidate their data to effectively harness its power.
Eliminating data siloes is the first and arguably, most important step for deploying an effective data analytics program. Banks must integrate data across all business lines and departments, storing it in a single, centralized repository. Once internal and external data from the bank’s core, general ledger, and third-party systems are combined, the bank can then accurately measure its performance and identify opportunities for revenue growth.
With centralized access to metrics, banks can fine-tune their marketing strategies and maximize profitability based on data, rather than guesswork. Backed by data, banks confidently answer the questions:
- What are our risks?
- What areas are most profitable?
- Which products and services are most used by our customers?
Building upon this foundation of data, banks can establish a strategy to accomplish their marketing objectives.
Analyze the past, present, and future.
Once data is consolidated and accessible from a central location, decisions can be based on actual metrics versus trends or industry benchmarks. However, when relying on data for strategic insight, it is advised that banks consider four specific types of analytics, including descriptive, diagnostic, predictive, and prescriptive.
- Descriptive analytics assess historic information, enabling banks to evaluate the past.
- Diagnostic analytics help banks understand current performance trends.
- Predictive analytics monitor current behavior to predict future behavior.
- Prescriptive analytics support tactical planning, allowing the bank to establish a strategy to achieve a specific objective.
Together, these analytics can empower the bank to drive positive change with confidence. Banks that leverage these four types of data analytics can see the big picture in its entirety and then develop a comprehensive plan to remain competitive in a fast-changing and increasingly crowded market.
Do not ignore profit risk.
Effective use of data analytics also supports the mitigation of profit risk, which can be defined as income statement concentrations.
Banks with high profit risk often rely heavily on a singular product, relationship, or market opportunity, with the belief that market and customer demands and interactions will remain the same. However, in the event of a shift, the bank’s profitability is threatened.
Profit risk can be calculated by dividing the profit generated by the top 10% of customer relationships divided by the net income of the institution. If a small portion of relationships are driving a majority of the bank’s profit, the level of profit risk is unhealthy. If a risky concentration is identified, the bank must diversify with the appropriate combination of products for its customers in a way that is profitable and sustainable for the bank. This would be near impossible without a solid understanding of target customers and a well-executed data analytics program.
Banks can obtain valuable insight about their profit risk and opportunities, as analyzing data about each individual, account, household, product, demographic, market, officer, segment, and channel promotes increased visibility across the institution. This allows the bank to accurately determine the next best product that will provide the most value to both the customer and the bank’s bottom line.
In short, data analytics enable a bank to recognize which activities and relationships positively impact the institution’s profitability—and then, conclude which marketing channels and messages are most successful in each customer interaction.
This level of certainty in correlating marketing efforts to revenue dollars means that banks can significantly enhance their business strategy, deepening relationships with customers while driving sustainable revenue.
Focused marketing over brand awareness.
Ultimately, successfully deploying data analytics helps banks of all sizes develop a deeper and stronger understanding of their target customers. To make the most of this knowledge, banks should demonstrate to customers that their needs are heard by delivering targeted marketing messages tailored to the customer’s financial behavior and communication preferences. Successful marketing is less about brand awareness and more about personalization. Relying on generic email blasts and direct mail lists will no longer suffice, as today’s consumers demand targeted communication that is personalized according to their distinct financial needs.
Do not squander the power of data analytics with poorly targeted and delivered marketing messages.
Banks must infuse data analytics with customer outreach to optimize the ROI of marketing efforts. With data analytics guiding the way, banks do not have to settle for mediocre response rates, as data highlights where banks should focus their attention and how to best nurture customer relationships, in turn generating impressive conversion rates and revealing new revenue opportunities to positively impact the bottom line.
James McHale is Senior Vice President and General Manager of Analytics at Baker Hill, a leading provider of technology solutions for common loan origination, relationship management and smart data analytics. Leveraging more than 20 years of financial services experience, McHale helps financial institutions harness the power of data to achieve strategic growth objectives. Twitter.