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How to Turn Big Data into Profits & Great Market Share

How to Turn Big Data into Profits & Great Market Share

The biggest banks on earth are getting bigger. And while the regulatory environment has made it tougher for community and mid-tier banks to keep up with the major industry players, advanced data analytics (also known as Big Data) is increasingly a factor in banks grabbing a bigger market share.

While market shocks like the financial crisis of 2008-09 drove customers and assets to larger banks, a focus on data science has also helped the five largest banks increase U.S. market share dramatically — gaining 108 percent from 2000-2013, according to the FDIC. And there are 640 fewer FDIC institutions from 2013 to 2014, a loss of 4.7 percent.

For those banks that are leveraging Big Data analytics technology — and can scale it quickly and easily — it is leading to greater profitability, increased market share and improved customer service.



Big Data analytics is the process of examining large data sets containing a variety of data types (both structured and unstructured) to uncover hidden patterns, unknown correlations, market trends, customer preferences and other opportunistic business information.

With the proper advanced analytics technology in place, these newfound analytical insights can provide a marked transformation for your bank in multiple areas— from more granular targeting of marketing campaigns, to better cross-selling and up-selling based on customer needs and history.

Many banks are already using Big Data analytics in big ways. The institutions that are implementing Big Data technologies, and building teams (or finding partners) to leverage it, are gaining a true competitive and profitable advantage from advanced analytics. The more information and knowledge a financial institution has about its customers, the better they can deliver what customers want and need.

Community banks, in particular, have deeper relationships with customers that power the institutions. This, coupled with Big Data and analytics that quantify and measure action, will result in best practices that the largest banks cannot attain.

Community banks in Texas have another advantage: Local relationships. Community banks have the advan-tage of relationships and intuition that have kept them strong. Combining strong relationships with actionable Big Data and intuitive analytics delivers a great combi-nation that drives profitability.

Simply put, the more information and knowledge a financial institution can collect on its best customers, the more it can predict what its customers want and need. Soon, major decisions to grow revenue, control costs, and identify risk will be driven by data analytics.

According to McKinsey’s DataMatics 2013 survey, banks that are adopting a Big Data strategy plan have a 23 times greater chance of customer acquisition, six times greater likelihood of customer retention and 19 times greater likelihood of customer profitability.



The right Big Data technology can be transformative. Consider these facts:

  • According to Information Age, Wells Fargo, the world’s largest bank, recently announced they had hired their first chief data officer and allotted a budget of $100 million to a team of 600 dedicated to refining customer data. Their goal? Make big bank data actionable— and the bank more profitable.
  • A recent study conducted by the Saïd Business School at the University of Oxford found that 63 percent of banks recognized using Big Data as a huge competitive advantage.
  • A recent Gartner report revealed the No. 1 spending priority in 2014 for financial institution CIOs is investing and utilizing business intelligence and data analytics.
  • In a survey conducted by FC Business Intelligence, 72 percent of bank executives who participated in the survey indicated that they use data analytics strategies specifically to improve the customer experience.
  • M&A activity rose 18 percent in 2014. According to Thomas Muchaud, CEO and president of KBW, increased consolidations in 2015 will continue as larger banks and regional champions leverage sophisticated models to extract more value from smaller and mid-sized banks that are not able to withstand the growing competition.
  • Bain Consulting estimated financial services firms spent $6.4 billion in Big Data-related hardware, software and services in 2015.



As the banking industry continues to face tight margins, profit challenges and fears of consolidation, it’s vital for financial institutions to uncover new opportunities to reduce expenses, stay competitive and generate new revenue sources.

Creating a competitive advantage from Big Data implementations requires both the technology and tools to quickly collect, slice and dice the Big Data itself, and the skills of an experienced analytics professional to draw insights from it. Here are six ways Big Data analytics help banks gain a competitive advantage:

1. Gain actionable knowledge

Advanced analytics allow a better understanding of behavioral trends and purchasing influences of your most profitable and loyal customers. When armed with data about which products customers are mostly likely to acquire, it takes the guesswork out of cross-sell opportu-nities. Customer service is enhanced, as cross-sell efforts are more timely and relevant.

2.  Improve customer retention

Banks that have a unified, 360-degree view of their customers, loans, deposits and profits are able to provide a consistent experience across every channel. They can also uncover opportunities to develop relationship pricing for loyal customers. This includes having a metric to identify “loyalty” and identify their churn risk.

3.  Create targeted, cost-effective marketing

Develop more effective marketing campaigns that are targeted to the right person, with the right message at the right time. Having a system that allows you to segment, manage and track specific actions will enhance your marketing R.O.I.

4.  Predict and mitigate risk

Enhance risk and fraud management by easily and quickly spotting pattern changes that are an indicator of potential risk. Predict a customer’s risk of defaulting or becoming delinquent on one or more loans.

5.  Quickly take action

Once key data segments are identified, the bank can take targeted action and measure the effect overtime. Incremental improvement leads to best practice results as you refine your strategies for years to come.

6.  Operate efficiently

Advanced data analytics provides banks with intelligence to make better, faster and smarter decisions. This knowledge leads to reducing duplicative systems, manual reconciliation tasks and redundant information technology costs.



Understandably, bigger banks have bigger budgets to invest in data talent and advanced technology. However, smaller banks are rapidly catching up by adopting a Big Data strategy with the low-cost scalability of an analytics partner with expertise and tools that fit their institution. If you are ready to deploy a plan, consider these four questions:

1.Staff and budget

Do you have the budget to hire, train, retain and manage highly skilled, knowledgeable, data-savvy tech and analytics personnel?

2. Analytics partner

If you look for an analytics partner, consider whether the company offers an advanced analytics solution that is fast, does the heavy lifting and is still competitively priced. Also ask whether they will help you utilize, cus-tomize and configure their advanced analytics product.

3. Analytics

How advanced is your current data or business intelli-gence manager regarding advanced analytics and data science coupled with experience of analyzing bank data?

4. Capabilities

What tools and software will you use to capture, secure, store, search, share, analyze and visualize your data? Can your veteran technology providers perform and deliver the new systems needed to lead your bank into the future?

Smart banks will continue to invest in customer analytics to gain new customer insights. This will empower the bank to effectively segment their clients in order to determine pricing, new products and services, the right marketing approaches, which channels customers are most likely to use and how likely customers are to switch banks.

The time has come for banks of all sizes to embrace the importance and benefits of Big Data analytics. Without a solid plan and the right information, implementing business analytics across the bank can be daunting and potentially expensive. However, with the right analytical tools — whether it is an advanced analytics solution, a team of data scientists or both — Big Data can be simplified. This can lead financial institutions of all sizes to much more than just data exploration.

It can lead to answers, action and analytics that turn into profit.

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