Planning for the Bank’s Future: Why Advanced Data Analytics Is Paramount in 2017?

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2016 has been a big year for advanced analytics. Banks that are already turning customer data into smart, actionable insights see major pay-offs in new business, better customer targeting and segmentation, faster decision-making, efficiencies in operations, and progress in risk management. As the banking industry approaches strategic planning season, it’s time for leadership to think long-term about the bank’s goals and future growth and how advanced analytics can support these objectives. Why should data science and advanced analytics be a critical component to the bank’s 2017 business plan? Consider these compelling reasons:

Differentiate in a Customer-Centric Economy


For those banks that are leveraging the power of advanced analytics, they can now identify models and algorithms that lead to improved customer relationships, loyalty and efficiency. The rewards translate into the ability to quickly solve business challenges, mitigate risk, and make the next best offers in real time via digital channels. A bank’s best customers are a prime target of the competition’s advanced analytical model. The algorithms and models that create actionable insights will help banks deliver highly personalized and tailored services to customers that will render increased market share, revenue and customer loyalty for years to come.

Transformational Journey is a Long-Term Commitment

Deploying an advanced analytics initiative and building a data culture takes time.  Turning the data generated by customers into actionable, comprehensive insights is a big task. The initial steps should include establishing short-term and long-term business goals. Once ready with an effective process to collect, cleanse, compute and consume the data, banks need to assign staff with the right skills to then use this complex data for discover insights that identify business opportunities and can interpret results meaningfully.

On average, an analytics program implementation can take 120-180 days. However, after the initial implementation period, banks can enjoy the pay-offs by establishing a set of recurring best practices derived by specific actions on specific data segments that enable banks to serve up the right offers, through the right channel, to the right customers at the right time. While there are many immediate actions that will deliver a return on investment, building prescriptive action models require an average of 18 to 36 months; that’s why it’s important to plan for this journey now.

A Vision for the Future



Advanced analytics should be a key component to the bank’s 2017 strategic plan and budget. Banks that are moving forward with a plan will be able to take action on specific data segments based on customer contribution, next best product models and algorithms that identify opportunity. Such action lists are an important resource to front-line teams and should both align with strategic goals and be measured through scorecard or incentive module feedback. Top performers at the bank can be recognized and learning organization theory can lift the performance of others. Over time, the bank’s culture begins to shift, strategic initiatives are measured, resources are provided to team members in the form of information and action lists, and performance can be assessed. Data science is the next step in business intelligence and will drive improved customer service, retention and contribution.

The banks that are investing in and embracing the potential of data science and advanced analytics can improve and reinvigorate all aspects of bank. Standing still is no longer an option for banks to maintain their competitive edge, advance their business and build lasting relationships with existing and new customers.


About the Author

Steven D. Simpson, Senior VP of Financial Institutions Solutions @ Saggezza

Steven Simpson provides a wealth of experience in the community banking and credit union industry and a deep understanding of the software and data analytics used by financial institutions. He has led numerous mergers and acquisitions, converted over 55 core banking systems, and guided a startup specializing in internet banking to IPO. With a successful 30 year career, Steven has held C-level positions at Bank of America, Sheshunoff Management Services, The Independent Bankers’Bank, and Caja Madrid.