Banks have historically relied on demographic data to segment customers, looking at characteristics such as age, race, gender, education level, and location to devise marketing plans and other customer engagement strategies. But technological innovations—coupled with the changing preferences and needs of younger generations—are paving the way for banks to be much more strategic and tailored in their approach. The key to this transformation is analyzing behavioral data to better understand consumers’ habits, preferences, and financial needs and abilities.

Two key drivers are contributing to this trend:

  • The rise of machine learning (ML): ML models can easily enable banks to use predictive analytics to identify patterns among vast amounts of data, giving them the ability to look at customers’ historical activity to determine which services would be of most use to them—rather than relying on demographic profile information to make an assumption.
  • Digital services: With people using countless digital products and services on a daily basis, banks have access to a variety of third-party data sources they can use to supplement their own information and devise more targeted offers.

The personalization engendered by behavioral analytics will be particularly important for banks as Generation Z enters the workforce. By 2020, this group will comprise 20 percent of the workforce, and that number will only increase in the next several years. As the author of a recent opinion piece in American Banker wrote, “There are important factors about how Gen Z views the world, which will influence how banks can reach them. Gen Z is more socially aware than prior generations and resistant to traditional definitions of race and gender, making it difficult to apply such traditional demographic factors.”

If you’re a bank looking to tap into behavioral data for more informed and successful customer strategies, below are a few key things to consider:

  • Set specific goals, KPIs, and metrics: Cleary defining specific objectives and the ways in which you will measure success should be the first step in any behavioral analytics strategy.
  • Determine your data sources: Your data sources will likely vary depending upon your goals and objectives. For example, the data you would analyze to target prospective buyers with a new mortgage offer would differ from the data informing a certificate of deposit (CD) program for existing customers.
  • Ensure the quality of this information: As mentioned above, banks now have access to numerous third-party data sources. This presents numerous opportunities for actionable analysis, but it’s critical that banks first ensure the quality of this information before running any analytics.

Of course, the benefits outlined above would not be possible without a solid business intelligence and analytics platform in place. In order to avail of behavioral analytics (and any other form of analytics, for that matter), it’s critical that companies invest in the right core technology to efficiently access, cleanse, and integrate data from disparate sources and to easily make this information actionable to the variety of stakeholders who need it.

For more on behavioral analytics in the banking sector, check out the American Banker piece in its entirety here.