Three tactics to best utilize data and behavioral analytics
Financial services organizations have access to some of the richest data and behavioral analytics around. They know how people bank, borrow, save, transact and live their financial lives. But most organizations have limited ideas about how to harness that data, build strategies around it and use it to shape future performance.
Thus more than ever, it pays to focus on this truth: Data and analytics generated by the customer provide a valuable blueprint for how to engage that customer in the future.
While creating a highly personalized digital experience occupies the minds of all financial services leaders, data analytics and application to drive performance can prove a game changer. Investing in data analytics technology, warehousing or marketing automation only mark the first steps. Banks also need the right people, processes and strategy to move data from interesting side notes to true business intelligence, strategy and profit-driving execution.
Most banks have data collection and storage systems, but often not linked. Many banks fail to cultivate specific ideas or strategies to collect what they want from the data—and determine how it can reshape customer experiences and performance. As the customer landscape continues to shift amid a digital and mobile revolution, banks must figure out how to use data to define growth strategies, create easier and simpler consumer engagement and ultimately grow wallet and market share.
Quantity, quality, strategy
Future growth with a demanding consumer audience depends on innovation, with enhanced customer experiences driving Net Promoter Scores and healthy referrals. Financial leaders need to use their data to identify their ideal existing target audience behaviors and patterns. This not only leads to better customer retention: It helps the organization grow.
Learning how to capture, cultivate and utilize the right data can help organizations marry qualitative knowledge and quantitative insights. This approach provides a wealth of data and opens the door for informed decisions, market analysis and modeling to create bold new growth strategies.
Providers, privacy, products
Many insurance providers have made major strides with data analytics. They use algorithms to identify web-shopping patterns and build innovative models such as online policy price comparisons—while traditional banking providers have lagged in their use of data modeling. Because financial services organizations gather sensitive and confidential data, part of the challenge rests with addressing internal concerns over the balance of online privacy with delivering more innovative services.
That fear does not hold back a barrage of new online disruptive FinTech players—such as Acorns, Simple and Venmo—from creating rich new apps to make banking, payments, saving and investing simpler and more engaging.
One growing digital success story comes from Citigroup. As one of the world’s largest financial services organizations, Citigroup has adopted a robust, data-driven approach to provide simpler banking services and to grow market share. The company uses model testing to deconstruct its customer data analytics and to better understand how to engage with customers.
Financial services organizations can use analytics to mine their data and find new insights, which can reduce process complexity, improve customer channel experiences and bolster product performance strategies by reaching customers at the exact moment of need.
Here, then, are three tactics for making the best use of data:
1. Evaluate patterns, trends and triggers
Financial services organizations should focus on customers’ preferences, needs and behaviors to facilitate the organization’s growth. But first, determine what these are. Collect data and analyze trends using a strategic process to define customer behaviors and channel usage to help build future predictive models.
Organized data provides vital insights to sets of patterns, trends and triggers that define the customers’ choices and where the organization has succeeded (or failed) at responding to those moments. This can help define future digital actions and growth strategies.
2. Strategize your growth rise
This should start with identifying the most committed, productive and profitable customers. While financial services leaders know that not all customer relationships are equal in value, few can quantify which customer segments fall into the ideal 10 or 20 percent of users by product, profit generation and recency—and then find those segments in the general population to grow more of them.
Conducting client and market analysis based on rich psychographic and lifestyle segmentation adds incredible value to data and market analytics. Lifestyle segmentation allows you to focus on laser targeting strategies well beyond basic demographics or vague clusters such as Millennials. By geocoding customer household data and tying it to market financial analytics and big data, we can now understand behaviors and market share, as well as forecast growth and predict performance trends.
When organizations can pinpoint future targeted growth segments and market performance, the profitability of each, and their growth in market population, they can better understand their market and how to best reach customers to optimize growth. Then it’s time to utilize behavioral data to identify patterns of actions for targeting.
3. Prioritize through models
By ranking and weighting specific tailored growth criteria, financial services leaders can build customized market algorithms that model future priorities. This can help pinpoint underperforming locations and future growth markets, increasing performance as a result. By leveraging data analytics, forecasting and market scoring, banks can model growth strategies out five years to target the most lucrative real estate opportunities.
As for the present, financial services organizations sit on a wealth of data analytics and information, but do they use it to its fullest potential?
Start with the right process of defining growth plans, profitable products, distinctive brand experiences and value proposition. Then build the right data model and long-range growth strategy and performance model that will set the organization up for success. After all, nothing beats crunching the data that results from a stellar uptick in performance.