By: Becky Summers and Eric Wittekind

big da·ta

The Oxford Dictionary defines big data as extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially relating to human behavior and interactions. Sounds like what we all need, but it is easier said than done.

Financial institutions are all dabbling in the use of data to determine behavioral changes and improve the accountholder experience. In speaking with clients during recent roundtable discussions with financial institution executives on organizational data use, participants shared that many of the technology applications focus on process improvements and ways to be in front of the next opportunity even before the accountholder may have identified a need. Trying times have also illustrated different financial needs that became the focus during COVID-19.

Data During a Pandemic

Bill Handel, general manager and chief economist at Raddon, encourages executives to “consider COVID-19 as an accelerant instead of a disruptor.” In the past, IT teams focused on systems, but today it is about a critical infrastructure built in a way that helps the organization move forward with data analytics at the center of decisions.

During the pandemic, an emerging need to improve financial wellness measures and identify accountholders in financial distress moved to the forefront for many clients. But the first stage of the pandemic was to react quickly. The need to solve the immediate accountholder and employee needs for physical safety was the top priority, then quickly shifted to providing loan relief for those with financial stress due to job loss or changes in income.

The next stage of the pandemic is to move from being reactive to becoming proactive. Roundtable client discussions indicated a grassroots movement to solve for financial support over time. This comes in different forms for different institutions. A $1.9 billion financial institution mined predictive data in a way to segment more affluent accountholders with decreased direct deposits or with severance payments to connect them with financial planning resources. These accountholders did not need loan deferments; they needed long-term planning instead. Without deep segmentation using clean data, the messages would have not been personalized, but the risk was the message was incorrect and ignored.

Data Identifies Opportunities

Executives discussed the need to use clean data to analyze transactional and behavioral data, resulting in more accurate accountholder interest and intent. Suggestions included looking at the data within the loan portfolio to see the age and vehicle information that lead to the next loan opportunity. With a deep analysis of collateral in the portfolio, average loan length was correlated with vehicle models to help determine when other accountholders with similar vehicles may be looking for a new auto loan. While other variables come into play, this data analysis allowed for a deeper look beyond the seasoning of a loan to determine the opportunity for another loan product. Loan seasoning alone improves cross-selling results, but cross-matching it to loan type and tenure helped improve response rates with deeper segmentation.

Each minute, there are 188 emails sent, 390,000 apps downloaded, 18 million texts sent and 4.5 million videos watched, according to Domo: Statista, Internet Live Stats, National Association of City Transportation, Officials, WIRED. Given the volume of information and contacts that a consumer receives, relevancy is a requirement. The best way to accomplish this is through the use of targeted segmentation down to the individual level. Executives noted several uses of data and segmentation to identify opportunities and personalize messages. Consider these ideas:

Predictive Analytics – Clients noted the use of cleansed and tagged transactional and behavioral data to determine consumer interest and intent while making a financial difference.

• Improve financial wellness, help those in financial distress with specific offers
  • Long-term savings options with earnings options for those relying on retirement savings
  • Loan refinances or payment deferments
  • Financial literacy communications including budgeting and savings ideas
• Identify held-away business and anticipate future needs by reviewing transaction data to expose payments made to other financial institutions for competitive products
  • Identify opportunities to recapture lost business with specific competitive offers
    • Consider demographic preferences
    • Personalize messaging to highlight your competitive advantage and focus on the right benefits
  • Consider concentration risk of held-away accounts; if many accountholders are using a certain competitive product, understand why. Is it a product gap issue? Should we reconsider product fees? Is it time for an accountholder survey to ask some questions about product features and benefits?
• Conduct attrition modeling to create prescriptive team member communications, touchpoints and ways to prevent the accounts from leaving
• Predict future bankruptcies: servicing, delinquency and recovery strategies

Dynamic Campaign Management – Quickly identify and define targeted audiences, and set up and launch targeted campaigns. Campaigns may focus on product, or oftentimes the executive talked about expanding the relationship.

• Create nurturing conversations from application to funding
• Conduct preapproval campaigns for accountholders and prospects
• Send the right messages that include preferences: Tell the consumer how the product can make a difference in their life and use images that resonate with the individual. Personalize the message illustrating hobbies or interests based on spending to catch their attention quickly and easily

Multichannel Message Delivery – Automatically serve targeted messages. Deliver to digital channels (public and private websites, mobile devices and millions of third-party websites) as well as to assisted channels (branch, call center, email and direct mail marketing systems).

• Timely, targeted offers through the right channels
• Leveraging digital and open internet channels

Data Creates Efficiency

Quickly, executive discussions turned to using data analysis to improve efficiency and accountholder experiences. The first efficiency discussed was driven by volume since every mortgage staff member has worked well into the night to keep up. Vetting lending with predictive analysis, especially for higher risk jumbo loans, has allowed processes to be streamlined, freeing up needed resources for loans that require more vetting. Prescreening data elements included loan-to-value ratio and prior lending experience with the accountholder, along with other lending criteria. This type of automated data analysis allowed for prioritization and preapprovals to keep the process moving quicker with only minimal human intervention. The measurements for success were increased loan application-to-funding ratio, automated-approval ratios and time-to-close analysis.

Roundtable participants noted that efficiency in the call center has been another focus over the past several months. Using data about the accountholder has made it possible to nurture conversations by representative assignment and topical areas, and even to increase call efficiency through new routing based on abandonment, representative skill sets and accountholder product mix.

The conversation revealed that data certainly is a focus for financial institutions, but it sounds easier than it is. Making data the center of all decision making starts with clean and tagged data to make it actionable to ensure decisions are truly based on a foundation of accuracy and truth. Discussions confirmed that great strides are being made, but as the U.S. Chamber of Commerce tells us, 90 percent of the data we have was created in the past two years. This is a journey, not a destination.

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