Transform member relationships with data analytics

According to PWC’s 2020 Retail Banking Survey, 61% of bankers say a customer-centric business model is ‘very important,’ but only 17% are ‘very prepared’ for it. Digital transformation is one key component in facilitating a customer-centric model. Consumers commonly equate digital access with convenience, ease-of-use, and a time saver. While digital enablement and adoption is not a new concept for financial institutions, there is growing popularity among consumers of all ages to favor more self-service, digital options, such as P2P payment apps and budget tools. 

While Millennials and Gen Z continue to be high digital adopters, the trend is becoming more popular among older generations as well. In 2017, 57% of American adults reported using a P2P payment app service which increased to 70% in 2020 according to the 2020 PaymentInsights report provided by Mercator Advisory Group. On top of that, a report released from Chase shared that 30% of P2P payment app users have signed up just in the past six months. 

Research by the Digital Banking Report found that 75% of financial institutions considered themselves ‘not adept’ at leveraging their data and analytics effectively. More automation, artificial intelligence, and machine learning tools are being introduced that help provide actionable insights for both members and credit unions. Digital sources provide more data on who, what, where, and how members are interacting and engaging with your products and services than ever before. However, if there’s not a strategy in place to analyze these data sources it makes it hard for financial institutions to make sound, informed decisions. 

Big data can feel unstructured and impossible to manage, but when strategically tackled, can help pinpoint consumer wants and present a more complete and accurate picture of strategic growth opportunities. Being able to consolidate and analyze data sources is a powerful tool for financial institutions to leverage, and ultimately transform, their member experience. 

3 Ways Data Analytics Can Transform Member Relationships

1. Expand new product and service opportunities for members

Having the right data can provide the insight you need to safeguard your institution and proactively develop plans to succeed with guidance on future investments to best manage your portfolio. Data can share insights on: 

• • Member profitability
  • Campaign measurement
  • Services and pricing
  • Capital deployment
  • Mergers and acquisitions

With an analytical, well-rounded approach financial institutions can potentially find new borrowers, find new ways to serve existing borrowers, or identify alternative ways to help drive consumer decisions with financing options that work best for them. Data can help determine new service guidelines, financing opportunities, or desirable tools and products to invest in to best serve members.

2. Identify different consumer segments

Analyzing data trends on consumer behavior and preferences can give financial institutions good insight on different consumer segments that exist as part of the larger loan portfolio and how to price for them under future economic scenarios. 

The modern consumer credit journey includes:

• • Student Loans
  • Credit cards
  • Auto
  • Mortgage
  • Retirement 
  • Refinancing 

Understanding these segments and where consumers are in their own credit journey can help address product and servicing needs. These insights can open new opportunities or identify new ways to personalize offerings and enhance the member experience. Institutions could create risk/reward profiles to make new product decisions and identify which technology tools would best serve their members. Identifying who is currently interacting with your products and how services can help them creates new ways to better serve existing members and borrowers.

3. Evaluate risk red flags

Credit risk modeling can help predict, monitor, and communicate potential roadblocks or sources of increased default and other loan concerns. Risk assessment is integral to protecting business from both expected and unexpected loan risks. Modeling analytics can look at risk of loan defaults, projected financial loss, and other risk-driven red flags.

Consolidated data metrics is an important piece of the data strategy puzzle for financial institutions. These metrics can help allocate capital more effectively across the loan portfolio, in real-time, and present a clearer picture of the unique needs of the members being served – both individually and at the community level. These keen data insights can provide the ‘who, what, where, when, and how’ of consumer behavior that can help transform the member experience with personalized service interactions and relevant product offers.

The Bottom Line

Ultimately, the future is uncertain with the changing economy, the members you target, what products you sell, and prices you charge. An enterprise approach to analytics is the key to driving profitable growth.