Leverage Transaction Data to Deliver the Personalization Consumers Expect

In today’s financial landscape, there is an unfortunate mismatch between what consumers expect financial institutions (FIs) to know about them and what FIs actually know. The power of the consumer is increasing. They have near-limitless access to information, an ever-growing choice of products and services, and a wealth of opportunities to share their experiences and provide product feedback. As a Deloitte study so accurately describes, consumers have become “critics and creators” and as a result, demand more personalized service.

Many FIs can’t deliver the level of personalization consumers are looking for because they have not equipped themselves with technology that provides insights to fully understand the needs and financial activities of their account holders. As a result, many FIs are not able to evaluate transaction data quickly enough to prevent their users from finding alternative products and providers when they are in the market.

Committed to elevating the online banking user experience (UX) and developing operational efficiencies for FIs, Alkami acquired Segmint, a leader in making transaction data usable and meaningful for FIs to deepen account holder relationships, uncover hidden profit opportunities, and grow revenues. This strategic acquisition combines Segmint’s data insights with Alkami’s digital account opening and digital banking capabilities to activate FIs’ user data along the account holder journey. By leveraging Key Lifestyle Indicators (KLIs) based on account holders’ transaction behaviors, FIs can engage customers and members with a personalized UX. So how can FIs achieve the level of personalization consumers want?

According to The Financial Brand, a shocking number of FIs do not use data-driven insights to improve the user experience. Based on a survey of bank and credit union marketers, they cited:

• 65% of respondents say they do not use their institution’s data as often as they should to educate and cross-sell users
• 27% say they do minimal or no personalization in their marketing
• 19% say they don’t use data-driven insights at all

How can FIs hope to bridge the gap between consumer expectations for personalization and their own ability to deliver accordingly if they don’t use the transaction data they have at hand? Here are a few steps FIs can take toward that end.

Realize the overwhelming task that is analyzing payment data

The data an institution stores about its users’ financial transactions are filled with valuable and actionable insights that identify their users’ patterns of merchant spending, trends in lifestyle activities and interests, life events, usage of held-away products and services, and more.

This transaction data is available and plentiful within the institution. However, it’s also messy, cluttered, often cryptic, and infrequently standardized.

And the effort to analyze this data is significant. For example, payments for a Chase Bank credit card could have more than a quarter-million unique transaction descriptions. Similarly, payments to Home Depot could have nearly 100k unique transaction descriptions. Condensing the billions of deposit and credit account transactions processed into a distinct set of competitors and retailers is a mind-boggling task that banks just don’t have time to take on.

Jump start your data strategy with merchant payment cleansing 

Many financial institutions (FIs) are considering merchant payment cleansing as a key ingredient to a data strategy. Merchant payment cleansing is a critical tool to help FIs better understand account holder transaction behavior and model spend patterns, taking raw transaction strings and identifying a cleansed business name, along with assigning an appropriate business category.  Segmint’s banking data solution cleanses and categorizes billions of inconsistently labeled transactions, which helps an FI leverage this clean, tagged data to provide a more organized online banking experience, avoid transaction disputes by providing better online banking statements, and reduce IT hours, lag time and quality issues.

The four most important factors when working with a data cleansing vendor are match rate, speed, categorization and taxonomy, and privacy and security. It is impossible to truly understand every transaction string, but FIs should expect more from their merchant payment cleansing partners than they are getting today, and find a partner who will deliver the match rate, speed, quality, and privacy to build the groundwork for a solid data game plan, allowing an FI to put this data into action.

Look for the consumer story in transaction data

FIs already know which products users have at their institution and their associated transactions. But that data only provides a sliver of the overall view of a user’s full financial profile.

An FI’s ability to know their users holistically enables them to drive strategic business decisions, deepen account holder relationships, and drive growth. And the critical dependency on this knowledge is the ability to aggregate, categorize, and make sense of transaction data. In turn, these FIs will realize stronger growth and performance than competitors.

In fact, the data itself is currency—it’s that valuable. With transaction data, institutions can target and craft deeply relevant and meaningful personalized experiences, execute quickly, and shape brand loyalty that can provide significant return over time. Without it, FIs are left in the dark, relying on outdated assumptions and imprecise generalizations.

Analyzing the entirety of users’ transactions: online bill payments, ACH payments, incoming fund transfers, competitive payments, and debit or credit card payments can provide an FI a more holistic view and identify a consumer’s life priorities, depicts patterns in spending behavior and current life stages, such as having children, buying a house, sending kids to college, or enjoying retirement. This is what account holders expect their FI to know and understand their motivations, current financial needs, and, most importantly, financial concerns.

The same Financial Brand article stated that an institution’s brand value can be measured based on the quality of the UX they provide. The following data points were gathered from their third-party research:

• 63% of consumers are highly annoyed with generic advertising
• 80% of consumers say they are more likely to do business with a company if it offers personalized experiences
• 91% of consumers would switch brands if a different brand with the same products and services was more authentic and purpose-driven

These stats shouldn’t be surprising. Consumers experience relevant numerous digital interactions every day — Amazon, Netflix, Starbucks, and Google searches are just a few examples. Consumers are demanding their FI to use the data it has available to help them to secure the right products, create wealth, reach their financial goals and wellness, and master their finances.

Take it a step further with AI Modeling

Because Segmint leverages transaction and product data observed on a daily basis, we provide modeling using an on-demand approach. The personalization we referenced above – how about taking it a step further? Segmint’s AI Modeling platform quickly identifies shifts in spend categories and banking behaviors, using those indicators to predict future account holder behaviors. Segmint has developed an AI Modeling platform specifically designed to use KLIs as model inputs – ideal data for predictive modeling. The platform is designed to analyze each KLI in an unbiased manner and identify the most predictive variables to incorporate into any model.  With a full suite of KLIs always at the ready, Segmint deploys out-of-the-box or custom predictive models within weeks, making AI attainable by FIs of all sizes.

Segmint + Alkami

These insights are made available to FIs via intuitive visualizations within the Alkami platform or through Segmint’s data services API, for ease of integration with an institution’s business intelligence toolset, providing opportunities to analyze the data and identify data-driven service strategies.