The following is a guest post by Mit Shah, Co-founder & COO of Method Financial.
From 2018 to 2020, Americans paid $120 billion in credit card interest and fees, contributing to the almost trillion dollars in nationwide household credit card debt.
In today’s rate-shopping environment, Americans must be able to accurately compare credit products and make the best possible decisions for their financial future.
The CFPB actively strives to ensure Americans can shop in a competitive market and reduce costs. The bureau is currently working on this by creating a neutral data source that can facilitate credit comparison shopping and the bureau’s 1033 rulemaking, which is intended to fuel competition and strengthen consumer rights.
However, most consumers have more than just credit card debt. Many also have mortgages, auto loans, student loans, and other liabilities to pay each month.
Comparing rates and offers for all these liability accounts can overwhelm many consumers. And many don’t realize they are eligible for reduced rates from consolidation opportunities.
Meanwhile, financial institutions intensely compete for deposits and loans, hoping their offers are more attractive than competitors.
The good news is the right data can help facilitate healthy market competition in a way that serves consumers and the financial service providers they engage with.
Do financial institutions and fintechs have the data necessary to eliminate the guesswork and target consumers with personalized offers quickly and at a competitive price point?
Personalization is essential, and the data you use matters
A study from the Boston Consulting Group found that financial institutions can experience annual revenue increases of 10%, as well as lower rates of customer attrition when they hyper-personalize offers, proving that personalization at scale is critical to building loyalty, profits, and customer retention.
Supporting consumers’ financial health is vital to overall customer satisfaction and can positively impact financial institutions’ growth.
According to a recent Financial Health Network study, customers are three times more likely to be satisfied with their primary financial institution if they feel their institution truly cares about their financial wellness. They are also five times more likely to purchase additional products from their financial institution in the future.
Yet, accomplishing this requires access to the right real-time data, not just demographic data.
Personalization requires more than assuming that a consumer may be interested in an auto loan or mortgage simply because of their age and income level.
This surface-level demographic data is not always enough to deliver personalization that moves the needle.
Instead, financial institutions and other service providers need comprehensive data that tells a consumer’s entire financial story, including their liability data.
Data on their student loans, credit cards, auto loans, mortgages, and all other types of debts, as well as real-time liability details such as balance amount, payment history, due date, and interest rate, can help tell that story.
With this level of data, financial institutions can strategically target consumers with the right message at the right time, improving segmentation and cross-sell efforts rather than relying on “spray and pray” marketing campaigns, which often miss the mark and do not give consumers a compelling reason to convert.
From personalized to pre-qualified
By analyzing consumer liability data, banks, credit unions, and fintechs can learn which financial products and services consumers would be most interested in and which will help them reach their financial goals.
This enables financial service providers to confidently target the right consumers with the most competitive, pre-qualified offers that move the needle for the consumer and the provider’s bottom line.
More comprehensive data on a consumer’s liabilities can reduce credit risk and default rates. Consumers can be pre-qualified for the most relevant and competitive offers based on the current balances on their debt stack, which means financial institutions can more easily segment consumers and pre-qualify them for suitable loan offers based on their institution’s credit policies.
As a result, institutions can hit the sweet spot when making offers – they can competitively price offers in a way that helps consumers save on interest and fees while minimizing risk.
An opportunity for lasting loyalty
According to Oliver Wyman, acquiring new customers can cost financial service providers anywhere from $100 to $200. With the right data, customer acquisition can be much more cost-effective and result in product offers that help consumers achieve their financial goals, which helps nurture long-lasting, loyal relationships.
As the CFPB finalizes its Section 1033 rule around consumer data sharing, financial institutions, and fintechs should consider how open banking data will pose new challenges and opportunities in today’s rate-shopping environment.
