In retail banking as in all of the financial services industry, it is important to examine distinct groups of customers to understand their unique characteristics. One example that illustrates how powerful this approach can be is a study of the differences between credit revolvers, who carry a balance on their credit cards, and credit transactors, who pay their cards off in full every month.
Different target audiences vary in terms of the actionable attributes that allow marketers to develop personalized marketing campaigns, optimize product offerings, identify cross-sell opportunities, deepen customer relationships and improve overall customer experience.
These attributes — known as predictive data — go beyond basic information and traditional demographics. They allow marketers to take action based on customer behaviors and motivations.
By segmenting customers based on their behavior — like credit revolving vs. credit transacting — financial services companies can develop more relevant promotions and rewards that resonate with each group’s specific needs and preferences. Data teams also can build statistical models utilizing predictive analytics to forecast customer behavior based on whether they are revolvers or transactors. But only with the right data.
Credit revolvers and credit transactors have recognizable characteristics beyond just their payment behaviors. These include spending and investment habits, unique product preferences, different lifestyle interests and more.
Below we’ll examine the research-based differences in the behaviors and motivations of these two customer groups and how marketers at banks, credit unions and other financial services companies can leverage such insights to improve results with all customers.
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Credit Revolvers vs. Transactors: Spending Habits
According to predictive consumer research, credit transactors are likely to spend nearly 40% more on discretionary items annually when compared to credit revolvers. Transactors also allocate more of their total annual discretionary spend to dining out than they do to other categories like apparel, education and entertainment. In contrast, revolvers spend 16% less on discretionary items than the average population.
It may not seem like it on the surface, but the discretionary spending habits of each group have implications for marketing and product development for retail banks and others as the difference in spend level naturally results in differences in where credit revolvers and credit transactors shop. Revolvers are over 200% more likely to purchase clothes from consignment or thrift stores compared to transactors. Transactors, on the other hand, are more than twice as likely to purchase luxury brands. With data like this, credit card issuers could offer rewards programs that target transactors’ spending habits, such as cash back on dining out or shopping for apparel.
Additionally, credit transactors are significantly more likely to donate to a wide range of charitable causes and are also more likely to be high-dollar donors that give more than $1,000 over the course of a year. On the other hand, credit revolvers are more likely to be motivated to donate to healthcare organizations than transactors are. This data suggests that credit transactors may be a more attractive target for just about any charitable organization looking to solicit donations, while credit revolvers may require a more targeted approach, such as focusing on healthcare-related causes that they are more likely to support. Financial services companies can use these insights to inform their corporate social responsibility initiatives and support the causes that matter to their customers.
Outside of what they actually do with their money, credit transactors and revolvers also have their own preferences when it comes to payment methods. Recent research shows that credit revolvers are over 750% more likely to be interested in buy now, pay later payment options and over 330% more likely to use payment platforms like PayPal when compared to transactors. While the outcome may be the same — a payment — banks, credit unions and other financial institutions should consider such customer preferences when it comes to payment methods to best meet the needs of different customer groups and provide seamless experiences.
Correlation Where It Counts:
Credit revolvers are far more likely than transactors to be interested in using buy now, pay later options. How much more likely?750%
Could the differences that revolvers and transactors exhibit in spending behaviors be to due differences in income? Let’s examine.
According to the data, credit transactors have an annual household income nearly twice that of credit revolvers, approximately $133,800 annually compared to approximately $72,200. It is safe to assume that this affects the level of discretionary spending. However, in the absence of current income data, how might one come to logically predict future spend level? After all, only employers and the IRS truly know income data.
This is an area where looking at groups like credit revolvers vs. transactors can help. By considering data points that are predictive of other behaviors, marketers and data scientists can glean actionable insight — such as differences in spend level — that have historically been drawn from more traditional data points like income.
Credit Revolvers vs. Transactors: Product Preferences
There is a vast array of financial products available to consumers today, and with predictive data on their customers, banks, credit unions and others in financial services can match prospects to the products they are most likely to need or be interested in.
When it comes to credit cards, transactors are far more likely — 3,600% so! — to be in the market for an airline credit card than revolvers. Revolvers are 850% more likely to be in the market for balance transfer credit cards and nearly 500% more likely to be in the market for a low interest card compared to transactors.
In addition, transactors are approximately 2,300% more likely to invest in stocks and bonds than revolvers. This difference suggests that transactors may be more interested in long-term investment products — such as retirement accounts — than revolvers. Revolvers may prefer products that offer more immediate benefits.
There are even differences in how these two groups care for their furry friends. Revolvers are nearly 60% more likely to purchase pet insurance than transactors. However, transactors are over 220% more likely to make regular vet appointments for their pet compared to revolvers. With this in mind, offering pet insurance cross-sell opportunities to credit revolvers would be a logical consideration for financial services organizations that have such products.
“Transactors are far more likely — 3,600% so! — to be in the market for an airline credit card than revolvers.”
— Theresa Blue, AnalyticsIQ
Besides specific product preferences and purchase intentions, credit revolvers and transactors also have preferred methods of how they access, use, and experience those products. When it comes to banking specifically, credit revolvers are more likely to access their bank accounts through mobile platforms. In fact, nearly one-third of all credit revolvers are highly likely to prefer to access their bank accounts and related information through mobile platforms, and they are 800% more likely to prefer accessing their bank accounts through mobile platforms compared to transactors. In contrast, transactors much prefer to access their bank accounts and information online or in person, with online access being their most preferred method. Such insights are important as they shed light on how financial institutions can provide the right experience for different types of customers.
But is it possible that differences in product preferences such as these are driven by age and gender?
While an understandable assumption, it may not be the main factor in this case as credit revolvers have an average age of 51 while transactors have an average age of nearly 56 — which is not a notable difference. Additionally, there is no significant difference between the breakdown of males and females when comparing credit revolvers and credit transactors to the average population, removing gender as a way to explain these differences and reinforcing the importance of predictive data points to aid in understanding consumers.
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Credit Revolvers vs. Transactors: Interests & Lifestyle
Insight into an individual’s lifestyle can provide marketers with contextual information needed to truly meet their needs, and credit revolvers and transactors display unique traits even outside of the financial arena.
For example, revolvers are over three times more likely to be active on TikTok and nearly 250% more likely to be cooking enthusiasts than transactors. However, their transacting counterparts are nearly eight times more likely to be active on LinkedIn and are nearly 500% more likely to be foodies on the hunt for trendy restaurants compared to credit revolvers. Data on customer channel preferences and personal interests provide marketers with guidance on where to target prospects and what content or rewards are important to them, such as shopping at grocery stores versus eating at restaurants.
Once again, it is natural to draw the conclusion that those interested in TikTok would be younger than those more active on LinkedIn. However, as we examined previously, there is not a significant age difference between the two groups.
But upon further examination, one difference is that credit revolvers are nearly 33% more likely than transactors to have children present in their household. This is another predictive attribute that could be driving household preferences for different social platforms that advertisers should consider.
Another interesting difference between credit revolvers and transactors is how they maintain their vehicles, which can be valuable when it comes to auto financing services. Credit revolvers are 185% more likely to maintain their cars themselves compared to transactors who are 900% more likely to have their car maintained by a dealership than revolvers. Transactors are also more likely to be in the market for a new vehicle compared to revolvers, who are more likely to shop for a used vehicle.
Since revolvers are more likely to maintain their cars themselves, marketers could consider offering them financing options for car repairs and creating partnerships with auto parts stores or offer discounts on auto maintenance services. Meanwhile, since transactors are more likely to be in the market for a new car, marketers targeting them could focus on promotions for those purchases along with competitive financing rates.
Credit revolvers are more brand conscious than transactors and more interested in the values of the companies they patronize. An emphasis on the values and mission of the financial institution in marketing campaigns should help to attract and retain credit-revolving consumers.
Differences even exist in how revolvers and transactors choose the brands they buy. Revolvers are 163% more likely to value brand name and brand status when compared to transactors. Transactors lean in the opposite direction, as they are nearly 40% less likely than the average population to value brand name and brand status.
Brand loyalty from revolvers comes easier when values align or when they get an incentive. Predictive data suggests that, compared with transactors, revolvers are 105% more loyal to retail brands when beliefs align and 74% more loyal when they benefit from a reward or points program.
Based on this insight, banks, credit unions and others would do well to create an attractive loyalty program to encourage revolvers to use their credit cards more often. This could include offering exclusive perks, discounts, and other incentives that are only available for higher-spending customers.
An emphasis on the financial institution’s values and mission in marketing campaigns also should help to attract and retain revolvers. This could include highlighting a commitment to sustainability, social responsibility and community engagement.
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Optimizing Marketing Strategy with Predictive Data
Banks have first-party data that indicates whether a customer is in the credit-revolving or credit-transacting category. By appending predictive data and insights to first-party data, bank marketers can get a better understanding of who people are, what they do, and even what they are likely to do in the future. Data points such as these can drive powerful use cases such as:
• Customer Segmentation: By segmenting customers based on behavior, financial services companies can craft targeted and relevant messages that resonate. This is especially important when key information like income and age may be missing.
• Targeted Advertising: When leveraging the insights gained from analyzing consumer behavior, businesses can tailor their advertising campaigns so that ads are served to the right audience on the right channels based on their interests, behaviors, demographics, and other characteristics.
• Product Personalization: By analyzing consumer data, marketers can identify actionable preferences and needs in order to match products to the right customers. Marketers can focus on creating a positive customer experience by offering personalized service such as simplifying the account opening process, providing relevant resources and educational materials, and identifying cross-sell opportunities.
• Predictive Modeling: With the right data, data scientists can build statistical models leveraging predictive analytics to forecast customer behavior, identify high-value customers, and optimize relationship management strategies based on consumer behaviors like credit revolving and transacting.
The need to examine and fully understand all aspects of consumer behavior — even those behaviors that may not seem important — is growing in banking and beyond. With predictive data that goes beyond traditional, known, first-party data points, marketers can focus on actions that produce results and provide the seamless, personalized experiences expected today. In the absence of income as an indication of spend level or in the absence of age or household composition data to explain potential preferences, nontraditional data points such as credit-revolving and credit-transacting behavior can offer predictive insights that help uncover other valuable and actionable conclusions.
About the author:
Theresa Blue is director of strategic partnerships for the predictive data company AnalyticsIQ. She has more than two decades of experience working in marketing data and analytics.