Influencing a customer’s decision to choose a specific credit card for any given transaction from her wallet can be challenging for card issuers. In the typical lifecycle of managing a bankcard customer, driving activation and usage is one of the most critical aspects of engagement.
This is indeed a challenge for issuers, because consumers have many options available to help them meet their purchasing needs. In the fourth quarter of 2014, for example, 32% of U.S. consumers were active on all open cards in their wallets, while only 24% were active on only one card. In a nutshell, consumer activity on multiple cards at the same time is a challenge for lenders who are striving to be the most-used card in the wallet. There’s even a nomenclature developed to discuss this dimension of loyalty; the card that receives the largest share of a consumer’s spend is referred to as the “top-of-wallet” card.
It’s all about revenue. Attaining a higher share of a customer’s spend generates incremental interchange (per-transaction revenue), along with potentially more interest income from revolvers, or those who carry a balance from month to month and therefore get charged interest. Card issuers spend millions of dollars annually to engage existing customers in order to attain a higher share of spend, all with the goal of driving incremental revenue.
With an increasingly complex marketplace, the pressure to do this effectively has increased over time. Yet getting the right data for the job can be tough. Here’s a new perspective on the top-of-wallet customer gained through enhanced data.
Wallet Share Segmentation
Traditionally, card marketers try to answer two questions when evaluating a customer for a marketing treatment: first, how much does this customer spend on my cards, and second, how much of the customer’s total spend does that represent? Usually, issuers look to two specific data elements to answer these questions: historical “on-us” spend and wallet share. On-us spend is available from the issuer’s own masterfile data, while wallet share calculations could be obtained in part from one of the networks (such as MasterCard or Visa) or from a data aggregator, like Argus Information and Advisory Services, LLC.
With these data, lenders might segment their customers in a myriad of ways. The following is an example of a segmentation along the wallet share dimension:
100% wallet share: Loyalists who use one card for 100% of their needs.
High wallet share: Customers who spend 70% to 99% on one card.
Medium wallet share: Those who use one card for 50% to 69% of their purchases.
Low wallet share: The least engaged group of customers, who spend less than 50% on any one card.
A segmentation along the on-us annual spend dimension might be as simple as a three-tiered approach: under $1,000; at least $1,000 and up to $10,000; and over $10,000. For illustration purposes, we’ll use the four wallet share tiers and the three on-us spend tiers defined above to build a 12-cell segmentation framework. Once the marketing segmentation has been determined, treatment strategies must then be identified. For this illustration we’ll limit ourselves to five different treatments:
No treatment: Consumers who demonstrate low levels of annual spend on cards overall are usually not considered for a marketing treatment because it’s usually not worth the expense.
Product migration: Loyalists who incur low levels of spend may be considered for a different product such as a debit card. This is often driven by the belief that maintaining a relationship with the customer is important, even if that relationship is not through a credit card product.
Retain/maintain: For consumers who use one specific card for 100% of their spend and are medium to high spenders overall, issuers work to retain loyalty with superior customer service and thank you notes, and other creative and cost-effective ways to retain the loyalists.
Selective offers: For customers who use one card for a high ratio (but not 100%) of their spend, card issuers provide selective marketing offers to incent the customer to allocate a higher proportion of spend to the on-us card.
Aggressive offers: Customers who use a specific issuer’s card for a low-to-medium share of their overall purchase needs but have high overall spend might fall into an aggressive offer bucket. There is a higher level of economic opportunity available from less engaged but high spenders to shift wallet share toward the marketer’s card.
TransUnion recently conducted a study using a sample of 9.5 million depersonalized consumer credit records. We observed card preferences and spend dispersion between all general purpose credit cards in the selected consumers’ wallets over a two-year period to analyze performance.
This analysis was performed with the top-of-wallet lender as the incumbent. One could easily perform this same analysis from the perspective that the top-of-wallet card is off-us. Here’s how it worked: Suppose you are a card marketer trying to determine, based on 2013 data, what marketing treatments to apply to each of your customers in 2014. Assume that you have three marketing treatments available to you: an aggressive offer that will cost you $200 in marketing spend, a selective offer that will cost you $125, and the no-treatment option, which will cost you nothing.
Using the traditional approach defined above, our sample looked like this:
2.4 million consumers were loyalists who used one specific card for 100% of their needs. We did not include these consumers in our marketing matrix, as we already have their loyalty.
3.3 million consumers fell into the high wallet share category because they spent 70% or more of their purchases on one card.
Another 3.2 million consumers used one card for 50% to 69% of their purchase needs.
The least engaged group of consumers, who diversified their spend across multiple cards, made up 600,000 of this sample.
Using our traditional marketing strategy, 15% of these “customers” would be bucketed into an aggressive offer, 78% would receive a selective offer, and 7% would receive no active marketing treatment. The effective cost of marketing would be $128 per consumer treated with an offer. This benchmark cost gave us a starting point that we believe we could reduce with our enhanced, multi-dimensional credit metrics.
Redefining the Customer—Enhanced Credit Data
Credit data have made tremendous progress over the past few years. We have more comprehensive reporting on the credit file, including actual payment amounts. Our credit file has also been transformed from a static view to a dynamic view of the consumer, with history in key metrics over time. These enhancements have afforded us tremendous insights, such as clear identification of revolver behavior, as well as much more powerful trend analysis capabilities.
Enhanced credit data allow us to understand how consumers spend and revolve balances on other cards in their wallets. To evaluate the benefit of our advances in data reporting in this marketing context, we performed a simulated marketing exercise using enhanced credit data for the same sample of consumers as above. Leveraging off-us spend and off-us revolving balance metrics, we were able to assign a gross revenue opportunity to each consumer. In other words, how much incremental revenue could I receive if I successfully convinced the customer to put all of her spend on my card? We assumed an interchange rate of 1.85% and an effective annual interest rate for revolving balances of 12%.
We then segmented the sample into the same offer categories discussed earlier: aggressive, selective and no treatment. To do so, we assumed we had to meet a net revenue marketing threshold of $25. Given that the lowest offer expense is $125 (selective offer cost), lenders may not market to any consumer who has a gross revenue opportunity of less than $150. A consumer who offers a gross revenue opportunity of $225 or more would be qualified for the aggressive offer that costs $200. And, all consumers with a gross revenue opportunity in the $151 to $225 range would be qualified for a selective offer treatment.
The results demonstrated a dramatic shift from traditional marketing treatments and are summarized below:
The traditional 15:78:7 mix shifted to a 24:25:51 mix between aggressive, selective and no treatment respectively. As a result, the effective cost for the redefined marketing strategy dropped to $79. Delivering a higher return on investment (ROI) by marketing more relevant offers is a great step forward.
Importantly, 54% more consumers qualified for an aggressive offer in the enhanced approach versus the traditional approach.
And, 12% of the consumers who would not have been eligible for a treatment now are qualified for an aggressive (4%) or selective (8%) offer.
In other words, you spend less overall per-treatment, but you also offer more consumers an aggressive, top-tier offer. Marketing return is achieved by extending economically feasible offers to consumers who have the potential to become high-value consumers. Opportunities and savings can both be delivered by utilizing this redefined approach.
To validate that these consumers were bucketed into relevant segments based on 2013 performance, we followed spend behavior for on-us and off-us cards for this sample over the course of 2014. The results were compelling.
We found that customers who were traditionally bucketed into an aggressive offer actually represented average annual gross revenue opportunity ranging from $161 to $323 based on their 2014 spend and revolving balances.
Under our redefined approach, customers receiving an aggressive offer presented a much higher and narrower range of annual revenue opportunity, with averages ranging from $313 to $337.
Similarly, customers who merited no treatment under the traditional strategy presented revenue opportunities ranging on average from $188 to $337. In contrast, under our redefined strategy no treatment customers presented average revenue opportunity ranging from $161 to $188.
Traditional measures of top-of-wallet are only indirectly able to estimate revenue opportunity. Enhanced credit data allow us to redefine the top-of-wallet consumer in a manner that more directly yields revenue opportunity insights, allowing lenders to segment customers into more effective offers. This can be a powerful tool in the search for strong marketing returns in our complex and dynamic lending marketplace.
Ms. Verma is director of research and consulting in the Financial Services unit of Chicago-based TransUnion. She can be reached at [email protected]. Mr. Becker is senior vice president of research and consulting in the same unit and can be reached at [email protected].
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