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Driving Better Results for Credit Marketers Through the Use of Alternative Data

  • Stephen Gausch

    Stephen Gausch

    Consultant Senior Director

Created at July 29th, 2019

Driving Better Results for Credit Marketers Through the Use of Alternative Data

In a previous blog post, I outlined the continued prominence of direct mail as a leading new account acquisition channel for personal lenders and credit card issuers.  This is primarily due to the tight integration between direct mail and the prescreening process, which leverages consumer credit data maintained and managed by the three credit bureaus and allows lenders to deliver consumers pre-selected or pre-approved offers of credit.  

To illustrate, research by Mintel Group indicates that in April, more than 75% of all direct mail credit card solicitations were prescreened.  While this statistic varies slightly from month to month, it’s clear that prescreened offers represent a significant majority of personal loan and credit card offer mail volume.  And this is with good reason, as consumer credit data, whose use for credit marketing purposes requires lenders and issuers to extend firm offers of credit, is the most powerful, predictive and valuable data at issuers’ disposal.  It’s unparalleled at identifying which consumers are most likely in-market for a new loan or card, gauging a consumer’s risk profile, and understanding potential profitability. However, issuers and lenders can drive even better marketing results when they combine the use of credit data with other alternative, non-credit- based data

What is alternative data for consumer credit marketing?  

For our purposes, alternative data can be defined as consumer information generated from sources other than a consumer credit reporting agency or credit bureau.   This data includes information such as demographics; lifestyle and psychographic data; property and homeownership information; product purchase, usage and propensity data; and brand and channel preference information. 

Typical demographic data may include attributes such as age, estimated income, and presence of children in the household.  Lifestyle data focuses on a consumer’s avocations, interests and hobbies such as gardening, sailing, or golf. Property and homeownership data indicates whether the consumer owns a home, when it was purchased, the purchase price and how much was borrowed.  Product usage and purchase data identifies the type of consumer goods the individual tends to consume, such as electronics, high-end fashion, or cruise packages. Brand and channel preference indicates whether the consumer, for example, prefers American or Southwest airlines or banks on-line or mostly via the branch.

This type of alternative data is available from a multitude of private and public sources and is most efficiently acquired from a firm that specialize in compiling and aggregating this data from a multitude of sources.  Much like consumer credit data, alternative data is maintained at the consumer (individual) or household level and resolves to a specific name and address. A typical high-quality provider of alternative data will maintain data on virtual every adult consumer in the country, more than 200 million records. 

The complementary nature of credit and alternative data for credit marketing

While credit data has proven powerful for targeting prospects based on predicted responsiveness, revenue potential and creditworthiness, it does have its limitations. It provides little insight into the profile of the consumers who are your most desirable prospects.  What do they look like, what is motivating them to respond to your solicitation or offer, how and why are they most likely to use your card or loan, and what marketing messages resonate best with them? Attempting to answer these questions based solely on the prospect’s credit data attributes such as bankcard utilization, or total available credit or number of 30-day delinquencies, will prove challenging at the least.  However, if marketers also know the prospect’s life stage, dominant lifestyle, affluence level, brand preferences, disposable income and purchase propensities, they can much more effectively tailor the creative and messaging to the individual prospect or a segment of similar prospects. 

In this manner, credit data is used to quantify the desirability of the prospect from a financial perspective, and alternative data is used to develop and extend the marketing message that will most compel a response.

Leveraging alternative data to create segmented creative and marketing messages

As an example, consider marketing an unsecured personal loan.  Research has shown that two of the most common motivations for consumers to pursue a personal loan are debt consolidation and home improvement projects.  In addition, the research has shown that consumers with sub- or near-prime credit are much more likely to use a personal loan for debt consolidation, while prime and prime-plus consumers are significantly more likely to use the loan for home improvements.  A lender might employ two creative executions, one focused on debt consolidation, the other on home improvement and target the prospect universe accordingly based on credit score. While this approach will prove more effective than a single message and creative for all prospects, it still may be ineffective for a high net worth empty-nest couple with prime credit that no longer owns a home and now rents.  In this case, a message focused on taking the loan for international travel or the vacation of a lifetime may prove even more effective. 

Even for prospects within the same segment, messaging and creative can be tailored and distinct based on the prospects’ alternative data profile.  Extending the example above, consider two prospects, both homeowners with prime credit. Both will receive an offer for a personal loan, and the messaging for both will stress using the loan for home improvements.  However, if one of the prospects is determined, based on property data, to have owned the home for a dozen or more years, maybe the message focuses on replacing and upgrading dated furnishings and décor, while for the other who purchased the home as new construction within the past three years, the message focuses on using the loan to add new features and amenities like a deck or patio.

Reaching underserved prospects

Another valuable use of alternative data for credit marketers is to reach prospects who they typically, albeit inadvertently, ignore – credit bureau opt-outs.   These are consumers who have proactively contacted one or more of the major credit reporting agencies and requested they be excluded from receiving prescreened offers of credit.  Estimates of the size of this population range from 30 to 60 million consumers.  

While these consumers cannot be solicited with prescreened offers generated from credit bureau data, it is permissible to solicit them with invitation-to-apply offers, using data from other non-regulated sources.  Given the fact these consumers have specifically requested to be excluded from receiving new credit offer solicitations, you would think they would be highly unresponsive to these offers. However, work Acxiom has conducted for a number of our credit card and personal loan clients indicates that, quite to the contrary, selective targeting of opt-outs can generate very favorable results.  This likely is due to the fact that opt-outs receive fewer offers than opt-in consumers and give more consideration to offers they do receive.

The process used to identify and target this underserved segment includes resolving consumer identities across a credit marketer’s prescreen and ITA marketing universes, and leveraging alternative data to drive models that segment and target those prospects most responsive to an offer and most likely to be approved.

Augmenting credit data-based models with alternative data

In a similar fashion, issuers can also use alternative data to augment or supplement the credit data they use in their prescreen marketing models.  While generally less powerful in these specific applications than credit data, alternative data can “round out” predictive prescreen scores and add incremental improvements in performance.

As an example, travel propensity data may be useful in adding incremental lift in models targeting a rewards card that is heavy on travel and entertainment rewards.  Or data indicating a household has one or more near-adult age children could add value in models targeting supplemental card or authorized users marketing campaigns.

Driving targeted digital display advertising

Research by Mintel Group indicates that as far back as February 2018, over half of all consumers who opened a new credit card account obtained the offer from a digital source.  Additional research from Winterberry Group forecasts on-line media spending to increase by more than 15% in 2019 versus 2018. Clearly, card issuers and lenders are continuing to increase their investment in digital advertising for both new account and existing customer marketing.  

That said, credit marketers seeking to conduct prescreen display advertising for new account acquisition face somewhat of a challenge, as most available solutions lack the scale and scope lenders desire and require.  As a result, many have turned to alternative data and invitation-to-apply offers in the digital display channel. They are integrating both off-line and on-line data to target and reach their desired audience of responsive, engaged and creditworthy consumers at greater scale and with more flexibility.

In summary, while the uses and applications of credit data are undeniable for credit marketers, integration of alternative non-credit data sources enable even more powerful and effective direct-to-consumer marketing in both off-line and on-line channels.