The data imperative for credit cards

Over the past dozen years, numerous US regional have relaunched consumer card programs on a self-issued basis. At the outset, the growth component for many of these relaunch strategies relied heavily on branch channels, customer loyalty and the desire to consolidate banking relationships. In recent years, the banks’ credit card programs have been plateauing to low, single-digit growth rates without obvious incremental prospects for growth in accounts, spend and balances. Although credit card portfolio health and returns continue to be favorable, without the ability to demonstrate further stepwise growth potential, these programs are at risk of atrophy in key areas, such as attention from senior executives and ongoing investment in innovation.

Often, the keys to reinvigorating growth include identifying and addressing root-cause growth inhibitors (which often relate to approval rates, credit line assignment and service experiences), and finding ways to digitize and integrate customers’ credit card experiences with those of their overall banking relationships. exhaust created by these card programs and other players in the payment value chain could hold a secret to vast amounts of information value to unlock growth opportunities.

Card issuers and, in particular, the payments industry generally have been early adopters of data-driven insights to grow their business; and rightly so, since the industry generates a massive volume of data. Banks are increasingly recognizing and reaching the point at which they need to drive innovative applications of the insights in functions that traditionally do not leverage them fully or consistently—for example, for enhancing customer experience or devising new product strategies.

In addition, as depicted in Figure 1, prospect and customer segmentation can be a key component of focusing growth strategy investments on areas of greatest opportunity. For instance, segmentation can help a bank determine areas for product refinement to both improve experiences for existing credit cardholders and tap into unserved or underserved markets. We also see segmentation as the prudent way for many banks to carefully venture outside of their existing retail banking customer bases through twinning analysis to identify characteristics their most profitable cardholder segments may share with non-relationship prospect pools.

Figure 1. Actionable segmentation driving key customer/prospect insights
The data imperative for credit cards fintech
Source: Accenture research and analysis

Card issuers see only one dimension of customers. However, there is significant information asymmetry with other players in the value chain, namely, payment networks, merchant acquirers and merchants. Issuers capture data about and cardholder details only, while merchant acquirers see details on merchants and transactions, merchants collect data on their customers and purchase basket, and the payment networks record data on movement of funds between these players and authorization tokens. Building a cross-payment cycle data view allows creation of rich micro-segments for hyper-personalization (Figure 2). It also enables banks to conduct merchant, store and product-level marketing studies, generate early warning indicators for fraud and delinquency, and create visibility into customer and industry trends.

Figure 2. Types of data captured and analyzed across different parties involved during the payments process
The data imperative for credit cards fintech
Source: Accenture research and analysis

Collection, cleaning and deciphering this data exhaust is an onerous task. However, advancements in artificial intelligence capabilities, like machine learning and Big Data, is making it easier and faster than ever before.

Capabilities, such as Accenture’s Intelligent Enterprise Platform that sits on top of the Accenture Insights Platform allows banks to layer third-party data from social media, web browsing and geo-tagging over the payments data. This further deepens card issuers’ understanding by manifolds around customer needs and behavior. It’s opening previously unimagined use cases, like real-time mood-/persona-based recommendations, geo-tagging and location-based offers to customers.

Looking forward, we anticipate that a cross-payment cycle data ecosystem together with machine learning will play a broader role in how banks generate new growth in accounts, spend and balances, as well as how they harvest value in their credit card programs.

We invite you to read about data as the new ecosystem currency in our report, The New, New Normal: Exponential Growth

Special thanks to Sanjay Ojha for his insights, as well as Rajat Mawkin and Uday Gupta, who also contributed to this blog. 

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