Data commerce is the next big revenue opportunity for banks. We’ve already seen some of the most innovative global banks get a head start on this, driving hundreds of millions in revenue with this new data commercialization business model. As Jim Marous writes in The Financial Brand, “Banks and credit unions that recognize the value of competing with data and analytics across the entire organization will be the best positioned to realize the full benefits of digital banking transformation.”

Household names like JP Morgan, Citibank, Barclays, and BBVA are already seeing success with data monetization. And they’re managing to do so in a way that doesn’t compromise sensitive customer data. Banks that aren’t actively pursuing data commerce are bound to be left behind. Let’s take a look at the opportunities and threats at play.

Challenges facing the banking sector

Banks are facing unprecedented challenges. Let’s look at a few of them:

  • Fierce competition from innovative and agile fintechs and challenger banks
  • High degree of regulatory control — and the necessity of keeping sensitive information out of the wrong hands
  • Data silos within organizations. This results in many data-related issues, chief among them being a lack of a unified customer view.
  • Pressure to increase margins and identify new revenue streams

Acceptance and appetite

This may surprise you, but an overwhelming majority — 97%, according to Fujitsu European — of banking customers are happy for their data to be used in exchange for better quality of services. If we look at business customers of banks, over 70% of small and medium-sized businesses (SMB) want customer insights from their bank.

Clearly, there is an appetite for valuable insights built on bank data, and an acceptance from customers that their data will be used, as long as it drives valuable experiences. Examples of monetizable data-driven insights abound in the industry. Referring to consumer card transaction data, the Managing Director of SMB at JP Morgan Chase said, “Small businesses can use this [card] data to figure out exactly where to set prices and store hours and staffing.”

At another major US bank, transaction data is used to generate models for accurate demand forecasting. A major fast food retailer combines this transactional data with its own data in order to make better operational decisions (inventory, opening hours, personnel, etc.) at both individual store and regional levels. This has resulted in significant improvement in demand accuracy, fewer inventory stockouts, and less excess inventory wasted. And it didn’t hurt their bottom line either, with $15 million of extra revenue gained through this process.

Making data a priority

At JP Morgan Chase’s 2022 Investor Day, they announced that one of their six strategic priorities for the coming year is to “Leverage data and technology to drive productivity and agility.” They are clearly not alone, with a number of innovators across the sector putting particular emphasis on their data strategy.

According to Accenture, banks can increase revenue 1-2% through data monetization. To put this in perspective, in the US alone, the top three banks average over $100 billion per year in revenue. Let’s look at a few of the key ways that banks can develop their data value proposition:

Acting as a data merchant. This can quickly generate revenue streams, but in order to maximize margins, banks need to focus on high-value data products rather than commoditized bulk data. Banks would be well-served to follow the approach of companies like Moody’s Analytics and build a strong core offering. Doing so unlocks several key value propositions:

  • The capacity to cross-sell and up-sell data products and services
  • Discovery of customer use cases, leading to continually more valuable data products. This virtuous cycle enhances the product-market fit of the data products.
  • Healthy margins due to owning the customer relationship

Providing insights. The key to realizing the value of banking data is providing actionable insights, not the data itself. Valuable models and insights are drawn from aggregated data and can be delivered without exposing sensitive information. Trends and benchmarks can be a great way to share valuable insights.

Customer offers. Accenture identifies this as the model with the highest potential value. However, this comes with a high level of risk and requires sophisticated data capabilities, which may be out of reach of highly siloed banks. As banks are some of the most trusted consumer brands out there, they need to be careful to have robust data governance and technology measures in place. Still, if done well, the reputational risks can be mitigated.

Internal optimizer. This can be considered a business-as-usual activity, yet with significant upside in terms of informing up-sell, cross-sell, and cost optimization. Many banks are doing this, and some of the same capabilities that enable this internal optimization can also unlock external monetization.

A bright (and profitable) future

Banks that embrace data commerce are already seeing encouraging returns. Yet there is much untapped potential, and those that develop their data commerce capabilities will be well-placed to capitalize on the possibilities. With proper technological and organizational frameworks, data monetization should develop without risk to reputation and data leaks. To learn more about how banks are capitalizing on the future with data commerce, get in touch.