Data-driven payments: How financial institutions can win in a networked economy

Electronic payments have essentially become a commodity for financial institutions, and most recent innovation has come from new market entrants, which are capitalizing on mobile platforms, social media and other technology. Financial institutions can capture this opportunity as well, provided they can leverage the wealth of data they control.

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Data-driven payments How financial institutions can win in a networked economy


Chicago Mark Flamme Partner +1-312-578-4530 mark.flamme Kevin Grieve Partner +1-312-578-4768 kevin.c.grieve Mike Horvath Principal +1-312-578-4519 michael.j.horvath



About the authors

Kevin Grieve is a partner with Strategy& based in Chicago, a member of the firm’s financial-services team, and head of the North American card and payments practice. He has more than 25 years of consulting and business experience in financial services. Mark Flamme is a partner with Strategy& based in Chicago and a member of the digital business and technology practice, where he specializes in the financial-services industry. Mike Horvath is a principal with Strategy& based in Chicago and a member of the firm’s financial-services team.



Executive summary

Over the past 30 years, the electronic payment facilitation services that financial institutions (FIs) offer have essentially become a commodity, with limited upside and growing regulatory, competitive, and pricing pressure. Meanwhile, most of the recent innovation in payments has come from new market entrants. These companies are creating a wave of new offerings — capitalizing on mobile platforms, social media, and location-based technology to expand their presence along the consumer’s entire path to purchase and capture a greater slice of each transaction. FIs can capture this opportunity as well, provided they can access and leverage the wealth of proprietary transaction data that they control — coupled with data from external sources — to generate a clear marketing ROI for their merchant customers. Doing so will require that FIs address four important attributes: (1) rapid, responsive access to data throughout the business; (2) coherent data architecture; (3) a culture of data-driven decision making, rather than intuition; and (4) data-savvy talent. The right approach — including all four elements — will help FIs make the most of data, allowing them to create new value propositions for both merchants and consumers, and increase their payments revenue and extend their existing payment franchise.



A changing world of payments

The U.S. payments industry is flourishing, currently processing more than 200 million transactions per day, according to the Federal Reserve, and generating more than US$180 billion in annual revenue. Yet the industry has optimized electronic payments facilitation for credit, debit, and prepaid accounts during the past 30 years to the point that such facilitation — whether payment acceptance, authentication, authorization, settlement, charge-back processing, or account statement generation — has essentially become a commodity. Even the 1 to 3 percent transaction fee that financial institutions (FIs) now earn for payments facilitation faces ongoing competitive and regulatory pressure. The Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank) has already shifted $8 billion away from FIs to retailers by placing caps on debit interchange rates, according to Forbes. Compliance with other aspects of Dodd-Frank is further costing large FIs as much as $34 billion annually, and FIs have cut their credit card lending by $70 billion.1 Simultaneously, dozens of non-FIs have entered the market with new value propositions, innovating beyond traditional transaction processing to offer pre- and post-purchase services and causing leading payments players to reexamine their current offerings (see Exhibit 1, next page). Milo, for example, performs online searches for specific products in stores near its users, offering product ratings, reviews, and price comparisons. RedLaser allows consumers to scan a product’s barcode in the store and immediately uncover the cheapest price for that product, whether online or at nearby retailers; users may then purchase the product online or visit another merchant. LevelUp offers post-purchase mobile loyalty and reward services. Major retailers are getting into the game as well. In early 2009, Starbucks launched a highly successful mobile payments and loyalty app that reduces the payment transaction fees the company must pay to FIs; the app now accounts for 15 percent of all U.S. retail sales. Walmart recently launched its Savings Catcher price comparison service, which matches the price of any local competitor’s identical product advertised in a print ad, giving the customer the difference. Apple’s Apple Pay
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mobile payment service, available on iPhone 6, uses near-field communication antennas to allow payments with just a finger on the Touch ID sensor. Apple Pay has already shifted a portion of interchange fees to Apple, will act as a potential catalyst for a new category of mobile interchange rates, and is expected to be a platform for future innovation.

Exhibit 1 Recent payment innovation is moving beyond the transaction into other aspects of the customer’s path to purchase
Generate demand Find local merchant Compare local merchants

Decision making
Contact/ arrive at store Decide on purchase


Review business/ tell friends Loyalty/ decide to return


Groupon: Helps merchants drive demand through targeted/ large audience discount offers Edo Interactive: Helps merchants find new customers, or reengage and build loyalty with existing customers, through an Edo card-linked marketing program

Cellfire: Users can sign up for deals and receive coupons triggered by their location AroundMe: Users can find nearby merchants using location-based searches

Yelp/ Google Maps: Users can get reviews, contact info, directions, etc.

ShopSavvy: Users can scan barcodes for comparison with other merchants/ products

Uber: Reduces payment friction; simplifies the process for drivers and customers Starbucks: Mobile wallet with stored value card reduces payment friction

LevelUp: Reduces cost of transaction, which allows business to pass along savings to customers in the form of discounts and loyalty rewards Belly: Provides merchants with personalized loyalty programs; allows merchants to track customer data and launch targeted media campaigns

Source: Strategy& analysis



Fueled by data

Most of these new services appearing in the payments space, from search through post-transaction, are being fueled by the vast and complex data sets now generated by social, location-based, and mobile technologies. Successful players are aggregating, analyzing, manipulating, and combining this data with existing transactional information to offer innovative value-added services to consumers. They are also gaining an improved understanding of the consumer, which they are leveraging to help merchants grow their businesses — whether boosting prospecting through the delivery of targeted offers or improving retention with the creation of next-generation loyalty and reward solutions. As pressure on traditional payments revenue mounts, FIs have the opportunity to leverage their own wealth of cross-merchant consumer transaction data, in combination with data from external sources, to provide enhanced solutions — offering services across the value chain while improving their core operations. This abundance of data, like a company’s brand and its culture, is one of the few proprietary assets available to a business today. Though the future is yet to be written, several incumbent FIs are already using this invaluable proprietary data asset to advance their core payments business, boosting customer acquisition through improved targeting, underwriting, cross-selling, and up-selling. An added benefit to FIs of leveraging their data as an asset is that data as an input and factor of production will cost substantially less in the future, with the price performance of data storage doubling every 12 months. In 1980, for example, the hard-drive cost per gigabyte (GB) was approximately $1 million; today, it is less than 10 cents. In fact, Google charges just two cents per month for 1 GB of storage.2



New opportunities

When Jack Welch was running GE in the early 1980s, he famously challenged his managers to define their business as only 10 percent of a broader market, looking at adjacent markets for new growth opportunities. As FI leaders look to the external market for new datadriven revenue opportunities, they would benefit from taking this same perspective, challenging themselves to think of a world of zero interchange and then imagining how they will create value going forward. As Exhibit 2, next page, shows, potential options include payments for audience, impressions, click-through, and purchase. In this effort, FIs should first look to aid merchants and their chief marketing officers (CMOs) in the pursuit of their perennial “holy grail”: traceable and precise marketing ROI. Without that, CMOs may hesitate to explore new and innovative services. CMOs have made progress in the online world — where a consumer’s digital path to purchase can more easily be tracked. However, in the offline world, in which 90 percent of retail transactions take place, CMOs have struggled to link a given marketing stimulus to a specific purchase transaction. In the future, this situation will begin to change when two recent payment innovations gain scale, together enabling accurate marketing ROI attribution. The first is from Square, a San Francisco–based payments company that has effectively moved the retail point-of-sale into the mobile cloud, blurring the lines between “click” and “brick.” The second is mobile payments from Google, Apple, and others that allow a consumer ID to be linked directly to a transaction ID, thereby closing the marketing loop between stimulus and sale. Niche players are already forging a path to measurable ROI. Mocapay, for example, manages and tracks mobile campaigns at the individual consumer level, combining mobile payments and mobile marketing on behalf of merchant brands to drive triple-digit marketing ROI. Scale players, too, are developing new and innovative services, such as JPMorgan Chase’s “intelligent solutions,” created to offer its customers products based on data and analytics. In addition, MasterCard has acquired Truaxis, a provider of credit and debit card–linked offers to
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Niche players are already forging a path to measurable ROI.

consumers through merchants and financial institutions, to provide combined payments and commerce propositions to merchants. Before an FI can enter the market with new data-driven service offerings or ROI metrics, however, it must first address key challenges inside the business relative to leveraging its data assets.

Exhibit 2 Greater engagement during each stage of an online transaction can lead to greater revenue for financial institutions

Pay for audience
Pricing based on circulation, ratings, viewer demos, etc. Examples include websites for CNN, Fox News, and MSNBC.

Pay for impressions
Pricing based on digitally measured views of an ad. Examples include AOL, Facebook, Google, and Yahoo.

Pay for click-through
Pricing based on measures of engagement — e.g., actual clicks of the ad (typically offered with payfor-impressions models). Examples include Bing and Google.

Pay for purchase
Pricing based on verifiable top-line impact driven by precise targeting and analytics. Examples include Amazon, Groupon, and OpenTable. – Improved data collection capabilities at point-ofpurchase – Usage and preference data coupled with location information to personalize future deals

Conversion rates (% of views)

0.9 –1.5%
Revenue potential (% of sales)




1– 2%




Source: Strategy& analysis



Four key challenges

Despite their scale and access to massive amounts of transactional data, FIs have not fully exploited their data assets in today’s information-based, networked economy. Historically designed, built, and operated in a linear fashion, they have tended to hoard their data in functional silos. Information flow between systems has been narrow and unidirectional. In addition, factors such as channel and product proliferation, geographic expansion, multiple payment methods, purpose-built IT applications, proliferating customer segments, and company acquisitions have made it difficult to create a single view of a customer across the organization. As a result, these institutions face four key challenges (see Exhibit 3, next page). Limited access The first challenge for traditional players attempting to leverage their wealth of data will be poor data access, as data is typically brokered and delivered exclusively by IT. Business users do not generally have easy access to timely, accurate, and relevant data; instead, they are left to run the business on static, historical reports. Direct, ad hoc queries and data analytics by business users are generally not well supported. If we were to locate a traditional FI along a spectrum of analytics capabilities from hindsight to insight to foresight, as shown in Exhibit 4, page 14, it would likely appear in the lower left or center portion of the chart. To improve their data access and analytics, traditional FIs can follow the example of their more data-ready peers, which have developed detailed and business-friendly classifications for their data assets across the full organizational ecosystem. One leading payments business created a taxonomy of data categories that helped it understand the entirety of the complex data landscape in which it operated. In fact, the company filtered approximately 5,000 data elements down to 54 data types, then narrowed those to the 12 most critical types for immediate usage. As a result, it was able to cut back dramatically on the number of terms used to describe each aspect of its operations and begin to speak consistently
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Exhibit 3 FIs face four key challenges in leveraging information to improve payment offerings

Limited access

Business users not empowered to access the full range of data themselves; business does not support the evolving nature of data access and analytics

Data architecture and environment

Complex data environment, with siloed data and complex repositories, leading to inconsistent information across business units

Data-driven culture

Tendency to make decisions through intuition rather than through fact-based evidence

Talent gap

Fundamental holes in the talent profile of business and IT roles required to keep pace with advances in data technology

Source: Strategy& analysis



Exhibit 4 An analytics capability requires people skilled as analytics producers and consumers in using data for directed analytics as well as free-form discovery


m an ag er s

an d

ec ut iv es


Analytics consumers


se ni

st ry

an al ys ts







Advanced analytics (mining, models, optimization, simulation)






st Bu om s er ines s, s an un d its su , pp lie r

s an ines al s ys in ts fo r







Interactive reports





Low Low
Reporting Monitoring Investigation Prediction


Static reports







Analytics producers








Dashboards and scorecards





Business insight



Ad hoc queries and online analytical processing






A m nal od yt el ica er l s






Analytics capability
Source: Strategy& analysis



and clearly across functions, offices, and geographies — simplifying the capture, storage, and retrieval of relevant business data. Some institutions have also developed business-friendly user interfaces for their data repositories: American Express, for example, set a goal of allowing its executives to address 80 percent of their business data needs in a self-service manner.3 Data architecture and environment Data management is typically diffused throughout a given financial institution. As a result, data repositories tend to be complex and fragmented, making it difficult to analyze cross-functional, crosschannel, cross-product, and even single-customer views. By contrast, the organization’s other proprietary assets, such as its brand, tend to be highly managed. Brand architects and brand governance are in place to determine where, when, and how the brand can be used, reducing complexity and confusion both inside and outside the firm. Given the data-based opportunities available today, FIs should begin to treat their data as they treat their brand, introducing rigorous management, employing data architects, and measuring and managing data quality. In the current environment, there may be 17 different repositories for the same customer information, but in the future, FIs will need to develop a single source of truth — one that is designed, built, managed, and maintained to give executives a real-time, horizontal view of their business. Data-driven culture Even a quick look at an organization can provide a view into the ways in which everyday tactical business decisions are made — decisions that, on a cumulative basis, will either enable or impede the organization’s long-term strategic plans. If decisions in a recent meeting were based on “hippo” — the highest-paid person’s opinion — for example, the organization may have a way to go. Shifting to a more data-driven culture will take time, and for most, it will begin with incremental behavioral changes. Amazon, for example, made its data central to all business initiatives by embedding data analytics usage into its goals, with product managers expected to use data analytics to inform their business decisions.4 Capital One trains its business executives on its information-based strategy and performs scientific testing on a massive scale to tailor its products and services.5

FIs should begin to treat their data as they treat their brand, introducing rigorous management, employing data architects, and measuring quality.



Financial institutions will eventually need the ability to do extensive experimentation, making decisions based on real-time consumer data and analysis rather than static or intuitive information. Starting at the top will help, rewarding data-driven decision making over intuition. IT can assist the effort by embedding data-savvy employees directly into business units. Eventually, users should begin to view data and analytics as accessible and beneficial, incorporating them into their day-to-day activities and beginning to think of their work as a contribution to the entire enterprise data environment. Talent gap To abet this cultural shift, institutions should hire data analysts and data scientists who are comfortable in a data-centric world. GE, for example, has built an analytics center in San Ramon, California, and hired more than 1,000 individuals with skills in data science, product development, and technology to focus on data analytics applications for the industrial Internet of things.6 Yet most FIs currently lack the skills to develop an optimal data environment, with traditional talent and hiring programs coming up short. They will need to retool the existing business, bringing in younger and more flexible talent who can look at the business in a new light. In addition, they should increasingly focus on quantitative skills such as statistical modeling and analysis, even for business roles such as marketing. They should look for candidates with experience in creating online and multichannel marketing campaigns, developing complex programming, executing marketing initiatives, and building targeted insights, along with the analytical skills to guide the sales force toward the best customers and the most appropriate prospects for lending.




As financial institutions move to generate revenue outside their traditional payments role, offering innovative new services along the value chain, they will find themselves increasingly reliant on internal and external data to fuel their growth and expansion. If they are to fully leverage this data-based opportunity, they will need to develop and nurture their data access, management, culture, and talent to position the business for success in this networked and data-driven economy — treating their wealth of data as the valuable asset it is.

Abby McCloskey, “Dodd-Frank’s Costs Will Be Paid For by Low-Income Bank Customers,” Forbes, Sept. 26, 2013
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Google pricing found at

Roberto Zicari, “Big Data Management at American Express: Interview with Sastry Durvasula and Kevin Murray,” ODBMS Industry Watch, Oct. 12, 2014

George Anders, “Inside Amazon’s Idea Machine: How Bezos Decodes Customers,” Forbes, Apr. 23, 2012

Bharat N. Anand, Christopher H. Paige, and Michael G. Rukstad, “Capital One Financial Corp Case Study,” Harvard Business Review, Apr. 24, 2000
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