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May 2001, Volume 69, Number 3

Credit Card Revolutionary

In the conservative world of banking and finance, it takes a bold and passionate leader like Richard Fairbank, MBA ’81, to change an industry.
By MIKE McNAMEE
HAVE YOU EVER TRIED TO EXPLAIN a credit card to a child—why the supermarket will give you valuable items like Fruit Loops, Lunchables, and pudding cups, and all you do is swipe a piece of plastic through a machine? “It’s like, the bank gives the money to the store, then the bank sends me a bill, and ... well, it’s kinda complicated.”
If you think that’s tough, try to see your credit card the way Richard D. Fairbank does. To the founder and CEO of Capital One Financial Corp., that slab of plastic is the key to an information machine—a machine fueled by data on who you are, what sort of people you live among, whether you’ll carry a balance or avoid finance charges at all costs. This data machine may offer the closest thing ever invented to perpetual motion, because the more you use the card, the more data it produces. The machine takes your bytes, combines them with transactions by millions of other cardholders—and before long, it can pinpoint what you’re likely to buy next, whether you’ll respond to a pitch for long-distance telephone service, and how likely you are to sign up for Internet banking.
Fairbank, MBA ’81, built that data machine—and revolutionized the credit industry. Before Fairbank, all credit cards cost the same: 19.8% annual percentage rate, $20 fee. Before Fairbank, no one had ever heard of teaser rates, “superprime” cards charging just 9.9% interest, or credit cards printed with your kids’ pictures. Since Fairbank—to be precise, since October 1991 when he rolled out the first cards to break the plastic price barrier—credit cards have become hotly competitive, customized products with thousands of combinations of rates, fees, credit lines, rewards, and services.
Fairbank’s insight—that credit cards offer digital finger-prints of consumer behavior—has two results of which he’s particularly proud. Before 1991, half of America couldn’t get a credit card at all, because banks turned thumbs down on anyone who didn’t fit their one-price-fits-all approach. Fairbank realized that the data machine could design cards to make those screened-out borrowers profitable while maintaining Capital One’s low default rates. Millions of Americans now can rent cars, book airline tickets, and shop online as members of the credit card economy. “We’ve democratized credit,” Fairbank boasts. ….
Finally, late in 1991, one idea showed promise: Offer potential customers a low initial interest rate if they consolidate debts from other accounts. The new card instantly built up big balances that brought in hefty interest payments once the three-month “teaser” rate expired. The teaser-rate balance-transfer card was born—and Capital One mobilized 100 employees in a week just to mail checks to pay new customers’ old debts.
The idea worked because Capital One’s data machine broke an ancient rule of credit: The people who want to borrow a lot of money are the ones you least want to lend to. Combing through the thousands of accounts Capital One had opened almost at random, Fairbank’s team found ways to identify good credit risks who were also heavy chargers. Using their demographic and financial profile, the bank could search for similar prospects and hit them with heavy promotions—raking in new customers, profits, and new data to fuel the information turbine. …. Fairbank and Morris applied their information-based strategy to every corner of the business. Take customer retention—a backwater for most card companies. By 1997, thanks to Capital One and its imitators, partygoers were talking about credit cards—and the one who paid the higher rate would be on the phone the next Monday claiming he’d switch to another bank unless he got a better deal. To help figure out which card-hoppers were serious, Fairbank ordered a test. When a card-hopper called, the customer service agent’s computer randomly ordered one of three actions: Match the claimed offer, split the difference in rates or fees, or just say no. “We called a lot of bluffs, because many people didn’t really have a new offer,” Fairbank says. Thousands of accounts were upgraded or closed— at random—and the company gathered data on who left, who stayed, and how they behaved. Now, when a card-hopper calls, computers can calculate the odds that this particular customer actually does have a better offer—and the lifetime profits that would be lost if she leaves. Within hundredths of a second, the operator gets a script spelling out the precise terms he can offer—or orders to bid the card-hopper a cheery farewell.
In Capital One’s lexicon, that experiment was a “scientific test.” While it involved thousands of accounts, it was just one of 14,000 tests that Capital One’s busy analysts and marketers conducted in 1997. In 2000, the company carried out 45,000 tests. Anyone in the company can propose a test, and if the results are promising, Capital One rushes the new product or approach into use immediately. “We don’t hesitate, because our testing has already told us what will work,” Fairbank says.
Fairbank and Morris also say thorough testing has saved the company from expensive mistakes. Take their approach to the Internet. When other card companies were paying top dollar to lock up prime advertising slots on Internet portals in 1999, Capital One was almost nowhere to be seen. The Web’s model—customers find you—flies in the face of Capital One’s targeted marketing strategy. Worse, early research showed that people who sought cards via the Web were worse credit risks than mail applicants—and 10 times as likely to commit fraud. So Capital One launched intensive tests to find key factors, such as the clickstream that brings a customer to its Web site, that help identify fraudsters, computer-generated applications, and poor risks. It also refined its pitches and tested ad strategies: “When everyone else was just buying up space, we had to convince portals to let us test ads on Tuesday against ads on Sunday,” Morris says.
The payoff: By mid-2000, with the dot-com world imploding and online ad costs plummeting, Capital One was ready to ramp up. By the end of the year, it was the largest Internet advertiser, and in November and December, capitalone.com was the most-visited financial Web site. Online account servicing—for which 2.5 million customers have signed up—offers even greater rewards. Those customers log on 2.7 times a month—nine times as frequently as offline customers contact the company, creating nine times as many opportunities to pitch targeted offers for new cards or other products.
Now Capital One sees two great advantages on the Internet. First, credit cards are the currency of the Web, promising to eclipse all other forms of payment—cash, checks, and fund transfers—as more and more commerce flows onto the Net. Just as important, the Internet fuels the data machine—and the direct marketing revolution that Fairbank hopes to ride far into the future. “We’ve hitched ourselves to an enormous macro trend, and I don’t see any end to it,” he says. Knowing Fairbank, he’s got the data to prove it.
The Information Age interview
Credit card company Capital One attributes its rapid customer growth to the innovative use of cutting-edge technology. European CIO Catherine Doran talks about the systems that have fuelled that runaway success.
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| |About the company | |
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| |Over the past five years, credit card company Capital One has picked up numerous awards for its | |
| |innovative use of technology. Established in 1995, the company has grown its customer base to almost| |
| |50 million worldwide, and founders Rich Fairbank and Nigel Morris attribute that success almost | |
| |entirely to the unique way in which data analysis technologies are used at Capital One to identify | |
| |profitable credit card holders from a vast database of potential and existing customers. | |
| |Under its 'Information-Based Strategy', Capital One can address a much wider group of potential | |
| |credit card customers, offering credit facilities to individuals who traditional card companies | |
| |regard as high risk. To do so, it aggregates as much data as possible on customers - from credit | |
| |checking information to lists of people's hobbies. This strategy is backed by large-scale investment| |
| |in data warehousing, business intelligence and analytics technologies. | |
| |Catherine Doran, Capital One's European chief information officer (CIO), has joint responsibility | |
| |(with her US counterpart) for all aspects of the company's IT delivery and service. Talking to | |
| |Information Age, she reveals how the close relationship between the business and information | |
| |technology functions of Capital One is a key to its success; which technologies support the | |
| |company's pioneering business model; and how it has translated effective use of technology into | |
| |better customer service. | |
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Information Age (IA): Capital One has consistently won awards for the way it uses technology - why do you think it has earned this recognition and what is so unique about the company's philosophy towards investment in and deployment of technology?
Catherine Doran (CD): I think the one respect in which Capital One is very different from most other companies is that, whereas most other companies had a business model and then computerized it, so the technology's an adjunct to the main business, Capital One was put together with technology right at the core of the business. Loads of companies say that and most of the time it's junk. But Capital One is definitely different in this respect.
When Capital One's founders Nigel Morris and Rich Fairbank started the company, they looked at how they might use technology differently. They thought: 'There's got to be an opportunity here. If we use technology to understand our customers, and understand how people work, we should be able to offer products to different segments of the population with different price points and different characteristics.' They realized they could offer a broader range of products to a wider group of customers and still make a healthy profit, meanwhile undercutting the traditional players.
They had this notion of dividing up the [potential credit card] population by segmenting our data, analyzing it and drawing conclusions to see what works and what doesn't work - and then using that to target market to customers. They first applied this philosophy at Signet Bank in the US. It worked so well that, after a few years, it was such as highly profitable dimension that they spun it off as Capital One. So technology is right at the heart of Capital One's genesis. Our strategy is all centered around understanding and analyzing information.
IA: Despite that firm commitment to analyzing information, the credit card industry is dogged by low conversion rates from direct marketing activities. From a technology perspective, how have you managed to improve customer interaction levels?
CD: Any fool can lend money, the brains is in getting people to pay it back. If you think about the credit card business and the way it was established, you had the traditional players with a single, 'one size fits all' product and many of them charging huge interest rates. Until recently, you either went to Barclays and got a Barclaycard or NatWest and got an Access card. It was the same in the US too, where Capital One started up.
The traditional players have not touched people in the higher credit risk bracket with a barge pole. As a general rule, they target the low-risk end of the market. The philosophy within the Capital One environment is that, actually, if you understand your data properly, you will find that even in the high-risk bracket, most people do actually pay back and you can still make a healthy profit. The trick is working out who won't. We have some of the lowest rates of default in the industry, so we do know how to do it.
At Capital One we have deployed an information-based strategy, which is based around getting customer data from whatever sources are available, loading it onto our systems and analyzing the hell out of it. This means we can put together products that will appeal to different types of people, by testing whether hypotheses are right, looking at the result of these tests, modifying the hypotheses and testing again, and so on.
IA: Given the stress you put on data, this must place a huge strain on Capital One in terms of data management. What kind of data warehousing and data mining infrastructure do you have in place to support your information-based strategy?
CD: I can't remember how many terabytes we have exactly, but I do know that it's the equivalent of 354 million telephone directories. We have a huge machine in the US and a huge machine in the UK and one of the things we do is share data between the two. It's just vast.
IA: With this immense amount of information at your fingertips, analyzing it must be an enormous challenge. Where does Capital One source its data on existing and potential customers, and how do you go about analyzing it?
CD: We're data hungry beasts and try to glean data from as many different sources as we can, providing it's legal and ethical. So we get data from standard places such as the Post Office address file and credit checking agencies such as Equifax and Experian. But we offer a range of 'lifestyle' products, such as a gardening credit card, so we get data from organizations that might target people with such interests.
We bring all the data in, we normalize it and clean it up using a software package from Trillium. And then we use tools from business intelligence software vendors such as SAS Institute and Brio for deep analysis. You end up with a body of knowledge that has accumulated over time. Soon you begin to see that the behavior people exhibit indicates whether they are more or less likely to default.
For instance, if you offer someone payment protection insurance, their response to that offer is an indicator as to whether you needed to offer it to them in the first place. Some of these indicators are counter-intuitive, so when a result turns up that you're not expecting, it's really very interesting. It's all about the 'Ah-hahs' in life.
IA: How have you turned the success you've had in analyzing and using data into better customer service?
CD: The main benefit for customers is that that people that otherwise would not find it easy to get credit can get credit from us. Although some of the more traditional players are starting to dip their toe into the higher credit risk bracket, they've only done so very slightly. At the end of the day though, we're not a charity, we're a business. Part of what we do is to tailor the products and the charges associated with them relative to the profile of the various customer segments. The whole purpose of our data analysis machine is that we want to identify upfront who are the people that are going to pay us back and who are not.
IA: This technology-centric, information-based strategy places a lot of focus on your position as CIO. How does Capital One bridge the traditional divide between the IT and business functions, and how does this affect your role on a day-to-day basis?
CD: It is certainly true to say that technology is a core component of how we do our business. In more traditional or historical organizations, the IT guys tend to have a sense of inferiority, but that doesn't happen here. Before I joined Capital One I actually had a conversation with one of the founders, Nigel Morris, about the relationship between business and IT. Having worked in some of the larger institutions, I knew what different types of relationships there could be. Morris said that in Capital One, he thought the relationship [between the IT and business functions] was the best he'd ever seen, but it still wasn't where he wanted it to be.
So when we have discussions about new strategies and products, both operations and IT are involved in those discussions rather than us getting a letter on Monday morning saying, 'Can you implement this on Wednesday?' That's not to say it's perfect. Sometimes if someone's got a bright idea they forget to think about some of the IT logistics. But that's the exception. The norm is that we get involved. | |
|Author: Jo Faragher |
|jfaragher@infoconomy.com |
|Date: 1 August 2002 |
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MISM 2301

Strategic Use of Information Systems Assignment
Capital One Case Questions

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Read the Capital One Case materials. http://academicwritingtips.org/component/content/article/34-business-studies-/1069-capital-and-the-rivals-of-the-business.html 1. What is Capital One’s business and who are their competitors? Capital One’s business • Financial Services Company that specializes in Credit cards, banking, auto loans, and home loans and saving products. • Founded in 1988 concentrated in mass marketing of credit cards Competitors • Bank of America • American express Company • HSBC bank • Other Financial Company who tends to offer similar products

2. What is their strategy to grow profitably and compete? Give examples. • Capital one is different from its competitors as it deployed the technology at the core of the business while others started with a business model and then tried to computerize it • They uses the technology to analyze and understand its customer as well as how they work, In order to provide products to various kinds of customers with different prices and characteristics • Developed data machines that could made detection of frauds easier as well as identifying the risk of each products • Concentrated on internet advertising • 2.5 million customers who signed up for the credit card online

3. How do they use information to implement their strategy? What kinds of information do they use and where does it come from?

Information to strategy • The company greatly appraises information technology that assists it to slice its data, analyze and make recommendations to make appropriate decisions concerning the market. • Despite the credit card industry having low conversion rates , the company utilizes the information technology to improve customer relations intensity • The company designed credit cards with digital fingerprints of consumer character which made them easily accessible to many people • The data machine could design cards to involve the left out borrowers while still maintaining low lending rates

What kinds of information • Capital one targets personal information of their customer in order to know the people who can afford to pay back the loan/ money • The data collected from the post office address files and credit checking agencies such as Equifax and Experian which offer reliable and useful data.

4. List and describe the information technologies used to support their strategy?

Information- based strategy • Gathers information • Loads into their systems and analyzing it • This ensure their products appeal most of their customers as well as considering different kinds of people tastes and preferences Well managed data warehouses/ mining infrastructure • Shared among the various company branches to enhance decision making

Trillium software/ Business Intelligence software (SAS institute and Brio) • Analyze data with business intelligence software to assist in making well-informed decisions • Assists in customer services as they adapt products • Charges depending on the profile of the customers • Avoiding losses from customer who cannot afford to pay

5. Describe their notion of a “scientific test”. Contrast this approach to product development and launch with a more traditional approach that a manufacturing company might use. Scientific test • Use of window bases software to program device tests, capture test data as well as modifying the available data • Used for production, quality control and final test • The capital one company utilizes the software to analyze and capture data about the potential credit card customers • The company has carried various experiments that involves thousands of account and conducted by its competent analyst and marketers.

Product development • The creation of an idea , designing the product and engineering the information to bring forward necessary contribution • The market should be researched and analyzed, in order to understand various segments of customer • Address the changing customer’s tastes and preferences

Traditional approach • Setting up the company then trying to computerize the data later • Focuses on the manual analyses of data