Credit Cards… Late 20th Century
The credit card industry in the eighties was defined by uniform, non-tailored interest rates, imprecise credit risk decisions based on human intuition, little product development, and universally adopted metrics of cardholder risk. Credit cards were also highly profitable, although credit losses varied widely by issuing bank.
Capital One achieved phenomenal success in this industry by adopting their Information Based Strategy at a time when no rivals cared to change. More specifically, 'What we want are markets where people can see the product being sold, but not the algorithms behind it. Your business magic lies in the algorithms of customization…' They then pursued this strategy through the application of quantitative methods to create a powerful competitive advantage: scalable micro-segmentation.
Scalable Microsegmentation
Capital One’s CEO summarized their strategy as, ‘The right product to the right customer at the right time for the right price.’ This required the replacement of manual credit review because the such processes ('relying on your tummy to make [credit] decisions') were neither scalable nor did they allow new insights to be readily transferred to other analysts. '[CapOne management also] claimed that their complex statistical models would be better than credit analysts at distinguishing between creditworthy cardholders and their apparently similar, but unworthy peers.' The use of these statistical models to replace human intuition made these processes instantaneous, less labor intensive, unbiased, and for the first time scalable. Once such risk models were implemented, their results could be used as an input in pricing decisions. The interest rate necessary to cover defaults and operational costs would be set as the absolute minimum, while the profit margins could be adjusted based on the expected longevity of the customer relationship, projected changes in spending behavior or repayment patterns.
Applications of Quantitative Methods
• Credit Risk: Loan officers were replaced by ‘complex statistical models’ of risk, thereby eliminating human bias.
• Customized Pricing: Stopped offering $20 annual fees and 19% interest rates to all customers, and used on statistical models to maximize profitability and customer acquisition.
• Value Determination: Lifetime NPV is calculated for every cardholder.
• Customer Retention: If the expected customer NPV turns negative, CapOne stops retention efforts.
• Product Life-cycling: When the customer lifetime NPV drops from one campaign to the next (as competition sets in) they drop the product from their line. (This is more of an 'actuarial approach' to risk.)
• Sales Prediction: CapOne knows which incremental products they want to sell their existing customers at all times, even tailoring the choices to the channel by which they’re contacted. (i.e. Whether you access their website with Chrome, Firefox, or Internet Explorer determines the interest rates you’re offered)
• Sales & Customer Service Optimization: ‘In the time that phones took to generate their first ring, Capital One’s computers identified the caller and predicted – with greater than 70% accuracy – what the caller wanted, and what the caller might be willing to buy.’
• Best Practice Propogation: Sharing best practices was simplified because they were implemented through quantitative methods rather than through intuitive, human intensive systems.
• Information Extraction: CapOne held approximately 5 single spaced pages of information on every person in the United States, and used this information to make their pricing, underwriting, and acquisition efforts more powerful. Deriving value from this much information is only possible with data mining methods.
Performance Analysis
• “From its IPO in 1994 to 2000, CapOne’s stock price had increased more than 1,000% while the S&P500 had increased just under 300%.”
• “[CapOne’s Parent company] was the best performer on the NYSE... until it spun off CapOne” in 1994.
• “The company’s average annual growth rate of 46% was the highest in the industry (excluding growth through mergers and acquisitions).”
• Grew from “a minor credit card subsidiary of a small, regional bank – and turned it into the nation’s seventh largest credit card issuer.”
• $1.1 billion IPO in October 1994 when the credit card division was spun off into an independent company.
• Credit card revenues grew from 25% of company revenues to 67% from 1988 to 1994.
Personal Thoughts
I first read this case as an undergrad, and it made a strong impression on my professional future. Rereading the case though, it highlighted two benefits of quantitative methods that I had forgotten: Transferability and Scalability. Statistical models are highly portable… a new employee can see the model and understand the company's underwriting standards quickly. Furthermore, CapOne didn’t need to increase the size of their underwriting department by 46% annually despite revenues growing that much because the decision process was automated. This stands in sharp contrast to GE, where they quickly hired 3,000 loan officers upon entering the Thai auto loan market... no wonder that CapOne’s operation was growing faster, even though GE was competing in the credit card industry at the same time!
The case also used an industry buzz word that I previously didn’t associate with quantitative methods: Micro-segmentation. Micro-segmentation requires customized pricing, customer service, risk evaluation and you can’t achieve this without quantitative methods. Some will say that companies don’t need quantitative methods for this… that they could just classify 100 market segments. I’d argue though that treating customers as individuals requires that pricing and risk cover the continuum of possibilities.
Lastly, CapOne’s CEO also emphasized a profound advantage of quantitative methods by stating their pursuit of, 'Markets where the algorithms behind the product can’t be reverse engineered.' Quantitative methods are therefore valuable because of the obfuscation built into them. It is time consuming, resource intensive, and difficult to reverse-engineer algorithm based business processes and that difficulty makes them more valuable competitive advantages.
‘Capital One Financial Corp’ Harvard Business School Case 9-700-124 Revised May 1, 2001.
http://cb.hbsp.harvard.edu/cb/product/700124-PDF-ENG
Straight from the Gut. Welch, Jack. Copyright 2001. Warner Books, New York, NY.
