Mor Sela - IKNS 4304 - Assignment 2- Analytical...

Mor Sela -‐ IKNS 4304 -‐ Assignment 2-‐ Analytical Plan.docx Columbia University 1

Capital One Analytical Plan Mor Sela, IKNS 4304, Assignment #2, March 16th 2014

Introduction I’d like start with a personal story that illustrates how Capital One outcompetes its rivals with its “information-‐based strategy”. Back in 2001, my job at Comverse Technology provided me with the opportunity to relocate from Israel to the US. So, my wife and I moved to NYC with our twin babies and began our new life here. Now, while I had a really stable and well paying job, and while we had a nice six-‐digit dollar amount of cash in the bank, neither my wife nor I had an established credit history in the US. So for a couple of months, none of the credit card companies agreed to issue us a credit card. We had to use a debit card instead. We were told that it would take at least 6 months for us to establish our credit history and qualify for a card (assuming we paid all our bills on time). Well, being in such a good financial situation, and after having credit card for many years in Israel, we thought that this situation made no sense but we accepted this reality and stopped applying for cards. To our good surprise, just about 3 months to our arrival in the US, a letter came in the mail from Capital One (a company we never heard about before) stating that we are pre-‐approved for a credit card. The interest rate on that card was relatively high and we had to pay an annual fee, but we were happy to do so in order to benefit from a card and accumulate credit history faster.

Clearly, that was a great move for Capital One. They were able to identify low-‐risk potential customers who would pay a premium for the service and were able to grab this opportunity before other more prominent credit card companies would. Now that I know about Capital One’s unique information-‐based marketing segmentation, I realize that this was the reason they were able to do so. Unlike their competitors who simply relied on the credit agencies’ traditional methods for approving new customers, Capital One used proprietary analysis of the credit data1 and additional data (mostly based on proprietary testing that they conducted2) to assess the risk level of individual potential customers.

Data The Prerequisite for Everything Analytical

My analysis of Capital One’s data management is based on publically available information. I couldn’t find specific public information about Capital One’s data Structure, Integration, Quality, Access, Privacy and Governance. What I did find is an indication that Capital One has been developing its own Unique customer/prospect data. In addition to traditional credit score data, Capital One used other available data to identify customers/prospects who represent relatively low risk despite their low credit score. Using that data, the company would test thousands of offerings and marketing campaigns to different


market segments (or customer profiles). The results of these tests were basically proprietary data that they company used for their large scale marketing campaigns.3

At what stage is the organization? Based on the above sources, I conclude that Capital One is at Stage 4 of the analytical maturity model when it comes to its Data Management. While the company used to be at the cutting edge of data management as a credit card issuer, I got the impression that recently they have slightly fallen behind the latest innovation in big data management. Specifically, the company doesn’t yet leverage Hadoop or equivalent distributed file system for storing and processing Big Data (they test Hadoop in a pilot environment, but not yet in production). Capital One data warehouse is currently using Ab-‐Initio ETL tools, as well as Oracle and Microsoft SQL relational database management systems technologies4. This indicates that the company’s ability to store and process huge amounts of unstructured data is relatively limited (at least in the short term).

What stage should the organization aspire to achieve over a 3-‐year period? Capital One should certainly aspire to regain the Stage 5 spot. As a company, which rightfully attributes its success to its “information-‐based strategy”, Capital One should keep exploiting the latest Big Data technologies for effective management of the enormous amount of data they could exploit (including structured, semi-‐structured and unstructured data).

How would the organization be different if it were to reach the desired stage? Having accurate, consistent, integrated, accessible, relevant and rich data is really a prerequisite for achieving Stage 5 in all other DELTA elements. By leveraging the latest Big Data technologies, Capital One could potentially store and analyze more data, and do it more efficiently. With Hadoop or equivalent technologies, the company will be able to store large volumes of unstructured data and process this data by running highly scalable algorithms over large number of computing processors (cores). From business perspective, It will allow Capital One to be more competitive by more quickly loading vast amounts of unstructured data such as social media communications, emails, industry reports and news; and processing it for improved decision-‐making, customer segmentation, risk analysis and other applications.

What are the key obstacles to reaching the desired state? It seems that at this point of time, the key challenges in moving to Hadoop are:

1. The complexity and risk of migrating from the existing relational databases to Hadoop. 2. The knowledge and expertise required to manage and develop BI applications in this new

and different framework. 3. Clear business justification for this investment. 4. Time to market (such transformation may take many months if not years). 5


What specific actions and recommendations would help Capital One reach Stage 5? In general, my recommendation for Capital One is to constantly be as agile as possible in leveraging new data gathering, storage, and analytics technologies across the organization. With today’s technologies, not analyzing unstructured big data is a missed opportunity. It doesn’t necessarily mean replacing existing data warehouses. As a first stage, I would recommend to develop big-‐data infrastructure that would address the needs of one or few strategically important analytics applications/targets that require large volume of unstructured data and support of real-‐time data sources. Once gained initial success with these applications, the company can scale its big data infrastructure to additional functional units and additional applications.

Enterprise Integrating Across Organizational Silos

At what stage is the organization? As mentioned at the About Capital One appendix, Capital One has gone through major acquisitions over the past decade. Integrating the acquired companies, both from technology/data and cultural perspectives, has been a significant challenge. While each new hire in Capital One is required to have a relatively high level of analytical skills (they all have to pass a math test, from clerks to executives), such standard did not apply to the thousands of employees that automatically joined the company through acquisitions. Also, while the IT department is relatively very efficient in integrating the IT assets of their acquisitions, this process takes a long time and is not yet complete. Therefore, I assess the organization as Stage 4 from Enterprise analytics maturity perspective.

What stage should the organization aspire to achieve over a 3-‐year period? Capital One should certainly aspire to regain the Stage 5 spot it had before its acquisitions. As a company, which rightfully attributes its success to its “information-‐based strategy”, Capital One should keep integrating its various legacy systems, applications, and databases. In addition, the company should keep striving to institute its analytical culture across the organization.

How would the organization be different if it were to reach the desired stage? By integrating data analytics technology and culture across its various subsidiaries and functional units, Capital One will be able to elevate its information-‐based strategy to a new height. Such integration would drive better data/information/knowledge sharing between the company’s analytics center of excellence and the various functional units, as well as among the


functional units. It will also enable all functional units to benefit from larger data pools and more sophisticated analytics/BI tools. As a result, the company as a whole will likely see improvement in its business metrics such as more consistent customer service, customer satisfaction, better marketing segmentation, better risk management, higher efficiencies and increased profitability.

What are the key obstacles to reaching the desired state? Probably the key obstacles are related to culture, personalities and ego. While the technology/data integration is certainly not a simple challenge, it is clearly doable once all the relevant stakeholders are united in their goals and motivation. However, creating such consensus and willingness to collaborate across corporate functions and independent subsidiaries/units has proven to be a big challenge. Each unit is comprised of different cultures, different geographies, different legacy technologies, different legacy processes and different set of objectives.

What specific actions and recommendations would help Capital One reach Stage 5? My recommendations are focused on the following two parallel tracks:

1. Technology/Data: a. Consolidate the various analytics-‐related systems and data warehouses b. Integratate legacy systems and data warehouses that could not be consolidated

2. People/Culture: a. Identify the resources and skill sets required for analytics excellence in every

functional unit b. Use the center of excellence to train employees from across the organization c. Hire and/or allocate analytical professionals for units who lack such talent d. Support the above activities with continuous top management commitment to

analytical excellence. For example, insist on relying on data when making important decision (whenever possible).

Leadership The Deciding DELTA Factor

At what stage is the organization? Given that Capital One’s co-‐founder, Rich Fairbank is still the Chairman of the Board, President, and Chief Executive Officer is a testament to the fact that the company’s leadership is fully committed to analytics and is qualified for the prestigious Stage 5 analytical maturity level. After all, it was Mr. Fairbank who


(together with his co-‐founder Nigel Morris) coined the term “information-‐based strategy” and made it a defining component of what Capital One is. Capital One is still today very much driven by the same concept.

What stage should the organization aspire to achieve over a 3-‐year period? The banking industry is growing more and more proficient and creative in data analytics, so to sustain its competitive analytics advantage, Capital One should strive to continually maintain its Stage 5 maturity. Given the market competitive dynamics, maintaining this status does not mean keeping the status quo. It requires on-‐going improvement and innovation.

What are the key obstacles to reaching the desired state? Maintaining a culture of analytics excellence while growing the business from practically a start up in 1988 (as a small division of Signet Financial Corp) to becoming a Fortune 500 company with about 42 thousands employees is probably the number one challenge of Rich Fairbank. This challenge has been amplified many times given that a key element of that huge growth comes from large acquisitions of well-‐established banks. While Capital One had always insisted on hiring exceptionally analytical talent employees (and leaders), as they acquired other companies, they had to deal with different levels of talent that came with those acquisitions.

What specific actions and recommendations would help Capital One retain Stage 5? Given the size of the organization, and assuming Mr. Fairbank will not stay in his position forever, I believe that the company should create policies that would allow it to sustain its analytical leadership for the long run. Few specific recommended actions:

1. Continue to practice and insist on fact-‐based decision making at the top executive level 2. Explicitly include "information-‐based strategy” within the core values of the company 3. Make sure to communicate these core values on a regular basis to all employees 4. Explicitly include analytical traits as a key requirement within the job qualifications of every

leader in the company 5. Codify policies, standards, frameworks and processes that demand the use of data and facts

for decision making and operational excellence 6. Reward analytical and fact-‐based decision makers

Targets Picking Your Spots for Analytics

At what stage is the organization? While the credit card business of Capital One is clearly at Stage 5 of its Analytics Targeting maturity, I believe that the organization as a whole is still at Stage 4. Parts of the banking business are still not leveraging


the full potential of data analytics and I’m not aware of any strategic initiatives that are currently in place for leveraging data analytics across the various subsidiaries and functional units of Capital One.

What stage should the organization aspire to achieve over a 3-‐year period? As with the other DELTA elements, I believe that Capital One should strive for Stage 5 maturity of its analytical targeting. With the huge amount of data widely available for banks, and given the unlimited potential opportunities for effectively using this data to drive business success and better customer experience, I see no reason not to aspire for the highest level of maturity.

How would the organization be different if it were to reach the desired stage? Simply put, moving from Stage 4 to Stage 5 of its analytical targeting means that the organization as a whole is better in exploiting more strategic data-‐driven business opportunities and/or expanding its data-‐driven opportunities to parts of the organization, which are currently underutilizing this technology/application.

What are the key obstacles to reaching the desired state? Once other elements of the DELTA model are in place, reaching Stage 5 of analytical targeting shouldn’t be that hard. Also, the fact that Leadership is already at Stage 5 is a key success factor for approving and driving strategic analytical project. The key challenge would be to stay “hungry” for constant innovation and keep defying the status quo in a mature organization.

What specific actions and recommendations would help Capital One reach Stage 5? While the organization is working on elevating its big data capabilities and working on integrating its various silos, leaders should start to build the business case for cross-‐functional strategic data analytics applications that can significantly improve Capital One’s competitiveness and bottom line. To support these business cases, the company should pursue pilot projects that would test the assumptions and reduce the risk associated with these initiatives. Armed with the pilot results, these leaders will be in a position to “sell” the business case to top management and the board of directors. Once these opportunities have been identified and supported by top management, it would be easier to justify both the Big Data infrastructure investments and the Enterprise integration efforts required to execute them.

Which 3 functional applications would you target for more sophisticated analytics? 1. Innovative market segmentation and related product/promotion/price offering: Just as

Capital One innovated credit card offering for many years, it should now innovate its commercial banking offering. While today banking services and features are basically fixed for most customers (with few variations depending on business/private and on the amount of money in deposit), I can see a potential for creativity around more customized service offering and pricing. For example, the bank may identify customers who are active social


media influencers (e.g. people with many twitter followers) and make sure they get uniquely great service and/or offer them incentives for referring new customers.

2. Cross-‐silo synergies: Capital One can improve customer satisfaction by providing benefits for customers of both its credit card and banking services. For example, automatic transfer of money from the customer’s credit card balance to cover overdraft in the checking account. Note: I think that overdraft charges by most banks are just ridiculously high and are a big source of frustration to many customers (similar to the ridiculous late fee charges by Blockbuster, which led to many customers moving to Netflix when it introduced an innovative offering with no late fee).

3. Performance Management: Now that Capital One has reached a significant scale (42k employees and more than 900 branches), sophisticated performance management, especially one that includes predictive analytics, can have a meaningful and even strategic impact on the company’s business performance as a whole. One example of such application may be sophisticated analysis of how various workplace factors (such as the office layout, the way meetings are managed, the ability to telecommute, etc.) affect productivity, employee satisfaction, customer service, and the ability to draw great talent.

Analysts Managing Scarce and Valuable Talent

At what stage is the organization? Capital One is certainly a leader in its management of analysts. The company has been very proactive in luring “heavy duty” PHD statisticians and uniquely analytical MBA graduates from top universities. The company employs a Center of Excellence model very effectively.6 Capital One emphasizes analytical skills to the point that each new hire (even a clerk) is required to pass a math test. However, such standard did not apply to the thousands of employees that automatically joined the company through the many acquisitions conducted by Capital One (see About Capital One). Therefore, until the company is able to standardize its analytic talent across all its subsidiaries and functional units, I would argue that it is at Stage 4 (or perhaps 4.5).

What stage should the organization aspire to achieve over a 3-‐year period? Capital One should certainly aspire to regain the Stage 5 spot (for the reasons below).

How would the organization be different if it were to reach the desired stage? Having talented, motivated, and well-‐managed analysts across all functional unites of the organization is instrumental to the company’s success in sustaining its analytical competitive advantage. It also has a snowball effect: the higher the standard is in managing analysts, the


more attractive the company is for new analytical talent. Really smart people want to work in a place that require smart people and knows how to motivates them.

What are the key obstacles to reaching the desired state? While many of the employees of the acquired companies could be trained and elevated to the desired analytical standards of Capital One, there are some who simply don’t have the required intellectual capacity or the attitude needed for adopting to the new standard.

What specific actions and recommendations would help Capital One retain Stage 5? 1. Identify the resources and skill sets required for analytics excellence in every functional unit 2. Use the center of excellence to train employees from across the organization 3. Hire and/or allocate analytical professionals for units who lack such talent 4. Replace employees who are not capable of meeting the analytical standards of their role 5. Support the above activities with continuous top management commitment to analytical

excellence. E.g., insist on relying on data when making important decision (when possible).

Conclusion

Capital One is perhaps the most analytically mature large financial services company in the US. That said, while it was clearly a Stage 5 company up to 2005, the significant non-‐organic growth of the company since then has created challenges in maintaining that level of enterprise-‐wide analytical maturity. Furthermore, while it has some of the biggest RDBMS-‐based data warehouses, Capital One hasn’t yet taken full advantage of advanced big data technologies.

To regain its desired analytical mastery level, I recommend taking the following actions:

• Data: Develop big-‐data infrastructure that would address the needs of strategically important analytics targets, which require large volume of unstructured data and support of real-‐time data sources.

• Enterprise: Consolidate/Integrate all analytics-‐related systems and data warehouses and enhance the analytical mastery level of employees across the organization.

• Leadership: Create policies that would allow the organization to sustain its analytical leadership for the long run.

• Targets: Cross-‐functional strategic data analytics applications that can significantly improve Capital One’s competitiveness and bottom-‐line.

• Analysts: Bring the analytical mastery level of employees across the organization (mostly at the acquired companies) to the Capital One’s standard.

Note that these are not sequential steps. These actions should be taken concurrently in order to get Capital One to the desired state as quickly as possible.

This effort is expected to create the culture and the infrastructure that would allow the organization as a whole to continuously develop more effective strategies and make better decisions. Thus, driving innovation, increasing market share, improving customer satisfaction,


enhancing productivity, increasing operational efficiencies and overall maximizing long-‐term competitiveness and shareholder value.

Figure 1 -‐ "Capital One Thrives on Innovation" -‐ an image taken from Capital One's career site

Appendix 1: About this Paper This paper assesses the analytical maturity of Capital One vis-‐à-‐vis the Analytical DELTA7 model of Tom Davenport, Jeanne Harris and Robert Morison. For each of the Analytical DELTA elements, the paper provides answers to the following questions:

• At what stage is the organization? • What stage should the organization aspire to achieve over a 3-‐year period? • How would the organization be different if it were to reach the desired stage? • What are the key obstacles to reaching the desired state? • What specific actions and recommendations would help the organization advance to

this next stage?

In conclusion, the paper aims to provide innovative, actionable, and targeted ideas for how Capital One could improve the use of analytics to further enhance its competitive advantage and maximize shareholder value.


Appendix 2: About Capital One Capital One Financial Corporation is the eighth largest bank holding company in the United States. The company operates in three segments: Credit Card, Consumer Banking, and Commercial Banking. As of December 31, 2013, the company has $204.5 billion in deposits and $297 billion in total assets, with $20 billion annual revenues and $4.3 billion in annual income. Headquartered in McLean, Virginia, Capital One has about 42k employees and more than 900 branches.8 9

Since its inception in 1994 as a credit card issuer, Capital One was one of the most analytical companies and was known for its “information-‐based strategy”.10

Since then the company entered the commercial banking business by acquiring Hibernia National Bank in 2005; and North Fork Bank and Chevy Chase Bank in 2008. Furthermore, in 2012 the company acquired ING Direct (now Capital One 360) and HSBC’s U.S. card business.

With these acquisitions along with customers’ growing demand for mobile and online channels, Capital One launched a multi-‐faceted digital transformation project designed to quickly integrate these businesses and position Capital One at the forefront of where the banking industry is going. In October 2013, Capital One was named to the 2013 InformationWeek 500 -‐ a list of the top technology innovators in the US. Capital One was recognized as a leader in leveraging technology to drive innovation and digital banking capabilities that are enabling a digital-‐first customer experience. A major focus of that innovation was related to data analytics.11

Figure 2: Brian Cobb, managing vice president for information technology operations at Capital One, Rich Fairbank, chairman, CEO, and founder of Capital One and Virginia Gov. at an opening ceremony of a new data center (Mar 14, 2014)


References: 1 Davenport, Thomas, Harris, Jeanne, and Morison Robert, Analytics at Work (Harvard Business School Publishing, 2010), 26 2 Davenport, Thomas and Harris, Jeanne, Competing on Analytics (Harvard Business School Publishing, 2007), 42 3 Davenport, Thomas, Harris, Jeanne, and Morison Robert, Analytics at Work (Harvard Business School Publishing, 2010), 26 4 Director Data Service Job Description, http://jobs.capitalone.com/dallas/information-‐technology/jobid4516369-‐director-‐data-‐services-‐(ai)-‐jobs (last accessed on 3/14/2014) 5 Dan Linstedt, Introduction to Hadoop, Big Data and Data Warehousing, http://danlinstedt.com/datavaultcat/introduction-‐to-‐hadoop-‐big-‐data-‐and-‐data-‐warehousing/

6 Davenport, Thomas, Harris, Jeanne, and Morison Robert, Analytics at Work (Harvard Business School Publishing, 2010), 107 7 Davenport, Thomas, Harris, Jeanne, and Morison Robert, Analytics at Work (Harvard Business School Publishing, 2010), 19-‐116 8 Capitalone.com (last accessed 3/10/2014) 9 Finviz.com, http://finviz.com/quote.ashx?t=cof (last accessed 3/10/2014) 10 Davenport, Thomas, Harris, Jeanne, and Morison Robert, Analytics at Work (Harvard Business School Publishing, 2010), 152 11 Capital One Earns Spot on the 2013 InformationWeek 500 List of Top Technology Innovators Across the U.S., http://press.capitalone.com/phoenix.zhtml?c=251626&p=irol-‐newsArticle&ID=1861335&highlight=analytics (last accessed 3/15/2014)

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