Developing a Credit Card Strategy Leveraging Big Data

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1 Elan Financial Services 2014 Developing a Credit Card Strategy Leveraging Big Data Big data presents great opportunity for targeting and incentivizing customers with valuble card offers. By Elan Financial Services October 2014

2 Today s always-connected fast moving digital world is doubling in size every two years. By 2020, the digital universe will reach 44 trillion gigabytes of data 1. 2 What Is Big Data and Where Does It Come From? Today businesses can find a plethora of data available for use; and for credit card issuers, analyzing cardholder transaction data can provide valuable insights on how customers are using or not using their credit cards. By analyzing key transaction level spending behaviors, financial institutions can find attractive incentives that deepen customer engagement and drive credit card usage. This type of data analysis and subsequent strategy falls under the label of big data. In this article the benefits, challenges, and security management of cardholder data transactions will be discussed. A simple google search on big data turns up 800 million results and thousands of articles. What is this popular topic about? According to McKinsey & Company, big data is defined as large pools of data that can be captured, communicated, aggregated, stored, and analyzed 2. These pools of information have existed for some time but in recent history have exponentially grow. In addition, most industries now possess the digital and analytical bandwidth to start leveraging big data. For example, the grocery industry has worked for a number of years to understand and leverage transactional data. The data available to a grocery chain to examine customers choices around products, frequencies, brand loyalty, price sensitivity, etc. is enormous. This data continues to be a key building block to many grocery loyalty programs with the end goal of better engaging and servicing their customers. When looking at just the credit card pool to drive similar data, Visa has 2.2 billion credit cards worldwide that complete over 89 billion transactions per year 3. MasterCard has over 1.9 billion credit cards worldwide that complete over 65 billion transactions per year 4. According to the Nilson Report 5, in 2012, consumer and commercial cards in the U.S. accounted for $3,778B in purchase volume.

3 Why Worry About Big Data? Card issuers have to figure out how to leverage cardholder transaction data, where information on cardholder activities like purchases, ATM withdrawals, and balance transfers are stored. With a proactive and analytical approach, card issuers can develop actionable solutions that lead to substantial benefits that might not be possible otherwise without this data. Three potential benefits in capturing and analyzing cardholder transaction data include: 3 1) Predicting future customer behaviors and transactions based on characteristics and spending patterns of existing cardholders. 2) Using transaction data to develop customized products and services that meet customers needs, resulting in higher acquisition and retention. 3) Quickly identifying fraud via changes in customer behavior and transaction patterns. Potential Hindrances to Use and How to Overcome Challenges A challenge for most financial institutions is figuring out how to analyze and leverage transaction data. Existing analytical tools are found to be challenging to use as outdated bank systems and their information infrastructures are not adapted for today s real-time environment. In addition, older systems have multiple silos of information that are dispersed throughout the bank in different regions and formats 6. Another concern when working with substantial blocks of data is the security and storage of that data. Dominic Venturo, the chief innovation officer for U.S. Bank Payment Services, recently wrote a piece called Penny For Your Thoughts. He mentions that, Consumers might opt into a marketing program in which they share anonymous purchase information in exchange for valuable offers from merchants that want to earn their business. All of these preferences must be managed, and the data must be protected and stored in increasingly massive and secure data centers 7. Once the proper set of data has been accumulated and analyzed, financial institutions need to invest in the systems and human resources with the experience and capabilities to select the actionable, valuable trends out of this stream of findings. With big data, card issuers are looking for a needle in the haystack.

4 Getting Started With Big Data Once data is secured and aggregated, the next item to address is how to initiate the analysis of cardholder transaction data. Using the backdrop of a credit card program in this example, during this first phase there are three important questions to address: 1) Who should be involved in the project? A team with a high-level of analytical and technical skillsets is needed to understand what is feasible and what is not. This team needs to be able to understand how this information can be used in conjunction with a business s core values and beliefs. 2) What is needed to make this project a success? Supplying the right resources and analytical tools to the team enables them to find valuable insights. Overall this process of gathering, analyzing and creating actionable strategies takes a lot of resources. These resources include time, IT bandwidth and money. 3) Where to look and what to look for? By piloting and testing out various scenarios, card issuers can find trends and patterns that drive cardholder behavior and actions. The ability to influence these trends and patterns will lead to more engaged and lasting member relationships. During this point in the process, a card issuer should step back and consider the multiple varieties of potential data sources available. When looking into what data a card issuer may have access to, it is important to examine all the potential sources. These data sources could include internal customer performance metrics (behavioral views, trends, profitability), external customer performance metrics (credit bureaus, non-fi payment behavior), customer-specific descriptors (demographics, relationships), and usage metrics (transaction detail, channel weighting, loyalty/rewards behavior). Selecting the right sources allows a card issuer to get to the right data, driving the most meaningful results. What will be the benefit to the card issuer once these trends have been identified? By spending time thinking through these questions before diving into a project, it should better prepare a financial institution to think through the cost-benefit analysis that can justify the upfront investment in big data. In addition over the long term, this process will help card issuers focus on projects and activities that will strengthen their core points of differentiation within the industry. 4 How Elan Financial Services is Tackling This Challenge Card issuers can follow a strategy of starting with a manageable pilot program to test and learn what the most effective approaches are. This step will enable card issuer to discover their capabilities, inefficiencies, and the infrastructure needed to build out a meaningful program. With a successful pilot, a card issuer can then proceed with a broader program that brings in a range of data sources. Elan Financial Services is piloting a suite of tools built on cardholder transaction data and other big data sources. These tools should provide direct insight into how customers use their payment cards. There are three types of programs that will be tested: customer segmentation, performance triggers, and merchant recommendation. By piloting tools and testing out programs, Elan hopes to better understand key spending drivers and how to create the most meaningful program generating the most positive change.

5 Elan s Pilot Points of Focus Customer segmentation looks at cardholders preferred spending habits. By looking at behavioral patterns, value proposition reinforcement and discount offers can incentivize targeted cardholders to use their cards more often. The right credit card products and services are then aligned to the cardholders needs. Performance triggers involve monitoring key changes in cardholders spending habits and quickly reacting to them with top-of-wallet strategies and sales stimulation. Product upgrades and sales stimulation such as higher status products and targeted reward programs can be offered. These value-added programs help position the card at the top-of-wallet. Merchant recommendation programs are about understanding cardholders preferred spending habits to better align products and services via merchant partnerships. For example, cardholders renovating their home and purchasing items at home improvement stores will receive targeted offers from popular home improvement stores. These programs can carry significant influence with the consumer if your financial institution can align with the right merchant partnerships. 5 Customer Segmentation Who Needs What? Performance Triggers Driving the Best Behaviors. Merchant Recommendation Merchant A Merchant B Merchant C Who Wants What?

6 Elan s Plan: Customer Segmentation Pilot The customer segmentation pilot should help develop product up-sell, second card strategy, usage and new account strategies leveraging the lifestyle spend segmentation. Hopefully by analyzing clusters of segmentation across the whole credit card portfolio, specific populations of customers will be defined within the Elan program. To put these groupings to use, Elan may look at spend by cluster and Merchant Category Codes to see how the credit spend in each category indexed against the average. This analysis would help Elan develop a strategy for this pilot to direct the appropriate offers based on a cardholder s spending patterns to individual cardholders to lift spend in key categories. The end goal will be to change (lift) cardmember spend in a couple of key spend segments for a variety of programs. 6 Elan s Plan: Performance Triggers Pilot A potential performance triggers pilot involves developing triggers based on probability expectations of purchases in key spending categories such as gas, grocery and restaurants. By proactively communicating with cardholders who have a strong history of strong spending behavior in a certain category but have started to exhibit behavior indicative of a decline in future sales, these performance-based triggers will attempt to re-engage the cardholder. During the pilot phase, channels such as , statement and mobile may be leveraged for quick response to these negative spend changes. By tying these communications to value proposition reinforcement and incentive/bonus reward offers, Elan hopes to reverse any declines in spend behavior driven by a competing product. 2,500 Performance Trigger Hypothetical Example 2,000 1,500 1, Normal Monthly Spend on Sporting Goods Monthly Spend for Month of the Trigger Amount of Spend Post Trigger

7 7 Elan s Plan: Merchant Recommendation Sample Transaction data can be leveraged to create merchant similarity indices that identify merchants that will likely appeal to a card issuer s cardholder. Powered by transaction data, one of the most prevalent examples of this type of marketing is found in online retailers. During the checkout phase, a reminder is provided to the cardholder, Other customers interested in this product also looked at. These types of offers aim to increase customer spending, enhance the customer experience and potentially drive revenue from select partners. Sample Merchant Recommendation: Matt s Gas Spending History Triggered Offer: Get 10% off gas purchases at Gas-Go for the next 30 days when you use your Elan card Rank Merchant # of Transactions % on Credit Card 1 Bob s Auto 4 0% 2 Hall s Station 4 25% 3 Gas-Go 3 100% 4 Filler Up 3 0% Elan is looking into leveraging this approach to big data developing a program to analyze cardholders recent activity across all known channels. Based on this activity, a number of merchant recommendations can be made along with a corresponding financial offer (5% off, etc.). Overall, Elan hopes this program adds value to both the cardholder and merchant while rewarding positive behaviors (spend, activation, etc.). Conclusion The pilot programs that Elan is developing represent one approach to help unravel the big data opportunity all card issuers are faced with today. If a proactive organized approach is taken, real benefits may be realized quickly. Overall, during this process, one must consider the benefit of continued testing of pilots, the facilitation of communicating insights and the expanding expectations of consumers. A patient and methodical approach is necessary in finding the needle in the haystack of big data that represents the true insights that will drive growth. About Elan Elan is a leading credit card provider in the industry and offers partners the availability of immediate access to a suite of credit card products that competes with National issuers, technology solutions that cater to audiences across the spectrum, and free access to a marketing engine that helps generate new accounts. Elan has created unique platforms to help their partner institutions compete in the digital space. For over 47 years, Elan has delivered exceptional credit card products and service to more than 1,500 financial institutions. For more information, call or visit

8 8 Sources: 1. Discover The Digital Universe Of Opportunities: Rich Data And The Increasing Value Of The Internet Of Things. EMC.com. April, Web. August 2, <http://www.emc.com/leadership/digital-universe/index.htm>. 2. McKinsey_Views from the frontlines_ Visa visa-fact-sheet_ Kearns, Gary. Innovative strategies to Leverage Big Data: Drive Co Brand Growth and Core Business Sales. Slideshare.com March 19, Web. August 3, <http://www.slideshare.net/morellimarc/mastercardbig-data-2013> 5. Nilson Report Chart 6. Groenfeldt, Tom. Banks Betting Big on Big Data and Real-Time Customer Insight. Bloomberg Businessweek. September Venturo, Dominic. Penny For Your Thoughts. BankTech.com. July 8, Web. August 2, <http://www. banktech.com/data-management/penny-for-your-thoughts/a/d-id/ >

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