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The Economic Value of Data

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Big data analytics promises to boost customer centricity and profitability for financial services firms, especially when applied to market research, customer segmentation, product testing, product …

Big data analytics promises to boost customer centricity and profitability for financial services firms, especially when applied to market research, customer segmentation, product testing, product development and customer service.


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  • 1. • Cognizant 20-20 InsightsThe Economic Value of DataPart 1 in a multipart seriesBig data analytics promises to boost customer centricity andprofitability for financial services firms, especially when applied tomarket research, customer segmentation, product testing, productdevelopment and customer service. Executive Summary type of data in the right format for analysis; too much data, as some organizations have learned, Big data has gained significant influence in recent may not always be beneficial. We also advise years and is rapidly transforming the business, companies to start small and take manageable operations and technology landscape for a steps toward incorporating big data analytics into myriad of industries. Early adopters — particularly their operating models. in retail and consumer products — have already derived significant business insights from big This white paper is the first in a series that data management best practices, such as analysis presents our perspective on the economic value of both the growing pools of structured trans- that can be derived from big data analytics by actional data from operations systems and the financial services companies. Subsequent white unstructured and semi-structured data generated papers will cover specific functional areas across by social media interactions. the financial services spectrum in which big data analytics can have a significant impact. According to a recent report by International Data Corp., big data is the next essential business From the Beginning capability and a foundation for the intelligent economy. According to the report, the worldwide Coined by McKinsey & Co., the term “big data”2 big data market is expected to grow from $3.2 describes large datasets that cannot be captured, billion in 2010 to $16.9 billion in 2015, a compound managed or processed by commonly used software annual growth rate (CAGR) of 40%.1 tools within a reasonable amount of time and at a reasonable cost. According to IDC, about 90% of Investments in big data solutions have helped available data today has been generated in just the enterprises achieve customer centricity and last two years. In fact, IDC estimates that: material gains in pricing and profitability. This whitepaper posits potential opportunities that big • Data volumes are growing at 50% per year, or more than doubling every two years. data analytics can create for financial services companies, citing specific business opportunities • Machine-generated data is projected to rise and benefits. Our empirical experience suggests from today’s 200 exabytes to 1,000 exabytes it is critical to generate the right amount and by 2015. cognizant 20-20 insights | february 2013
  • 2. • Multi-structured data content is the primary can now be aggregated and analyzed to identify driver of new data. 80% of this new data is escalation and complaint triggers, understand digital, which is complex to analyze in its native fraud patterns, manage alerts, reduce credit risk structure. and build social media dashboards. These devel- opments can help financial institutions tailor their • Digital data is growing at 62% annually vs. products and build strategy roadmaps aligned structured data at 22%.3 This explosion of data with customer expectations. Effective use of coupled with the growth in social networking big data will be a key driver for competition in and virtualization, has introduced unprece- financial services, and companies that use data dented opportunities for companies to better more effectively will secure an edge in the mar- connect with consumers and ketplace. Most companies understandwhere the markets as well as their sentiments, have yet to find are heading. Retaining customers and satisfying consumer expectations are among the most serious precise answers to Most companies have yet to challenges facing financial institutions. the challenges posed find precise answers to the Sentiment analysis and predictive analysis are by fast-changing challenges posed by fast- changing consumer demands; two techniques that they can use to effectively address these and other key challenges. consumer demands; many lack the ability to process many lack the ability data in near real-time or Capturing Customer Feedback Through Sentiment Analysis to process data in convert interactions into trans- actions. Big data analytics, Consumers today are just as willing to share near real-time or therefore, has become one of their thoughts on social media platforms such convert interactions the most frequently discussed as Facebook and Twitter as express them to into transactions. topics for many business leaders. a customer service representative over the phone, Web site or in person. When captured and managed, such information can provide valuable Emerging big data tools provide companies with insights into what customers are thinking. In the ability to analyze far greater quantities and addition, online customer reviews on Web sites types of data in a shorter span of time. It includes such as Amazon and Yelp are fast emerging as structured datasets, such as information stored major influencers of vendor/provider selection in databases; semi-struc- and purchasing behavior. This means financial tured data like XML files and Snippets of RSS feeds; and unstructured services institutions must carefully review this proliferating stream of unfiltered content to unstructured data datasets, such as images, gauge customer expectations and opinions oncan be interpreted and videos, text messages, e-mails product offerings and then act accordingly. analyzed, delivering and documents. New technolo- gies can help uncover insights Traditionally, companies have collected consumer insights that can hidden within these large feedback using survey and focus group results. determine likely datasets. While retailers and These tools may gauge consumer sentiment, but consumer response technology companies such as Google, Walmart, Amazon and they may not necessarily capture emerging trends or hidden insights, particularly on a real-time (both favorable Sears have made significant basis. A negative opinion of a bank’s offering can and unfavorable) to developments on this front, potentially lead to dramatic customer churn. For decisions made the doorsthe financial services open for are just starting to example, in September 2011, Bank of America announced its decision to charge customers a by the bank. industry, which stands to gain monthly debit card fee. Three days later, the bank significant advances in areas withdrew the decision after a customer uproar such as market research, customer segmenta- and threat of attrition. The reversal occurred after tion, product testing, product development and customers petitioned the bank and mobilized to customer service. close their accounts and take their banking and investment business elsewhere.4 For example, text captured from credit applica- tions, account opening interviews, call center Sentiment analysis tools aim to capture customer notes, mortgage application notes, social media feedback from social media platforms and chatter and other customer service interactions customer service interactions, among other cognizant 20-20 insights 2
  • 3. sources, and help banks evaluate the potential service features. This can help banks generateimpact of such decisions. Sentiment analysis customer “wish-lists” and incorporate these intoenables organizations to associate words used their product roadmaps.in unstructured communications and tie themto consumer emotions and sentiment on a topic. Sentiment analysis can also help banks rewardThese findings can serve as key inputs into customers effectively. This is extremely importantstrategic decision-making. across the industry because account switching costs are relatively low and customer churn isThe idea is to use technology to create codes a major challenge. By examining customer con-that analyze the Web and provide insights into fidence indices that areconsumer sentiment on a much larger scale and driven by specific dataat a much faster rate than the findings revealed elements (product, func- By examining customerby surveys or focus groups. Snippets of unstruc- tionality, content and confidence indices thattured data can be interpreted and analyzed, price), banks can judge thedelivering insights that can determine likely mood of the market and are driven by specificconsumer response (both favorable and unfavor- decide how to best reward data elements (product,able) to decisions made by the bank. their customers. Success- functionality, content ful execution drives loyaltyConsider the following customer scenarios and and also attracts new cus- and price), banks canstatements: tomers. Figure 1 illustrates judge the mood of the how banks can effective- market and decide how• “ABC Bank’s small business offering is useful ly satisfy a disgruntled for new businesses and entrepreneurs. The customer using the afore- to best reward their lack of a same-day payment facility is a downer, mentioned technique. customers. Successful though.” execution drives loyalty• “The feature to view both business and Although the technologies personal accounts is really cool, although they behind sentiment analysis and also attracts new really need to improve their customer service.” are still maturing, many of customers. the tools and techniquesThe sentiment analysis tool would pick up words are advanced enough for financial services insti-like “useful,” “lack” and “improve” and attach tutions to derive incremental value by under-contextual meaning to generate graphs and standing customer likes, dislikes and preferencesreports, which can then be used by the bank for product and service improvements. Clearly,to satisfy customer expectations. Additionally, early adoptors will gain a competitive advantagereports can be generated to illustrate trends and going forward.opinions on individual product and customerConverting Detractors into Advocates Michael has recently • The nature of the e-mails • The bank sends Michael a registered several suggests a disgruntled personal note addressing his complaints with customer customer that is likely to churn. concerns. care at his bank. The bank recognizes this and takes immediate action. • The bank offers to refinance his auto loan at a much better • The bank knows Michael has rate, saving him money and a new car loan. gaining his loyalty in return.Figure 1 cognizant 20-20 insights 3
  • 4. Using Predictive Analytics to churn or favorable response to a particular Capitalize on Customer Insights marketing campaign. For example, our work with Merchant Rewards International, a provider of Customers across the globe are increasingly credit card processing services, indicates a higher demanding simple, fast and inexpensive means response rate for offers aligned with previous to conduct both financial and purchasing trans- transaction behavior and buying propensity. actions. However, consumer needs are becoming more diverse and unpredictable, Predictive analytics can help banks build models Institutions can placing tremendous pressure based on customer spending behavior and create pre-defined on companies to fulfill them. Ongoing economic challenges, product usage to pinpoint products and services that customers might find more useful and that profiles, thereby accelerating globalization and financial institutions can deliver more effectively.revealing a history of provider choice means financial Such a model can help banks develop an efficient higher fraud volume services firms must meet and exceed traditional expecta- cross-sell offer, helping them increase their share of wallet, garner loyalty and increase profitability. through purchase tions. While failing to respond types and to dynamically changing expec- For example, profiling technology can help credit ticket sizes. tations is problematic, a larger challenge is correctly predict- card companies identify transactions, cardhold- ers and merchants that exhibit a high probabil- ing consumer needs and desires ity of fraud. Institutions can create pre-defined and responding, just in time, with the right set of profiles, thereby revealing a history of higher products and services. fraud volume through purchase types and ticket sizes. Predictive analytic techniques can be used to mine large amounts of historical data and determine Furthermore, predictive analysis can identify the likely occurrence of events in the future. aberrant behavior patterns and help financial By querying, visualizing and reporting these institutions prevent fraud. Collecting data from datasets, companies can generate actionable multiple sources, such as Web behavior and point- insights. Changing data over time can illuminate of-sale inputs, and correlating it with aggregated behavioral and transactional patterns that can data compiled from other financial services help with move-forward decisions on product and firms by third-party providers, can help banks service strategies. and brokers detect fraud earlier than existing approaches. Big data analytics not only helps Regression and response models are among the financial institutions preserve the long-sought- techniques that financial institutions can use to after “instant transaction user experience,” but it determine, for example, the likelihood of customer can also safeguard them against fraud. Unleashing Machine Intelligence Michael uses his credit • The predictive analytics system • The system allows the card to perform an online determines Michael’s location, transaction to go through transaction. time, transaction category and if the generated score merchant. probabilistically determines that the • The system then compares purchaser is actually these details with Michael’s Michael. past purchase behavior and calculates a score. Figure 2 cognizant 20-20 insights 4
  • 5. A good example is the ongoing refinement of talent to accelerate their analytics efforts, as theyneural network technology5 to assess whether often lack internal expertise or cannot afford toa credit card transaction is being performed stretch existing resources. In some cases, insteadby the real cardholder or someone committing of hiring and motivating talent internally, they arefraud. The transaction is scored against a pre- engaging third-party providers to supply talentdefined profile, and if the score passes an estab- on an “as needed” basis.lished cutoff, it is approved; otherwise, it is heldfor a fraud check. Banks have used this type of Looking Aheadartificial intelligence technology since the early Big data analytics can help financial institutions1990s to perform pattern recognition and spot derive significant benefits by increasing customerfraudulent transactions. However, big data tech- satisfaction, retention and expansion throughnologies make the process faster and more cost- more effective cross-selling and improvement ofefficient, accurate and robust. their fraud and risk management capabilities. The economic value of data will beFinancial institutions can also create ‘”predictive realized only when financialscorecards,” which can help determine the institutions fully endorse As risk managerslikelihood of customers defaulting on payments big data analytics and invest frequently say,in the near future. Among the parameters to in innovation. Although the it is better to beconsider are late utility bill payments, late car possibilities are endless,insurance payments, increases in purchases numerous challenges must approximately rightcompared with monthly averages and listening be addressed before the than precisely wrong.and learning from relevant social media conver- benefits can be fully realized.sations. In a special report in The Economist, authorAs with sentiment analysis, additional research Kenneth Cukier reveals that the recent globaland development is required to improve the financial crisis sheds light on how banks andaccuracy and effectiveness of predictive analytic ratings agencies relied on models requiringtechniques. However, when deployed strategical- vast volumes of information but failed to reflectly, these tools can help banks gain a significant financial risk in the real world.6 Therefore, toadvantage in a competitive macro-economic envi- capitalize on big data analytics’ opportunities andronment. realize significant business value, it is advisable for financial institutions to start small and growAreas such as delinquency propensity, loss gradually. Firms must find the right balance ofmitigation and cross-sell/next-best-offer scripting required information and desired insight. As riskare all specific areas offering a solid business case managers frequently say, it is better to be approx-for use of analytics techniques. Financial institu- imately right than precisely wrong.tions are making investments and hiring outsideFootnotes1 “Worldwide Big Data Technology and Services 2012-2015 Forecast,” IDC, March 7, 2012, http://www.idc.com/getdoc.jsp?containerId=prUS23355112#.UQwxhuTAeE4.2 “Big Data: The Next Frontier for Innovation, Competition and Productivity,” McKinsey Global Institute, May 2011, http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_ next_frontier_for_innovation.3 ”Worldwide Big Data Technology and Services Forecast,” IDC.4 Tara Siegel Bernard, “In Retreat, Bank of America Cancels Debit Card Fee,” The New York Times, Nov. 1, 2011, http://www.nytimes.com/2011/11/02/business/bank-of-america-drops-plan-for-debit-card-fee. html?_r=0.5 Donald F. Specht, “Probabilistic Neural Networks,” ScienceDirect, 1990, http://www.sciencedirect.com/science/article/pii/089360809090049Q.6 “Data, Data Everywhere,” The Economist, Feb. 27, 2010, http://www.emc.com/collateral/analyst-reports/ ar-the-economist-data-data-everywhere.pdf. cognizant 20-20 insights 5
  • 6. References• “Crunching the Numbers,” The Economist, May 19, 2012, http://www.economist.com/node/21554743, http://www.information-management.com/news/predictive-analytics-making-little-decisions-with- big-data-10023151-1.html.• Julianna DeLua, “Big Data Meets Sentiment Analysis,” The Informatica Blog, June 27, 2011, http://blogs.informatica.com/perspectives/2011/06/27/big-data-meets-sentiment-analysis/.• James Taylor, “Predictive Analytics: Making Little Decisions with Big Data,” Information Management, Sept. 12, 2012, http://www.oracle.com/technetwork/topics/entarch/articles/oea-big- data-guide-1522052.pdf.• “Financial Services Data Management: Big Data Technology in Financial Services,” Oracle Corp., June 2012, http://www.oracle.com/us/industries/financial-services/bigdata-in-fs-final-wp-1664665.pdf.• David Wallace, “Big Data Management for Retail Banks,” SAS, The Knowledge Exchange, July 6, 2012, http://www.sas.com/knowledge-exchange/risk/integrated-risk/big-data-management-for- retail-banks/index.html.• Christopher Papagianis, “Can Silicon Valley Fix the Mortgage Market?” Reuters, April 25, 2012, http://blogs.reuters.com/christopher-papagianis/tag/big-data/.About the AuthorsVin Malhotra is a Partner with Cognizant Business Consulting’s Banking and Financial Services Practice.He has 25-plus years of experience in management consulting, focused on retail, commercial andmortgage banking clients. His clients have included international and regional banks, credit unionsand Fortune 1000 firms in the BPO, payments and financial technology space. He has served clients inmultiple geographies, with project delivery in the U.S., Latin America, Central America and Europe. Vincan be reached at Vin.Malhotra@cognizant.com.Sudhir Jain is a Senior Manager within Cognizant Business Consulting’s Banking and Financial ServicesPractice. He has 10-plus years of experience in capital markets, risk management, collateral managementand margining with top-tier banks in the U.S., Singapore and India. Sudhir leads a team of business con-sultants who provide advisory services and software development to leading banks. He can be reachedat Sudhir.Jain@Cognizant.com.Rahul Kumar is a Senior Consultant within Cognizant Business Consulting’s Banking and FinancialServices Practice. Rahul has three-plus years of experience in consumer banking at one of the world’slargest banks. Rahul can be reached at Rahul.Kumar8@cognizant.com.About CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out-sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered inTeaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industryand business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50delivery centers worldwide and approximately 156,700 employees as of December 31, 2012, Cognizant is a member ofthe NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performingand fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant. World Headquarters European Headquarters India Operations Headquarters 500 Frank W. Burr Blvd. 1 Kingdom Street #5/535, Old Mahabalipuram Road Teaneck, NJ 07666 USA Paddington Central Okkiyam Pettai, Thoraipakkam Phone: +1 201 801 0233 London W2 6BD Chennai, 600 096 India Fax: +1 201 801 0243 Phone: +44 (0) 20 7297 7600 Phone: +91 (0) 44 4209 6000 Toll Free: +1 888 937 3277 Fax: +44 (0) 20 7121 0102 Fax: +91 (0) 44 4209 6060 Email: inquiry@cognizant.com Email: infouk@cognizant.com Email: inquiryindia@cognizant.com©­­ Copyright 2013, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by anymeans, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein issubject to change without notice. All other trademarks mentioned herein are the property of their respective owners.