Turning Customer Knowledge into Business Growth


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By embracing big data and predictive analytics to create multidimensional customer profiles, companies can make more informed business decisions that better anticipate customer needs, wants and desires.

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Turning Customer Knowledge into Business Growth

  1. 1. Turning Customer Knowledge into Business Growth By embracing big data and predictive analytics to create multidimensional customer profiles, companies can make more informed business decisions that better anticipate consumer needs, wants and desires. | KEEP CHALLENGING
  2. 2. Executive Summary Customers today can access an unprecedented volume of information via varied channels before making an informed purchase. For organizations, this means continuously learning from customer behavior to stay relevant. But while there is no dearth of customer data available, organizations often grapple with the challenge of developing clear, complete and fully updated profiles of their customers. In a 2012 study, conducted by Columbia Business School and New York American Marketing Association,1 39% of corporate marketers said their company’s customer data was collected too infrequently and was not up to date. Meanwhile, a January 2013 study by Aberdeen Group2 found that top-performing companies are more likely than others to use a rich set of data sources to feed their predictive analytics models, including internal transaction data and unstructured or real-time data, to provide actionable guidance for decision-makers (see Figure 1). Creating Rich Customer Profiles Data Source Leaders Followers Internal transactional records 93% 74% Internal customer records 75% 80% Customer sentiment data 57% 29% External customer information 56% 36% Customer interaction data 56% 36% Clickstream data 40% 18% Unstructured data 38% 29% Base: 157 Source: Aberdeen Group report, January 2013 Figure 1 2 KEEP CHALLENGING December 2013
  3. 3. The use of big data and analytics can be extended to customer relationship management (CRM), as companies need to combine structured and unstructured data with powerful analytics tools to create a multidimensional customer profile. This white paper describes a solution concept and implementation approach to developing a multidimensional customer profile and deriving actionable insights with the help of big data and analytics. TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 3
  4. 4. The Data Challenge Organizations have traditionally used structured customer data stored in their enterprise systems to develop customer profiles. Additionally, a few have attempted to incorporate external data purchased from third-party agencies, by converting it into structured formats that can then be stored in their enterprise systems. However, this approach results in customer profiles that, at best, are incomplete in the following ways: • Data stored in enterprise systems is dated and restricted to past interactions. Many times, data integrity is questionable; for instance, a promotional mailer may use a customer address from the CRM system, but if the customer has relocated, the promotion campaign is rendered ineffective. • Agency data is based on extrapolated customer surveys, which can never replace actual data insights on individual customers. • Customers no longer use only company-operated channels. Consumers have a much broader footprint through social media to broadcast their experience with the company’s products or services or even their intent to switch to a competitor’s offerings. For any sales and marketing team, it is vital to keep current with the pulse of the customer, and this cannot be accomplished by relying solely on internal enterprise data. Because of these factors — and with the fast uptake of social, mobile, analytics and cloud technologies (the SMAC Stack™), creating customer profiles without semi- or unstructured data can render an organization uncompetitive and even irrelevant. The Customer’s Multiple Dimensions For any sales and marketing team, it is vital to keep current with the pulse of the customer, and this cannot be accomplished by relying solely on internal enterprise data. Information avenues that can provide crucial insights include social media activity, browsing behavior, mobile app downloads, games played, past purchases, photos shared, music/video preferences and vacation choices. We call the accumulation of all these activities a Code Halo™, which is essentially the digital footprint created by enterprises, customers, employees and processes from their online behavior. Business leaders such as Amazon and Google have quickly risen to the top of their industries by deriving meaning from the intersections of Code Halos and building their strategies around these insights. (For more on this topic, read our white paper, “Code Rules: A Playbook for Managing at the Crossroads.”) A true view of the customer, then, needs to link the details stored in enterprise systems with Code Halos, or external customer information. This consolidated or augmented view presents a near-real-time and complete picture of the customer (or potential customer) with which the business is interacting. Because the traditional view completely ignores the social aspects of the customer, it can best be described as a dormant description that is waiting to be brought to life by social information and the customer’s Code Halo. 4 KEEP CHALLENGING December 2013
  5. 5. However, this does not happen automatically; semi- and unstructured data that supplies information on customer activity in the external world needs to be analyzed and indexed before it can be melded with structured data from enterprise systems and delivered in the form of a multi-dimensional customer profile. We call this process AIM (or analyze, index and meld) & Deliver. The multidimensional customer profile is like a coin with two sides; the face of the coin depicts the structured data elements of the customer, and the back depicts the unstructured data elements. When both of these aspects are melded and delivered together, the true customer profile can be derived. The multidimensional customer profile can also be visually represented by a sphere (see Figure 2). Note that when you slice this sphere, you can look at various aspects of the customer and company from both structured and unstructured perspectives. Once the multidimensional customer profile is available, it opens up multiple use cases that drive real-time actionable insights. The insights can be made available Creating the Multidimensional Customer Profile Creating the Multidimensional Customer Profile Unstructured Events Mobile Life events Apps Games Photo ent Environm er y Weath Econom Audio Searches Video Downloads Comments Docs Sharing Favorites Company Web site Web/mobile clickstream Store Browsing behavior Product pages visited Footfalls interest Product Location Intelligence Frequently used Web site Social Videos Podcast ntiment se /brand Product nce l Influe fessiona Pro Social s fluencer in Product PAS Current residence Frequent visits Device preferences Competitor purchase interest Interaction history Travel/vacation Points of interest Blogs Boards Job profile networks Social networks Professional networks Forums Skill set Likes Dislikes Bookmarks Product failures Product comments Sharing Circle Demographics 3 Age Allied product interest Payment history Credit history history Professional Customer influencers Product/brand interest groups/ Product y hierarch Contact center Professional Influence Social Micro-blogs Customer surveys y Compan program Loyalty Benefits Tiers tabase Offers da tions tore loca S Gender Other Customer Offer responses Credit-worthiness Past offers Credit terms Channel Chat Direct mail Grievances groups/ Product affinity E-mail Search keywords Accept/ignore Service history Cases Product interest Purchase history Preferred mode Contact preference orks er netw Partn y Inventor y lit availabi Data Elements Attributes Structured Figure 2 TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 5
  6. 6. The CRM Analytics Continuum Creating the Multidimensional Customer Profile Customer trigger Analytics insights lead to decision maps for executives. Analytics-based insights lead to decision matrix for field service reps. Connect with real-time customer profile.* Unstructured Product Affinity Mobile Apps Games Photo Economy Audio Searches nt Environme Weather Video Downloads Comments Product pages visited E-mail Frequently used Web site Location Intelligence PAS Customer Customer Product Cross-Sell ID Profile Purchased Offer Web/mobile clickstream Browsing behavior Points of interest Boards Social networks Professional networks Likes Dislikes Bookmarks Sharing ase Loyalty Tiers ions Store locat Partner Age Allied product interest program Benefits Gender Offer responses Credit-worthiness Past offers Credit terms Offers datab B2 Brand B Brand C Y 123 Demographics Customer surveys 3 Y Product comments Circle Payment history Credit history Company Brand C Skill set Micro-blogs history Brand A XYZ Job profile networks Product/brand interest Channel A1 Professional Customer influencers Forums ABC Interaction history Professional Influence Social Product failures groups/ Product hierarchy Brand C CrossSell Success Direct mail Contact center Travel/vacation Blogs Chat Search keywords Device preferences Competitor purchase interest Current residence Frequent visits Grievances groups/ Product affinity Docs Sharing Favorites Company Web site Store Footfalls interest Product Social Videos ment Podcast rand senti Product/b nce al Influe Profession Social influencers Product High A1 Customer Profile Acceptance Events Life events A1 Brand C Brand A Y DEF C3 Brand D Brand B Y Other Customer Accept/ignore Service history Cases Product interest Brand A Brand B High Low C3 Brand E Brand D Brand B Purchase history Low Preferred mode Contact preference networks Inventory availability Structured *Powered by the AIM & Deliver process. Figure 3 5 and customized for different stakeholders in the form of decision matrices/maps that can be leveraged for real-time data-driven decision-making. The effectiveness of decisions using this approach drives continuous closed-loop feedback (see Figure 3). An example of this is real-time cross-sell offers, in which the decision matrices/ maps can vary for different stakeholders (see Figures 4 and 5). Using the multidimensional customer profile derived from big data and analytics, the contact center agents, sales representatives and any other customer-facing personnel have access to the exact real-time offers they need to entice customers or prospects. This kind of decision-making is more operational in nature and targeted to the timing of the customer trigger. 2 At the same time, the multidimensional customer profile can deliver the muchneeded fuel to power analytics for executive decisions. In order to understand which offers performed well and the changes needed to improve the offer management process, executives would need a dashboard providing planning insights such as purchases made to date, potential pairing across products and categories, and customer profile acceptance levels to boost success rates. Cross-Sell Decision Matrix for the Customer Operations Team Customer Profile Product Purchased Cross-Sell Offer Cross-Sell Success ABC A1 Brand A Brand C Y XYZ B2 Brand B Brand C Y 123 A1 Brand C Brand A Y DEF C3 Brand D Brand B Y Customer ID Figure 4 6 KEEP CHALLENGING December 2013
  7. 7. Cross-Sell Decision Map for Executives Product Affinity High A1 Customer Profile Acceptance Brand C Brand A Brand B High Low C3 Brand E Brand D Brand B Low Figure 5 Implementation Approach To implement the solution, we recommend a four-phased approach (see Figure 6). Phase 1: AIM & Deliver To initiate the first phase of the AIM & Deliver process, the underlying data elements must be identified. This entails merging the customer details available within and outside the enterprise (see Figure 7, next page). Disparities across the data sources need to be ironed out to associate customer data within the enterprise with the right data sources in the external world. This can be done with advanced analytics. By combining automatic entity extraction with name matching, users can automatically identify entity mentions in unstructured data and link them with structured information. This linkage simplifies the process and combines data about an entity into a complete customer profile. AIM & Deliver Phase 3 Phase 2 Phase 1 Four-Step Implementation Process Phase 4 5 Analyze Index Meld Deliver • Entity extraction • Document clustering • Attribute matching • Customer name matching • Link structured data • Link customer to enterprise applications • Real-time customer profile • Location intelligence Evaluate business case and stakeholders Define stakeholders and detail the use case. Assess business relevance, technology and economic hurdles. Big data architecture Design the big data architecture after use case crystallization. Analytics engine Configure analytics engine for actionable insights. Derive real-time multidimensional customer profile. Deliver augmented real-time multidimensional customer profile Figure 6 TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 7
  8. 8. Merging Two Worlds of Data Loyalty Program Data Contact Preferences Orders/ Pipeline / Analytics BI Structured Data s Contracts En t Channel History Credits/ Terms Structured Surveys Social Events Scan Curation Documents Contact History eS pris ystem er Payment History Campaign Data Quote Purchase History Service History Agency Data Quantified Self Transactions Influence Professional Network Activities E-commerce Social Bookmarking Index Voice Portal/IVR Agency/ Semi-/Unstructured Data Analyze Photo Sharing M-commerce Location Intelligence Contact Center Data Web Clickstream Direct Mail Video Sharing Social Activity Boards/ Forums/ Activities Boards/ Forums/ Activities Online Searches Mobile Activity Blogs/ Microblogs Unstructured Surveys POS Store Transactions E-mails/ Surveillance Chat Figure 7 The key steps involved with combining these two different genres include: • Analyze the different types of data, clustering them based on specified parameters and extracting entities, such as customer name, organization, product name, location, etc. • Index the clustered data sets and create structured metadata for each entity, enabling fast filtering and searching by people, places, company names or other entities. 8 KEEP CHALLENGING December 2013
  9. 9. • Meld the extracted entities with near-perfect attribute matches (i.e., accurate customer names with existing customer data in the CRM system). • Deliver the augmented customer profile, enhanced with location intelligence for easy consumption by CRM systems, BI/analytics or any other point solutions. Phase 2: Evaluate the initiative’s business case and stakeholders. This crucial step can make or break the overall initiative. We offer a proven approach to creating and finalizing the business case for big data analytics that is specifically relevant from a CRM perspective. The use case-driven approach can help map the business requirements tightly with the big data technology design considerations, such as relational storage and query, distributed storage and processing, and low latency/in-memory. This cost-benefit analysis-based approach can help define the stakeholders and detail the use case while also assessing ROI. It helps answer questions such as: • How do you approach your first big data implementation? • Do you have the information necessary to determine the approach? • How can you ensure you receive the business value of the big data journey? • What metrics and cost factors affect the success of your big data program? The output of this step provides the company with a business case and an ROI calculation to ensure management will fund the initiative. More than a proof of concept, this process results in a proof of value and helps customers understand the business relevance, technology challenges and economic hurdles of a typical big data/analytics engagement. Phase 3: Design the big data architecture and configure the analytics engine. Once the business use cases have been crystalized, the big data architecture and analytics engine needs to be designed for focused analysis and to derive actionable insights for different stakeholders. This significantly reduces the time to value and also brings a sharp focus to the expected business outcomes. The use case-driven approach can help map the business requirements tightly with the big data technology design considerations, such as relational storage and query, distributed storage and processing, and low latency/in-memory. This, in turn, leads to a sustainable and scalable architecture. The analytics engine must then be configured for linking datasets around an entity (e.g., what do I know about this customer?) or around a relationship (e.g., how is this customer related to others?) Successfully configured, such analytics can produce qualitatively new insights that result in business value, such as reduced customer churn rate, next best action and better predictions of risk and failure. TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 9
  10. 10. Making Meaning from Digital Fingerprints Applications Improved Upsell/Cross-sell Real-Time Offers Enhanced Marketing Effectiveness Proactive Servicing Personalized Campaigns Service customers proactively using social listening. Use the multidimensional profile to personalize campaigns. • Positive • Campaign acceptance Objectives Delight customers and cross-sell/upsell by making intelligent, realtime recommendations. Create offers and next best offers on the fly based on updates to realtime customer profiles. Fine-tune offers and channel effectiveness during campaign planning and creation. Success Metrics • Increase revenue generated from cross-sell/upsell offers. Increase share of wallet. Increase customer satisfaction scores. • Increase number of • Reduce marketing • • real-time offers sent. Improve offer acceptance rate. Reduce customer offerrelated spending. Reduce turnaround time for offer. • • • campaign costs. Increase lead conversion. sentiment service level. Customer loyalty. rate. • • Figure 8 Phase 4: Create real-time, multidimensional customer profiles. Once the multidimensional customer profile is established, the possibilities are endless. The profile provides access to customer data residing not only in the enterprise but also from every other area in the external world with which the customer has interacted. In essence, the profile captures every digital trace that the customer creates. This invaluable data can now be exploited for driving several applications (see Figure 8). Challenges Along the Way Companies can expect to be faced with several challenges when developing multidimensional customer profiles, including: • Data explosion: Customers are increasingly interconnected, instrumented and intelligent. Accordingly, an unprecedented velocity, volume and variety of data is being created. As the amount of data created about consumers grows, the percentage of data that businesses can process quickly decreases, because traditional systems cannot store, process and analyze massive amounts of structured and unstructured data. Business systems are not designed for today’s unstructured data, rapidly changing schema and elastic scaling of storage. • Privacy and regulatory issues: Another issue is regulatory and privacy issues. Data collectors bear a tremendous responsibility to provide full disclosure of what they plan to do with customer data. But an even greater challenge is the sharing of data. For instance, if a consumer grants one company permission to use his or her data, what rules (if any) will regulate how that information is shared across multiple companies? Such questions will become one of the biggest sticking points in terms of trying to navigate the right policies. 10 KEEP CHALLENGING December 2013
  11. 11. Data collectors also need to make it easier for customers to opt in or out of having their information used, similar to opting into mailing lists or using an “unsubscribe” option to opt out. When consumers feel they’re getting a tangible benefit for their personal information, their resistance to data collection starts to fade. Loyalty and rewards programs are a good example of how companies can persuade customers to reveal more details about behaviors such as shopping habits. Looking Forward Leading organizations are already gearing up to create multidimensional customer profiles using both structured and unstructured data sources. Complete and continuously up-to-date customer profiles enabled by big data and analytics are increasingly an essential tool in the arsenals of organizations across industries and geographies. ` Footnotes 1 “Marketing ROI in the Era of Big Data: The 2012 BRITE-NYAMA Marketing in Transition Study,” Columbia Business School and NYAMA, 2012, http://www4.gsb. columbia.edu/null/2012-BRITE-NYAMA-Marketing-ROI-Study?exclusive=filemgr. download&file_id=7310697&showthumb=0. 2 “Maximizing Customer Lifetime Value with Predictive Analytics for Marketing,” Aberdeen Group, February 2013, http://www.aberdeen.com/_aberdeen/public/viewlookinside-pdf.aspx?cid=8362. About the Authors Sairam Iyer is a Senior Information Management and Analytics Consultant with Cognizant Business Consulting’s Enterprise Information Management Practice. His core responsibilities include providing thought leadership in the areas of business intelligence and analytics, and consulting with clients across industry verticals. Sairam has nine years of rich experience with Fortune 100 companies, specializing in CXO and business leader-level workshops to understand business processes and concerns and convert them into business intelligence and analytics solutions. As a multidisciplinary BI strategy expert, he has hands-on experience in executing information management and analytics engagements from concept to delivery. Sairam obtained his M.B.A. from the Xavier Labor Relations Institute (XLRI), Jamshedpur, specializing in marketing and strategy. He can be reached at Sairam.Iyer@cognizant.com. Vikas Singhvi is a Senior CRM Consultant with CBC’s Enterprise Applications Services (EAS) Practice. Vikas’s core responsibilities include working on consulting projects in the sales, marketing and customer service domains across industry verticals. He has four-plus years of progressive experience in business strategy, customer relationship management consulting, digital marketing consulting, sales and marketing process consulting and business development. His consulting experience includes extensive multicountry project exposure across the hightechnology, retail, manufacturing-logistics, information services and transportation domains. Before joining Cognizant, Vikas worked with Microsoft India as an APEX (Accelerated Professional Experiences) member, which is a program for highpotential entry-level employees. Vikas received his M.B.A. from the prestigious Indian Institute of Management at Indore, specializing in marketing and strategy. He can be reached at Vikas.Singhvi@cognizant.com. TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 11
  12. 12. About Cognizant Business Consulting With over 3,600 consultants worldwide, Cognizant Business Consulting (CBC) offers high-value consulting services that improve business performance and operational productivity, lower operational expenses and enhance overall performance. Clients draw upon our deep industry expertise, program and change management capabilities and analytical objectivity to help improve business productivity, drive technologyenabled business transformation and increase shareholder value. To learn more, please visit http://www.cognizant.com/business-consulting or email us at inquiry@cognizant.com. About Cognizant Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50 delivery centers worldwide and approximately 166,400 employees as of September 30, 2013, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant. World Headquarters 500 Frank W. Burr Blvd. Teaneck, NJ 07666 USA Phone: +1 201 801 0233 Fax: +1 201 801 0243 Toll Free: +1 888 937 3277 inquiry@cognizant.com European Headquarters 1 Kingdom Street Paddington Central London W2 6BD Phone: +44 (0) 207 297 7600 Fax: +44 (0) 207 121 0102 infouk@cognizant.com Continental Europe Headquarters Zuidplein 54 1077 XV Amsterdam The Netherlands Phone: +31 20 524 7700 Fax: +31 20 524 7799 Infonl@cognizant.com India Operations Headquarters #5/535, Old Mahabalipuram Road Okkiyam Pettai, Thoraipakkam Chennai, 600 096 India Phone: +91 (0) 44 4209 6000 Fax: +91 (0) 44 4209 6060 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 any means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.