Social Media Analytics: Enabling Intelligent, Real-Time Decision Making


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Companies that rely on social media to interact with employees, vendors and customers can take advantage of advanced and predictive analytics to extract and analyze the increasing amounts of unstructured data generated by social media, and use that information to understand customers' issues and preferences; gauge marketing campaigns; enhance their competitive intelligence, and improve decision making throughout the organization.

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Social Media Analytics: Enabling Intelligent, Real-Time Decision Making

  1. 1. • Cognizant Reports Social Media Analytics: Enabling Intelligent, Real-Time Decision Making Today, an increasing number of organizations rely on social media for interacting internally, as well as with external constituents. Using advanced and predictive analytics applied holistically via a centralized “command center,” companies can mine growing pools of unstructured data, deliver more timely and actionable insights, and better inform business and operational strategies. cognizant reports | August 2013
  2. 2. Executive Summary With more than 1.5 billion users worldwide,1 social media offers a treasure trove of information in the form of real-time, interactive communications made available through blogs, tweets, updates, images and videos. Not surprisingly, organizations are growing more and more reliant on social media to understand and work more responsively with employees, vendors and customers, and better gauge the competition. However, mining and analyzing the huge volumes of unstructured data generated by social media is no easy task. Using social media analytics, organizations can mine and decipher vast amounts of data from various social media platforms to discover customer sentiment about brands, trends, the issues customers face, the efficacy of marketing campaigns and competitor intelligence, for example. Findings can be used by sales, marketing and other functions to support more informed and timely decisions. By adding predictive analytics, organizations can more accurately forecast customer needs and behaviors, and anticipate and deal with issues before they can damage the business’s reputation. Yet achieving this level of knowledge can be a real challenge. We have developed a framework, LAEI, that allows companies to be successful in their social media initiatives. The first step for any organization is to listen to conversations. This could be active listening using certain tools and a dedicated team for owned and earned media, or passive listening during a time of crisis. Listening helps in collecting all the data, and using the power of technology and human interactionsto analyze that data for business insights. The next step in the journey is the most critical, and one where we see most customers experience a disconnect. Once you have insights, it is important to use them as part of your engagement strategy. This could be as simple as responding back to one-on-one conversations for customer service or as complex as using the insights to drive content strategy for marketing. The last step is to integrate the social data and combine it with enterprise data to obtain the digital profile of your customer. Organizations with limited capabilities and budgets can pursue analytics as a service (AaaS), an emerging service delivery model that provides access to third-party specialists that can cognizant reports offer analytical insights – dynamically shifting the cost of owning the technology infrastructure, processes and talent from the organization to an expert partner. Driving Forces The Rise of Social Media According to an Experian Marketing Services study,2 U.S. consumers spend 27% of their total Internet time on social networking sites and forums. Facebook has more than 1.1 billion active users. Twitter, on average, records 58 million tweets every day. These statistics, combined with the millions of blog posts on the Internet and discussions that occur by the minute on forums and social networking sites, for example, can provide a rich and growing pool of data on market trends and such things as consumer interests and perceptions. Still, data is one thing; analyzing it successfully to gain useful insights is quite another. The Growth of User-Generated Content Many consumers enthusiastically post their experiences with brands and write product reviews on various social media platforms like wikis, blogs and social networking sites. Such user-generated content (UGC) is perceived by pundits to carry more value and make brands more trustworthy than any company advertisement. According to a 2012 Nielsen survey of 28,000 global Internet users, 92% of consumers trust recommendations from friends and family more than any other form of advertising. Seventy percent of customers place their trust in online consumer reviews – making this medium the second most trusted form of advertising.3 Marketers, too, are encouraging users to comment, submit pictures and videos, rate products and write reviews. However, user-generated content is mostly informal, and analyzing it can be difficult – especially when trying to ascertain the rationale underlying certain comments and ratings, what customer posts mean, and the significance of the type of medium used, for example. The Challenge of Unstructured Data According to Gartner, 80% of enterprise data – documents, e-mails, call logs, corporate blogs and the like – is unstructured (i.e., it does not fit into any traditional database).4 The proliferating use of social media data (including tweets and comments in colloquial style, images, videos, 2
  3. 3. blog posts, etc.) is exponentially increasing the amount of unstructured data to be sorted, analyzed and used to gain meaningful insights. Yet most organizations do not have the resources or tools needed to sift through and interpret the vast quantities of social media data they have at their disposal without making considerable changes to their IT infrastructure, operational processes and organizational structure. brand mention, customer feedback and discussions, for example. The scope of data collection depends on the business purpose, such as gauging the market’s perception of a new product, monitoring marketing campaigns, creating brand awareness and performing competitor intelligence. Data-gathering tools (free or subscription-based) can help organizations collect customers’ tweets, blog posts, status updates, etc., in real time from various social media sites, based on pre-set search parameters. This allows companies to track and respond to individual customer updates and tweets as soon as they are received. For example, from Q2 2012 to Q2 2013, brands improved their response rates on Facebook by 143%, according to a survey by Social Bakers. The airlines industry led in social customer care by answering 79% of customer questions, closely followed by finance (78%) and telecom (75%). Dutch airline KLM is the most socially devoted brand – answering 98% of questions on Facebook at an average response time of 45 minutes.6 While the “digerati” seem to agree that social media data presents significant opportunities, few organizations appear to have the strategies, skills and tools in place to analyze the data. In fact, analyzing and applying data of all types and formats are the biggest data-related challenges in 2013 for 45% of the 700 marketers surveyed by Infogroup Targeting Solutions and Yesmail Interactive (see Figure 1).5 Advanced social analytics can help organizations analyze and quickly draw inferences from burgeoning unstructured social media and enterprise data, and convert it into actionable insights. Harnessing Social Media Data Using the LAEI Framework In our view, organizations need a holistic strategy for exploiting social media’s full potential. We recommend that companies build a social media analytics framework around four critical steps – listen, analyze, engage and integrate – to effectively use social media for intelligent decision making (see sidebar, next page). • Listen: The first step involves identifying and collecting relevant social media data around • Analyze: The next step involves analyzing the collected data to understand customer sentiment. However, the data will contain plenty of irrelevant information – especially if organizations use social media crawlers to find comments with brand mention. This makes it difficult to pin down what customers are actually saying. Removing the “noise” around the data will help improve the accuracy of the analysis. Semantic analysis is an advanced data-cleansing method that groups large amounts of data Data Challenges in 2013 Hiring Qualified Employees 8% Real-time Data Collection 11% Collecting Data 11% Protecting Customer Data and Privacy 12% Cleaning Data 13% 20% Applying Data Analyzing Data 25% Source: Infogroup Targeting Solutions and Yesmail Interactive, 2013 Figure 1 cognizant reports 3
  4. 4. segments based on their behavioral patterns. Segmentation helps shed light on issues specific to each group, and address group patterns as a whole. It can be used to design marketing campaigns for each target segment. For instance, marketers can offer high-value customers greater discounts and other incentives to persuade Social network them to stay. analytics allow based on the relationship between words and/or phrases. It provides a higher level of refinement than text analytics or natural language processing tools, and exceeds other traditional methods that involve correcting typos and errors, removing duplicates and using Boolean operators such as “and, or, and no” to limit the search, for example. Semantic analysis goes beyond classifying customer comments into positive, negative and neutral, and provides insights into what customers think about products, including what they like and what improvements they would like to see. organizations » Identifying influencers: Customers share to identify the varying degrees of strength of these relationships with relationships and other individuals within a group. Social how information network analytics flows within groups. allow organizations to Most important, identify the strength of these relationships and these tools enable how information flows companies to within groups. Most target group important, these tools enable companies to influencers target group influenc- who can best ers who can best affect affect members’ members’ decisions. Influencers can be decisions. used to quickly bring a new service or product to market, attract new customers and prevent mass defection through incentives like special offers. The latest cloud-based tools for customer feedback analysis support multiple languages at advanced levels – providing accurate and realtime information about various markets. For instance, Finnish retailer Kesko uses Etuma’s text feedback analysis to understand customer experiences by analyzing numerous surveys and customer feedback. This has helped the retailer identify and resolve issues related to customer dissatisfaction, enhance its ability to react to problems, and improve product availability and day-to-day operations.7 Further, analyzing social media data helps organizations in the following aspects: » Customer segmentation: Using customer demographics and other personal information collected from different sources, organizations can divide customers into Quick Take Applying the LAEI Framework A leading global pharmaceutical company organizing a fund-raising event wanted to monitor the Twitter conversations of attendees to understand what they thought about the company. It also wanted to moderate and display tweets manually, in real time, on a large screen during the conference. We created a federated Twitter governance tool that captured all conference-related tweets in real time – allowing multiple moderators to filter and update their feeds and display approved feeds on the screen. An additional layer for checking regulatory compliance was incorporated during the requirements analysis phase. More than 6,000 conversations were monitored and moderated during the conference. The tool helped identify the top tweets, trending topics and what key opinion leaders were talking about. More important, it enabled the company to identify key influencers, and understand attendees’ sentiments of the company. The exercise increased attendees’ engagement levels, as evidenced by their heightened Twitter activity. It also created a buzz and user-generated content on social channels, which had a positive impact on the brand – resulting in the company raising US$70,000 — a substantial increase over the US$50,000 it had targeted. cognizant reports 4
  5. 5. ready to purchase, and direct them to the nearest stores. » Gain insight into customer spending habits, improve location-based services and identify locations for real-world marketing campaigns. Tools such as Klout8 can be used to gauge a person’s online influence based on their responses to social media posts. • Engage: Customers who are engaged with companies through social media spend 20% to 40% more than other customers, reveals a Bain & Co. study of more than 3,000 customers.9 Analyzing social media posts provides a deeper perspective on trending topics, hot brands and the type of content that is being shared, for example. This kind of analysis can be used to drive relevant content posts on channels like Twitter, Facebook, Instagram and blogs, and propel content shares. Predictive analytics can also be used to understand what would interest customers, and the ideal time to publish content to “sweeten” content performance. For instance, Adobe Social predicts engagement levels and proposes the best time to post content on Facebook in order to improve content engagement and interaction. • Integrate: This stage involves integrating unstructured data across the organization with enterprise structured data to obtain a 360-degree view of customers. To achieve this, organizations must integrate their social media platforms with their existing master data management (MDM) systems. Once a customer’s social media data flows into the organization, the MDM hub can search to determine whether the customer profile already exists within the enterprise database. If so, it can automatically add relevant social media data to the master customer file. It can also update customer profiles whenever changes are made in source systems to reflect the latest customer information. Integrating social data with the MDM hub offers multiple benefits by enabling companies to: » Create digital profiles of customers to uncover various types of relationships and influencers. » Provide insights on customer activity across social channels. » Pull user location data as soon as customers update their location, using the check-in feature on social media sites. Sales can use this information to reach out to customers who are on the move and cognizant reports Social Media Command Centers: The One-Stop Shop A social media command center collects relevant conversations in real time, and then analyzes them to provide insights about customer sentiment, brand performance and the competition, for example, to inform decisions across various functional areas within the organization. Companies such as Dell, Cisco and Gatorade have implemented social media command centers primarily to listen and respond to customer conversations quickly.10 By combining data visualization tools, social media platforms and analytics, command centers allow organizations to monitor relevant online chatter in real time. This information can be used to quickly reach out to customers and support them in suitable ways, thus helping to secure their loyalty. For instance, T-Mobile uses a social media command center to prevent customer churn. Auto companies are employing these centers to predict recalls. General Electric has a command center to help the company quickly locate power outage areas and repair electric grids.11 Real-time monitoring can help adjust content based on hot topics, make on-the-fly changes to marketing campaigns and design content to improve customer engagement, for example. The latest tools allow companies to add data from other systems, such as customer relationship management (CRM), and configure data visualizations for smartphones, PCs and other mobile devices, apart from large television screens. Social media command centers have also been employed by sports organizers and non-profit organizations. The organizers of the Super Bowl, for example, launched a social media command center in 2012 to enhance the experience of the estimated 150,000 fans who visited the game site in Indianapolis. In this case, the center provided information about safety and service. The command center used keyword-based monitoring and geo-targeting of the Indianapolis/Indiana area across major social media sites12 to identify 5
  6. 6. • Marketing: Organizations can no longer rely on analyzing yesterday’s customer chatter to devise today’s marketing campaigns. Social media analytics helps marketers cope with fast-changing customer preferences through real-time marketing. By discovering trending topics, marketers can quickly hone tweets and social media updates to align with hot topics, stay relevant and drive customer engagement. Companies such as Dell and McDonald’s use social media analytics to listen to customers in real time and adjust ad campaigns and content on the fly to resonate with social media users. In fact, based on social media feedback, Fifth Group Restaurants decided to tone down one of its Mexican dishes made with chilies, in spite of an internal debate.14 Marketers can also use image recognition technologies to see what images are being shared by customers and their impact on sales, for example. (See sidebar, next page). • Sales: Predictive analytics, such as affinity or market-basket analytics, provides details about products that are often bought together, as well as the right combination of products and services for customers – such as a game and a movie based on the game. This information can be used for cross-selling and up-selling, and customizing products and services. Customer sentiment can be used to forecast sales and revenues, and prepare in advance for any spikes in demand. The following are areas where social media analytics can have a big impact: • Innovation: Product development teams can tap into social media to understand what customers like or dislike about a brand, the desired product features Organizations that a target demographic can no longer wants, and popular features of competitors’ products. rely on analyzing This information can be yesterday’s used to fix defects in the customer chatter next iteration, trigger new ideas, and also review curto devise today’s rent ideas and products in marketing development. Most crowdcampaigns. sourcing campaigns now use social media to fuel ideas and contributions. Feedback on new product demonstrations can also provide inputs on customer preferences in various markets. • Customer service: Social media channels can help companies identify potential customerservice issues before they spiral and inflict damage to a brand’s reputation. By monitoring social media for real-time feedback during a new product release, the customer service team can identify issues and proactively reach out to customers to fix glitches. Customer service can also forecast what type of problems customers may encounter during specific times and prepare accordingly. • Competitive intelligence: In business, nothing can be more valuable than solid competitive intelligence. Social media analytics allows companies to track competitor mentions on social media, and understand how competitors are leveraging various social media platforms for brand promotion and customer engagement, for example. This information can be cognizant reports 6 and respond immediately to visitors who posted on Twitter, Facebook and other social media platforms on event-related issues. The command center staff answered attendees’ inquiries about the event, routes, parking, food, cab service, hotels, tourist attractions and emergency tips, and provided real-time updates about traffic, weather, etc. The initiative was a hit, and managed to attract 50,000 fans – 10 times more than expected, and at a 3.6 to 1 positive to negative sentiment ratio.13 While numerous big brands have built their own command centers, others are undecided – fearing the repercussions of huge investments. Companies can build their own state-of-the-art command centers by partnering with technology providers, forming joint ventures, using managed services or choosing another evolving business model. The Case for Advanced Social Analytics Social media analytics has grown from simply being a tool for collecting customer likes and comments to an opportunity to gain critical business insights and make quick and effective decisions. By augmenting social media analytics with predictive capabilities, organizations can more accurately forecast what their customers are likely to do. Predictive analytics involves the use of regression models and advanced techniques, such as neural networks, to provide a complete view of customers and their future actions based on their transactional, social-media and other data.
  7. 7. useful for reviewing and strengthening current social media strategies. Monitoring reviews and posts by bloggers and thought leaders about competitive products can provide valuable inputs that can be used to enhance various functions across the organization. Embracing Analytics as a Service Analyzing social media and other enterprise data is a difficult task. Handling huge volumes of data poses a significant challenge for organizations and requires substantial investments in people, processes, IT tools and infrastructure. Other challenges, such as a lack of domain capabilities and budgets, disparate databases and organizational silos can prevent organizations from effectively using social media data (see Figure 2, next page). A partner with the ability to handle complex analytics tasks can help companies take better advantage of analytics. With process virtualization and cloud computing, opportunities now exist for cost-cutting through global sourcing via the Business Process as a Service (BPaaS)15 delivery model. This can save precious capital expenditures (Cap-Ex) – estimated by some industry sources at up to 30% – by eliminating the cost of acquiring expensive hardware, software and key talent through outcomes-based and consumption pricing models. A subset of BPaaS, analytics as a service (AaaS) combines traditional knowledge process outsourcing (KPO) and business process outsourcing (BPO) capabilities with more efficient, cloudenabled ways of delivering analytical insights. This approach allows organizations to deploy analytics solutions tailored to their needs. The service can be increased or decreased as business requirements dictate, providing more flexibility in controlling operating expenses. Organizations should seek a partner that can seamlessly marry analytics with technology, rather than a pure-play analytics services provider that lacks industry-specific domain expertise. The key analytical component is derived from the ability to understand various business-use cases and develop predictive models capable of comprehending complex relationships and learning from historical data. A qualified partner must have expertise in extracting meaningful insights from social networks and social media and performing complex analyses on the data. Such a Quick Take Image Recognition Analytics Social networking sites such as Facebook, Pinterest, Instagram and Flickr receive and host billions of photos, with thousands added every minute. Some of the images can be of brands, company logos and products, without any text to reference them. Since traditional social media monitoring tools can only track text (such as user comments and posts mentioning a brand), marketers often do not know what customers are referring to, who is using their company’s products, or if counterfeit versions of those products exist. Analytics with image recognition capabilities can help companies overcome this challenge and leverage images to enhance their market knowledge and extend their reach. Advanced image analytics with pixel-level analysis is gradually gaining acceptance among large retailers and advertising agencies. Companies such as Piqora and Curalate have developed image recognition technologies for social media sites such as Facebook, Pinterest and Instagram – allowing them to identify the most popular shared images from their Web sites, the most influential individual visitors, and the traffic that an image diverts to a target Web site, for example. A case in point: A coffee shop chain can use this technology to gather information about what its customers like and dislike; confirm the most popular shops in the chain; the number of times an image is shared and by how many people; its impact on sales; location-based knowledge and competitor information. The coffee chain can reach out to more customers, respond to user comments, engage with influencers and other prospective customers, and use images with positive comments for marketing after obtaining permission. cognizant reports 7
  8. 8. Barriers to Using Social Media Data Effectively Nothing is Preventing Lack of Awareness About Opportunities General Lack of Engagement with Social Media Organizational Silos/Lack of Joined-up Thinking Lack of Resources to Make Sense of Data No Budget/Lack of Buy-in from Top of Organization Social Analytics are Separate from Multichannel Analytics and Business Intelligence Social Data Stored in Disparate Tools Lack of Tracking Capabilities and Analytics 0% 10% Companies 20% 30% 40% 50% Agencies n = more than 650 marketing professionals from companies and agencies across North America and Europe. Source: Econsultancy and Adobe, September 5, 2012 Figure 2 partner must also be able to integrate advanced analytics with enterprise systems, and enhance business efficiencies. As analytics processes become standardized and can uniformly be applied via cloud-enabled models (harnessing the growing clout of utility computing architectures), we believe that organizations stand to benefit greatly by associating themselves with partners that have invested in such capabilities. Looking Forward To experience the full potential of analytics, we advise companies to consider the following: • Identify key areas for deploying analytics. • Enter into relationships with partners capable of providing AaaS. • Design a comprehensive strategy for the adoption and implementation of analytics. • Develop an enterprise-wide data architecture. • Formulate customized strategies to capitalize on unique data. • Develop a fact-based decision-making culture focused on achieving specific goals. • Continuously refurbish and renew the organization’s analytics implementation. Footnotes 1 “Social Networking Reaches Nearly One in Four Around the World.” eMarketer, June 18, 2013. “Experian Marketing Services Reveals 27 Percent Of Time Spent Online Is On Social Networking In 2012.” Prnewswire, April 16, 2012. 2 “Global Consumers’ Trust In Earned Advertising Grows In Importance.” Nielsen, April 10, 2012. http:// 3 4 “Big Content: The Unstructured Side of Big Data.” Gartner, May 1, 2013. darin-stewart/2013/05/01/big-content-the-unstructured-side-of-big-data/ cognizant reports 8
  9. 9. “Data-Rich and Insight-Poor: Marketers Planning to Turn Information into Intelligence in 2013.”, 2013. 5 “Socially Devoted: The Next Generation of Customer Care is Social.” Social Bakers, July, 2013. 6 “Retail Case Kesko: The Evolution of Kesko’s Customer Experience Using Etuma’s Free-form Text Feedback Analysis Services.” Etuma, July 6, 2013. 7 Klout allows users to measure their online influence. It currently tracks user activity around seven social media sites such as Twitter, Facebook, Instagram, etc., and assigns a Klout Score, a number between 1and 100. Higher Klout Score represents greater influence. 8 “Putting Social Media to Work.” Bain & Company, 2011. Putting_social_media_to_work.pdf 9 “Examples of Social Media Command Centers for the World’s Largest Brands.” Salesforce Blog, December 5, 2012. 10 “Social Media Command Centers Built For Brands Not NASA.” Intelligent HQ, May 6, 2013. http:// 11 “Super Bowl First: Social Media Command Center.” Today, January 23, 2012. tech/super-bowl-first-social-media-command-center-84788 12 “Learning From a Super Bowl’s Social Media Command Center.” Social Media Today, February 1, 2013. 13 “Social Media Isn’t All Marketing.” Monkeydish, June 17, 2013. articles/social-media-isn%E2%80%99t-all-marketing 14 BPaaS refers to the provision of business services encompassing underlying IT infrastructure, platform and skilled manpower, to run specific business processes within a virtual, globalized and distributed operating model. 15 Bibliography • “Customer Analytics in the Age of Social Media.” TDWI Research, 2012. 2012/07/best-practices-report-q3-customer-analytics-in-the-age-of-social-media/asset.aspx • “Who’s Sharing My Brand Images? Why Text-Based Social Media Monitoring Falls Short.” Adota, May 16, 2013. • “The Social Economy: Unlocking Value and Productivity through Social Technologies.” McKinsey & Company, July, 2012. economy • “Adobe Social Unveils Predictive Publishing for Facebook.” BusinessWire, April 24, 2013. http://www. • “Social Media Analytics Software Pulls Useful Info Out Of Online Muddle.” SearchBusinessAnalytics, 2013. cognizant reports 9
  10. 10. Credits Author and Research Analyst Vinaya Kumar Mylavarapu, Cognizant Research Center Subject Matter Expert Amit Shah, Manager, Cognizant Social Design Harleen Bhatia, Design Team Lead Suresh Satyavarapu, Designer 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 164,300 employees as of June 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 for more information. World Headquarters European Headquarters India Operations 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 Email: 1 Kingdom Street Paddington Central London W2 6BD Phone: +44 (0) 207 297 7600 Fax: +44 (0) 207 121 0102 Email: #5/535, Old Mahabalipuram Road Okkiyam Pettai, Thoraipakkam Chennai, 600 096 India Phone: +91 (0) 44 4209 6000 Fax: +91 (0) 44 4209 6060 Email: © ­­ 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.