From invisible to visible . . . to measurable

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From invisible to visible . . . to measurable

From invisible to visible . . . to measurable

Social analytics extends enterprise performance improvement

Social media can help companies open communication channels up, down, and across the enterprise to gain data-driven insights and improve business outcomes.

Executive summary

The convergence of social software platforms and big data analytics is creating new avenues to explore the factors driving business performance. From a marketing standpoint, businesses continue to make significant progress in examining social media interactions with and among their customers to learn about and address needs, desires, and opinions. And businesses are in the early stages of analyzing social data in combination with other data sets from both within and outside the enterprise to guide strategic decisions.1

To date, companies have made only limited inroads into exploring social media-based activities and interactions inside the enterprise and with supply chain and distribution partners. However, the results of a few nascent efforts hint at exciting emerging capabilities for using social media and big data analytics to identify patterns of social interaction internally, assess how those patterns affect performance, and then leverage those lessons in near real-time feedback loops to improve organizational performance.

Introduction

In the past several years, businesses have begun using analytics to mine social data in combination with big data to understand external customer priorities and interests. However, efforts to similarly analyze internal and business partner data have lagged, largely for lack of effective tools.

Such tools are now becoming available, and companies that develop a strategy for deploying enterprise social software and use the tools effectively in various organizational areas can capitalize on the vast amounts of data this activity will generate. Analyzing these data can provide tremendous visibility into patterns of interaction that go beyond email and phone logs. Examining enterprise social software data, along with data from other sources such as ERP systems and customer service operations, can help identify causal relationships between patterns of interaction and operating performance.

The uptake in social tools and adoption continues

DUP_627-000018237062Gartner estimates that, by 2016, 50 percent of large organizations will have internal Facebook-like social networks, 30 percent of which will be considered as essential as email and telephones are today.2 Forrester predicts that the market for social enterprise apps and related services will reach $6.4 billion in 2016.3

The dozens of social software tools available to help drive organizational improvement generally fall into two categories. Enterprise social network tools, such as Salesforce Chatter, Jive, Socialcast by VMWare, and Microsoft Yammer, are focused on finding and connecting people dispersed throughout the enterprise. Tools such as IBM Connections, Microsoft SharePoint, and SAPJam provide shared workspaces for workgroups and individuals to collaborate across an enterprise.

Announcements in 2013 by two social software vendors provided evidence of the growing role of analytics in the effective use of these platforms. A new version of Jive provides reports designed to “pinpoint business areas that could be tweaked to advance the attainment of specific goals like hitting sales targets, enhancing onboarding of new employees, and sharpening customer service.”4 An upgrade to the IBM Connections enterprise social networking platform provides “new analytics features so that administrators can monitor usage, such as collaboration trends among employees and engagement with customers in social media services like Twitter and Facebook.”5

Various forces can drive adoption of social software tools. In some cases, the impetus is bottom-up. A team within the enterprise may take the initiative to deploy a solution that will help its members collaborate, perhaps doing so under the radar or despite an explicit prohibition by the CIO. In other cases, a business unit executive might attend a conference, find social software solutions interesting, and set out to apply one in his or her part of the business. Or a senior executive may become enamored of a social solution and mandate its deployment across the enterprise.

Barriers to successful deployment can arise in any of these situations. IT may stand in the way of expanding on the self-starting team’s efforts. The business unit leader’s ad hoc initiative could die of inertia. The senior executive’s mandate may be rolled out halfheartedly, its adoption limited by inadequate understanding of its purpose and a sense that it’s simply added work.

Organizations often measure the effectiveness of social software solution deployments in terms of user adoption levels.6 However, adoption does not measure frequency or consistency of use, nor does it link usage to performance improvement or business benefits. A more effective approach is to target major pain points within a company, determine how social software can help address them, and then carefully track the impact of deploying a solution. Therein lies a starting point for social analytics.

Using social analytics to detect patterns of interaction

Although still relatively early in their development, organizations are beginning to use social analytics to make formerly invisible patterns of interaction visible, and using that information to boost social participation, facilitate collaboration, and even encourage behavioral change—ultimately leading to opportunities for performance improvement. VMWare, a virtualization and cloud infrastructure solutions provider, launched Socialcast internally after it acquired the enterprise collaboration platform company in 2011. Through a thoughtful rollout, VMWare was able to steadily increase Socialcast membership among its employees to 95 percent from 73 percent over a nine-month period.7

During the rollout, VMWare focused not only on increasing membership but also on converting members into active participants. The company believed that an actively engaged Socialcast community would help foster an environment where employees would share ideas, gain access to experts, receive recognition for their input, and collaborate with fellow employees anywhere in the world throughout the company.

The company used performance indicators that measured the level of engagement for the entire company, as well as for smaller departments and position types. Measuring levels of engagement for smaller employee groups allowed VMware to target specific groups with initiatives aimed at boosting participation. These efforts were customized to help members with lower involvement become more engaged, while continuing to encourage and reward groups with higher levels of adoption.

By targeting its engagement initiatives, VMware was able to increase community participation faster and with less investment. Over the same nine-month period in which overall membership grew to 95 percent, active participation grew from 26 percent of members to 39 percent. By continuing to measure these rates in combination with other analytics, VMware could expand its understanding of how its actions increased participation and apply this learning as needed.

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Sabre Labs is also using social to encourage collaboration and innovation. Sarah Kennedy Ellis, director of Sabre Labs, leads a group that incubates ideas and collaborates across business units to identify tools and technologies for producing insights. The group continually looks for partners and champions within the lines of business, so it is a natural fit to use social tools to identify organizational change agents, surfacing them through both internal and public social networks.

Ellis says her team is planting “social seeds” and tracking how ideas and information spread virally through the 10,000-person workforce. She relates a story about a company executive who once sent an email after reading Ellis’s external blog. Through that connection, Ellis gained access to an unexpected level of resources, support, and opportunity.8

Another potential benefit of social analytics is the ability to identify effective practices and behaviors and encourage employees to adopt them. Margarita Quihuis, co-director of the Stanford Peace Innovation Lab, believes that social networks and platforms can facilitate a “pay it forward” mindset and nurture individuals with specific, valuable interests and experience. In a manner similar to artistic patronage in Renaissance Italy, social provides the vehicle for aspiring talent to be identified, engaged, and supported across social and organizational boundaries.

Quihuis cited a study that described how executives at a Silicon Valley company grew concerned over data that indicated that only 200 of its 6,000 external partnering deals had gone through the legal department. But while formal channel metrics suggested inadequate reviews, which could potentially increase risk, social data analysis showed that many more deal reviews were actually being conducted and trafficked through informal, social-driven channels. Quihuis believes that social provided a platform to make visible those people who had the ability and time to conduct reviews and connect them with those needing assistance. This helped fulfill an organizational need with surplus resources, while providing advancement and development opportunities for up-and-comers in the company.9

The step change: Discovering causal relationships through measurement

The types of initiatives described above show the potential of social analytics to equip executives with new insights that can be used to elevate performance. To make this possible, however, leaders need to understand the causal relationships between patterns of interaction and operating performance. This requires a shift similar to that made by businesses when they stopped focusing exclusively on financial DUP_627_000018028900data, a lagging performance indicator, and started considering operational metrics, which can be leading indicators. A better understanding of interaction patterns detected through enterprise social data can help executives anticipate interactions’ impact on operating metrics, thus becoming a new, and even earlier, set of leading performance indicators. However, because the quantity of data available for analysis in today’s hyper-connected world is outpacing leaders’ ability to make use of it, asking the right questions is as important as getting the right answers.

Michael Wu of Lithium Technologies believes that many organizations are not realizing the full value of social data today because they analyze it without the necessary context. For example, Wu says, the use of sensors that passively collect data, such as those in mobile devices, presents an opportunity for real insight—if there is enough context to avoid misinterpretation. Demand is growing for specific, customized analytic tools that include context and metadata. Textual explanations and interpretation of data are increasingly needed to accompany analysis.

Wu also stresses the importance of taking an organization-wide view of social activity. Establishing leading indicators requires measurement and cross-correlation to know what types of social interactions drive performance and how companies can encourage these activities. Individuals within the workplace can start to recognize patterns as they develop, before the typical reaction point. And they can gain a better understanding of the larger system they operate in and how their own and others’ actions affect it.10

We can see this enterprise approach to measurement and analysis in the realm of exception handling. Exceptions can arise in any core operating process—in the manufacturing supply chain when a critical item doesn’t arrive as expected; in sales and marketing when a customer asks for terms and conditions that are outside policies and procedures; in product development, when a new iteration crashes.

Finding the right people to address the matter, often dispersed and not readily identifiable, is a major issue in exception handling. Also, creating an environment in which people in different parts of the organization, or even different countries, can come together quickly to reach a resolution can be a problem. And because exceptions are typically handled manually, no records exist in many cases of how many exceptions occurred, who addressed them, and what the results were.

Using social software, on the other hand, can allow companies to address exceptions systematically, determining which exceptions are one-off events and identifying those that are occurring with increasing frequency. Social software’s unique capabilities can be used to improve exception handling by allowing employees to communicate across boundaries and benefit from relationships. Also, by making these interactions visible, social software can help executives see patterns of exception handling.

For example, sales associates at Avaya, the provider of business collaboration and communications software and services, use Socialcast microblogs to tap into what their peers are saying.11 A sales associate who encounters an exception can search conversations on Socialcast to see if anyone else has dealt with a similar situation. This easy access to institutional memory saves time. If the associate does not find a discussion about a similar exception, he or she can post a question to the group, eliminating the time-consuming process of identifying the right person or e-mailing a massive list-serve and receiving redundant responses.

From measurable to actionable

Social software, as suggested by the preceding examples, can be a powerful tool for identifying, quantifying, and addressing business issues and requirements. But the potential impact of social analytics will be seen when businesses elevate enterprise performance by uncovering actionable insights throughout the organization and feeding them back to employees. By leveraging the leading performance indicators revealed through social analytics, such a feedback loop provides specific elements of knowledge and information—with appropriate context—to front-line workers in various parts of the organization to help them do their jobs better.

Using a dashboard, for example, employees can see where they are performing well and where they are lagging, continuously and in real time. They can also use social software to engender discussions that help them recognize the areas in which they need to improve, celebrate and share the areas where they are doing well, and possibly find groundbreaking new ways to do things. This virtuous cycle can benefit individual workers themselves, their colleagues across the enterprise, and the overall business.

We see an example of this in LiveOps, a provider of cloud contact center and customer service solutions.12 The company has developed a real-time feedback system in the form of a personalized performance dashboard, giving all agents the ability to know exactly what they do well and where they need to improve in terms of answering calls more effectively. To enhance agent performance, LiveOps also provides forums and chat tools through which experienced, accomplished agents provide tips and pointers to newcomers.

Because of LiveOps’s distributed and largely home-based workforce, the company has had to innovate to develop solutions that allow peer-to-peer learning, which enables its agents to grow and learn organically. The core of LiveOps’s solution lies in the internal forum tool that the company created for its agents. These tools include a “hot topics” forum with more than 60,000 topics and 1 million postings. But the importance of the dashboard cannot be overstated. By seeing a visual representation of their performance throughout their workday, LiveOps employees compete with themselves and their peers to improve their individual performance and, consequently, overall enterprise performance.

Improved performance: A natural product of social analytics

The real impact of making the invisible visible, and then measurable, comes when companies find ways to use social to open communication channels up, down, and across the enterprise. Doing so can be a catalyst for conveying the information and insights from social software and social analytics back to front-line employees themselves. Using these tools to move from descriptive to predictive to prescriptive information, and putting it in the hands of employees, can help organizations not simply anticipate what is likely to happen, but also identify the actions that are likely to have the greatest impact in improving performance.

The Deloitte Center for the Edge provides pragmatic frameworks and practical change methodologies focused on helping companies understand and profit from emerging opportunities on the edge of business and technology. Deloitte Consulting LLP’s suite of social business services includes services around holistic social business strategy, enterprise collaboration and development, social media and commerce, social monitoring and analytics, and social business governance and risk management. Contact the authors for more information.

Endnotes

View all endnotes
  1. For further discussion on the strategic application of social data, big data, and analytical tools, please see the Deloitte University Press article “Reengineering business intelligence,” http://dupress.com/?s=reengineering+business+intelligence.
  2. Gartner, Predicts 2013: Social and collaboration go deeper and wider, January 29, 2013, http://www.gartner.com/newsroom/id/2319215, accessed January 24, 2014.
  3. Henry Dewing, Social enterprise apps redefine collaboration—an information workplace report, Forrester, November 30, 2011, http://www.forrester.com/Social+Enterprise+Apps+Redefine+Collaboration/fulltext/-/E-RES59825?docid=59825, accessed January 24, 2014.
  4. Juan Carlos Perez, “Jive improves gamification, analytics in enterprise social suite,” InfoWorld Applications, July 16, 2013, http://www.infoworld.com/d/applications/jive-improves-gamification-analytics-in-enterprise-social-suite-222810, accessed January 24, 2014.
  5. Juan Carlos Perez, “IBM to beef up content management, analytics in Connections enterprise social product,” InfoWorld Applications, January 28, 2013, http://www.infoworld.com/d/applications/ibm-beef-content-management-analytics-in-connections-enterprise-social-product-211670?page=0,0, accessedJanuary 24, 2014.
  6. John Hagel III, John Seely Brown, Duleesha Kulasooriya, and Aliza Marks, Metrics that matter: Social software for business performance, Deloitte Development LLC, 2012, http://dupress.com/articles/metrics-that-matter/.
  7. Cameran Evens, “Social analytics for the enterprise,” Socialcast by VMWare Blog, May 22, 2012, http://blog.socialcast.com/social-analytics-for-the-enterprise/, accessed January 24, 2014.
  8. Sarah Kennedy Ellis, discussion at ON Social workshop, Deloitte University, February 2013.
  9. Margarita Quihuis, discussion at ON Social workshop, Deloitte University, February 2013.
  10. Michael Wu, discussion at ON Social workshop, Deloitte University, February 2013.
  11. Megan Miller, Aliza Marks, and Marcelus DeCoulode, Social software for business performance, Deloitte, 2011, p. 7, http://www.deloitte.com/assets/Dcom-UnitedStates/Local%20Assets/Documents/TMT_us_tmt/us_tmt_%20Social%20Software%20for%20Business_031011.pdf, accessed January 24, 2014.
  12. Tim Whipple (VP of service delivery, 2005-2011, LiveOps), interview with Deloitte Center for the Edge, October 2011.

About The Authors

Eric Openshaw

Eric Openshaw is vice chairman and US Technology, Media & Telecommunications leader, Deloitte LLP.

John Hagel

John Hagel is co-chairman of the Silicon Valley-based Deloitte Center for the Edge, which conducts original research and develops substantive points of view for new corporate growth.

John Seely Brown

John Seely Brown (JSB) is the independent co-chairman of the Silicon Valley-based Deloitte Center for the Edge, which conducts original research and develops substantive points of view for new corporate growth.

Acknowledgements

The authors would like to thank all the participants of the 2013 ON Social 2 workshop.

From invisible to visible . . . to measurable: Social analytics extends enterprise performance improvement
Cover Image by Kai and Sunny