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From invisible to visible
. . . to measurable
Social analytics extends enterprise
performance improvement
A report by the Deloitte Center for the Edge
About the authors
The authors would like to thank all the participants of the 2013 ON Social 2 workshop.
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
From invisible to visible . . . to measurable
Contents
Executive summary | 2
Introduction | 3
The uptake in social tools and adoption continues | 4
Using social analytics to detect patterns of interaction | 5
The step change: Discovering causal relationships through
measurement  | 7
From measurable to actionable | 9
Improved performance: A natural product of social analytics | 10
Endnotes | 11
Social analytics extends enterprise performance improvement
1
THE convergence of social software plat-
forms 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 enter-
prise 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 analyt-
ics 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.
Executive summary
From invisible to visible . . . to measurable
2
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 deploy-
ing 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 relation-
ships between patterns of interaction and
operating performance.
Social analytics extends enterprise performance improvement
3
The uptake in social tools
and adoption continues
GARTNER 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 avail-
able to help drive organizational improve-
ment generally fall
into two categories.
Enterprise social
network tools, such
as Salesforce Chatter,
Jive, Socialcast
by VMWare, and
Microsoft Yammer,
are focused on find-
ing 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 attain-
ment 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 custom-
ers 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 prohibi-
tion 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 initia-
tive could die of inertia. The senior executive’s
mandate may be rolled out halfheartedly, its
adoption limited by inadequate understand-
ing of its purpose and a sense that it’s simply
added work.
Organizations often measure the effective-
ness of social software solution deployments
in terms of user adoption levels.6
However,
adoption does not measure frequency or con-
sistency of use, nor does it link usage to per-
formance 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 care-
fully track the impact of deploying a solution.
Therein lies a starting point for social analytics.
From invisible to visible . . . to measurable
4
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 invis-
ible patterns of interaction visible, and using
that information to boost social participation,
facilitate collaboration, and even encour-
age behavioral change—ultimately leading to
opportunities for performance improvement.
VMWare, a virtualization and cloud infra-
structure 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 mem-
bership 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 convert-
ing members into active partici-
pants. 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 depart-
ments 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 mem-
bers 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 par-
ticipation faster and with less investment. Over
the same nine-month period in which overall
membership grew to 95 percent, active partici-
pation 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.
Sabre Labs is also using social to encourage
collaboration and innovation. Sarah Kennedy
Ellis, director of Sabre Labs, leads a group that
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.
Social analytics extends enterprise performance improvement
5
incubates ideas and collaborates across busi-
ness 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 informa-
tion 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 analyt-
ics 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 facili-
tate 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 identi-
fied, 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 assis-
tance. This helped fulfill an organizational
need with surplus resources, while providing
advancement and development opportunities
for up-and-comers in the company.9
From invisible to visible . . . to measurable
6
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 focus-
ing exclusively on
financial data, a lag-
ging performance
indicator, and
started considering
operational metrics,
which can be lead-
ing indicators. A
better understand-
ing of interaction
patterns detected
through enterprise
social data can help
executives antici-
pate interactions’
impact on operat-
ing 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 sen-
sors 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, custom-
ized analytic tools
that include con-
text and metadata.
Textual explanations
and interpretation
of data are increas-
ingly needed to
accompany analysis.
Wu also stresses
the importance of
taking an organi-
zation-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
The step change: Discovering
causal relationships
through measurement
A better understanding
of interaction
patterns detected
through enterprise
social data can help
executives anticipate
interactions’ impact on
operating metrics.
Social analytics extends enterprise performance improvement
7
exception handling. Exceptions can arise in
any core operating process—in the manufac-
turing supply chain when a critical item doesn’t
arrive as expected; in sales and marketing
when a customer asks for terms and condi-
tions that are outside policies and proce-
dures; in product development, when a new
iteration crashes.
Finding the right people to address the
matter, often dispersed and not readily identifi-
able, 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 bound-
aries 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 conversa-
tions 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 invisible to visible . . . to measurable
8
From measurable to actionable
SOCIAL software, as suggested by the pre-
ceding examples, can be a powerful tool for
identifying, quantifying, and addressing busi-
ness 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 perfor-
mance indicators revealed through social ana-
lytics, 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 recog-
nize the areas in which they need to improve,
celebrate and share the areas where they are
doing well, and possibly find groundbreak-
ing 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 devel-
oped a real-time feedback system in the form
of a personalized performance dashboard, giv-
ing 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 post-
ings. But the importance of the dashboard
cannot be overstated. By seeing a visual rep-
resentation of their performance throughout
their workday, LiveOps employees compete
with themselves and their peers to improve
their individual performance and, conse-
quently, overall enterprise performance.
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.
Social analytics extends enterprise performance improvement
9
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.
From invisible to visible . . . to measurable
10
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 col-
laboration 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+Collaborati
on/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 work-
shop, 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%20Busi-
ness_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.
Social analytics extends enterprise performance improvement
11
Eric Openshaw
Vice chairman and US Technology, Media & Telecommunications leader
Principal
Deloitte LLP
+1 714 913 1370
eopenshaw@deloitte.com
John Hagel
Co-chairman
Deloitte LLP Center for the Edge
+1 408 704 2778
jhagel@deloitte.com
John Seely Brown
Independent Co-chairman
Deloitte LLP Center for the Edge
+1 408 578 3182
jsb@johnseelybrown.com
Contacts
From invisible to visible . . . to measurable
12
The Deloitte Center for the Edge conducts original research and develops substantive points of
view for new corporate growth. The center, anchored in Silicon Valley with teams in Europe and
Australia, helps senior executives make sense of and profit from emerging opportunities on the edge
of business and technology. Center leaders believe that what is created on the edge of the competi-
tive landscape—in terms of technology, geography, demographics, markets—inevitably strikes at
the very heart of a business. The Center for the Edge’s mission is to identify and explore emerging
opportunities related to big shifts that are not yet on the senior management agenda, but ought
to be. While Center leaders are focused on long-term trends and opportunities, they are equally
focused on implications for near-term action, the day-to-day environment of executives.
Below the surface of current events, buried amid the latest headlines and competitive moves,
executives are beginning to see the outlines of a new business landscape. Performance pressures are
mounting. The old ways of doing things are generating diminishing returns. Companies are having
harder time making money—and increasingly, their very survival is challenged. Executives must
learn ways not only to do their jobs differently, but also to do them better. That, in part, requires
understanding the broader changes to the operating environment:
•	 What is really driving intensifying competitive pressures?
•	 What long-term opportunities are available?
•	 What needs to be done today to change course?
Decoding the deep structure of this economic shift will allow executives to thrive in the face of
intensifying competition and growing economic pressure. The good news is that the actions needed
to address short-term economic conditions are also the best long-term measures to take advantage
of the opportunities these challenges create.
For more information about the center’s unique perspective on these challenges, visit www.
deloitte.com/centerforedge.
About the Center for the Edge
Social analytics extends enterprise performance improvement
13
About Deloitte University Press
Deloitte University Press publishes original articles, reports and periodicals that provide insights for businesses, the public sector and
NGOs. Our goal is to draw upon research and experience from throughout our professional services organization, and that of coauthors in
academia and business, to advance the conversation on a broad spectrum of topics of interest to executives and government leaders.
Deloitte University Press is an imprint of Deloitte Development LLC.
This publication contains general information only, and none of Deloitte Touche Tohmatsu Limited, its member firms, or its and their
affiliates are, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice
or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or
action that may affect your finances or your business. Before making any decision or taking any action that may affect your finances or
your business, you should consult a qualified professional adviser.
None of Deloitte Touche Tohmatsu Limited, its member firms, or its and their respective affiliates shall be responsible for any loss
whatsoever sustained by any person who relies on this publication.
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of
member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/about for a detailed description
of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Please see www.deloitte.com/us/about for a detailed
description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules
and regulations of public accounting.
Copyright © 2014 Deloitte Development LLC. All rights reserved.
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From invisible to visible to measurable

  • 1. From invisible to visible . . . to measurable Social analytics extends enterprise performance improvement A report by the Deloitte Center for the Edge
  • 2. About the authors The authors would like to thank all the participants of the 2013 ON Social 2 workshop. 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 From invisible to visible . . . to measurable
  • 3. Contents Executive summary | 2 Introduction | 3 The uptake in social tools and adoption continues | 4 Using social analytics to detect patterns of interaction | 5 The step change: Discovering causal relationships through measurement  | 7 From measurable to actionable | 9 Improved performance: A natural product of social analytics | 10 Endnotes | 11 Social analytics extends enterprise performance improvement 1
  • 4. THE convergence of social software plat- forms 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 enter- prise 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 analyt- ics 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. Executive summary From invisible to visible . . . to measurable 2
  • 5. 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 deploy- ing 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 relation- ships between patterns of interaction and operating performance. Social analytics extends enterprise performance improvement 3
  • 6. The uptake in social tools and adoption continues GARTNER 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 avail- able to help drive organizational improve- ment generally fall into two categories. Enterprise social network tools, such as Salesforce Chatter, Jive, Socialcast by VMWare, and Microsoft Yammer, are focused on find- ing 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 attain- ment 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 custom- ers 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 prohibi- tion 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 initia- tive could die of inertia. The senior executive’s mandate may be rolled out halfheartedly, its adoption limited by inadequate understand- ing of its purpose and a sense that it’s simply added work. Organizations often measure the effective- ness of social software solution deployments in terms of user adoption levels.6 However, adoption does not measure frequency or con- sistency of use, nor does it link usage to per- formance 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 care- fully track the impact of deploying a solution. Therein lies a starting point for social analytics. From invisible to visible . . . to measurable 4
  • 7. 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 invis- ible patterns of interaction visible, and using that information to boost social participation, facilitate collaboration, and even encour- age behavioral change—ultimately leading to opportunities for performance improvement. VMWare, a virtualization and cloud infra- structure 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 mem- bership 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 convert- ing members into active partici- pants. 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 depart- ments 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 mem- bers 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 par- ticipation faster and with less investment. Over the same nine-month period in which overall membership grew to 95 percent, active partici- pation 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. Sabre Labs is also using social to encourage collaboration and innovation. Sarah Kennedy Ellis, director of Sabre Labs, leads a group that 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. Social analytics extends enterprise performance improvement 5
  • 8. incubates ideas and collaborates across busi- ness 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 informa- tion 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 analyt- ics 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 facili- tate 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 identi- fied, 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 assis- tance. This helped fulfill an organizational need with surplus resources, while providing advancement and development opportunities for up-and-comers in the company.9 From invisible to visible . . . to measurable 6
  • 9. 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 focus- ing exclusively on financial data, a lag- ging performance indicator, and started considering operational metrics, which can be lead- ing indicators. A better understand- ing of interaction patterns detected through enterprise social data can help executives antici- pate interactions’ impact on operat- ing 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 sen- sors 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, custom- ized analytic tools that include con- text and metadata. Textual explanations and interpretation of data are increas- ingly needed to accompany analysis. Wu also stresses the importance of taking an organi- zation-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 The step change: Discovering causal relationships through measurement A better understanding of interaction patterns detected through enterprise social data can help executives anticipate interactions’ impact on operating metrics. Social analytics extends enterprise performance improvement 7
  • 10. exception handling. Exceptions can arise in any core operating process—in the manufac- turing supply chain when a critical item doesn’t arrive as expected; in sales and marketing when a customer asks for terms and condi- tions that are outside policies and proce- dures; in product development, when a new iteration crashes. Finding the right people to address the matter, often dispersed and not readily identifi- able, 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 bound- aries 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 conversa- tions 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 invisible to visible . . . to measurable 8
  • 11. From measurable to actionable SOCIAL software, as suggested by the pre- ceding examples, can be a powerful tool for identifying, quantifying, and addressing busi- ness 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 perfor- mance indicators revealed through social ana- lytics, 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 recog- nize the areas in which they need to improve, celebrate and share the areas where they are doing well, and possibly find groundbreak- ing 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 devel- oped a real-time feedback system in the form of a personalized performance dashboard, giv- ing 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 post- ings. But the importance of the dashboard cannot be overstated. By seeing a visual rep- resentation of their performance throughout their workday, LiveOps employees compete with themselves and their peers to improve their individual performance and, conse- quently, overall enterprise performance. 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. Social analytics extends enterprise performance improvement 9
  • 12. 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. From invisible to visible . . . to measurable 10
  • 13. 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 col- laboration 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+Collaborati on/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 work- shop, 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%20Busi- ness_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. Social analytics extends enterprise performance improvement 11
  • 14. Eric Openshaw Vice chairman and US Technology, Media & Telecommunications leader Principal Deloitte LLP +1 714 913 1370 eopenshaw@deloitte.com John Hagel Co-chairman Deloitte LLP Center for the Edge +1 408 704 2778 jhagel@deloitte.com John Seely Brown Independent Co-chairman Deloitte LLP Center for the Edge +1 408 578 3182 jsb@johnseelybrown.com Contacts From invisible to visible . . . to measurable 12
  • 15. The Deloitte Center for the Edge conducts original research and develops substantive points of view for new corporate growth. The center, anchored in Silicon Valley with teams in Europe and Australia, helps senior executives make sense of and profit from emerging opportunities on the edge of business and technology. Center leaders believe that what is created on the edge of the competi- tive landscape—in terms of technology, geography, demographics, markets—inevitably strikes at the very heart of a business. The Center for the Edge’s mission is to identify and explore emerging opportunities related to big shifts that are not yet on the senior management agenda, but ought to be. While Center leaders are focused on long-term trends and opportunities, they are equally focused on implications for near-term action, the day-to-day environment of executives. Below the surface of current events, buried amid the latest headlines and competitive moves, executives are beginning to see the outlines of a new business landscape. Performance pressures are mounting. The old ways of doing things are generating diminishing returns. Companies are having harder time making money—and increasingly, their very survival is challenged. Executives must learn ways not only to do their jobs differently, but also to do them better. That, in part, requires understanding the broader changes to the operating environment: • What is really driving intensifying competitive pressures? • What long-term opportunities are available? • What needs to be done today to change course? Decoding the deep structure of this economic shift will allow executives to thrive in the face of intensifying competition and growing economic pressure. The good news is that the actions needed to address short-term economic conditions are also the best long-term measures to take advantage of the opportunities these challenges create. For more information about the center’s unique perspective on these challenges, visit www. deloitte.com/centerforedge. About the Center for the Edge Social analytics extends enterprise performance improvement 13
  • 16. About Deloitte University Press Deloitte University Press publishes original articles, reports and periodicals that provide insights for businesses, the public sector and NGOs. Our goal is to draw upon research and experience from throughout our professional services organization, and that of coauthors in academia and business, to advance the conversation on a broad spectrum of topics of interest to executives and government leaders. Deloitte University Press is an imprint of Deloitte Development LLC. This publication contains general information only, and none of Deloitte Touche Tohmatsu Limited, its member firms, or its and their affiliates are, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your finances or your business. Before making any decision or taking any action that may affect your finances or your business, you should consult a qualified professional adviser. None of Deloitte Touche Tohmatsu Limited, its member firms, or its and their respective affiliates shall be responsible for any loss whatsoever sustained by any person who relies on this publication. About Deloitte Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/about for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting. Copyright © 2014 Deloitte Development LLC. All rights reserved. Member of Deloitte Touche Tohmatsu Limited Follow @DU_Press Sign up for Deloitte University Press updates at DUPress.com.