The New Enterprise is adopting new tools and technology that utilize data, mobilize their workforce, and increase collaboration throughout the organization. In this new report, SVB Analytics examines the underlying industry sectors supporting this new business environment and offers data on venture funding, revenue models and valuations.

1. The Face of the New Enterprise
Market Overview and Proprietary
Financial Intelligence

2. SVB Analytics
provides business
analytics solutions
to every stakeholder
in the venture
capital ecosystem.
Advisory Services:
Client Focus:
 Due diligence support
 Valuation Guidance
 Proprietary market intelligence
Corporate venture,
innovation and
development groups
Valuation Services:
 409A Valuations (Stock Options)
 Purchase Price Allocation
(post-M&A Accounting)
 Goodwill and Intangible Asset
Impairment
Client Focus:
Venture-backed
companies
6,000
Valuations completed since 2006
Research:
1,200
 Proprietary Research and Data
 Customized Studies
 Thought Leadership
Active clients in 2012
Client Focus:
Corporate and
venture ecosystem
2

3. Table of Contents
The Face of the New Enterprise
6
Characteristics, Tools and Competencies
7
Market Forces Shaping the New Enterprise
Exploring the Data
11
Sector / Niche Segmentation
13
Exploring and Interpreting the Data
Summary
22
Final Thoughts
23
Bios and Contact Information
24
Glossary
3

4. Table of Contents
The New Enterprise is adopting
new tools and technology that
utilize data, mobilize their
workforce, and increase
collaboration throughout the
organization.
The Face of the New Enterprise
6
Characteristics, Tools, and Competencies
7
Market Forces Shaping the New Enterprise
Exploring the Data
11
Sector / Niche Segmentation
12
Segmentation
13
Exploring and Interpreting the Data
Summary
22
Final Thoughts
23
Bios and Contact Information
24
Glossary
4

5. The Face of the New Enterprise
More
Mobile
Work-life
Blur
Collaborate
Online
Social
Multiple
Devices
Bring
Your Own
Everything

6. Characteristics, Tools and Competencies
The New Enterprise
Tools and Competencies
Has unparalleled, predictive insight into
its key operating metrics – internal and
external
Big Data Collection, Harmonization, Storage,
Predictive Analytics and Business Intelligence
Has an increasingly mobile workforce
needing to work where they go and access
internal resources
Virtualization of the IT stack, BYOD,
Consumerization of IT, Security
Is becoming flatter and more transparent,
with self organizing teams and an increasing
share of knowledge workers
Social Collaboration, Distributed Workforce
Increasingly blurs boundaries between
employees, vendors, and customers
Social CRM, PaaS, Value Chain Collaboration
6

7. Market Forces Shaping the New Enterprise
Big Data, Predictive Analytics and Business Intelligence
By 2005 the world had generated a total of
130 Billion GB of data – a level
Companies in all industries have a
expected to increase to
Big Data Problem
40 Trillion GB by 2020
1
0.5% of 2.5B GB of
data generated every day
Only
Missed opportunities to not only
know why it happened, but
is examined for its analytic value1
predict what will happen
The Big Data Industry will grow from
$3.2B in 2010 to almost
$17B by the end of 2015
1
early-stage
investment happening
Significant
in this space
1 IBM Research
7

8. Market Forces Shaping the New Enterprise
Virtualization of the IT stack, BYOD,
Consumerization of IT, Security
200M
More than
mobile workers
will be using mobile business apps1
BYOD in the enterprise:
multiple device, OS support costs plus
security and compliance challenges
60% of North Americans
use their smartphone for work2
72%
At least
of companies report
increased productivity as a direct
result of flexible working practices3
68% claim it has led to
increased revenue3
Productivity trade-offs include
information security,
device management and
administration costs
Gartner sees BYOD programs as “the single most radical shift in the economics of
client computing for business since PCs invaded the workplace.”
1 Strategy Analytics, 2013
2 iPass Mobile Workforce Report 2013
3 Regus/Mindmetre 2012
8

9. Market Forces Shaping the New Enterprise
The Social Enterprise
Enterprise Social Networking market is
forecast to grow at a CAGR of
from 2012 through 20161
Heavy competition amongst large
and small companies to offer
52%
Social technologies can increase brand awareness
by
36% and customer conversions by 20%
83% of companies are using
technologies
Social and Collaborative tools must work
at least one social technology
73% of companies leverage social
technology internally
74% customers | 48% external partners
2
unified social
communication
inside and outside
the enterprise and
enable data sharing
without compromising security
2
1 MarketResearchReports.biz
2 “Evolution of the Global Enterprise”, McKinsey Global Survey
9

10. Table of Contents
We took a bottom-up approach
when segmenting the
underlying industry sectors
supporting the Face of the New
Enterprise.
The Face of the New Enterprise
6
Characteristics, Tools, and Competencies
7
Market Forces Shaping the New Enterprise
Exploring the Data
Sector / Niche Segmentation
13
We examined the business
models of the companies in our
data set, and characterized
them by granular focus
(“Niche”), which were then
organized into four primary
business functions (“Sector”).
11
Exploring and Interpreting the Data
Summary
22
Final Thoughts
23
Bios and Contact Information
24
Glossary
10

11. Exploring the Data…
Sector / Niche Segmentation
Enterprise Operations
Enterprise Mobility
Data Analytics & Business
Intelligence
Network Security
Big Data
Content and Collaboration
Business Intelligence
Supply Chain Management
Human Resources
IT Infrastructure
External Engagement
Cloud Infrastructure
Application Development/PaaS
Network Management Software
 Communications Technology
 Software Defined Networking
Consumer Payment Systems
Systems Management
Social Media
CRM
Marketing
Storage Technology
11

12. Exploring the Data…
Sector / Niche Distribution
Distribution of companies by sector and niche
26%
Enterprise Operations and IT Infrastructure show the
highest concentration of companies comprising the
new enterprise.
22%
19%
32%
Data Analytics and Business Intelligence
External Engagement
Enterprise Operations
IT Infrastructure
The following analysis indicates that this is a
reflection of the maturity of these sectors
(relative to DA/BI and EE), and the myriad
opportunities to improve security and efficiency
and reduce costs.
Parent Sector
Top Niches
DA and BI
Business Intelligence
12%
IT Infra
Storage Technology
11%
Ent Ops
Network Security
10%
DA and BI
Big Data
10%
12

13. Exploring the Data…
VC Funding Distribution
Distribution of financing by sector
5%
3%
9%
9%
Three of the four sectors profiled exhibit
strong concentrations in Series A and Series
B stage companies.
7%
18%
22%
28%
42%
33%
32%
Series D
Series C
Series B
Series A
Enterprise Operations, however, exhibits a
comparatively higher concentration of Series
C and Series D companies, indicating a
relatively mature sector.
32%
41%
45%
42%
30%
Data
External
Analytics and Engagement
Business
Intelligence
Enterprise
Operations
IT
Infrastructure
13

14. Exploring the Data…
The Age of the New Enterprise
Distribution of companies by year founded
9%
Sixty percent of Enterprise Applications
companies were founded between 2000 and
2007, compared to 72% of Data Analytics and
Business Intelligence companies founded in
2008 or later.
14%
22%
30%
32%
47%
50%
42%
37%
27%
23%
2011-2012
2008-2010
2004-2007
2000-2003
By examining both series of funding and years
since founding, it is clear that Data Analytics
& Business Intelligence and External
Engagement are in the early years of market
development relative to Enterprise Operations
and IT Infrastructure. This highlights that the
New Enterprise may increasingly adopt tools
and technology within these sectors going
forward.
19%
23%
5%
12%
8%
Data
External
Analytics and Engagement
Business
Intelligence
Enterprise
Operations
IT
Infrastructure
14

15. Exploring the Data…
Revenue Run Rate
Distribution of average trailing 12 months revenue by sector
8%
11%
3%
14%
One quarter of all Enterprise Operations
companies generated more than $20M on a
trailing 12-month basis.
9%
25%
17%
72% of DA/BI companies are Series A or B,
and 63% of these companies generate less
than $1M in revenue.
11%
18%
8%
14%
9%
12%
30%
22%
63%
87% of EE companies are Series A or B,
and 57% of these companies generate less
than $1M in revenue.
62% of EO companies are Series A or B,
and 33% of these companies generate less
than $1M in revenue.
57%
33%
Data
External
Analytics and Engagement
Business
Intelligence
$20M+
$10M-$20M
$5M-$10M
$1M-$5M
<=$1M
Enterprise
Operations
36%
IT
Infrastructure
75% of ITI companies are Series A or B, and
36% of these companies generate less than
$1M in revenue.
This confirms that DA/BI and EE are in
relatively nascent stages of development
compared to EO and ITI.
15

16. Exploring the Data…
Revenue Generation by Stage and Sector
TTM Revenue by Round ($M)
Examining trailing 12-months revenue by
round shows that revenue generation is
minimal at the Series A and B; however, at
Series C, a large chasm develops between
Enterprise Operations/IT Infrastructure and
Business Intelligence/External Engagement.
$9.6
$6.9
$3.7
$1.9
$1.0
$0.4
$0.2
Data
Analytics and
Business
Intelligence
$1.0
$0.6
$0.8
External
Engagement
$1.4
Enterprise
Operations
Series C
Series B
Series A
The compression of the Value-to-Capital
ratio from the previous page in Business
Intelligence and External Engagement
mirrors the comparative lower revenue of
companies in these spaces relative to
Enterprise Operations and Infrastructure.
This may be a reflection of more
developed markets in EO and ITI, or that
buyers of enterprise solutions are more
likely to purchase more “traditional”
solutions versus “newer” solutions.
$0.1
IT
Infrastructure
16

17. Exploring the Data…
Revenue Progression
Revenue by Stage of Development ($M)
$29.0
Commercialization and
revenue growth
$13.6
$10.5
Develop
sales force
There is also a larger disconnect
between LTM and NTM revenue at the
earlier stages. With better forecasting,
later-stage companies are able to scale
and forecast growth more accurately.
Product Development
$4.7
$3.5
$1.5
$0.1
Series Seed
$2.1
$0.5
$0.7
Series A
Series B
Median LTM Revenue
Series C
At the early stages, most companies are
focused largely on developing POC,
alpha or beta product/service offerings.
Cultivating a sales force and deploying it
at scale generates an explosion in
revenue from later stage Series C and
Series D companies.
Series D+
Median NTM Revenue
17

18. Exploring the Data…
Investments in the New Enterprise
Average Invested Capital by Stage and Sector ($M)
Looking at average invested capital by
round, we notice that Series B and
Series C investments are
commensurately higher in EO and ITI
companies.
$50.6
$45.9
Series C
Series B
$27.8
Series A
$22.5
This echoes the previously identified
trend of higher TTM revenue in EO and
ITI companies as they likely require
larger amounts of capital for product
development and sales.
$18.4
$15.5
$14.8
$6.4
$7.8
Data Analytics
and Business
Intelligence
External
Engagement
$18.0
$8.1
Enterprise
Operations
$12.2
IT Infrastructure
18

19. Exploring the Data…
Value Progression
73% of Series D rounds
are at higher valuations
than the Series C
Step Up Analysis
100%
Average Pre-Money Valuation ($M)
Series D
Series C
$139.9
Series B
Series A
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
86%
27%
7% 7%
Seed-to-A
$137.4
$190.4
A-to-B
Up
$89.8
$151.2
$87.6
$36.3
73%
70%
12%
B-to-C
Flat
18%
C-to-D
Down
The relatively higher proportion of down
rounds at the Series B and Series C
financing again highlights the scaling and
commercialization challenges mid-stage
companies face.
$42.9
$30.9
$12.4
Data Analytics
and Business
Intelligence
$42.8
$28.3
$33.9
$13.2
$16.8
$17.4
External
Engagement
Enterprise
Operations
IT
Infrastructure
Those that successfully execute are
rewarded with higher valuations in the
Series D round.
19

20. Exploring the Data…
Value Relative to Total Invested Capital
The ratio of the pre-money valuation
divided by total invested capital enables
cross-sector comparison of both
valuation and capital efficiency. For
instance, a Series C company that
recently raised a round at a pre-valuation
of $50M with total invested capital of
$25M would have a ratio of 2x.
Pre-Money / Total Invested Capital
1.7x
2.8x
1.5x
2.0x
2.0x
2.8x
1.6x
Series C
1.8x
Series B
Series A
2.3x
2.2x
1.7x
Data Analytics
and Business
Intelligence
External
Engagement
Enterprise
Operations
Series A valuations are typically a
function of capital and ownership
requirements. The size of the
addressable market, and strength of the
team and concept also drive value.
However, as the focus shifts from
product development to
commercialization, revenue generation
begins to play a role in later-stage
(Series C and beyond) valuations.
2.0x
IT Infrastructure
To illustrate this, compare the relatively
high Series A valuations in DA/BI to the
relatively low Series C valuations. The
inverse is true in IT Infrastructure.
20

21. Table of Contents
The Face of the New Enterprise
6
Characteristics, Tools, and Competencies
7
Market Forces Shaping the New Enterprise
Exploring the Data
11
Sector / Niche Segmentation
13
Exploring and Interpreting the Data
Summary
22
Final Thoughts
23
Bios and Contact Information
24
Glossary
21

22. Final Thoughts
Growth-Stage Focus
Early-Stage Focus
Business models for growth-stage companies
center around:
Business models for early-stage companies
center around:
Security
Engagement (internal and external)
Mobility
Data and Intelligence
Big Data
Collaborations and Efficiency
Value and Cost
The New Enterprise is shaped by technologies
employees pull in from their personal lives, and
explosive growth in the volume of data created inside
and outside the enterprise. The enterprise’s boundaries
melt into those of its employees, customers, and
partners. Flexibility, mobility, and collaboration present
significant opportunities for both entrepreneurs and
investors.
Later-stage companies in the market have provided the
enabling foundation for this transformation, and paved
the way for innovative new companies to explore and
disrupt the enterprise.
22

23. SVB Analytics Contacts
Steve Allan | Managing Director
Steve Allan is a managing director with SVB Analytics, responsible for
leading SVB Analytics in executing client engagements, issuing valuation
opinions for private companies, and conducting research in the technology
and life science private financing arena. Allan brings a strong financial
background and passion for entrepreneurship to his leadership role at SVB
Analytics.
Sallan@SVB.com | 415.764.3135
Rob Tompkins | Director
Rob Tompkins is a director with SVB Analytics and leads SVBA’s research, strategy and
business development initiatives. Tompkins has extensive experience valuing privately-held
technology companies with a focus on the intersection of energy and technology. Prior to
joining SVB, Tompkins provided strategic and financial advisory services to startups in the
U.S. and Latin America.
Rtompkins@SVB.com | 512.372.6769
Contributing authors:
Sean Lawson, Technology Senior Associate
Amrit Sareen, Technology Associate
23

24. Glossary
Term
Definition
API
Application Programming Interface
CES
Consumer Electronics Show
COGS
Cost of Goods Sold
IoT
Internet of Things
M2M
Machine-to-machine
MDM
Mobile Device Management
Metcalfe's Law
The value of a network is equal to the square of the number of devices connected to it
Moore's Law
The number of transistors on integrated circuits doubles approximately every two years
Organic Sales
Growth that comes from existing customers, word of mouth, and viral sources, versus from increased sales and
marketing efforts
RFID
Radio-frequency identification
ROI
Return on Investment
SaaS
Software-as-a-Service
Step-up
Refers to the percentage increase in the original issuance price of the preferred securities between two rounds of
financing
SVB
Silicon Valley Bank
WSN
Wireless Sensor Network
24

25. SVB Financial Group
3003 Tasman Drive
Santa Clara, California 95054
408.654.7400
svb.com
SVB Analytics
555 Mission Street, Suite 900
San Francisco, California 94105
800.760.9644
svb.com
This material, including without limitation the statistical information herein, is provided for informational purposes only. The
material is based in part upon information from third-party sources that we believe to be reliable, but which has not been
independently verified by SVB Financial Group and, as such, we do not represent that the information is accurate or complete.
The information should not be viewed as tax, investment, legal or other advice nor is it to be relied on in making an investment or
other decisions. You should obtain relevant and specific professional advice before making any investment decision. Nothing
relating to the material should be construed as a solicitation or offer to acquire or dispose of any investment or transaction.
©2013 SVB Financial Group. All rights reserved. Member Federal Reserve System. SVB>, SVB>Find a way, SVB Financial
Group, and Silicon Valley Bank are registered trademarks. SVB Analytics is a member of SVB Financial Group and a non-bank
affiliate of Silicon Valley Bank. Products and services offered by SVB Analytics are not FDIC insured and are not deposits or other
obligations of Silicon Valley Bank. SVB Analytics does not provide tax or legal advice. Please consult your tax or legal advisors for
such guidance. Rev. 06-17-13