Published on February 21, 2014
The Face of the New Enterprise Market Overview and Proprietary Financial Intelligence
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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
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
The Face of the New Enterprise More Mobile Work-life Blur Collaborate Online Social Multiple Devices Bring Your Own Everything
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
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
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
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
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
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
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
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
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
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 $1M in revenue. This confirms that DA/BI and EE are in relatively nascent stages of development compared to EO and ITI. 15
Exploring the Data… Revenue Generation by Stage and Sector TTM Revenue by Round ($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. $0.1 IT Infrastructure 16
Exploring the Data… Revenue Progression Revenue by Stage of Development ($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 $0.7 Series A Series B Median LTM Revenue Series C At the early stages, most companies are focused largely on developing POC, alpha or beta product/service offerings. Cultivating a sales force and deploying it at scale generates an explosion in revenue from later stage Series C and Series D companies. Series D+ Median NTM Revenue 17
Exploring the Data… Investments in the New Enterprise Average Invested Capital by Stage and Sector ($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 $12.2 IT Infrastructure 18
Exploring the Data… Value Progression 73% of Series D rounds are at higher valuations than the Series C Step Up Analysis 100% Average Pre-Money Valuation ($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
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
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
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
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
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
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
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