1、1 Q4 2019 Sector Update Software Important disclosures appear at the back of this report GP Bullhound LLP is authorized and regulated by the Financial Conduct Authority GP Bullhound Inc is a member of FINRA 2 GP Bullhound is a leading technology advisory and investment firm, providing transaction ad

2、vice and capital to category leaders with an emphatic focus on software globally We publish a quarterly SaaS market report to provide quantitative insights into public and private market valuations across the transaction spectrum and qualitative insights into company business models, technology and

3、trends We feature many of the leading SaaS companies and CEOs across interesting sub-sectors Based in our San Francisco office, Jonathan Cantwell is the head of our software practice with over 15 years of experience Completed over 50 transactions with public and private tech companies globally Raise

4、d $400m of capital for our clients over the last 12 months Several of our recent software advisory transactions and investments are showcased below: Software perspectives EXECUTIVE SUMMARY UNDISCLOSED$177MUNDISCLOSED$50M ValiditySignaviodeltaDNAPartnerizeEcoVadis ACQUIRED BYINVESTMENT BYACQUIRED BYI

5、NVESTMENT BYINVESTMENT BY AppBuddyApax DigitalUnityAccel - KKRCVC $200M 3 Key sector takeaways Recent software transaction trends 1 Agenda 2 Public market SaaS analysis SaaS CEO commentary Key SaaS metrics Appendix A: GP Bullhound SaaS index 3 4 5 6 Appendix B: software public comparables by categor

6、y7 Appendix C: GP Bullhound credentials eliminating the need for traditional coding language. This allows businesses to hire less expensive talent, scale their applications quicker with new consumer trends and reduce the length and complexity of the project lifecycle all of which lead to a quicker a

7、nd easier deployment As 2020 progresses, expect more businesses to implement low-code / no-code platforms and the amount of developers with non- traditional IT or software developer backgrounds to increase significantly Low-code / no-code platforms are driving innovation at all levels of a business

8、2 Computer vision is going to drive new developments across several industry verticals Developments in the functionality and application of artificial intelligence (AI) are driving advancements across all sectors of technology. One area that is gaining major traction, and will continue to do so thro

9、ugh 2020, is computer vision. It utilizes a deep learning algorithm that powers a neural network to decipher images and derive contextual meaning The applications of computer vision are incredibly diverse and can be incorporated across all industries, such as healthcare, retail and transportation. I

10、t can be used to analyze medical reports and images to provide more accurate diagnoses, track customer movement and purchases in-store and guide autonomous vehicles, respectively Throughout the upcoming year, advancements in AI and deep learning are going to push the boundaries of computer vision, a

11、utomating routine processes to make them quicker, simpler and more precise 7 Current trends in software KEY TRENDS 3 The edge computing market is set to grow 10 x from $1.7Bn in 2018 to $16.6Bn by 2025, making it one of the fastest growing segments in the software space1 Edge computing is particular

12、ly useful in time sensitive situations (think oil well sensors, where milliseconds can be the difference between a major disaster and fixing an issue), Gartner predicts that by 2023 there could be more than 20 times as many smart devices at the edge of the network as in conventional IT roles Edge co

13、mputing alone has limited storage capability and still requires its centralized counterpart to maximize its full computing capabilities. Going forward, edge computing will present a third option to the traditional private versus public cloud structures, utilizing a combination of both centralized an

14、d decentralized data processing. Businesses can utilize the benefits of each architecture and implement a hybrid cloud and edge computing solution to optimize their networks for efficiency and greater scalability Edge computing begins its exponential growth as technology enables devices to process d

15、ata in real time without the latency 4 Automated ML modeling in enterprise solutions Machine learnings rapid adoption across all industry segments made it one of the most important areas of technological innovation. However, building successful data models that can deliver actionable insights in any

16、 field requires significant technical knowledge and talent that many companies cant afford to hire. Companies like Google, IBM and Amazon understand this pain point and are kicking off the next wave of innovation in machine learning automated machine learning (AutoML) AutoML is essentially the proce

17、ss of algorithm selection, hyperparameter tuning, and modeling assessment which optimizes data models with little to no human interaction. This increases productivity by automating repetitive tasks / processes and allows users to deploy complex data models without having to hire expensive talent In

18、2020, expect more companies to leverage automated machine learning platforms to deploy dynamic and consistently optimized models. This will expand user capabilities beyond the traditional data scientist role, making this technology available to all levels of an enterprise (1) Allied Market Research

19、8 Current trends in software KEY TRENDS (1) Crunchbase 5 Scale premium and greater access to sources of financing insulates public markets from increasing focus on profitability in the private markets 6 Late stage financing is trending down in 2H19 and will continue to do so in 1H20 Late stage VC fu

20、nding is down 13%1in Q4 year over year and demand is lower than expected for traditional equity as companies seek to avoid down rounds by raising through traditional debt vehicles instead. Demand is also lower than expected for convertible debt because often a return is tied to an IPO price, yet liq

21、uidity comes much later after the IPO lockup period and thus face an increased risk A significant driver behind the decline in funding arose from a series of high-profile misses in Softbanks Vision fund, and other mega funds like it; which was primarily enabled by their ability to write larger check

22、s, command higher valuations and make long term investments that other investors find too risky. Mega fund-backed companies are separating themselves very quickly, leaving less capital for the rest of the market; forcing other VCs to assess their next investment carefully in order to catch the next

23、unicorn Going into 2020 expect a similar trend in total funding amount and volume as the market corrects for the mega fund missteps, which will in turn force companies toward alternative sources of financing We have experienced a change in attitudes on behalf of investors and buyers in the software

24、transactions we work on. Increasingly, profitability or a clear path to profitability are being valued much more highly than the old growth at all costs approach. The anecdotal evidence we are receiving is that unless revenue growth is in the 70%+ category, multiples are negatively impacted if there

25、 is no path to profitability Public market data does not reflect the same pattern though, with unprofitable companies valued more highly than profitable companies on an EV/Revenue basis 6.4x EV/Rev for profitable vs 8.7x EV/Rev for unprofitable. This is likely because there is a scale premium applie

26、d to these much larger businesses, whereby access to financing decreases the risk of investing in an unprofitable business, and growth implies market share advantage/gain and leadership position in the space 9 Section 2 Recent software transaction trends 10 Announce dateTargetBuyerTarget description

27、 Value ($m) Implied EV/LTM Rev Dec-18 Digital advertising solution that helps advertisers create, launch and iterate 1,8177.9x Dec-17 Unified communication however, they have not been independently verified and are not guaranteed as being accurate. The information contained in this research report i

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