transaction trends in a.i. · source: mckinsey global institute, digital europe: pushing the...
TRANSCRIPT
Strictly private and confidential
DEALMAKERS IN TECHNOLOGY
TRANSACTION TRENDS
IN A.I.
Munich · May 2019
2
Confidential
Today’s speaker
▪ Director at GP Bullhound, Berlin
▪ Doctorate in finance from Leipzig Graduate School of
Management
▪ TMT investment banking since 2005
▪ 12 years in London; in Berlin since 2017
▪ Focus on fundraisings and exits
▪ Selected recent transactions:
– $30m growth financing of Mambu by Bessemer
– Buyout of Magix by Capiton
– Sale of Elbkind to Reply
– Sale of Acmeo to H.I.G.-backed Inifigate
– $15m growth financing of TIS by 83North
▪ Focus on software, digital services, (I)IoT and cloud
infrastructure
Dr. Nikolas Westphal
Confidential
3
Dealmakers in Technology
530+successful transactions
$19bn+of value delivered to clients
Fintech
Entertainment
Software
Digital Services
eCommerce
100+professionals
4
Table of Content
Introduction: Riding the smart enterprise wave1
Venture environment: New platforms nearing maturity2
M&A market: The global race for AI3
Key takeaways for strategic acquirors and investors4
Supplementary materialsA
5
Confidential
I.
INTRODUCTION:Riding the smart enterprise wave
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A train carriage containing controlled nuclear materials was stolen in Cincinnati today. Its whereabouts are unknown.
The incident occurred on the downtown train line, which runs from Covington and Ashland stations.
In an email to Ohio news outlets, the U.S. Department of Energy said it is working with the Federal Railroad Administration to find the thief.
“The theft of this nuclear material will have significant negative consequences on public and environmental health, our workforce and the economy of our nation,” said Tom Hicks, the U.S. Energy Secretary, in a statement. “Our top priority is to secure the theft and ensure it doesn’t happen again.”
The stolen material was taken from the University of Cincinnati’s Research Triangle Park nuclear research site, according to a news release from Department officials.
The Nuclear Regulatory Commission did not immediately release any information.
According to the release, the U.S. Department of Energy’s Office of Nuclear Material Safety and Security is leading that team’s investigation.
“The safety of people, the environment and the nation’s nuclear stockpile is our highest priority,” Hicks said. “We will get to the bottom of this and make no excuses.
A routine news report…
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Confidential
The text on the preceding page has been written at the
first attempt by an unsupervised machine learning
model called GPT-2. The GPT-2 has been trained simply
to predict the next word in 40 GB of Internet text.
The technology has not been released due to concerns
about potential malicious applications.
Source: OpenAI Foundation,
“Better Language Models and Their Implications”
https://openai.com/blog/better-language-models/
February 2019
…or is it?
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8
The smart enterprise revolution: agile, intelligent, non-linear
Source: Palantir
Electronic Tools
Semi-conductor
ConsumerTelecomEnterpriseSmart
Enterprise
Silicon Valley Technology Trends
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9
Data: the new oil of the global economy…
Source: Morgan Stanley Equity Research, “Engineering the 21st Century Digital Factory”
Annual data creation by industry (petabytes)
1,812
911
776 773
424 397 375 336
Manufacturing Government Banking Securities
Investment Services
Communications
& Media
Retail Professional
Services
Healthcare
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…driving immense efficiency gains on the back of AI
Source: Accenture and Frontier Economics
Note: (1) Gross Value Added
Impact of AI on industry output (Real Gross Value Added (1) in 2035 in the USA in $trn)
8.4
7.5
6.2
4.03.7
3.42.8
2.12.3
1.51.0
12.2
9.3
8.4
4.9 4.7 4.6
3.32.9 2.7
2.01.3
Baseline AI "steady state" scenario
Manufacturing Prof.
Services
Wholesale
& Retail
Public
Services
Media &
Comms.
Financial
Services
Construction Logistics Healthcare Hospitality Utilities
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A range of repeatable tasks can be automated today
Source: Adobe – “Digital Trends Report 2018”; BCG Gamma / Ipsos
Areas of Task Automation
Forecasting
Goal-oriented communication
Planning and organisation
Pattern recognition
Monitoring and observation
Repetitive manual labour
31%of companies
expected to add
AI over next
year
47%of digitally mature
organisations have
AI strategies
4.5xincrease in jobs
requiring AI
skills since
2013
70%of employees feel
that AI has increased
the appeal of their
work
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II.
VENTURE ENVIRONMENTNew platforms nearing maturity
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13
Continuously increasing VC funding into AI
Source: Pitchbook
Artificial Intelligence & Machine Learning VC Funding ($ Bn)
0.61.4 1.4
2.6
7.0
9.6
14.816.1
25.9
165
3,295
2010 2011 2012 2013 2014 2015 2016 2017 2018
Funding ($ Bn) Number of Rounds
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Continuously increasing VC funding into AI (continued)
Source: Pitchbook, GP Bullhound Analysis
Artificial Intelligence & Machine Learning VC Funding ($ Bn) – excluding Uber
0.61.4 1.4
2.6
4.6
6.0
9.2
16.1
25.9
165
3,295
2010 2011 2012 2013 2014 2015 2016 2017 2018
Funding ($ Bn) Number of Rounds
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15
2014
2004
2014
2011
2011
2016
2015
2012
2005
2015
2014
2015
2013
2013
2009
2003
Computer Vision
Enterprise
Automotive
Computer Vision
Digital Media
Automotive
PaaS
FinTech
Enterprise/RPA
Marketplace
Urban Mobility
Robotics
Automotive
Bio/Nanotech
Data/BI
Enterprise/RPA
Best-funded AI start-upsTop-15 best funded AI start-ups (selected)
Source: Pitchbook, GP Bullhound Analysis
Founded VerticalCompany
3,640
2,758
2,100
1,668
1,360
1,032
1,000
993
977
975
790
700
626
592
572
550
Country Total funding received ($ m)
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16
Regional trends in funding
Source: Pitchbook, GP Bullhound Analysis
Funding amounts over time ($ Bn) Total AI funding by region 2010-2018 ($ Bn)
0.5 1.1 1.12.3
6.37.5
12.2
7.7
14.10.4
0.8
1.4
2.0
1.4
1.4
6.1
8.2
0.3
0.41.0
1.6
0.61.4 1.4
2.6
7.0
9.6
14.816.1
25.9
2010 2011 2012 2013 2014 2015 2016 2017 2018
N America Europe Asia Other
52.9
5.1
17.6
3.7
N America Europe Asia Other
6,876 3,292 1,668 1,569
67% 6% 22% 5%% of
Total
# of
Deals
17
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III.
M&A MARKETThe global race for AI
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0.0
10.0
20.0
30.0
40.0
2013 2014 2015 2016 2017
Big strategics starting to focus on AI
Source: CB Insight
Quarterly earnings calls: mentions of “Artificial Intelligence” Quarterly earnings calls: mentions of “Machine Learning”
0.0
10.0
20.0
30.0
40.0
50.0
60.0
2013 2014 2015 2016 2017
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19
1325
4 2 413
18 19
47
80
90
4 2 4
1319 20
48
93
115
2010 2011 2012 2013 2014 2015 2016 2017 2018
Platform Vertical
M&A transaction trends in AI
Source: Mergermarket; GP Bullhound Analysis
Annual AI M&A (number of transactions)
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N America
216
62%
Europe
57
16%
Asia
64
19%
Other
10
3%
N America Europe Asia Other
N America
200
57%
Europe
96
28%
Asia
41
12%
Other
10
3%
N America Europe Asia Other
Regional differences in AI M&A
Source: Mergermarket; GP Bullhound Analysis
Share of inbound (no. of transactions 2010-2018) Share of outbound (no. of transactions 2010-2018)
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1
2
4
1
3
2
2
1
1
1
1
7
6
6
1
4
2
3
2
4
3
3
2
1
1
8
8
6
5
5
5
5
4
4
3
3
3
2
1
1
1 Platform Vertical
Most active acquirors
CountryInvestor Number of AI acquisitions 2010-2018
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Largest AI acquisitions 2010-2018
2017
2018
2013
2018
2018
2018
2016
2018
2018
2014
2019
2019
2013
2018
2016
91 Wireless Websoft
InvestorTarget Year
12,288
1,374
1,227
791
682
494
452
397
286
241
231
227
223
193
166
Deal size ($ m)
Source: Mergermarket; GP Bullhound Analysis
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Selected characteristics of AI acquisitions
Source: Mergermarket; GP Bullhound Analysis
25% Percentile 50% Percentile 75% Percentile
Size(€m)
EV/Revenue(x)
9 23 74
3.1 7.1 28.4
Maximum
12,228
550.2
Boston AI
91 Wireless Websense
24
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IV.
KEY TAKEAWAYSfor strategic acquirors and investors
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25
-10 -5 0 5 10 15 20
High tech and
telecommunications
Automotive and assembly
Financial serv ices
Resources and utilities
Transportation and logistics
Consumer packaged goods
Retail
Education
Health care
Building materials and
construction
Professional serv ices
Travel and tourism
Define an AI strategy around your key verticalsAI adoption across the value chain driving companies’ performance
Source: McKinsey Global Institute, Digital Europe: Pushing the frontier, capturing the benefits; Digital America: A tale of the haves and have-mores
AI adoption in key industries across the value chain AI adoption vs. profit margins
Relatively low Relatively high
AI adopters with
proactive strategy
Partial AI adopters
or experimentersNon-adopters
1 Operating profit margin for selected sectors as a share of turnover, for continuing operations and
before exceptional items.2 Firms that are big data and cloud service users and report their strategic posture towards AI to be:
“Disrupting our industry using AI technology is at the core of our strategy”,”We have changed our
longer-term corporate strategy to address the AI threat or opportunity disruption,” or “We have
developed a coordinated plan to respond to the AI threat or opportunity but have not changed
out longer-term corporate strategy.”
Self-reported current profit marginDifference from industry average (unweighted) (percentage points )
AI index Assets Usage Labor
Ove
rall A
I in
de
x
MG
I D
igit
iza
tio
n in
de
x
De
pth
of
AI t
ec
hn
olo
gie
s
AI s
pe
nd
Su
pp
ort
ing
dig
ita
l ass
ets
Pro
du
ct
de
ve
lop
me
nt
Op
era
tio
ns
Su
pp
ly c
ha
in a
nd
dis
trib
utio
n
Cu
sto
me
r e
xpe
rie
nc
e
Fin
an
cia
l an
d g
en
era
l
ma
na
ge
me
nt
Wo
rkfo
rce
ma
na
ge
me
nt
Exp
osu
re t
o A
I in
wo
rkfo
rce
AI r
eso
urc
es
pe
r w
ork
er
High tech and
telecom.
Automotive and
assembly
Financial
serv ices
Resources and
utilities
Media and
entertainment
Consumer
packaged
Transportation
and logistics
Retail
Education
Professional
serv ices
Health care
Materials and
construction
Travel and
tourism
1
26
Confidential
Build an early-stage ecosystem around your AI strategyExample: strategic venture investments in industrial automation / smart manufacturing
2
2
2
8
1
5
9
3
3
2
4
4
4
9
13
9
9
17
4
4
4
11
7
6
12
12
13
8
2
5
5
1
2
15
10
11
23
1
9
10
16
16
10
7
7
8
19
12
17
23
31
34
36
42
43
49
75
Wearables & VR/AR Data & analytics Simulation & design Robotics & (additive) manufacturing IIoT Platforms & Hardware
CountryInvestor Number of venture investments by vertical
27
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Plug strategic capability gaps with acquisitionsExample: Microsoft / XOXCO and Oracle / DataFox
3
▪ Recent M&A:Acquired by Microsoft
▪ Vertical:Conversational
chat bots
▪ Key future engagement tool
▪ Microsoft Bot Framework:
already 360,000+ developers
▪ Recent M&A:Acquired by
Oracle
▪ Vertical:Automated data extraction
▪ Extracts and structures
content from millions of
public sources
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Be prepared to invest at strategic valuation levels“Rule of 40” software valuations vs. selected AI transaction multiples
Sources: Capital IQ, Mergermarket, GP Bullhound Analysis
Note: Data based on GP Bullhound SaaS Index, excludes outliers and companies with “N/A”
“Rule of 40” software valuations… …vs. selected AI acquisition multiples
4
CRM
WDAY
NOW
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SPLK
SHOP
ULTI
DBX
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DOCURNG
CDAY HUBS
PFPT
RP
NEWR
PVTL
JCOM
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TLNDUPLD
SINCH
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MOBLBCOV
AMBRAMBR
JIVE
0x
1x
2x
3x
4x
5x
6x
7x
8x
9x
10x
11x
12x
13x
14x
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70%
EV
/2019E R
eve
nu
e M
ultip
le
2019E Revenue Growth + 2019E Unlevered FCF Margin
550xMaximum AI
EV/Revenue
acquisition
multiple 28x75% percentile AI
EV/Revenue
acquisition
multiple7xMedian AI
EV/Revenue
acquisition
multiple
29
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SUPPLEMENTARY MATERIALS
30
Why now?Examples of vertical platforms starting to gain critical mass
Source: Pitchbook; Company Information
▪ Recent funding:
$225 – Series C,
Sep-18
▪ Vertical:
Robotic process
automation
▪ Automation of
back-office tasks
▪ Rapid take-up,
especially via
channels
▪ Recent funding:
$180 – Series A,
Oct-18
▪ Vertical:
Industrial
automation
▪ Combines AI and
computer vision
with agile robots
▪ Vision of “lights-out
factory”
▪ Recent M&A:
Acquired by
Microsoft
▪ Vertical:
Conversational
chat bots
▪ Key future
engagement tool
▪ Microsoft Bot
Framework: already
360,000+ developers
▪ Recent M&A:
Acquired by
Oracle
▪ Vertical:
Automated data
extraction
▪ Extracts and
structures content
from millions of
public sources
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31
Technology universe share price development
Source: Capital IQ
Share Price Development of Selected Indices
75.0%
125.0%
175.0%
225.0%
275.0%
Jan-17 May-17 Sep-17 Jan-18 May-18 Sep-18 Jan-19 May-19
Tech Majors IT Services E-Commerce & Marketplaces Enterprise Software & SaaS Index S&P500 Dax30
+173.5%
+115.8%
+66.0%
+29.2%
+6.8%
+1.94%
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5.0x
15.0x
25.0x
35.0x
45.0x
55.0x
Jan-14 Apr-14 Jul-14 Oct-14 Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19
Enterprise Software & SaaS Index Tech Majors E-Commerce / Marketplaces IT Services DAX 30 S&P 500
Valuation development over time
Source: Capital IQ
Note: EV/EBITDA multiples > 75.0x considered as not meaningful
Historical EV / NTM EBITDA Trading Performance of Selected Indices
Median EV / NTM EBITDA
5Y 3Y 1Y Today
Enterprise Software & SaaS Index 30.4x 33.0x 35.9x 43.4x
Tech Majors 11.2x 12.2x 12.4x 13.1x
E-Commerce / Marketplaces 16.0x 15.3x 13.6x 13.7x
IT Services 8.7x 8.6x 8.6x 8.9x
DAX 30 8.3x 8.4x 8.1x 8.9x
S&P 500 11.2x 11.6x 11.5x 11.9x
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0
100
200
300
400
500
600
700
800
900
1,000
1,100
1,200
De
c-0
9
De
c-1
0
De
c-1
1
De
c-1
2
De
c-1
3
De
c-1
4
De
c-1
5
De
c-1
6
De
c-1
7
Se
p-1
8
Dry
Po
wd
er ($
bn
)
Venture Capital Buyout Growth Other Private Equity*
Private equity is awash with capital …
Source: Pitchbook.com, Preqin.com
Note: (1) as at July 2018
Private Equity: Dry Powder by Fund Type, 2009 - Q3 2018 PE Funds in Market over Time, Q1 2013 – Q3 2018 (1)
Largest Private Equity Funds Closed in Q2 2018
0
500
1,000
1,500
2,000
2,500
3,000
3,500
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3
2013 2014 2015 2016 2017 2018
No. of Funds Raising Aggregate Capital Targeted ($bn)
Fund Firm Fund Size (m) Fund TypeGeographic
Focus
Carlyle Asia Partners V Carlyle Group 6,550 USD Buyout Asia
Bain Capital Europe V Bain Capital 4,350 EUR Buyout Europe
Nordic Capital Fund IX Nordic Capital 4,300 EUR Buyout Europe
TowerBrook Investors V TowerBrook Capital Partners 4,250 USD Buyout US
General Atlantic
Investment Partners 2017General Atlantic 3,289 USD Growth US
Thoma Bravo Discover
Fund IIThoma Bravo 2,400 USD Buyout US
Blackstone Capital
Partners AsiaBlackstone Group 2,300 USD Buyout Asia
BGH Capital Fund I BGH Capital 2,600 AUD Buyout Australasia
Inflexion Buyout Fund V Inflexion Private Equity Partners 1,250 GBP Buyout Europe
Atlas Capital Resources III Atlas Holdings 1,675 USD Turnaround US
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34
European buyout activity
… leading to an increasingly crowded market and bigger
and bigger deals
Source: Pitchbook.com, Preqin.com
Strategies Targeted by PEs in the NTM, Q3 2016 – Q3 2018
Regions Targeted by PEs in the NTM, Q2 2016 – Q2 2018
51%53%
31%
11%
19%
37%
50%52%
30%
11%
19%
39%
56%
61%
31%
9%
16%
45%
0%
10%
20%
30%
40%
50%
60%
70%
North
America
Europe Asia-Pacific Rest of
World
Emerging
Markets
Global
Pro
po
rtio
n o
f Fu
nd
Se
arc
he
s
Q2 2016
Q2 2017
Q2 2018
18
5€
19
3€
16
3€
23
9€
32
2€
43
7€
23
1€
35
3€
40
6€
798
860 866
900
1,175
1,398
1,1661,171
986
0
200
400
600
800
1000
1200
1400
1600
0
50
100
150
200
250
300
350
400
450
500
2010 2011 2012 2013 2014 2015 2016 2017 2018
Deal Value (EURbn) # of Deals closed
70%
50%42%
16% 15%
9%
5% 2%
71%
51% 51%
14% 13%
5% 5% 3%
70%
53% 50%
16% 15%
8%6%
2%0%
10%
20%
30%
40%
50%
60%
70%
80%
Bu
yo
ut
Ve
ntu
re
Ca
pita
l
Gro
wth
Fu
nd
of
Fu
nd
s
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co
nd
ary
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Co
-In
ve
stm
en
t
Ba
lan
ce
dPro
po
rtio
n o
f Fu
nd
Se
arc
he
s
Q2 2016
Q2 2017
Q2 2018
35
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TEAM
SPLK
SHOP
VEEV
PAYCULTI
OKTA
ZEN
RNG
CDAY
HUBS
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COUP
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MDB
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PCTYMDSO
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(20.0%)
(15.0%)
(10.0%)
(5.0%)
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
(5%) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65%
2019E U
nle
ve
red
FC
F M
arg
in
2019E Revenue Growth
Rule of 40% – Public SaaS2018E Growth and Profitability of Public SaaS Companies in GPB Index
Source(s): Capital IQ
Note: Data based on GP Bullhound SaaS Index, constituents can be found in Appendix A,
excludes outliers and companies with “N/A”
The Rule of
40% Line
36
Rule of 40% – Public SaaS (Cont’d)
Source(s): Capital IQ
Note: Data based on GP Bullhound SaaS Index, excludes outliers and companies with “N/A”
Sub-Performers Market-Performers Out-Performers
Companies whose summed revenue growth rate and FCF margin is greater than 40%, are valued higher
by investors than those below this 'rule of 40' line
CRM
WDAY
NOW
SQ
SPLK
SHOP
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DBX
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DOCURNG
CDAY HUBS
PFPT
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APPF
APPN
WK PS
EVBG
YEXT
EGHT
LPSN
SPSC
SCWX
FSCT
BNFT
INSTTLND
UPLD
SINCH
CBLK
MOBLBCOV
AMBR
AMBR
JIVE
0x
1x
2x
3x
4x
5x
6x
7x
8x
9x
10x
11x
12x
13x
14x
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70%
EV
/2019E R
eve
nu
e M
ultip
le
2019E Revenue Growth + 2019E Unlevered FCF Margin
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37
75%
60%
46% 46%
32% 32%
25%
40%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Horizontal vs. Vertical Focused Cloud Software TrendsVertical-specific has lower Sales & Marketing spend as a percentage of revenue
However, generally lower growth and valuation multiples
Horizontal Software Vertical-Specific Software
Median: 21%Median: 46%
Source(s): Capital IQ
Note: GP Bullhound SaaS Index constituents can be found in Appendix A
13.0x 25.0x 10.1x 16.2x 6.4x 15.2x 7.0x 4.7x 6.8x 12.8x 18.6xEV/LTM
Revenue
FY19/18E
Revenue
Growth6.9% 50.7% 26.8% 31.3% 18.3% 31.7%
2.4x
0.3% 16.1% 4.8% 13.7% 34.6% 25.9%
9.0x
20.2%
31%
24% 23%19% 18% 17%
0%
10%
20%
30%
40%
50%
60%
70%
80%
38
▪ From the 1910s to the 2000s, labor distribution has changed to reflect shifts in society
» Farmers in the U.S. declined from 24% to <1% of labor force
» Technical professions rose 20%, teachers doubled, and health workers grew by 7x
» The concept of an engineer meanwhile went from almost non-existent, to 5% of the workforce
▪ Though concerns exist around impact of AI and workforce automation, we believe
working conditions will improve
» AI will augment tasks that existing employees spend far too much of their time completing
» More flexible work hours and work environments
» Less confinement and time commuting = increased productivity
▪ Many innovators in the space are painting a picture of symbiosis between humans and
artificial intelligence
» Potential to assist many professions including surgeons, machinists, soldiers and chefs
» “EQ” professions will become more important again
▪ Ethics still need to be explored
» E.g.: is it right to delegate core HCM tasks to a machine learning algorithm?
Outlook: what AI may hold in store for us
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