Annual review by Mary Meeker of Kleiner Perkins
About Techcelerate Ventures
Tech Investment and Growth Advisory for Series A in the UK, operating in £150k to £5m investment market, working with #SaaS #FinTech #HealthTech #MarketPlaces and #PropTech companies.
INTERNET TRENDS 2018
Mary Meeker
May 30 @ Code 2018
kleinerperkins.com/InternetTrends
2
Thanks
Kleiner Perkins Partners
Ansel Parikh & Michael Brogan helped steer ideas and did a lot of heavy lifting. Other
contributors include: Daegwon Chae, Mood Rowghani, Eric Feng (E-Commerce) & Noah
Knauf (Healthcare). In addition, Bing Gordon, Ted Schlein, Ilya Fushman, Mamoon Hamid,
Juliet deBaubigny, John Doerr, Bucky Moore, Josh Coyne, Lucas Swisher, Everett Randle &
Amanda Duckworth were more than on call with help.
Hillhouse Capital
Liang Wu & colleagues' contribution of the China section provides an overview of the world's
largest market of Internet users.
Participants in Evolution of Internet Connectivity
From creators to consumers who keep us on our toes 24x7 + the people who directly help us
prepare the report. And, Kara & team, thanks for continuing to do what you do so well.
3
Context
We use data to tell stories of business-related trends we focus on. We hope others take the ideas, build on
them & make them better.
At 3.6B, the number of Internet users has surpassed half the world's population. When markets reach
mainstream, new growth gets harder to find - evinced by 0% new smartphone unit shipment growth in
2017.
Internet usage growth is solid while many believe it's higher than it should be. Reality is the dynamics of
global innovation & competition are driving product improvements, which, in turn, are driving usage &
monetization. Many usability improvements are based on data - collected during the taps / clicks /
movements of mobile device users. This creates a privacy paradox...
Internet Companies continue to make low-priced services better, in part, from user data. Internet Users
continue to increase time spent on Internet services based on perceived value. Regulators want to ensure
user data is not used 'improperly.'
Scrutiny is rising on all sides - users / businesses / regulators. Technology-driven trends are changing so
rapidly that it's rare when one side fully understands the other...setting the stage for reactions that can have
unintended consequences. And, not all countries & actors look at the issues through the same lens.
We focus on trends around data + personalization; high relative levels of tech company R&D + Capex
Spending; E-Commerce innovation + revenue acceleration; ways in which the Internet is helping
consumers contain expenses + drive income (via on-demand work) + find learning opportunities. We review
the consumerization of enterprise software and, lastly, we focus on China's rising intensity & leadership in
Internet-related markets.
4
Internet Trends 2018
1) Users
5-9
2) Usage
10-12
3)
Innovation + Competition + Scrutiny
13-43
4) E-Commerce
44-94
5) Advertising
95-99
6) Consumer Spending
100-140
7) Work
141-175
8) Data Gathering + Optimization
176-229
9) Economic Growth Drivers
230-237
10) China (Provided by Hillhouse Capital) 237-261
11) Enterprise Software
262-277
12) USA Inc. + Immigration
278-291
5
INTERNET DEVICES + USERS =
GROWTH CONTINUES TO SLOW
6
Global New Smartphone Unit Shipments =
No Growth @ 0% vs. +2% Y/Y
Source: Katy Huberty @ Morgan Stanley (3/18), IDC.
New Smartphone Unit Shipments vs. Y/Y Growth
0%
30%
60%
90%
0
0.5B
1.0B
1.5B
2009 2010 2011 2012 2013 2014 2015 2016 2017
Y/Y GrowthNew Smartphone Shipments, GlobalAndroid
iOS
Other
Y/Y Growth
7
Source: United Nations / International Telecommunications Union, USA Census Bureau. Internet user data is as of mid-year. Internet user
data: Pew Research (USA), China Internet Network Information Center (China), Islamic Republic News Agency / InternetWorldStats / KP
estimates (Iran), KP estimates based on IAMAI data (India), & APJII (Indonesia). Note: Historical data (particularly in Sub-Saharan Africa)
revised by ITU in 2017 to better account for dual-SIM subscriptions (i.e. two Internet subscriptions per single smartphone user).
Global Internet Users =
Slowing Growth @ +7% vs. +12% Y/Y
0%
4%
8%
12%
16%
0
1B
2B
3B
4B
2009 2010 2011 2012 2013 2014 2015 2016 2017
Y/Y GrowthInternet Users, GlobalGlobal Internet Users
Y/Y Growth
Internet Users vs. Y/Y Growth
8
Global Internet Users =
3.6B @ >50% of Population (2018)
24%
49%
0%
20%
40%
60%
2009 2010 2011 2012 2013 2014 2015 2016 2017
Internet Penetration, Global Internet Penetration
Source: CIA World Factbook, United Nations / International Telecommunications Union, USA Census Bureau. Internet user data is as of mid-year.
Internet user data: Pew Research (USA), China Internet Network Information Center (China), Islamic Republic News Agency / InternetWorldStats
/ KP estimates (Iran), KP estimates based on IAMAI data (India), & APJII (Indonesia). Note: Historical data (particularly in Sub-Saharan Africa)
revised by ITU in 2017 to better account for dual-SIM subscriptions (i.e. two Internet subscriptions per single smartphone user).
9
Internet Users
Growth Harder to Find After
Hitting 50% Market Penetration
10
INTERNET USAGE =
GROWTH REMAINS SOLID
11
Digital Media Usage @ +4% Growth...
5.9 Hours per Day (Not Deduped)
Source: eMarketer 9/14 (2008-2010), eMarketer 4/15 (2011-2013), eMarketer 4/17 (2014-2016), eMarketer 10/17 (2017). Note:
Other connected devices include OTT and game consoles. Mobile includes smartphone and tablet. Usage includes both home and
work for consumers 18+. Non deduped defined as time spent with each medium individually, regardless of multitasking.
Daily Hours Spent with Digital Media per Adult User
0.2
0.3
0.4
0.3
0.3
0.3
0.3
0.4
0.4
0.6
2.2
2.3
2.4
2.6
2.5
2.3
2.2
2.2
2.2
2.1
0.3
0.3
0.4
0.8
1.6
2.3
2.6
2.8
3.1
3.3
2.7
3.0
3.2
3.7
4.3
4.9
5.1
5.4
5.6
5.9
0
1
2
3
4
5
6
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Hours Spent per Day, USAOther Connected Devices
Desktop / Laptop
Mobile
12
Internet Usage
How Much = Too Much?
Depends How Time is Spent
13
INNOVATION + COMPETITION =
DRIVING PRODUCT IMPROVEMENTS /
USEFULNESS / USAGE +
SCRUTINY
14
Devices
Access
Simplicity
Payments
Local
Messaging
Video
Voice
Personalization
Innovation + Competition =
Driving Product Improvements / Usefulness / Usage
15
Devices =
Better / Faster / Cheaper
Source: Apple, Google, Katy Huberty @ Morgan Stanley, IDC. *ASP Based on Morgan Stanley's new smartphone shipment breakdown
by taking the midpoint of each $50 price band & assuming a $1,250 ASP for smartphones over $1,000. Note: Deloitte estimates that
120MM used smartphones were traded in 2016 and 80MM in 2015 which may further reduce smartphone costs to consumers as the
ratio of used to new devices rises. Apple 2016 = iPhone 7 Plus, 2017 = iPhone X. Google 2016 = Pixel, 2017 = Pixel 2.
Apple iPhone
2016
2017
'Portrait' Photos
Water Resistant
Face Tracking
Full Device Display
Wireless Charging
2016
2017
Google Assistant
'AI-Assisted'
Photo Editing
'Lens' Smart
Image Recognition
Always-On Display
Google Android
$0
$100
$200
$300
$400
$500
2007 2009 2011 2013 2015 2017
New Smartphone Shipment ASP, Global*New Smartphone Shipments ASP
16
Access =
WiFi Adoption Rising
WiFi Networks
Source: WiGLE.net as of 5/29/18. Note: WiGLE.net is a submission-based catalog of wireless
networks that has collected >6B data points since launch in 2001. Submissions are not paired
with actual people, rather name / password identities which people use to associate their data.
0
100MM
200MM
300MM
400MM
500MM
2001
2003
2005
2007
2009
2011
2013
2015
2017
WiFi Networks, Global
17
Simplicity =
Easy-to-Use Products Becoming Pervasive
Media
Spotify
Source: Telegram (5/18), Square (5/18), Spotify (5/18).
Messaging
Telegram
Commerce
Square Cash
18
Payments =
Digital Reach Expanding
1%
2%
3%
4%
4%
7%
7%
8%
9%
15%
40%
Other
Wearables / Contactless
Smart Home Device
QR Codes
Other In-App Payments
Mobile Messenger Apps
P2P Transfer
Other Mobile Payments
Buy Buttons
Other Online
In-Store
0%
10%
20%
30%
40%
50%
% of Global Responses (9/17)
Transactions by Payment Channel
Thinking of your past 10 everyday transactions, how many were made in each of the following ways?
Source: Visa Innovations in a Cashless World 2017. Note: Full question was 'Please think about the payments you make for everyday transactions (excluding rent,
mortgage, or other larger, infrequent payments). Thinking of your past 10 everyday transactions, how many were made in each of the following ways?', GfK
Research conducted the survey with n = 9,200 across 16 countries (USA, Canada, UK, France, Poland, Germany, Mexico, Brazil, Argentina, Australia, China, India,
Japan, South Korea, Russia, UAE), between 7/27/17 9/5/17. All respondents do not work in Financial Services, Marketing, Marketing Research, Advertising, or
Public Relations, own and currently use a smartphone, have a savings or checking account; own/use a computer or tablet, and own a credit or debit card.
60% =
Digital
19
Payments =
Friction Declining...
Source: China Internet Network Information Center (CNNIC). Note: User defined as active user of mobile-
passed payment technology for everyday transactions, as well as more complex transactions, such as bill
paying in the relevant period. Includes all forms of transactions on mobile (e.g., QR codes, P2P, etc.)
0
200MM
400MM
600MM
2012
2013
2014
2015
2016
2017
Mobile Payment Users, ChinaChina Mobile Payment Users
20
Payments =
Digital Currencies Emerging
Source: Coinbase. Note: Registered users defined as users that have an account on Coinbase.
Coinbase Users
1x
2x
3x
4x
January
March
May
July
September
November
Coinbase Registered Users, Global (Indexed to 1/17)2017
21
Local =
Offline Connections Driven by Online Network Effects
0
50K
100K
150K
200K
2011
2012
2013
2014
2015
2016
2017
2018
Active Neighborhoods, USASource: Nextdoor (5/18). Note: There are ~130MM households in USA. Nextdoor estimates
that there are ~650 households per average neighborhood (~200K USA neighborhoods).
Nextdoor Active Neighborhoods
22
Messaging =
Extensibility Expanding
0
0.5B
1.0B
1.5B
2011 2012 2013 2014 2015 2016 2017
WhatsApp
Facebook Messenger
WeChat
Instagram
Twitter
Messenger MAUs
QQ
WeChat
Messaging
Tencent (2000 2018)
Source: Facebook, WhatsApp, Tencent, Instagram, Twitter, Morgan Stanley Research. Note: 2013 data for Instagram &
Facebook Messenger are approximated from statements made in early 2014. Twitter users excludes SMS fast followers.
MAUs (Monthly Active Users) are defined as users who log into a messenger on the web or through an application.
23
Video =
Mobile Adoption Climbing...
Source: Zenith Online Video Forecasts 2017 (7/17). Note: Based on a study across 63 countries. The
historical figures are taken from the most reliable third-party sources in each market including Nielsen
and comScore. The forecasts are provided by local experts, based on the historical trends,
comparisons with the adoption of previous technologies, and their judgement.
0
10
20
30
40
2012
2013
2014
2015
2016
2017
2018E
Daily Mobile Video Viewing Minutes, GlobalMobile Video Usage
24
Video =
New Content Types Emerging
Source: Twitch (3/18). Note: Tyler "Ninja" Blevins Twitch stream has 7MM+
followers (#1 ranked) as of 5/29/18 based on Social Blade data.
0
6MM
12MM
18MM
2012 2013 2014 2015 2016 2017
Average Daily Streaming Hours, GlobalTwitch Streaming Hours
Fortnite Battle Royale
Most Watched Game on Twitch
25
95%
95%
70%
80%
90%
100%
2013
2014
2015
2016
2017
Word Accuracy RateGoogle
Threshold for Human Accuracy
Voice =
Technology Lift Off
Google Machine Learning Word Accuracy
Source: Google (5/17). Note: Data as of 5/17/17 & refers to recognition accuracy for
English language. Word error rate is evaluated using real world search data which is
extremely diverse & more error prone than typical human dialogue.
26
Voice =
Product Lift Off
Source: Consumer Intelligence Research Partners LLC (Echo install base, 2/18), Various
media outlets including Geekwire, TechCrunch, and Wired (Echo skills, 3/18)
Amazon Echo Installed Base
0
10MM
20MM
30MM
40MM
Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Installed Base, USA0
10K
20K
30K
2015
2016
2017
2018
Number of SkillsAmazon Echo Skills
2015
2016
2017
27
Devices
Access
Simplicity
Payments
Local
Messaging
Video
Voice
Personalization
Innovation + Competition =
Driving Product Improvements / Usefulness / Usage
28
Personalization =
Data Improves
Engagement + Experiences
Drives Growth + Scrutiny
29
Personal + Collective Data =
Provide Better Experiences for Consumers
Source: Facebook (5/18), Pinterest (5/18), Spotify (5/18), Netflix (5/18).
Note: Facebook Q1:18 MAU (4/18), Pinterest MAU (9/17), Spotify Q1:18
MAU (5/18), Netflix Q1:18 global streaming memberships (4/18).
Music
Video
Newsfeed
Discovery
170MM
Spotifys
125MM
Netflixes
2.2B
Facebooks
200MM
Pinterests
30
...Personal + Collective Data =
Provide Better Experiences for Consumers
100MM+
Snap Map
MAUs
17MM**
Nextdoor
Recommendations
20%
UberPOOL Share of All
Rides, Where Available*
100MM+
Waze
Drivers
Real-Time
Social Stories
Often Real-Time
Local News
Real-Time
Transportation
Real-Time
Navigation
Source: Facebook (5/18), Waze (2/18), Snap (5/18), Nextdoor (5/18) *Active Markets = Atlanta, Austin, Boston, Chicago, Denver, Las Vegas, Los
Angeles, Miami, Nashville, New Jersey, New York City, Philadelphia, Portland, San Diego, San Francisco, Seattle, Washington D.C., Toronto, Rio de
Janeiro, Sao Paulo, Bogota, Guadalajara, Mexico City, Monterrey, Lima, Paris, London, Ahmedabad, Bangalore, Chandigarh, Chennai, New Delhi,
Guwahati, Hyderabad, Jaipur, Kochi, Kolkata, Mumbai, Pune, & Sydney. **Refers to cumulative recommendations as of 11/17.
31
Internet Companies
Making Low-Priced Services Better, in Part, from User Data
Internet Users
Increasing Time on Internet Services Based on Perceived Value
Regulators
Want to Ensure User Data is Not Used 'Improperly'
Privacy Paradox
32
Rising User Engagement =
Drives Monetization + Investment in Product Improvements...
Facebook Annualized Revenue per Daily User
$16
$34
$0
$10
$20
$30
$40
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1
Source: Facebook (4/18). Note: Facebook Daily Active Users (DAU) defined as a registered Facebook user who
logged in and visited Facebook on desktop or mobile device, or took action to share content or activity with his or
her Facebook friends or connections via a third-party website that is integrated with Facebook, on a given day.
ARPDAU calculated by dividing annualized total revenue by average DAU in the quarter.
2015
2016
2017
2018
Annualized Average Revenueper Daily Active User (ARPDAU), Global
33
...Rising Monetization + Data Collection =
Drives Regulatory Scrutiny
Source: European Union (5/18), European Commission (6/17, 10/17,
12/16), Bundeskartellmt (German Competition Authority (12/17).
German Federal Ministry of Justice and Consumer Protection (10/17).
Competition
Commission fines Google 2.42 billion for abusing
dominance as search engine by giving illegal
advantage to its own comparison shopping service.
- European Commission, 6/17
Commission approves acquisition of LinkedIn by
Microsoft, subject to conditions.
- European Commission, 12/16
Commission finds Luxembourg gave illegal tax
benefits to Amazon worth around 250 million.
- European Commission, 10/17
Taxes
Data / Privacy
The Germany Network Enforcement Act will require
for-profit social networks with >2MM registered
users in Germany to remove unlawful content
within 24 hours of receiving a complaint.
- German Federal Ministry of Justice & Consumer
Protection, 10/17
Safety / Content
The European Data Protection Regulation will be
applicable as of May 25th, 2018 in all member states
to harmonize data privacy laws across Europe.
- European Union, 5/18
Facebook's collection & use of data from
third-party sources is abusive.
- German Federal Cartel Office, 12/17
34
Internet Companies =
Key to Understand Unintended Consequences of Products...
Source: Facebook (4/18).
We're an idealistic & optimistic company.
For the first decade, we really focused on all the good that
connecting people brings.
But it's clear now that we [Facebook] didn't do enough.
We didn't focus enough on preventing abuse & thinking
through how people could
use these tools to do harm as well.
- Mark Zuckerberg, Facebook CEO, 4/18
35
Regulators =
Key to Understand Unintended Consequences of Regulation
Source: Bloomberg (5/18).
This month, the European Union will embark on an
expansive effort to give people more control
over their data online...
As it comes into force, Europe should
be mindful of unintended consequences
& open to change when things go wrong.
- Bloomberg Opinion Editorial, 5/8/18
36
It's Crucial To Manage For
Unintended Consequences
But It's Irresponsible to Stop
Innovation + Progress
37
USA Internet Leaders =
Aggressive + Forward-Thinking
Investors for Years
38
Global USA-Listed Technology IPO Issuance &
Global Technology Venture Capital Financing
Investment (Public + Private) Into Technology Companies =
High for Two Decades
Technology IPO & Private Financing, GlobalSource: Morgan Stanley Equity Capital Markets, *2018YTD figure as of 5/25/18, Thomson ONE. All global USA-listed
technology IPOs over $30MM, data per Dealogic, Bloomberg, & Capital IQ. 2012: Facebook ($16B IPO) = 75% of 2012
IPO $ value. 2014: Alibaba ($25B IPO) = 69% of 2014 IPO $ value. 2017: Snap ($4B IPO) = 34% of 2017 $ value.
NASDAQ Composite0
2,000
4,000
6,000
8,000
$0
$50B
$100B
$150B
$200B
1990
1995
2000
2005
2010
2015
Technology Private Financing
Technology IPO
NASDAQ
2018YTD*
39
Technology Companies =
25% & Rising % of Market Cap, USA
USA Information Technology % of MSCI Market Capitalization
Source: FactSet, Katy Huberty @ Morgan Stanley. MSCI, Formerly Morgan Stanley Capital
International = American provider of equity, fixed income, hedge fund stock market indexes, and
equity portfolio analysis tools. Data refers to MSCI's index of USA publicly traded companies.
0%
10%
20%
30%
40%
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017
33%
March, 2000
25%
April, 2018
IT % of MSCI Market Capitalization, USA
40
Technology Companies =
6 of Top 15 R&D + Capex Spenders, USA
USA Public Company Research & Development
Spend + Capital Expenditures (2017)
Source: SEC Edgar, Katy Huberty @ Morgan Stanley. Note: All figures are calendar year 2017. Amazon R&D = Tech &
Content spend. General Motors does not include purchases of leased vehicles. AT&T capex does not include interest
during construction, just purchases of property, plant, & equipment. Verizon capitalizes R&D expense (i.e. reported as
capex). General Electric R&D = GE funded, not government or customer. Bold indicates tech companies.
$0
$10B
$20B
$30B
$40B
Merck
General Electric
Johnson & Johnson
Chevron
Facebook
Ford
General Motors
Exxon Mobil
Verizon
AT&T
Microsoft
Apple
Intel
Google / Alphabet
Amazon
2017 R&D + Capex
Capex
R&D
Gogle / Alp
I
Micros
+45% Y/Y
+23%
+11%
+5%
+6%
-4%
+1%
-4%
+5%
+5%
+40%
-26%
+12%
+2%
+3%
Fac
41
Technology Companies =
Largest + Fastest Growing R&D + Capex Spenders, USA
Research & Development Spend + Capital
Expenditures Select USA GICS Sectors
$0
$100B
$200B
$300B
2007
2009
2011
2013
2015
2017
R&D + Capex, USATechnology
Healthcare
Energy
Materials
Industrials
Discretionary
Staples
Utilities
Telecom
Technology
+9% CAGR
+18% Y/Y
Healthcare*
+4% CAGR
+8% Y/Y
Discretionary
0% CAGR
-22% Y/Y
Source: ClariFi, Katy Huberty @ Morgan Stanley. GICS = Global Industry Classification Standard, an industry taxonomy developed in 1999 by MSCI and Standard & Poor's
(S&P) for use by the global financial community. CAGR = Compounded annual growth rate from 2007-2017. Note: Amazon, Netflix and Expedia removed from Discretionary
Sector & added to Technology. Discretionary includes companies that sell goods & services that are considered non-essential by consumers such as Starbucks (restaurants) &
Nike (apparel). See appendix for detailed GICs definition. ClariFi does not have R&D or Capex data from Financial Services. *Healthcare Includes pharmaceutical companies.
42
Technology Companies =
Rising R&D + Capex as % of Revenue18% vs. 13% (2007)
USA Technology Company Research & Development
Spend + Capital Expenditures vs. % of Revenue
13%
18%
0%
5%
10%
15%
20%
$0
$200B
$400B
$600B
$800B
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
% of RevenueR&D + Capex, USAR&D + Capex
% of Revenue
Source: ClariFi, Katy Huberty @ Morgan Stanley. GICS = Global Industry Classification Standard, an industry taxonomy developed in
1999 by MSCI and Standard & Poor's (S&P) for use by the global financial community. Note: Amazon, Netflix and Expedia removed
from Discretionary Sector & added to Technology. Discretionary includes companies that sell goods & services that are considered
non-essential by consumers such as Starbucks (restaurants) & Nike (apparel). See appendix for detailed GICs definition.
43
USA Tech Companies
Aggressive Competition +
Spending on R&D + Capex =
Driving Innovation + Growth
44
E-COMMERCE =
TRANSFORMATION ACCELERATING
45
E-Commerce =
Acceleration Continues @ +16% vs. +14% Y/Y, USA
E-Commerce Sales + Y/Y Growth
Source: St. Louis Federal Reserve FRED database. Note: Historic data
(Pre-2016) adjusted / back-casted in 2017 by USA Census Bureau to better
align with Annual Retail Trade + Monthly Retail Trade Survey data.
0%
4%
8%
12%
16%
20%
$0
$100B
$200B
$300B
$400B
$500B
2010
2011
2012
2013
2014
2015
2016
2017
E-Commerce Sales
Y/Y Growth
E-Commerce Sales, USAY/Y Growth
46
E-Commerce vs. Physical Retail =
Share Gains Continue @ 13% of Retail
E-Commerce as % of Retail Sales
0%
4%
8%
12%
16%
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
E-Commerce Share, USASource: USA Census Bureau, St. Louis Federal Reserve FRED database.
Note: 13% = Annualized share. Penetration calculated by dividing
E-Commerce sales by "Core" Retail Sales (excluding food services, motor
vehicles / auto parts, gas stations & fuel). All figures are seasonally adjusted.
47
Amazon =
E-Commerce Share Gains Continue @ 28% vs. 20% in 2013
E-Commerce Gross Merchandise Value (GMV) Amazon vs. Other
Source: St. Louis Federal Reserve FRED database, Brian Nowak @ Morgan Stanley (5/18).
Morgan Stanley Amazon USA GMV estimates exclude in-store GMV and assume 90% of
North American GMV is USA. Market share calculated using FRED E-Commerce sales data.
0%
20%
40%
60%
80%
100%
$0
$100B
$200B
$300B
$400B
$500B
Share of E-Commerce, USAGMV
2017
2013
2013
2017
Other
Amazon
$52B GMV = 20% Share
$129B GMV = 28% Share
48
E-Commerce =
Evolving + Scaling
49
E-Commerce =
Mobile / Interactive / Personalized / In-Feed + Inbox / Front-Doored
Source: Instacart (5/18)
Find
Local Store
Explore
Custom Savings
View + Share
Recommendations
Pay
Seamlessly
Update
Instacart
50
E-Commerce =
A Look @ Tools + Numbers
Payment
Online Store
Online Payment
Fraud Prevention
Purchase Financing
Customer Support
Finding Customers
Delivering Product
51
Offline Merchants =
Set Up Payment System
0
1MM
2MM
3MM
2013 2014 2015 2016 2017
$0
$30B
$60B
$90B
GPV
Active Sellers
Estimated Active Sellers &
Gross Payment Volume (GPV)
Source: Square (5/18). Note: Active Sellers have accepted five or more payments using Square in the last 12
months. In 11/15 Square disclosed it had 2MM users and in 3/16 disclosed it was adding 100K sellers per quarter
assuming seller trends remained constant, Square had approximately 2.8MM active sellers at the end of 2017.
(~2.8MM = 2017E)
Square
Points of Sale (POS)
Software Services
Payroll
Loans
Invoices
Analytics
Estimated Active Sellers, GlobalGPV, Global
52
...Build Online Store
0
300K
600K
900K
2013 2014 2015 2016 2017E
$0
$10B
$20B
$30B
GMV
Merchants
Active Merchants, GlobalGMV, GlobalActive Merchants &
Gross Merchandise Volume (GMV)
Shopify
Online Stores
Source: Shopify, Brian Essex @ Morgan Stanley. Note: Active Merchants refers to
merchants with an active Shopify subscription at the end of the relevant period. 2017
Active merchants and GMV are estimates based on periodic disclosures. (609K = 2017E)
53
Integrate Online Payment System
Stripe
Payment API Implementation
Source: Stripe (5/18).
54
...Integrate Fraud Prevention
0
4K
8K
12K
2015
2016
2017
Merchants
Active Merchants, GlobalSignifyd
Fraud Prevention
Source: Signifyd (5/18). Note: Merchants refers to retailers using
Signifyd services to monitor for fraud @ period end. (10K = 2017)
Increase Revenue
Fast Decisions (milliseconds)
Shift Liability
55
Integrate Purchase Financing
Affirm
Financing
Source: Affirm (5/18).
1,200+ = Merchants
$350
56
Intercom
Real-Time Support
Integrate Customer Support
Customer Conversations
Source: Intercom (5/18). Note: Conversations started include messages initiated by business & customers. (500MM = 2018)
0
100MM
200MM
300MM
400MM
500MM
2013 2014 2015 2016 2017 2018
Conversations Started, Global
57
Find Customers
Source: Criteo (5/18). Note: Clients defined as active
clients @ relevant period end. (18K = 2017)
0
5K
10K
15K
20K
2013 2014 2015 2016 2017
Marketing Clients
Clients, GlobalCriteo
Customer Targeting
58
Deliver Products to Customers
Parcel Volume
UPS + FedEx + USPS*
0
2B
4B
6B
8B
10B
12B
2012 2013 2014 2015 2016 2017
Volume, USA* USPS
UPS
FedEx
Product Delivery
Source: UPS, FedEx, USPS, Caviar. *Combines USPS's domestic shipping &
package services volumes, FedEx's domestic package volumes, and UPS's
domestic package volumes. All figures are calendar year end except FedEx
which includes LTM figures ending November 30 due to offset fiscal year.
59
E-Commerce =
A Look @ Tools + Numbers
Payment
Online Store
Online Payment
Fraud Prevention
Purchase Financing
Customer Support
Finding Customers
Delivering Product
60
Building / Deploying Online Stores =
Trend Evinced by Shopify Storefront Exchange
Source: Shopify (5/18)
Shopify Storefront Exchange (Launched 6/17)
Loopies.com
61
Online Product Finding Evolution =
Search Leads
Discovery Emerging
Getting More...
Data Driven / Personalized / Competitive
62
Product Finding =
Often Starts @ Search (Amazon + Google...)
49%
36%
15%
Where Do You Begin Your Product Search?
Source: Survata (9/17). Note: n = 2,000 USA customers.
Amazon
Search Engine
Other
63
Product Finding (Amazon) =
Started @ Search...Fulfilled by Amazon
Product Search
Source: The Internet Archive, Amazon.
1-Click
Purchasing
Prime
Fulfillment
Sponsored
Product Listings
Voice
Search + Fulfillment
64
Product Finding (Google) =
Started @ Search...Fulfilled by Others
Organic Search
Paid
Search
Shopping
Product
Listing Ads
Shopping
Actions
Source: The Internet Archive, Google.
65
Online Product Finding Evolution =
Search Leads
Discovery Emerging
Getting More...
Data Driven / Personalized / Competitive
66
Product Finding (Facebook / Instagram) =
Started @ Personalized Discovery in Feed
Source: Facebook (5/18), Instagram (5/18).
67
Online Product Finding Evolution =
Search Leads
Discovery Emerging
Getting More...
Data Driven / Personalized / Competitive
68
Google = Ad Platform to a Commerce Platform...
Amazon = Commerce Platform to an Ad Platform
Source: Advia (Google 2000 image), TechCrunch (2/17), Amazon (5/18).
AdWords
Sponsored Products
1-Click Checkout
Amazon
Google Home Ordering
19972000
2018
69
E-Commerce-Related Advertising Revenue =
Rising @ Google + Amazon + Facebook
Amazon
$4B +42% Y/Y =
Ad Revenue
3x = Engagement Increase
For Top Mobile Product Listing Ad*
>80MM +23% Y/Y =
SMBs with Pages
Source: Google (7/17), Brian Nowak @ Morgan Stanley (Amazon Ad revenue estimate, 5/18), Facebook (4/18).
*Google disclosed that the leftmost listing in a mobile product listing ad experiences 3x engagement.
70
Social Media =
Enabling More Efficient
Product Discovery / Commerce
71
Social Media =
Driving Product Discovery + Purchases
Source: Curalate Consumer Survey 2017 (8/17). Note: n = 1,000 USA consumers ages 18-65. Left chart
question: 'In the last 3 months, have you discovered any retail products that you were interested in buying on
any of the following social media channels?' Right chart question: 'What action did you take after discovering
a product in a brand's social media post?' Never Bought / Other includes offline purchases made later.
Social Media Discovery
Driving Purchases
44%
11%
45%
55% = Bought
Product Online
After Social
Media Discovery
Social Media Driving
Product Discovery
22%
34%
59%
59%
78%
0%
50%
100%
Snap
% of Respondents, USA (18-65 Years Old)
% of Respondents that Have Discovered
Products on Platform, USA (18-34 Years Old)
Bought Online Later
Bought Online Immediately
Never Bought / Other
72
2%
6%
0%
2%
4%
6%
8%
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1
Social Media =
Share of E-Commerce Referrals Rising @ 6% vs. 2% (2015)
Social / Feed Referrals to E-Commerce Sites
Share, USA2015
2016
2017
2018
Source: Adobe Digital Insights (5/18). Note: Adobe Digital Insights based on 50B+ online
USA page visits since 2015. Data is collected on a per-visit basis across all internet
connected devices and then aggregated by Adobe. Data reflects 5/1/18 measurements.
73
Social Media =
Helping Drive Growth for Emerging DTC Retailers / Brands
$0
$20MM
$40MM
$60MM
$80MM
$100MM
0
1
2
3
4
5
6
7
Annual Revenue, USAYears Since Inception
Source: Internet Retailer 2017 Top 1,000 Guide. *Data only for E-Commerce sales and shown in 2017 dollars. Chart
includes pure-play E-Commerce retailers and evolved pure-play retailers. The Top 1,000 Guide uses a combination of
internal research staff and well-known e-commerce market measurement firms such as Compete, Compuware APM,
comScore, ForeSee, Experian Marketing Services, StellaService and ROI Revolution to collect and verify information.
Select USA Direct-to-Consumer (DTC) Brands
Revenue Ramp to $100MM Since Inception*
74
Social Media =
Ad Engagement RisingFacebook E-Commerce CTRs Rising
1%
3%
0%
1%
2%
3%
4%
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Facebook E-Commerce CTR, GlobalFacebook E-Commerce CTRs (Click-Through Rates)
Source: Facebook, Nanigans Quarterly Facebook Benchmarking Data. Note: Click-Through Rate is
defined as the percentage of people visiting a web page who access a hyperlink text from a particular
advertisement. CTR figures based on $600MM+ of ad spend through Nannigan's platform.
2016
2017
2018
75
Return on Ad Spend =
Cost Rising @ Faster Rate than Reach
-40%
0%
40%
80%
120%
Q1
Q2
Q3
Q4
Q1
Y/Y Growth, GlobaleCPM
CTR
Facebook E-Commerce
eCPM vs. CTR Y/Y Growth
2017
2018
CTR = +61%
Source: Booking Holdings Inc. (11/17), Nanigans Quarterly Facebook Benchmarking Data. Note: eCPMs are defined as the effective (blended across ad formats) cost per
thousand ad impressions. Click-Through Rate is defined as the percentage of people visiting a web page who access a hyperlink text from a particular advertisement. CTR
figures based on $600MM+ of ad spend through Nanigan's platform. In 2017, Booking Holdings spent $4.1B on online performance advertising which is primarily focused on
search engine marketing (SEM) channels. The quote on the left relates to historical long-term ad ROI trends as competition across performance channels intensified.
In performance-based
[digital advertising] channels,
competition for top placement has
reduced ROIs over the years &
been a source of margin pressure
- Glenn D. Fogel, CEO & President, Booking Holdings
Q3:17 Earnings Call, (11/17)
eCPM = +112%
76
Source: Salesforce Digital Advertising 2020 Report (1/18). Note: n = 900 full-time advertisers, media buyers,
and marketers with the title of manager and above. Respondents are from companies in North America (USA,
Canada), Europe (France, Germany, Netherlands, UK, Ireland) and Asia Pacific (Japan, Australia, New Zealand)
with each region having 300 participants. The survey was done online via FocusVision in 11/17.
Customer Lifetime Value (LTV) = Importance Rising as...
Customer Acquisition Cost (CAC) Increases
What Do You Consider To Be Important Ad Spending Optimization Metrics?
8%
13%
15%
18%
19%
27%
0%
10%
20%
30%
Multi-touch Attribution
Last-Click Attribution
Closed-Won Business
Brand Recognition & Lift
Impressions / Web Traffic
Customer Lifetime Value
% of Respondents, Global
77
Lifetime Value / Customer Acquisition Cost (LTV / CAC) =
Increasingly Important Metric for Retailers / Brands
Source: Facebook (3/17).
Facebook Ad Analytics Tools LTV Integration
78
Data-Driven Personalization /
Recommendations =
Early Innings @ Scale
79
Evolution of Commerce Drivers (1890s -> 2010s) =
Demographic -> Brand -> Utility -> Data
Source: Eric Feng @ Kleiner Perkins
Wikimedia, eBay, Stitch Fix.
Demographic
Brand
Utility
Data
Catalogs
Limited product
selection +
shopping moments
Department Stores /
Malls
Rising product
selection +
shopping moments
E-Commerce
Transactional
Massive product
selection + 24x7
shopping moments
E-Commerce
Personalized
Curated product
discovery + 24x7
recommendations
Sears Roebuck
Montgomery Ward
Macy's
GAP
Nike
Amazon
eBay
Amazon
Stitch Fix
1890s - 1940s
1940s - 1990s
1990s - 2010s
2010s -
80
Product Purchases =
Many Evolving from
Buying to Subscribing
81
Subscription Service Growth = Driven by...
Access / Selection / Price / Experience / Personalization
Online Subscription Services
Representative Companies
Subscribers
2017
Growth
Y/Y
Netflix
Video
118MM
+25%
Amazon
Commerce / Media
100MM
--
Spotify
Music / Audio
71MM
+48%
Sony PlayStation Plus
Gaming
34MM
+30%
Dropbox
File Storage
11MM
+25%
The New York Times
News / Media
3MM
+43%
Stitch Fix
Fashion / Clothing
3MM
+31%
LegalZoom
Legal Services
550K
+16%
Peloton
Fitness
172K
+173%
Source: Netflix, Amazon, Spotify, Sony, Dropbox, The New York Times, Stitch Fix, LegalZoom, Peloton.
Note: Netflix = global streaming memberships. The New York Times = digital subscribers. Sony PlayStation
Plus figures reflect FY, which ends March 31. Stitch Fix figures reflect FY, which ends January 31.
82
Free-to-Paid Conversion = Driven by User Experience...
Spotify Subscribers @ 45% of MAUs vs. 0% @ 2008 Launch
Source: Spotify (5/18). Note: MAU = Monthly Active Users.
Spotify Subscribers % of MAU
0%
20%
40%
60%
0
25MM
50MM
75MM
2014
2015
2016
2017
Subscribers % of MAUSubscribers, Global Subscribers
Subscribers % of MAU
83
Shopping =
Entertainment
84
Mobile Shopping Usage =
Sessions Growing Fast
Mobile Shopping App Sessions Growth Y/Y
-40%
-16%
-8%
-8%
-8%
20%
20%
33%
43%
54%
-60%
-40%
-20%
0%
20%
40%
60%
Lifestyle
Games
Personalization
Photography
Sports
News / Magazine
Utilities / Productivity
Business / Finance
Music / Media / Entertainment
Shopping
Session Growth Y/Y (Global, 2017 vs. 2016)
Source: Flurry Analytics State of Mobile 2017 (1/18). Note: n = 1MM applications
across 2.6B devices globally. Sessions defined as when a user opens an app.
Average = 6%
85
Taobao
1.5MM+ Active
Content Creators
Product + Price Discovery =
Often Video-Enabled
YouTube
Many USA Consumers View YouTube
Before Purchasing Products
Source: YouTube (3/16, 5/18), Alibaba (3/18), Right image: South China Morning Post (2/18). Note: Many
USA customers refers to data in a report published by Google, based on Google / Ipsos Connect,
YouTubeSports Viewers Study conducted on n = 1,500 18-54 year old consumers in the USA in 3/16.
86
Product + Price Discovery =
Often Social + Gamified
Source: Wish (5/18), Pinduoduo (1/18), Right image: Walkthechat (1/18).
Note: Wish user figures are cumulative users, not MAU.
Pinduoduo
Refer Friends to
Reduce Price
Wish
Hourly Deals
300MM+ Users
87
Physical Retail Trending =
Long-Term Growth Deceleration
88
-6%
-3%
0%
3%
6%
$0
$1B
$2B
$3B
$4B
2000
2002
2004
2006
2008
2010
2012
2014
2016
Volume
Growth Y/Y
Physical Retail =
Long-Term Sales Growth Deceleration Trend
Physical Retail Sales + Y/Y Growth, USA
Source: St. Louis Federal Reserve FRED Database. Note: Physical Retail includes
all retail sales excluding food services, motor vehicles / auto parts & fuel.
Physical Retail Sales, USAY/Y Growth
89
'New Retail' =
Alibaba View from China
90
Alibaba =
Building E-Commerce Ecosystem Born in China
Source: Alibaba Investor Day (6/17).
91
Alibaba & Amazon = Similar Focus Areas
Alibaba = Higher GMVAmazon = Higher Revenue (2017)
Source: Grace Chen (Alibaba) + Brian Nowak (Amazon) @ Morgan Stanley. *Alibaba has invested but does not have a majority ownership. **Alibaba Non-
China revenue = Alibaba International Commerce revenue (AliExpress, Lazada, & Alibaba.com). Amazon Non-USA revenue = Retail sales of consumer
products & subscriptions through internationally-focused websites outside of North America. Note: All figures reflect calendar year 2017. Alibaba GMV
includes Non-China GMV estimates, Y/Y Growth is FX adjusted using 6.76 RMB / USD average exchange rate for 2017. All figures refer to calendar year.
Market cap as of 5/29/18. Amazon GMV includes in-store GMV. FCF = Cash flow from operations - stock-based compensation - capital expenditures.
Tmall / Taobao / AliExpress /
Lazada / Alibaba.com /
1688.com / Juhuasuan / Daraz
Alibaba Cloud
Intime / Suning* / Hema
Ant Financial* / Paytm*
Youku / UCWeb / Alisports /
Alibaba Music / Damai /
Alibaba Pictures*
Ele.Me (Local) / Koubei (Local) / Alimama /
(Marketing) / Cainiao (Logistics) / Autonavi
(Mapping) / Tmall Genie (IoT)
Amazon.com
Amazon Web Services (AWS)
Whole Foods / Amazon Go /
Amazonbooks
Amazon Payments
Amazon Video / Amazon
Music / Twitch / Amazon Game
Studios / Audible
Alexa (IoT) / Ring (IoT) /
Kindle + Fire
Devices (Hardware)
Amazon
$783B = Market Capitalization
$225B = GMV(E) +25% Y/Y
$178B = Revenue +31% Y/Y
37% = Gross Margin
$4B = Free Cash Flow
31% = Non-USA Revenue as % of Total**
Alibaba
$509B = Market Capitalization
$701B = GMV(E) +29% Y/Y
$34B = Revenue +31% Y/Y
60% = Gross Margin
$14B = Free Cash Flow
8% = Non-China Revenue as % of Total**
Cloud Platform
Other
Digital
Entertainment
Payments
Online
Marketplace
Physical
Retail
92
through technology & consumer insights,
we [Alibaba] put the right products in front of right customers at the right time
our 'New Retail' initiatives are substantially growing Alibaba's total addressable
market in commerce
in this process of digitizing the entire retail operation,
we are driving a massive transformation of the traditional retail industry.
It is fair to say that our e-commerce platform is
fast becoming the leading retail infrastructure of China.
Since Jack Ma coined the term 'New Retail' in 2016,
the term has been widely adopted in China by
traditional retailers & Internet companies alike.
New Retail has become the most talked about concept in business
Alibaba has three unique success factors that are
enabling us to realize the New Retail vision.
Alibaba =
'New Retail' Vision Starts in China
Alibaba CQ1:18 & CQ4:17 Earnings Calls, 5/4/18, 1/24/18
93
Alibaba's
marketplace platforms handle billions of transactions each month
in shopping, daily services & payments.
These transactions provide us with the
best insights into consumer behavior
& shifting consumption trends. This puts us in the best position to
enable our retail partners to grow their business.
Alibaba is a deep technology company.
We contribute expertise in cloud, artificial intelligence,
mobile transactions & enterprise systems to help our
retail partners improve their businesses
through digitization & operating efficiency.
Alibaba has the most
comprehensive ecosystem of commerce platforms, logistics & payments
to support the digital transformation of the retail sector.
Alibaba =
'New Retail' Vision Starts in China
Alibaba CQ1:18 & CQ4:17 Earnings Calls, 5/4/18, 1/24/18.
94
Alibaba =
Extending Platform Beyond China
Source: Alibaba, Pitchbook. *Percentages represent international commerce revenue proportion of total revenue. Note: All
figures are calendar year. Revenue figures translated using the USD / CNY = 6.76, the average rate for 2017. Grey indicates
a majority control stake, all others are minority investments. Country based on headquarters, not countries of operation.
Alibaba International Commerce revenue includes revenue generated from AliExpress, Lazada, and Alibaba.com.
0%
30%
60%
90%
$0
$1B
$2B
$3B
2012 2013 2014 2015 2016 2017
International Commerce Revenue
International Commerce RevenueY/Y GrowthInternational Revenue =
8.4% vs. 7.9 Y/Y*
Revenue
Company Country
Category
Type
Date
Daraz.pk
Pakistan
Marketplace
M&A
5/18
Tokopedia
Indonesia Marketplace
Equity
8/17
Paytm
India
Payments
Equity
4/17
Lazada
Singapore Marketplace
M&A
4/16
Selected Investment
Alibaba Non-China E-Commerce Highlights
95
INTERNET ADVERTISING =
GROWTH CONTINUING...
ACCOUNTABILITY RISING
96
Advertising $ =
Shift to Usage (Mobile) Continues
% of Time Spent in Media vs. % of Advertising Spending
Source: Internet and Mobile advertising spend based on IAB and PwC data for full year 2017. Print advertising spend
based on Magna Global estimates for full year 2017. Print includes newspaper and magazine. ~$7B opportunity
calculated assuming Mobile (IAB) ad spend share equal its respective time spent share. Time spent share data based
on eMarketer (9/17). Arrows denote Y/Y shift in percent share. Excludes out-of-home, video game & cinema advertising.
4%
13%
36%
18%
29%
9%
9%
36%
20%
26%
0%
10%
20%
30%
40%
50%
Radio
TV
Desktop
Mobile
% of Media Time in Media / Advertising Spending, 2017, USATime Spent
Ad Spend
~$7B
Opportunity
97
Internet Advertising =
+21% vs. +22% Y/Y
Source: IAB / PWC Internet Advertising Report (5/18).
Internet Advertising Spend
$23
$26
$32
$37
$43
$50
$60
$73
$88
0%
10%
20%
30%
0
$30B
$60B
$90B
2009
2010
2011
2012
2013
2014
2015
2016
2017
Y/Y GrowthInternet Advertising Spend, USADesktop Advertising
Mobile Advertising
Y/Y Growth
98
Advertisers / Users vs. Content Platforms =
Accountability Rising...
The Wall Street Journal, February 2018
Adweek, July 2017
Many Americans Believe Fake News Is Sowing Confusion
Pew Research Center, December 2016
99
Source: YouTube (5/18, 12/17), Facebook (Transparency Report: 5/18,
5/17, 2/18). Note: All Google content moderators represent full-time
hires but Facebook content moderators are not all full-time.
...Advertisers / Users vs. Content Platforms =
Accountability Rising
Facebook (Q1:18)
Google / YouTube
Content Initiatives
8MM = Videos Removed (Q4:17)
81% Flagged by Algorithms
75% Removed Before First View
2MM = Videos De-Monetized For
Misleading Content Tagging (2017)
10K = Content Moderators (2018 Goal)
583MM = Fake Accounts Removed
99% Flagged Prior To User Reporting
21MM = Pieces of Lewd Content Removed
96% Flagged by Algorithms
3.5MM = Pieces of Violent Content Removed
86% Flagged by Algorithms
2.5MM = Pieces of Hate Speech Removed
38% Flagged by Algorithms
+7,500 = Content Moderators
3,000 Hired (5/172/18)
100
CONSUMER SPENDING =
DYNAMICS EVOLVING
INTERNET CREATING OPPORTUNITIES
101
Consumers
Making Ends Meet =
Difficult
102
Household Debt = Highest Level Ever & Rising
Change vs. Q3:08 = Student +126%...Auto +51%...Mortgage -4%
6%
4%
0%
5%
10%
15%
$0
$3T
$6T
$9T
$12T
$15T
2003
2005
2007
2009
2011
2013
2015
2017
Unemployment Rate, USAHousehold Debt, USA $13.1T
$12.7T
Household Debt & Unemployment Rate
Source: Federal Reserve Bank of New York Consumer Credit Panel / Equifax, Quarterly
Household Debt and Credit Report, Q4:17; St. Louis Federal Reserve FRED Database.
Mortgage
Student Loan
Auto
Credit Card
Home Equity Revolving
Other
Unemployment Rate
103
Personal Saving Rate = Falling @ 3% vs. 12% Fifty Years Ago
Debt-to-Annual-Income Ratio = Rising @ 22% vs. 15%
Source: St. Louis Federal Reserve FRED Database, USA Federal Reserve Bank. *Consumer debt-to-annual-income ratio reflects
outstanding credit extended to individuals for household, family, and other personal expenditures, excluding loans secured by real
estate vs. average annual personal income. Personal saving rate is shown as a percentage of disposable personal income (DPI),
frequently referred to as "the personal saving rate." (i.e. the annual share of disposable income dedicated to saving)
Personal Saving Rate & Debt-to-Annual-Income* Ratio
0%
5%
10%
15%
20%
25%
1968
1978
1988
1998
2008
2018
Personal Saving Rate
Debt-to-Annual-Income* Ratio
Ratio, USA
104
Relative Household Spending =
Shifting Over Past Half-Century
105
Relative Household Spending Rising Over Time =
Shelter + Pensions / Insurance + Healthcare
Relative Household Spending
12%
7%
5%
15%
8%
5%
17%
10%
7%
0%
5%
10%
15%
20%
Annual Spend, USA1972
1990
2017
$11K
$31K
$68K
Total Expenditure
Source: USA Bureau of Labor Statistics (BLS), Consumer Expenditure Survey. *Pensions + Insurance includes deductions for private retirement accounts, social
security, and life insurance. **Other Includes education and miscellaneous other expenses. Note: Results based on Surveys of American Urban & Rural Households
(Families & Single Consumers). 1972 data reflects non-annual survey conducted by BLS + Census Bureau to adjust CPI. 1990 and 2017 Data Based on Annual
Survey performed by BLS + Census Bureau. Healthcare costs include insurance, drugs, out-of-pocket medical expenses, etc. 2017 = mid-year figures.
106
Relative Household Spending Falling Over Time =
Food + Entertainment + Apparel
Relative Household Spending
15%
6%
5%
15%
5%
5%
12%
4%
3%
0%
5%
10%
15%
20%
1972
1990
2017
$11K
$31K
$68K
Total Expenditure
Annual Spend, USASource: USA Bureau of Labor Statistics (BLS), Consumer Expenditure Survey. *Pensions + Insurance includes deductions for private retirement accounts, social
security, and life insurance. **Other Includes education and miscellaneous other expenses. Note: Results based on Surveys of American Urban & Rural Households
(Families & Single Consumers). 1972 data reflects non-annual survey conducted by BLS + Census Bureau to adjust CPI. 1990 and 2017 Data Based on Annual
Survey performed by BLS + Census Bureau. Healthcare costs include insurance, drugs, out-of-pocket medical expenses, etc. 2017 = mid-year figures.
107
Food =
12% vs. 15% of
Household Spending 28 Years Ago...
108
Grocery Price Growth = Declining Trend
Owing To Grocery Competition
0%
25%
50%
75%
-2%
0%
2%
4%
6%
8%
10%
1990
1995
2000
2005
2010
2015
Grocery Price Change Y/Y & Market Share of Top 20 Grocers
Top 20 Grocer Market Share
Grocery* Price Y/Y Change
Price Change Y/Y, USA Market Share, USASource: USDA Research Services, using data from the USA Census Bureau's Annual Retail Trade Survey + Company Reports, USA Bureau of Labor
Statistics (BLS). *Grocery Price growth refers to the growth in prices for "Food at Home" as reported by the USA Census Bureau. Note: Includes all food
purchases in CPI, other than meals purchased away from home (e.g., Restaurants). Grocery @ 56% of Food Spend in 2017 vs. 58% in 1990 per BLS.
2017
109
Walmart = Helped Reduce Grocery Prices via Technology + Scale...
per Greg Melich @ MoffettNathanson
0%
5%
10%
15%
20%
1995
2000
2005
2010
2015
Walmart Grocery Share
2017
By using technology to reduce inventory, expenses & shrinkage,
we can create lower prices for our customers.
- Walmart 1999 Annual Report
Source: Greg Melich @ MoffettNathanson
Note: Share reflects retail value of food for off-site
consumption sold across all Walmart properties.
Grocery Share, USA
110
E-Commerce =
Helping Reduce Prices for Consumers
111
E-Commerce sales have
risen rapidly over the past decade.
Online prices are falling absolutely & relative to
traditional inflation measures like the CPI.
Inflation online is, literally, 200 basis points
lower per year than what the CPI has been showing.
To better understand the economy going forward,
we will need to find better ways to measure prices & inflation.
- Austan Goolsbee,
Professor of Economics, University of Chicago Booth School of Business, 5/18
112
Consumer Goods Prices = Have Fallen
-3% Online & -1% Offline Over 2 1/4 Years per Adobe DPI
Source: Adobe Digital Economy Project Note: Adobe Digital Economy Project measures prices and sales volume for 80% of online
transactions at top 100 USA retailers (15B site visits & 2.2MM products) then calculates a Digital Price Index (DPI) using a Fisher
Ideal model. CPI calculates USA prices using a basket of 83K goods, tracked monthly, & applied to a Laspeyeres model. DPI
Excludes Apparel. Austan Goolsbee serves as strategic advisor to Adobe DPI project.
$0.94
$0.96
$0.98
$1.00
$1.02
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Online Retail (DPI)
Offline Retail
Consumer Prices For Matching Products - Online vs. Offline
Prices (Indexed to 1/1/16), USAOnline = -3%
Offline = -1%
2016
2017
2018
113
Online vs. Offline Price Decline Leaders =
TVs / Furniture / Computers / Sporting Goods per Adobe DPI
(30%)
(20%)
(10%)
0%
10%
Price Change, Y/Y
(DPI vs. CPI), USA, 3/17-3/18
DPI vs. CPI
Difference (
)
5%
1%
1%
0%
0%
-1%
-1%
-1%
-2% -2% -3%
-4%
CPI
DPI
0%
0%
Source: Adobe Digital Economy Project Note: Adobe Digital Economy Project measures prices and sales volume for 80% of online
transactions at top 100 USA retailers (15B site visits & 2.2MM products) then calculates a Digital Price Index (DPI) using a Fisher
Ideal model. CPI calculates USA prices using a basket of 83K goods, tracked monthly, & applied to a Laspeyeres model. DPI
Excludes Apparel. Austan Goolsbee serves as strategic advisor to Adobe DPI project.
114
We've seen how technology can make
online shopping more efficient, with lower prices,
more selection & increased convenience.
We are about to see the
same thing happen to offline shopping.
- Hal Varian, Chief Economist @ Google, 5/18
115
Relative Household Spending =
How Might it Evolve?
Shelter Spend = Rising
Transportation Spend = Flat
Healthcare Spend = Rising
116
Shelter as % of Household Spending = 17% vs. 12% (1972)...
Largest Segment in % + $ Growth
Relative Household Spending
12%
15%
17%
0%
5%
10%
15%
20%
1972
1990
2017
$11K
$31K
$68K
Total Expenditure
Annual Spend, USASource: USA Bureau of Labor Statistics (BLS), Consumer Expenditure Survey. *Pensions + Insurance includes deductions for private retirement accounts, social
security, and life insurance. **Other Includes education and miscellaneous other expenses. Note: Results based on Surveys of American Urban & Rural Households
(Families & Single Consumers). 1972 data reflects non-annual survey conducted by BLS + Census Bureau to adjust CPI. 1990 and 2017 Data Based on Annual
Survey performed by BLS + Census Bureau. Healthcare costs include insurance, drugs, out-of-pocket medical expenses, etc.. 2017 = mid-year figures.
117
Shelter
118
USA Cities =
Less Densely Populated vs. Developed World
0
1000
2000
3000
4000
South
Korea
Japan
UK
Italy Germany Spain
France Australia USA Canada
Population Density Urban Areas*
Top 10 'Advanced' Economies**, 2014
Source: OECD, International Monetary Fund (IMF). *Urban areas defined as "Functional Urban Areas' per
OECD/EU with greater than 500K residents. **IMF determines 'Advanced Economies' designation using a
combination of GDP per Capita, Export Diversity, and integration into the global financial system.
Residents per KM2 (Urban Areas)17x
USA
6x
~2x
9x
5x
~3x
~3x
~2x
119
USA Homes =
Bigger vs. Developed World
Japan
~1,015
South Korea
~725
UK
~990
Source: Wikimedia, Japan Ministry of Internal Affairs, US Census Bureau, UK Office for National Statistics, Asian Development Bank Institute. *USA + Japan + UK =
2013. Korea = 2010, owing to lag in publication of government measured data. Note: Data reflects average occupied dwelling size across each nation.
Average Home Size* (Square Feet) Select Countries
USA
~1,500
120
USA Homes =
Getting Bigger...Residents Falling @ 2.5 vs. 3.0 (1972)
0
1
2
3
4
0
1K
2K
3K
4K
1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016
Average New Home Square Footage & Residents
New Home Square Footage, USAResidents per Home, USANew Home
Square Footage
Source: USA Census Bureau (6/17). Note: Data reflects newly built housing stock. Single Family homes includes newly built single family homes. Similar
growth trends are seen across all housing units, as single-family homes are the majority of new USA housing stock. Average size of multifamily new
dwelling in USA = 1,095 square feet in 1999 (earliest data available), 1,207 square feet in 2016. Residents per household based on all households.
Residents
per Home
121
USA Office Space =
Steadily Getting Denser / More Efficient
0
50
100
150
200
250
1990
1995
2000
2005
2010
2015
Occupied Office Space per Employee Square Feet
Square Footage per Employee, USA195
180
Source: CoStart Portfolio strategy Analysis of USA Leased office space & USA Employment Figures (2017).
2017
122
Shelter...
To Contain Spending
Consumers May Aim to
Increase Utility of Space
123
Airbnb =
Provides Income Opportunities for Hosts
Source: Airbnb, TechCrunch. Note: Airbnb disclosed in 2017 ~660K listings were in USA. A 2017 CBRE study of ~256K USA
Airbnb listings + ~177K Airbnb hosts in Austin, Boston, Chicago, LA, Miami, Nashville, New Orleans, New York City, Oahu,
Portland, San Francisco, Seattle, & Washington D.C. found that 83% of listings are made by single-listing hosts, or are listings for
spaces within otherwise occupied dwellings. This implies that there >500K individuals listing spaces on Airbnb in USA as of 2018.
0
2MM
4MM
6MM
0
30MM
60MM
90MM
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Active Listings, GlobalGuest Arrivals, GlobalGuest Arrivals
Active Listings by Hosts
Airbnb Guest Arrivals & Active Listings by Hosts
5MM Global Active Listings
124
Airbnb Consumer Benefits =
Can Offer Lower Prices for Overnight Accommodations
$306
$240
$220
$217
$193
$167
$118
$114
$187
$191
$93
$179
$114
$110
$65
$92
$0
$50
$100
$150
$200
$250
$300
$350
New
York City
Sydney Tokyo London Toronto
Paris Moscow Berlin
Hotel
Airbnb
Airbnb vs. Hotel Average Room Price per Night
Price, 1/18Source: AirDNA, HRS, Originally Compiled by Statista. Note: Hotel data based
on HRS's inventory of hotels. Euro prices converted to USD on 1/22/18.
125
Relative Household Spending =
How Might it Evolve?
Shelter Spend = Rising
Transportation Spend = Flat
Healthcare Spend = Rising
126
Transportation as % of Household Spending = 14% vs. 14% (1972)...
#3 Segment of $ Spending Behind Shelter + Taxes
Relative Household Spending
14%
16%
14%
0%
5%
10%
15%
20%
1972
1990
2017
$11K
$31K
$68K
Total Expenditure
Annual Spend, USASource: USA Bureau of Labor Statistics (BLS), Consumer Expenditure Survey. *Pensions + Insurance includes deductions for private retirement accounts, social
security, and life insurance. **Other Includes education and miscellaneous other expenses. Note: Results based on Surveys of American Urban & Rural Households
(Families & Single Consumers). 1972 data reflects non-annual survey conducted by BLS + Census Bureau to adjust CPI. 1990 and 2017 Data Based on Annual
Survey performed by BLS + Census Bureau. Healthcare costs include insurance, drugs, out-of-pocket medical expenses, etc.. 2017 = mid-year figures.
127
Transportation
To Contain Spending
Consumers Reducing Relative
Spend on Vehicles +
Increasing Utility of Vehicles
128
Transportation as % of Household Spending =
Vehicle Purchase % DecliningOther Transportation % Rising
0%
20%
40%
60%
1972
1990
2017
Source: USA BLS Consumer Expenditure Survey. Vehicle Age = Bureau of Transportation Statistics + I.H.S. Public Transit Trips = American Public Transit Association Note: *Vehicle
Operation + Maintenance includes Insurance, Repairs, Parking, and Other expenses. Other transportation includes all transportation outside of personal vehicles, including rise-sharing..
Results based on Surveys of American Urban and Rural Households (Families and Single Consumers). 1972 data reflects non-annual survey conducted by BLS + Census Bureau to
adjust CPI. 1990 and 2017 Data Based on Annual Survey performed by BLS + Census Bureau. Cars refers to all light vehicles (i.e. passenger cars + light trucks). Includes all actively
driven cars. Public transit trips reflect unlinked rides (i.e. one full journey). Note: Ride Share Statistics based on Q1:16 and Q1:17 Estimates from Hillhouse Capital.
Relative Household Spending
Transportation
Relative Transportation Spend, USAVehicle Purchases
Gas + Oil
Vehicle Operation +
Maintenance*
Other
Transportation
Relative Transportation Spending =
Vehicles Stay On Road Longer...
@ 12 vs. 8 Years (1995)
Average Car Lifespan
Other Transportation Rising
+30% vs. 1995
Public Transit Usage
~2x Y/Y (2017)
Ride-Share Rides
129
Uber =
Can Provide Work Opportunities for Driver-Partners
0
1MM
2MM
3MM
$0
$15B
$30B
$45B
2012
2013
2014
2015
2016
2017
Driver-Partners, GlobalGross Bookings, GlobalGross Bookings
Driver-Partners
Uber Gross Bookings & Driver-Partners
3MM Global Driver-Partners +50*%
Source: Uber. *Approximately +50% Y/Y. Note: ~900K USA Driver-Partners. Note: As of Jan 2015, ~85% of Uber driver-partners
drove for UberX based on historical growth rates, it is estimated that >90% of USA Uber driver-partners drive for UberX.
130
Uber Consumer Benefits =
Lower Commute Cost vs. Personal Cars 4 of 5 Largest USA Cities
Source: Nerdwallet Study, March 2017. Washington D.C. included in Top 5 due to including of Baltimore MSA population. *Car commute costs include Gas
(OPIS), Maintenance (Edmunds.com), Insurance (NerdWallet), & Parking (parkme.com). Note: Commute distances are from 2015 Brookings analysis.
Uber data is based on a suburbs-to-city-center trip mirroring average commute distance for a metro. Data collected at peak commute times in February
2017. Cheapest Option (UberX, UberPOOL, etc.) selected for Uber costs.
$218
$116
$130
$89
$65
$142
$77
$96
$62
$181
$0
$50
$100
$150
$200
$250
New York City
Chicago
Washington
D.C.
Los Angeles
Dallas
Personal Car
Uber
UberX / POOL vs. Personal Car* Weekly Commute Costs
5 Largest USA Cities, 2017
Weekly Cost
131
Relative Household Spending =
How Might it Evolve?
Shelter Spend = Rising
Transportation Spend = Flat
Healthcare Spend = Rising
CREATED BY NOAH KNAUF @ KLEINER PERKINS
132
Healthcare as % of Household Spending = 7% vs. 5% (1972)...
Fastest Relative % Grower
Relative Household Spending
5%5%
7%
0%
5%
10%
15%
20%
1972
1990
2017
$11K
$31K
$68K
Total Expenditure
Annual Spend, USASource: USA Bureau of Labor Statistics (BLS), Consumer Expenditure Survey. *Pensions + Insurance includes deductions for private retirement accounts, social
security, and life insurance. **Other Includes education and miscellaneous other expenses. Note: Results based on Surveys of American Urban & Rural Households
(Families & Single Consumers). 1972 data reflects non-annual survey conducted by BLS + Census Bureau to adjust CPI. 1990 and 2017 Data Based on Annual
Survey performed by BLS + Census Bureau. Healthcare costs include insurance, drugs, out-of-pocket medical expenses, etc.. 2017 = mid-year figures.
133
Healthcare Spending =
Increasingly Shifting to Consumers
134
USA Healthcare Insurance Costs = Rising for All
Consumers Paying Higher Portion @ 18% vs. 14% (1999)
Annual Health Insurance Premiums vs. Employee Contribution
Source: Kaiser Family Foundation Employer Health Benefits Survey (9/17). Note: n = 2,000 private, non-federal businesses
with at least 3 employees. Employers are asked for full person costs of healthcare coverage and the employee contribution.
14%
18%
0%
5%
10%
15%
20%
$0
$2K
$4K
$6K
$8K
1999 2001 2003 2005 2007 2009 2011 2013 2015 2017
% Employee ContributionAnnual Healthcare Insurance Premiums for Employee Sponsored Single Coverage, USAPremiums
% Employee Contribution
135
USA Healthcare Deductible Costs = Rising A Lot
Employees @ >$2K Deductible = 22% vs. 7% (2009)
Annual Deductibles vs. % of Covered Employees
with >$2K Deductibles
7%
22%
0%
10%
20%
30%
$0
$500
$1,000
$1,500
2006
2008
2010
2012
2014
2016
% of Employees Enrolled in a Single Coverage Plan with >$2K DeductibleAnnual Deductible Among Employees with Single Coverage, USAAnnual Deductible Among Covered Employees
% of Employees Enrolled in a Plan with >$2K Deductible
Source: Kaiser Family Foundation Employer Health Benefits Survey (9/17). Note: n = 2,000 private, non-federal businesses with
at least 3 employees. Employers are asked for full person costs of healthcare coverage and the employee contribution.
136
When Consumers Start Spending More
They Tend To Pay More
Attention to Value + Prices
Will Market Forces
Finally Come to Healthcare &
Drive Prices Lower for Consumers?
137
Healthcare Patients Increasingly
Developing Consumer Expectations
Modern Retail Experience
Digital Engagement
On-Demand Access
Vertical Expertise
Transparent Pricing
Simple Payments
138
Healthcare Consumerization
Source: One Medical, Web.Archive.org, Oscar, Capsule. Note: Oscar
data as of the first month of each year based on enrollments timing.
Office Locations
Memberships
Unique Conversations
One Medical
Modern Retail
Experience
0
40
80
2014
2016
2018
Offices0
150K
300K
2014
2015
2017
Memberships0
15K
30K
Unique Conversations2016
2017
Digital Healthcare
Management
Capsule
Oscar
On-Demand
Pharmacy
139
Healthcare Consumerization
Source: Nurx, Dr. Consulta, Cedar. *Medical interactions include prescriptions, lab orders, &
messages from MDs / RNs. **Cedar data represents the % of total collections using Cedar
over time at a multispecialty group with 450 physicians and an ambulatory surgical center.
Nurx
Women's Healthcare
Specific Solutions
InteractionsTransparent
Pricing
Cedar
Dr. Consulta
Simplified
Healthcare Billing
0
50K
100K
2016
2017
2018
0
500K
1,000K
2013
2015
2017
0%
50%
100%
0
31
60
91
PatientsMedical Interactions*
Patients
% of Collections**
Days
% of Collections
140
Consumerization of Healthcare
+ Rising Data Availability =
On Cusp of Reducing
Consumer Healthcare Spending?
141
WORK =
CHANGING RAPIDLY
INTERNET HELPING, SO FAR
142
Technology Disruption =
Not New...But Accelerating
143
Technology Disruption =
Not New
0%
25%
50%
75%
100%
1900
1915
1930
1945
1960
1975
1990
2005
New Technology Proliferation Curves*
Adoption, USAGrid Electricity
Radio
Refrigerator
Automatic Transmission
Color TV
Shipping Containers
Microwave
Computer
Cell Phone
Internet
Social Media Usage
Smartphone Usage
2017
Source: 'Our World In Data' collection of published economics data including Isard (1942), Grubler (1990), Pew Research,
USA Census Bureau, and others. *Proliferation defined by share of households using a particular technology. In the case
of features (e.g., Automatic Transmission), adoption refers to share of feature in available models.
144
Technology Disruption =
AcceleratingInternet > PC > TV > Telephone
New Technology Adoption Curves
Electricity
Telephone
Car
Dishwasher
Radio
Air Conditioning
Washer
Refrigerator
Television
Microwave
Personal Computer
Mobile Phone
Internet
0
15
30
45
60
75
90
1867
1887
1907
1927
1947
1967
1987
2007
Years Until 25% Adoption, USA2017
Source: The Economist (12/15), Pew Research Center (1/17), Asymco (11/13).
Note: Starting years based on invention year of each consumer product.
145
Technology Disruption Drivers =
Rising & Cheaper Compute Power + Storage Capacity...
1.E-07
1.E-06
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E+07
1.E+08
1.E+09
1.E+10
1900 1925 1950 1975 2000 2025
$1,000 of Computer Equipment
Analytical Engine
BINAC
IBM 1130
Sun 1
Pentium II PC
$0
$0
$1
$10
$100
$1,000
$10,000
$100,000
$1,000,000
$10,000,000
1956
1987
2017
Price per GB0GB
0GB
0GB
1GB
10GB
100GB
1000GB
10000GB
Hard Drive Storage Capacity Storage Price vs. Hard Drive Capacity
0.1GB
0.01GB
0.001GB
$0.1
$0.01
Calculations per SecondPrice Per GB
Capacity
IBM Tabulator
Source: John McCallum @ IDC, David Rosenthal @ LOCKSS Program Stanford): Kryder's Law. Time + Ray Kurzweil analysis
of multiple sources, including Gwennap (1996), Kempt (1961) and others. Note: All figures shown on logarithmic scale.
146
...Technology Disruption Drivers =
Rising & Cheaper Connectivity + Data Sharing
24%
49%
14%
33%
0%
10%
20%
30%
40%
50%
60%
2009
2010
2011
2012
2013
2014
2015
2016
2017
Penetration, GlobalInternet + Social Media Global Penetration
Internet
Social Media
Source: United Nations / International Telecommunications Union, USA Census Bureau. Internet user data is as of mid-year Internet user data: Pew
Research (USA), China Internet Network Information Center (China), Islamic Republic News Agency / InternetWorldStats / KP estimates (Iran), KP
estimates based on IAMAI data (India), & APJII (Indonesia). Population sourced from Central Intelligence Agency database. eMarketer estimates for
Social Media users based on number of active accounts, not unique users. Penetration calculated as a % of total population based on the CIA database.
147
New Technologies =
Created / Displaced Jobs Historically
148
New Technologies =
Job Concerns / Reality Ebb + Flow Over Time
Source: New York Times, 2/26/1928, article by Evans Clark. Originally sourced from Louis Anslow, "Robots have been about to take all the jobs for more than 200
years," Timeline, 5/7/16. The New York Times, 2/24/1940, article by Louis Stark. Originally sourced from Louis Anslow, "Robots have been about to take all the jobs
for more than 200 years," Timeline, 5/7/16. The New York Times, 5/4/1962, article by Milton Bracker. New York Times, 9/3/1940, article by Harley Shaiken. Originally
sourced from Louis Anslow, "Robots have been about to take all the jobs for more than 200 years," Timeline, 5/7/16. 2017 Article = The New York Times.
1920
1940
1960
1980
2000
2020
149
New Technologies =
Aircraft Jobs Replaced Locomotive Jobs...
0
100K
200K
300K
400K
1950
1960
1970
1980
1990
2000
2010
2015
Locomotive Jobs - Engineers / Operators / Conductors
Aircraft Jobs - Pilots / Mechanics / Engineers
Locomotive vs. Aircraft Jobs
Jobs, USASource: ITIF analysis of IPUMS data (Atkinson + Wu); St. Louis Federal Reserve FRED Database. Note: IPUMS data tracks historical employment
(since 1950) using 2010 Census occupational codes. (7140:Aircraft Mechanics + Service Technicians; 9030: Aircraft Pilots + Flight Engineers; 9200:
Locomotive Engineers + Operators; 9230: Railroad Brake, Signal, + Switch Operators; 9240: Railroad Conductors + Yardmasters).
150
New Technologies =
Services Jobs Replaced Agriculture Jobs
0
5MM
10MM
15MM
20MM
25MM
1900
1910
1920
1930
Jobs, USAAgriculture Jobs - Farming / Forestry / Fishing / Hunting
Services Jobs - Business / Education / Healthcare / Retail / Government / Other Services
Agriculture vs. Services Jobs
Source: Growth & Structural Transformation Herrendorf et al. (NBER, 2013) Services includes all non-farm jobs
except goods-producing industries such as natural resources / mining, construction and manufacturing.
151
Agriculture =
<2% vs. 41% of Jobs in 1900
0
30MM
60MM
90MM
120MM
150MM
1900
1915
1930
1945
1960
1975
1990
2005
Jobs, USAAgriculture Jobs - Farming / Forestry / Fishing / Hunting
Services Jobs - Business / Education / Healthcare / Retail / Government / Other Services
Agriculture vs. Services Jobs
1900 Agriculture =
41% of Jobs
2017 Agriculture =
<2% of Jobs
2017
Source: St. Louis Federal Reserve FRED Database, Growth & Structural Transformation Herrendorf et al. (NBER, 2013),
Bureau of Labor Statistics Note: Pre-1948 Agriculture data = Herrendorf et al. Post 1948 = Bureau of Labor Statistics. Pre-1939
services data = Herrendorf et al. Post 1939 services data = Bureau of Labor Statistics. Services includes all non-farm jobs
except excluding Goods-Producing industries such as Natural Resources / Mining, Construction and Manufacturing.
152
70 Years = New Technology Concerns Ebb / Flow...
GDP Rises...Unemployment Ranges 2.9 - 9.7%
0%
10%
20%
30%
$0
$6T
$12T
$18T
1929 1939 1949 1959 1969 1979 1989 1999 2009
Unemployment RateReal GDPReal GDP
Unemployment Rate
Source: St. Louis Federal Reserve FRED Database, Bureau of Economic Analysis, BLS.
Note: Real GDP based on chained 2009 dollars. Unemployment rate = annual average.
Real GDP vs. Unemployment Rate, USA
5.8%
70-Year Average
3.9% Unemployment Rate (4/18)
2017
153
Will Technology Impact Jobs
Differently This Time?
Perhaps...But It Would Be
Inconsistent With History as...
New Jobs / Services +
Efficiencies + Growth Typically
Created Around New Technologies
154
Job Market =
Solid Based on Traditional
High-Level Metrics, USA
155
Unemployment @ 3.9% =
Well Below 5.8% Seventy Year Average
Unemployment Rate
0%
10%
20%
30%
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Unemployment Rate, USAAverage = 5.8%
2018
Source: St Louis Federal Reserve FRED Database, Bureau of the Budget (1957). Note: Unemployment rate calculated
by diving the total workforce by the total number of unemployed people. People are classified as unemployed if they do
not have a job, have actively looked for work in the prior 4 weeks and are currently available for work.
156
0
20
40
60
80
100
120
1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012 2017
CCI (Indexed to 1964), USAConsumer Confidence = High & Rising
Index @ 100 vs. 87 Fifty-Five Year Average
Consumer Confidence Index (CCI)
Source: St. Louis Federal Reserve FRED Database. Note: Indexed to Q1:66 = 100. Consumer Confidence
Index (Michigan Consumer Sentiment Index) is a broad measure of American consumer sentiment, as
measured through a 50-question telephone survey of at least 500 USA residents each month.
87 =
55-Year Average
157
Job Openings = 17 Year High
@ 7MM~3x Higher vs. 2009 Trough
1.4MM = Professional Services + Finance
1.3MM = Healthcare + Education
1.2MM = Trade / Transportation / Utilities
879K = Leisure / Hospitality
661K = Mining / Construction / Manufacturing
622K = Government
486K = Other
0
1MM
2MM
3MM
4MM
5MM
6MM
7MM
2000
2005
2010
2015
Job Openings* Job Openings* USA
6.6MM Job Openings (3/18)
2018
Source: St Louis Federal Reserve FRED Database. *A job opening is defined as a non-farm specific position
of employment to be filled at an establishment. Conditions include the following: there is work available for
that position, the job could start within 30 days, and the employer is actively recruiting for the position.
158
Job Growth =
Stronger in Urban Areas Where 86% of Americans Live
Job / Population Growth Urban vs. Rural (Indexed to 2001)
90
100
110
120
2001
2006
2011
2016
Source: USDA ERS, BLS. Note: LAUS county-level data from BLS are aggregated into urban (metropolitan/metro) and rural
(nonmetropolitan / non-metro), based on the Office of Management and Budget's 2013 metropolitan classification. Metro areas
defined as counties with urban areas >50K in population and the outlying counties where >35% of population commutes to an
urban center for work. 'Rural' data reflects total non-metro employment, where population has been declining since 2011.
Population = +4%
Jobs = +19%
Jobs = +4%
Population = +15%
Urban
Rural
159
Labor Force Participation @ 63% =
Below 64% Fifty-Year Average...~3.5MM People Below Average*
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Labor Force Participation Rate, USALabor Force Participation Rate**
64% =
50 Year Average
Source: St Louis Federal Reserve FRED Database, BLS. *In March 2018, ~161.8MM Americans were in the labor force (62.9% participation). Participation @ 50-year
average of 64.3% would imply a labor force of 165.3MM. The labor force participation rate is defined as the section of working population in the age group of 16+ in the
economy currently employed or seeking employment. **For data from 1900-1945 the labor force participation rate includes working population over the age of 10.
160
0
2
4
6
8
10
12
14
Work
Caring for Household Members
Caring for Non-Household Members
Education
Household Activities & Services
Other (Including Sleep)
Other Socializing, Relaxing, Leisure
Watching TV
Hours per Day, USA, 6/16
Not In Labor Force
In Labor Force
Most Common Activities For Many Who Don't Work* =
Leisure / Household Activities / Education
Source: 2014 American Time Use Survey, CEA calculations, BLS. Note: Prime-age males defined as men
between the ages of 25-54. Daily hours may not add up to 24 since some individuals do not report all time spent.
Household activities include cleaning, cooking, yardwork & home maintenance not related to caregiving.
Males* (Ages 25-54) Daily Time Use
i
ter
cializi
, elaxi
, eis re
t
(I
l
i
l
)
Caring F
+3 Hours
+0.7
+0.6
+0.5
+0.3
+0.01
-0.02
-5
161
Job Expectations =
Evolving
162
Most Desired Non-Monetary Benefit for Workers =
Flexibility per Gallup
Source: Gallup 2017 State of the American Workplace Note: *Flexible schedule defined as ability to choose own hours of work. Gallup developed
State of the American Workplace using data collected from more than 195,600 USA employees via the Gallup Panel and Gallup Daily tracking in 2015
and 2016, and more than 31 million respondents through Gallup's Q12 Client Database. First launched in 2010, this is the third iteration of the report.
Would You Change Jobs to Have Access To
61%
54%
53%
51%
51%
48%
40%
35%
0%
25%
50%
75%
Health
Insurance
Monetary
Bonuses
Paid
Vacation
Flexible
Schedule
Pension
Paid
Leave
Profit
Sharing
Working
From
Home
Share, USA (2017)
163
Technology = Makes Freelance Work Easier to Find
Freelance Workforce = 3x Faster Growth vs. Total Workforce
Source: 'Freelancing in America: 2017' survey conducted by Edelman Research, co-commissioned by
Upwork and Freelancers Union. Note: Survey conducted 7/17-8/17, n = 2,173 Freelance Employees who
have received payment for supplemental temporary, or project-oriented work in the past 12 months.
69%
77%
50%
75%
100%
2014
2015
2016
2017
% Responding Positively, USA Has Technology Has Made It
Easier To Find Freelance Work?
100%
102%
104%
106%
108%
110%
2014
2015
2016
2017
Total
Freelance
Workforce Growth, USAWorkforce Growth
Freelance vs. Total
+8.1%
+2.5%
164
On-Demand Jobs =
Big Numbers + High Growth
Increasingly Filling Needs for Workers Who
Want Extra Income / Flexibility...
Have Underutilized Skills / Assets
165
On-Demand Workers =
5.4MM +23%, USA per Intuit
Source: Intuit (2017/2018). *2018 = Forecast from 2017 data. Preliminary 2018 results appear to be in line with forecast as of 5/16/18.
Note: On-demand workers defined as online marketplace workers including transportation and/or logistics for people or products, online
talent marketplaces, renting out space. Providing other miscellaneous consumer and business services (e.g. TaskRabbit, Gigwalk,
Wonolo, etc.). Workers defined as 'active' employees that have done 'significant' on-demand work within the preceding 6 months.
2.4
3.9
5.4
6.8*
0
2MM
4MM
6MM
8MM
2015
2016
2017
2018E
On-Demand Platform Workers, USA
Workers, USA
166
On-Demand Jobs =
>15MM Applicants on Checkr Platform Since 2014, USA
Checkr Background Check On-Demand Applicants
Top 100 Metro Areas, USA
Source: Checkr (2018)
167
On-Demand Jobs =
Big Numbers + High Growth
Real-Time
Platforms
Internet-Enabled
Marketplaces
0
1MM
2MM
3MM
$0
$15B
$30B
$45B
2014 2015 2016 2017
Driver-Partners, GlobalGross Bookings, GlobalGross Bookings
Driver-Partners
Uber @ 3MM Driver-Partners
0K
50K
100K
150K
200K
250K
2014 2015 2016 2017
Dashers, GlobalLifetime Dashers
DoorDash @ 200K Dashers
0
5MM
10MM
15MM
20MM
2014 2015 2016 2017
Freelancers, GlobalUpwork @ 16MM Freelancers
0
1MM
2MM
$0
$1B
$2B
$3B
$4B
2014 2015 2016 2017
Sellers, GlobalGross Merchandise Sales (GMS), GlobalGMS
Sellers
Etsy @ 2MM Sellers
0
2MM
4MM
6MM
0
30MM
60MM
90MM
2014 2015 2016 2017 2018
Active Listings, GlobalGuest Arrivals, GlobalGuest Arrivals
Active Listings
Airbnb @ 5MM Listings
Freelancers
Uber Source: Uber Note: ~900K USA Uber Driver-Partners. As of 1/15, based on historical growth rates, it is estimated that >90% of USA Uber driver-partners drive for UberX.
DoorDash Source: DoorDash. Note: Lifetime Dashers defined as the total number of people that have dashed on the platform, most of which are still active. Etsy Source: Etsy. Note: In
2017, 65% of Etsy Sellers were USA-based (1.2MM). Upwork Source: Upwork. Airbnb Source: Airbnb, Note: Airbnb disclosed in 2017 that ~660K of their listings were in USA. A 2017
CBRE study of ~256K USA Airbnb listings + ~177K Airbnb hosts in Austin, Boston, Chicago, LA, Miami, Nashville, New Orleans, New York City, Oahu, Portland, San Francisco,
Seattle, & Washington D.C. found 83% of hosts are single-listing hosts / non-full-home hosts. This implies >500K USA hosts.
168
On-Demand Jobs =
Big Numbers + High Growth
Filling Needs for Workers Who
Want Extra Income / Flexibility...
Have Underutilized Skills / Assets
169
On-Demand Work Basics + Benefits =
Extra Income + Flexibility, USA per Intuit
Source: Intuit, 2017 Note: Intuit partnered with 12 On-Demand Economy platforms which provided access to their
provider email lists. (n = 6,247 respondents who had worked on-demand within the past 6 months). The survey
focused on online talent marketplaces. Airbnb and other online capital marketplaces were not included.
Extra Income
Flexibility
37% = Run Own Business
33% = Use Multiple On-Demand Platforms
26% = Employed Full-Time (W2 Wages)
14% = Employed Part-Time (W2 Wages)
5% = Retired
71% = Always Wanted To Be Own Boss
46% = Want To Control Schedule
19% = Responsible for Family Care
9% = Active Student
57% = Earn Extra Income
21% = Make Up For Financial Hardship
19% = Earn Income While Job Searching
91% = Control Own Schedule
50% = Do Not Want Traditional Job
35% = Have Better Work / Life Balance
$34 Average Hourly Income
$12K Average Annual Income
24% Average Share of Total Income
11 Average Weekly Hours With
Primary On-Demand Platform
37 Average Weekly Hours of Work
(All Types / Platforms)
Benefits
Basics
170
On-Demand
Platform Specifics
171
$21 = Average Hourly Earnings
17 = Average Weekly Hours
30 = Average Trips Per Week
Uber =
3MM Global Driver-Partners +~50% Y/Y (2017)
Uber Driver-Partners (USA = 900K)
Basics
Motivations
Source: Drivers + Basics = Hall & Krueger (2016) 'An Analysis Of The Labor Market For Uber's Driver-partners In The United
States.' Other = Cook, Diamond, Hall, List, & Oyer (2018) 'The Gender Earning Gap in the Gig Economy' Note: % Statistics
based on 12/14 survey of Uber Drivers in 20 markets that represent 85% of all USA Uber Driver-Partners
80% = Had Job Before Starting Uber
72% = Not Professional Driver
71% = Increased Income Driving Uber
66% = Have Other Job
91% = Earn Extra Income
87% = Set Own Hours
85% = Work / Life Balance
74% = Maintain Steady Income
32% = Earn Income While Job Searching
172
Etsy =
2MM Global Active Sellers +9% (Q1)
Etsy Sellers (USA = 1.2MM)
Source: "Etsy SEC filings + "Crafting the future of work: the big impact of microbusinesses:
Etsy Seller Census 2017" Published by Etsy. Survey measured 4,497 USA-based sellers
on Etsy's marketplace In 2017, 65% of Etsy Sellers were USA-based (1.2MM).
$1.7K = Annualized Gross Merchandise Sales (GMS) per Seller
$3.4B = Annualized GMS +20% (Q1)
99.9% = USA Counties with Etsy Seller(s)
97% = Operate @ Home
87% = Identify as Women
58% = Sell / Promote Etsy Goods Off Etsy.com
53% = Started Their Business on Etsy
49% = Use Etsy Income for Household Bills
32% = Etsy Sole Occupation
32% = Have Traditional Full-Time Job
28% = Operate From Rural Location
27% = Have Children @ Home
13% = Etsy Portion of Annual Household Income
68% = Creativity Provides Happiness
65% = Way to Enjoy Spare Time
51% = Have Financial Challenges
43% = Flexible Schedule
30% = Use Etsy Income for Savings
Basics
Motivations
173
Airbnb =
5MM Global Active Listings (5/18)
Basics
Motivations
$6,100 = Average Annual Earnings per Host Sharing Space
97% = Price of Listing Kept by Hosts (9/17)
43% = Airbnb Income Used for Rent / Mortgage / Home Improvement
Airbnb Hosts (USA Listings = 600K+)
80%+ = Share Home in Which They Live
60%+ = 'Superhosts' Who Identify as Women
29% = Not Full-Time Employed
18% = Retirees
57% = Use Earnings to Stay in Home
36% = Spend >30% of Total Income on Housing
12% = Avoided Eviction / Foreclosure
Owing to Airbnb Earnings
Source: Average Earnings + Foreclosure Avoidance = 'Introducing The Living Wage Pledge" Airbnb (9/17), Superhost Gender Identity =
'Women Hosts & Airbnb" (3/17), Employment Status & Earning Usage = '2017 Seller Census Survey' (5/18). Note: A Superhost is an
Airbnb host with a 4.8+ rating, 90% response rate, 10+ stays/year, and 0 cancellations. Superhosts are marked as such on Airbnb.com
174
No [Uber] driver-partner is ever told where or when to work.
This is quite remarkable an entire global network miraculously
'level loads' on its own.
Driver-partners unilaterally decide
when they want to work and where they want to work.
The flip side is also true they have unlimited
freedom to choose when they do NOT want to work
The Uber Networkis able to elegantly match
supply & demand without 'schedules' & 'shifts'
That worker autonomy of both time & place
simply does not exist in other industries.
- Bill Gurley The Thing I Love Most About Uber Above the Crowd, 4/18
175
On-Demand + Internet-Related Jobs =
Scale Becoming Significant
176
DATA GATHERING + OPTIMIZATION =
YEARS IN MAKING
INCREASINGLY GLOBAL + COMPETITIVE
177
Data Gathering + Optimization =
Accelerates With
Computer Adoption...
Mainframes
(Early 1950s*)
* In 1952 IBM launched the first fully electronic data processing system, the IBM 701.
178
Data Gathering + Optimization (1950s ) =
Enabled by Mainframe Adoption
Mainframe Shipment Value & Units
0
4K
8K
12K
16K
$0
$4B
$8B
$12B
$16B
1960
1965
1970
1975
1980
1985
1990
Mainframe Units, USAMainframe Shipment Value, USAShipment Value
Annual Mainframe Shipments
Source: W. Edward Steinmueller: The USA Software Industry: An Analysis and Interpretive History (3/95).
179
Data Gathering + Optimization (1950s ) =
Government Mainframe Deployment
Source: Social Security Administration (75th Anniversary Retrospective), NASA 'Computers in Spaceflight', CNET "IRS Trudges on With
Aging Computers" (5/08). Note: Social Security includes Americans receiving retirement benefits, old-age / survivors insurance, unemployment
benefits, or disability benefits. Tax records includes include total households since all are required to file taxes regardless of amount owed.
1955
1960
1965
Social Security
Calculate Benefits for
15MM Recipients (62MM Now)
NASA
Calculate Real-Time
Orbital Determination
IRS
Calculate / Store
55MM Records (126MM Now)
180
Data Gathering + Optimization (1950s ) =
Business Mainframe Deployment
1955
1965
1975
Banks
Bank of America
Process Checks
Hospitals
Tulane Medical School System
Manage Patient Data
Credit Cards
Visa
Manage Merchant Network
Telecom
Bell Labs / AT&T
Optimize Telephone Switching
Airlines
American Airlines
Process Transactions / Data
Insurance
Aetna
Optimize Insurance Policies
Retail
Walmart
Track Inventory / Logistics
Source: Bank of America, IBM, Computer World (9/85), Network Computing (3/04), Computer History Museum, Walmart Museum. Note: Banks (1952):
Bank of America adopted 'Electronic Recording Method of Accounting' system developed by Stanford Research Institute. Telecom (1955): Bell Labs
installed the IBM 650 to facilitate engineering for complex automated telephone switching systems. Hospitals (1959): Tulane Medical School System
installed the IBM 650 to process medical record data. Airlines (1962): IBM computers integrated into SABRE system. Insurance (1965): Aetna installed
IBM's 360 to automate policy creation. Retail (1972): Walmart established a data processing facility. Credit Cards (1973): Year IBM partnered with Visa.
181
...Data Gathering + Sharing + Optimization =
Accelerates With
Computer Adoption...
Consumer Mobiles + The Cloud
(2006)...
182
Computing Big Bangs =
Cloud (2006) + Consumer Mobile (2007)
2006
Amazon AWS
Until now, a sophisticated &
scalable data storage infrastructure
has been beyond the reach
of small developers.
- Amazon S3 Launch FAQ, 2006
2007
Apple iPhone
Why run such a sophisticated
operating system on a mobile
device? Well, because it's
got everything we need.
- Steve Jobs, iPhone Launch, 2007
Source: Wikimedia, Apple, Amazon, Steve Jobs Photo by Tom Coates.
183
Amazon AWS # of Services
2006
2008
2010
2012
2014
2016
2018
2006
1 Service
2018
140+ Services
Computing Big Bangs =
Cloud (2006) + Consumer Mobile (2007)
2008
2010
2012
2014
2016
2018
2008
<5,000 Apps
2018
2MM+ Apps
Apple iOS # of Apps
Source: Amazon, The Internet Archive. Apple; AppleInsider. Note: Based on Apple releases.
Includes all iPhone/iPad/Apple TV applications available for download. Data as of 5/18.
184
...Computing Big Bangs Volume Effects =
Cloud Compute Cost Declines Continue -11% vs. -10% Y/Y...
-50%
-40%
-30%
-20%
-10%
0%
$0
$0.1
$0.2
$0.3
$0.4
$0.5
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Cost
Y/Y Change
Cost Per InstanceY/Y ChangeAWS Compute Cost + Growth*
Source: The Internet Archive. *Cost data reflects price of 'current generation' m.large on-demand Linux instance in USA-
East Virginia (m1.large = 2008-2013, m3.large = 2014-2015, m4.large = 2016-2017, m5.large = 2018). m.large chosen as
a representative instance of general purpose compute; pricing does not account for increasing instance performance.
185
...Computing Big Bangs Volume Effects =
Cloud Revenue Re-Accelerating +58% vs. +54% Q/Q
0%
25%
50%
75%
100%
$0
$2B
$4B
$6B
$8B
$10B
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1
Amazon AWS
Microsoft Azure
Google Cloud
Global RevenueY/Y Growth2015
2016
2017
2018
Cloud Service Revenue Amazon + Microsoft + Google
Source: Amazon AWS = Company filings, Microsoft Azure = Keith Weiss @ Morgan
Stanley (4/18), Google Cloud = Brian Nowak @ Morgan Stanley (5/18). Note: Google
Cloud revenue excluded in Y/Y growth rate calculation due to limited quarterly estimates.
186
0
0.5B
1.0B
1.5B
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Global ShipmentsData Gathering + Sharing + Optimization (2006 ) =
Enabled by Consumer Mobile Adoption...
Source: Morgan Stanley (Katy Huberty, 3/18), IDC.
Smartphone Shipments
187
...Data Gathering + Sharing + Optimization (2006 ) =
Enabled by Social Media Adoption
Source: Global Web Index (9/17), Telegram (2/16), Line (10/17), WeChat (11/17), Whatsapp (7/17).
Note: Per Global Web Index, social media time spent for Internet users aged 16-64. n = 61,196
(2012), 156,876 (2013), 168,046 (2014), 198,734 (2015), 211,024 (2016), 178,421 (2017).
Time Spent on Social Media
Messages per Day
0
20B
40B
60B
Telegram
(2/16)
Line
(10/17)
(11/17)
(7/17)
Messages per Day90
135
0
50
100
150
2012 2013 2014 2015 2016 2017
Global Daily Time on Social Media (Minutes)
188
...Data Gathering + Sharing + Optimization (2006 ) =
Enabled by Sensor Pervasiveness...
MEMS Sensor / Actuator Shipments
0
5B
10B
15B
2012 2013 2014 2015 2016 2017
Global ShipmentsShared
Transportation
Mobike
Predictive
Maintenance
Samsara
Fitness
Tracking
Motiv
Precision
Cooking
Joule
Sensors + Data = In More Places
Visual
Navigation
Google Maps
Home
Temperature
Nest
Source: IC Insights (2018), Google Maps, Mobike, Nest, Samsara, Motiv, Joule. Note: MEMS sensors and
actuators includes all MEMS-based sensors (e.g., Accelerometers, Gyroscopes, etc.), but does not include optical
sensors, like CMOS image sensors, also includes actuators made using MEMs processes, per IC Insights.
189
0
20
40
60
80
100
120
140
160
180
2004
2006
2008
2010
2012
2014
2016 2018E 2020E 2022E 2024E
Zettabytes (ZB)Information Created Worldwide
(per IDC)
...Data Gathering + Sharing + Optimization (2006 ) =
Ramping @ Torrid Pace
2005
0.1 ZB
2010
2 ZB, 9%
2015
12 ZB, 9%
Amount, % Structured
2020E
47 ZB, 16%
2025E
163 ZB, 36%
Source: IDC Data Age 2025 Study, sponsored by Seagate (4/17). Note: 1 petabyte = 1MM gigabytes, 1 zeta byte =
1MM petabytes. The grey area in the graph represents data generated, not stored. Structured data indicates data that
has been organized so that it is easily searchable and includes metadata and machine-to-machine (M2M) data.
190
Data =
Can Be Important Driver of
Customer Satisfaction
191
USA Internet Data Leaders =
Relatively High Customer Satisfaction
Source: American Customer Satisfaction Index (ASCI). *Netflix data from 2016, as ASCI score was not tracked in 2017. Instagram / Facebook
average score used as 'Facebook' score. Priceline.com used as 'Booking Holdings' score. Note: ASCI is a tool first developed by The University
of Michigan to measure consumer satisfaction with various companies, brands, and industries. ACSI surveys 250K USA customers annually via
email, responses to weighted questions are used to create a cross-industry score on a scale of 0-100. Top 2017 Score = 87 (Chick-fil-A).
85
82
72
79
78
Amazon (E-Comemrce)
Google / Alphabet (Search)
Facebook (Social Media)
Netflix (Video)
Booking Holdings (Lodging Inventory)
60
65
70
75
80
85
90
Google (Alphabet)
Amzon
Facebook / Instagram
Netflix*
Booking.com (Priceline)
77 = Q4:17 USA Average
American Customer Satisfaction Index (ASCI) Scores
(Internet Data Companies >$100B Market Capitalization, 5/18, USA)
2017 ASCI Score
E-Commerce
Search
Social Media
Video
Lodging Inventory
192
Google Personalization = Queries
Drive Engagement + Customer Satisfaction
Query Growth
(2015 -2017)
Data-Driven Personalization
Google Query Growth, Global (2015-2017)Source: Google (5/18). Note: Google queries only personalized for geo-location data. *Reflects mobile queries,
where location data is readily available / important.
60%
65%
900%
0%
200%
400%
600%
800%
1000%
__
For Me
Should I
__?
__
Near Me*
193
Spotify Personalization = Preferences
Drive Engagement + Customer Satisfaction
Source: Spotify, Benjamin Swinburne @ Morgan Stanley (4/18)
Note: Monthly unique artists listened to per user as of 5/18.
37%
44%
0%
25%
50%
2014
2015
2016
2017
Spotify Daily Engagement
User Preferences
DAU / MAU, Global68
112
0
40
80
120
2014
2015
2016
2017
Monthly Unique Artist Listened to Per User, GlobalUnique Artist Listening
Data-Driven Personalization
194
Toutiao Personalization = Interests
Drive Engagement + Customer Satisfaction
0
50MM
100MM
150MM
200MM
250MM
2015
2016
2017
MAUs
Data-Driven Personalization
Source: Toutiao (5/18), Snap (5/18), Instagram (8/17).
*Instagram data reflects time spent by users under the age
of 25, assumed to be representative of all Instagram users.
Main Page User A
Main Page User B
0
20
40
60
80
Snapchat
(5/18)
Instagram*
(8/17)
Toutiao
(5/18)
Minutes Spent per Day
MinutesMAUs, Global
195
Data =
Improves Predictive Ability of
Many Services
196
Data Volume = Foundational to Algorithm Refinement +
Artificial Intelligence (AI) Performance
25
30
35
40
10
100
1MM
30MM
100MM
300MM
Object Detection Precision (mAP @[.5,.95])Example Images in Training Dataset
Object Detection - Performance vs. Dataset Size
Google Research & Carnegie Mellon, 2017
Source: Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Sun, Shrivastava, Singh, & Gupta, 2017
Note: Chart reflects object detection performance when initial checkpoints are pre-trained on different subsets of JFT-300M tagged image
dataset. X-axis is the data size in log-scale, y-axis is the detection performance in mAP@[.5,.95] on "COCO minival" testing set.
197
Data Volume = Foundational to Tool / Product Improvement...
Artificial Intelligence (AI) Predictive Capability
Source: Amazon Artificial Intelligence on AWS Presentation (6/17). *Amazon Rekognition enables users to
detect objects, people, text, scenes, and activities in their photos and videos using machine learning.
AWS 'Data Flywheel' Amazon Rekognition*
More Data
Pricing
More Uploads = Lower Average Price
Accuracy
Regular Improvements
Better Analytics
Features
Regular Improvements
Better Products
Customers
Large / Small Enterprises + Public Agencies
More Customers
198
Artificial Intelligence (AI)
Service Platforms for Others =
Emerging from Internet Leaders
199
Amazon = AI Platform Emerging from AWS
Enabling Easier Data Processing / Collection for Others
Source: Amazon. AWS = Amazon Web Services.
Rekognition Image Recognition
SageMaker Machine Learning Framework
AI Hardware Scalable GPU Compute Clusters
Comprehend Language Processing
Amazon AWS AI Services / Infrastructure
200
...Google = AI Platform Emerging from Google Cloud
Enabling Easier Data Processing / Collection for Others
Google Cloud AI Services / Infrastructure
Source: Google
AI Hardware Tensor Processing Units
Google Cloud Vision API
Dialogflow Conversational Platform
Cloud AutoML Custom Models
201
AI in Enterprises = Small But Rapidly Rising Spend Priority
Per Morgan Stanley CIO Survey (4/18 vs 1/18)
January 2018
April 2018
Which IT Projects Will See The Largest Spend Increase in 2018?
Share of CIO Respondents, USA + E.U.Source: AlphaWise, Morgan Stanley Research. Note: n = 100 USA / E.U. CIOs. Note: Full Question Text =
'Which three External IT Spending projects will see the largest percentage increase in spending in 2018?'
0%
5%
10%
Networking Equipment
Artificial Intelligence
Hyperconverged
Infrastructure
202
Source: CNBC (2/18).
AI is one of the most important things
humanity is working on.
It is more profound than electricity or fire
We have learned to harness fire for the benefits of
humanity but we had to overcome its downsides too.
AI is really important, but we
have to be concerned about it.
- Sundar Pichai, CEO of Google, 2/18
203
Data Sharing =
Creates Multi-Faceted Challenges
204
Data + Consumers =
Love-Hate Relationship
Source: Cartoonstock, Artist: Roy Delgado
205
Most Online Consumers Share Data for Benefits
Source: USA Consumer Data = Deloitte To share or not to share (9/17)
Note: n = 1,538 USA customers surveyed in cooperation with SSI in 2016.
79%
Willing to Share Personal Data For 'Clear Personal Benefit'
>66%
Willing To Share Online Data With Friends & Family
USA Consumers per Deloitte
206
Most Online Consumers Protect Data When Benefits Not Clear
Source: Deloitte To share or not to share (9/17)
Note: n = 1,538 USA consumers in cooperation with SSI.
Consumers Taking Action To Address Data Privacy Concerns
9%
26%
27%
28%
47%
64%
0%
25%
50%
75%
Did Not Buy Certain Product
Closely Read Privacy Agreements
Didn't Visit / Closed Certain Websites
Disabled Cookies
Adjusted Mobile Privacy Settings
Deleted / Avoided Certain Apps
% of Respondents that Took Action in the Last 12 Months Due to Data
Privacy Concerns, USA
207
Internet Companies =
Making Consumer Privacy Tools More Accessible (2018)
Source: Facebook, Google
2008
2018
2008
2018
208
Data Sharing =
Varying Views
209
Privacy Act of 1974
Enacted = 12/31/74
General Data Protection Regulation
Enacted = 5/25/18
Personal Information Protection Act
Enacted = 09/30/11
Act on Protection of Personal Information
Enacted = 5/30/17
Source: Wikimedia, USA Congress, EU, Japan Government, South Korea Government, Argentina Government.
Note: Argentina proposed a 2017 draft amendment to the Personal Data Protection Act that would strengthen current regulation
and align with most GDPR requirements. Japan enacted an amendment to its Act on Protection of Personal Information that
went into effect on 5/30/17. All EU countries grouped due to passage of EU-wide GDPR laws.
EU / Asia / Americas =
Rising Regulatory Focus on Data Collection + Sharing
Enacted in Past 10 Years
Developing (2018)
Data Privacy Laws
210
China to Further Promote Government
Information Sharing & Disclosure
Xinhua State Press Agency, 12/7/17
...China =
Encouraging Data Collection
[Xi Jingping] called for building high-speed, mobile, ubiquitous &
safe information infrastructure, integrating government & social data
resources, & improving the collection of fundamental information...
[Xi stated] The Internet, 'Big Data,' Artificial Intelligence, &
'The Real Economy' should be interconnected.
- Xinhua State News Agency, 12/9/17
Ministry of Industry & Information
Training to Build 'Big Data' Datacenter
Xinhua State Press Agency, 5/07/17
China Launches 'Big Earth' Big Data Project
To Boost Science Data Sharing
Xinhua State Press Agency, 2/13/18
Source: Xinhua (PRC's official Press Agency).
.
211
0
5x
10x
15x
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Observed Malware Volume
Malware Volume (Indexed to (Q1:16), GlobalAdversaries are taking malware to
unprecedented levels of
sophistication & impact...
Weaponizing cloud services & other
technology used for legitimate purposes...
And for some adversaries,
the prize isn't ransom, but
obliteration of systems & data.
- Cisco 2018 Annual Cybersecurity Report, 2/18
Cybersecurity =
Threats Increasingly Sophisticated...Targeting Data
2016
2017
Source: Cisco 2018 Annual Cybersecurity Report. Note: Data collected by Cisco endpoint security equipment / software. While
Malware volume increased ~11x from Q1:16 to Q4:17, traffic events process by the same equipment only increased ~3x.
212
Global Internet Leadership =
USA & China
213
Economic Leadership...
214
Relative Global GDP (Current $) =
USA + China + India GainingOther Leaders Falling
Global GDP Contribution (Current $)
Source: World Bank (GDP in current $). Other countries account for ~30% of global GDP.
0%
10%
20%
30%
40%
1960
1970
1980
1990
2000
2010
% of Global GDPUSA
Europe
China
India
Latin America
26%
22%
4%
15%
40%
25%
3%
7%
3%
6%
2017
215
Cross-Border Trade =
Increasingly Important to Global Economy
0%
10%
20%
30%
40%
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Trade as % of Global GDP
Share2016
Source: World Bank. Note: 'World Trade' refers to the average of Imports &
Exports (to account for goods in-transit between years) for all nations.
216
Internet Leadership =
A Lot's Happened Over
5-10 Years
217
Today's Top 20 Worldwide Internet Leaders 5 Years Ago* =
USA @ 9China @ 2
Rank
Market Value ($B)
2018 Company
Region
5/29/13
1)
Apple
USA
$418
2)
Amazon
USA
121
3)
Microsoft
USA
291
4)
Google / Alphabet
USA
288
5)
USA
56
6)
Alibaba
China
--
7)
Tencent
China
71
8)
Netflix
USA
13
9)
Ant Financial
China
--
10)
eBay + PayPal**
USA
71
11) Booking Holdings
USA
41
12)
Salesforce.com
USA
25
13)
Baidu
China
34
14)
Xiaomi
China
--
15)
Uber
USA
--
16)
Didi Chuxing
China
--
17)
JD.com
China
--
18) Airbnb
USA
--
19) Meituan-Dianping
China
--
20)
Toutiao
China
--
Total
$1,429
Source: CapIQ, CB Insights, The Wall Street Journal, media reports. *Only includes public companies in
2013. **eBay + PayPal combined for comparison purposes though PayPal spun-off of eBay on 7/20/15.
Public / Private Internet Companies, Ranked by Market Valuation (5/29/18)
218
Today's Top 20 Worldwide Internet Leaders Today =
USA @ 11China @ 9
Rank
Market Value ($B)
2018 Company
Region
5/29/13
5/29/18
1)
Apple
USA
$418
$924
2)
Amazon
USA
121
783
3)
Microsoft
USA
291
753
4)
Google / Alphabet
USA
288
739
5)
USA
56
538
6)
Alibaba
China
--
509
7)
Tencent
China
71
483
8)
Netflix
USA
13
152
9)
Ant Financial
China
--
150
10)
eBay + PayPal*
USA
71
133
11) Booking Holdings
USA
41
100
12)
Salesforce.com
USA
25
94
13)
Baidu
China
34
84
14)
Xiaomi
China
--
75
15)
Uber
USA
--
72
16)
Didi Chuxing
China
--
56
17)
JD.com
China
--
52
18) Airbnb
USA
--
31
19) Meituan-Dianping
China
--
30
20)
Toutiao
China
--
30
Total
$1,429
$5,788
Source: CapIQ, CB Insights, Wall Street Journal, media reports. *eBay + PayPal combined for comparison purposes though PayPal spun-off of eBay
on 7/20/15. Market value data as of 5/29/18. The Wall Street Journey, Recode, TechCrunch, Reuters, and the Information articles detail the latest
valuations for Ant Financial (4/18), Xiaomi (5/18), Uber (2/18), Didi Chuxing (12/17), Airbnb (3/17), Meituan-Dianping (10/17), and Toutiao (12/17).
Public / Private Internet Companies, Ranked by Market Valuation (5/29/18)
219
Smartphones = China @ #1 Worldwide OEM...
@ 40% vs. 0% Share Ten Years Ago...USA @ 15% vs. 3%
Source: Katy Huberty @ Morgan Stanley (3/18), IDC. Note: OEM = Original Equipment Manufacturer.
Worldwide New Smartphone Shipments by OEM Headquarters
0%
40%
0%
20%
40%
60%
0
0.5B
1.0B
1.5B
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
China OEM ShareWorldwide ShipmentsChina
USA
Korea
Other
% from China OEMs
220
Internet Globally =
USA Platforms = Lead User Numbers
2.2B
2.0B
1.0B
0.7B
0
0.5B
1.0B
1.5B
2.0B
2.5B
(All Platforms)
(Android)
Tencent
(WeChat)
Alibaba
(E-Commerce)
Active Users by Platform, GlobalActive Users By Platform
Source: Facebook (4/18), Google (5/17), Tencent (3/18), Alibaba (5/18). Note: Facebook =
MAUs, Google = MAUs, Tencent WeChat = MAUs, Alibaba = Mobile MAUs.
221
Internet by Country =
China Platforms = Lead User Numbersin China
1.0B
2.2B
2.0B
0.7B
0
0.5B
1.0B
1.5B
2.0B
2.5B
(All Platforms)
(Android)
Tencent
(WeChat)
Alibaba
(E-Commerce)
Active Users by Platform, GlobalChina
Asia (ex-China)
Europe
North America
Rest Of World
Active Users By Platform
Source: Hillhouse Capital. Facebook (4/18), Google (5/17), Newzoo (Google Android USA estimate, 1/18), Tencent (3/18), Alibaba (5/18). Note: Facebook = MAUs,
Google = MAUs (Newzoo Global Mobile Market Report estimates that there are 125MM active Android smartphones in the USA in 2017), Tencent WeChat = monthly active accounts
vs. users as many Chinese users have multiple accounts (ex. 688MM users sent red envelopes during the 2018 Chinese New Year), Alibaba = Annual active consumers. Estimated
WeChat ex-China MAU <5% of total per Hillhouse. Estimate Alibaba ex-China annual active consumers (Lazada + Aliexpress) = 80MM annual active customers per Hillhouse.
222
China Feature + Data-Rich Internet Platforms =
Largest # of Users in One Country
Tencent
WeChat + WeChat Pay
PhotosFriendsGames
AppsFinancesBills
Alibaba
TaoBao + Alipay
SearchesNewsBrands
FeedbackFinances...Bills
Source: Tencent, Alibaba
223
China Internet Users =
More Willing to Share Data for Benefits vs. Other Countries per GfK
Source: GfK Survey (1/17). Note: n = 22K of internet users ages 15+. A scale of 1-7 were used to identify the level of agreement with the
following statement: "I am willing to share my personal data (health, financial, driving records, energy use, etc.) in exchange for benefits or
rewards like lower costs or personalized service" using a scale where "1" means "don't agree at all" and "7" means "agree completely."
.
Would you share personal data (financial, driving records, etc.)
for benefits (e.g., lower cost, personalization, etc.)?
8%
12%
12%
14%
15%
16%
16%
17%
20%
25%
26%
27%
28%
29%
30%
38%
0%
10%
20%
30%
40%
Japan
Netherlands
Germany
Canada
France
Spain
UK
Australia
South Korea
USA
Brazil
Global
Italy
Russia
Mexico
China
% of Global Respondents Very Willing to Share (6 or 7 on 7 Point Scale)
i
Globl
224
China Digital Data Volume @
Significant Scale & Growing Fast =
Providing Fuel for
Rapid Artificial Intelligence Advancements
225
Artificial Intelligence =
USA & China
226
Artificial Intelligence Competition =
Increasingly Complex TasksChina Momentum Strong
Source: International Computer Games Association, RoboCup, Image-Net, Stanford. Note: Stanford Question Answering Dataset is a set of 100,000+ human-generated
questions covering 500+ Wikipedia articles. Scores ranked by Exact Match Accuracy, which refers to the share of questions correctly parsed / answered. Highest Score
included for teams with multiple results (i.e. Google + Carnegie Melon) *National Affiliation refers to main campus of sponsoring Group / Company / University. Microsoft
submitting team based in Beijing (lead by Feng-Hslung Hsu who was lead developer of 'Deep Thought' while @ Carnegie Mellon). NEC team based in USA.
1) Deep Thought (USA)
2) Bebe (USA)
3) Cray Blitz (USA)
China = No Entrants
1) NEC-UIUC* (USA + Japan)
2) XRCE (France / EU)
3) University of Tokyo (Japan)
1985
2005
2018
1995
6th World Computer
Chess Championship
Large Scale Visual
Recognition Challenge 2010
1) CMUnited-99 (USA)
2) MagmaFreiburg (Germany)
3) Essex Wizards (UK)
RoboCup-99 Soccer
Simulation League
Stanford Question Answering
Dataset (Ongoing)
1) Google + Carnegie Mellon (USA)
2) Microsoft* + NUDT (USA + China)
3) YUANFUDAO (China)
4) HIT + iFLYTEK (China)
5) Alibaba (China)
China = No Entrants
China = 11th Place
227
Natural Science & Engineering Higher Education =
China Graduation Rates Rising Rapidly per National Science Foundation
0
20K
40K
60K
2000 2002 2004 2006 2008 2010 2012 2014
Annual Natural Science & Engineering Degrees
(Agricultural Sciences / Biological Sciences / Computer Sciences / Earth, Atmospheric & Ocean Sciences / Mathematics / Engineering)
Doctorate Degrees, Selected Countries / Economies Source: USA National Science Foundation analysis of National Bureau of Statistics (China), Government of Japan, UNESCO, OECD, National Center for Education Statistics, IPEDS, & National Center for Science /
Engineering data. Note: Data for the majority of the countries were collected under same OECD, EU, and UIS guidelines & field groupings in the ISCED-F are similar to fields used in China, a major degree producer.
Natural sciences include agricultural sciences; biological sciences; computer sciences; earth, atmospheric, and ocean sciences; & mathematics. EU-Top 8 for doctoral degrees includes UK / Germany / France / Spain /
Italy / Portugal / Romania / Sweden. EU-Top 8 for first university degrees includes UK / Germany / France / Poland / Italy / Spain / Romania / The Netherlands. The # of S&E doctorates awarded rose from about 8K in
2000 to more than 34K in 2014. Despite the growth in the quantity of doctorate recipients, some question the quality of the doctoral programs in China (Cyranoski et al. 2011). The rate of growth in doctoral degrees in
S&E and in all fields has considerably slowed starting in 2010, after an announcement by the Chinese Ministry of Education indicating that China would begin to limit admissions to doctoral programs & focus on quality
of graduate education (Mooney 2007). Also in China, first university degrees increased greatly in all fields, with a larger increase in non-S&E than in S&E fields. China experienced an increase of almost 1.2MM degrees
and up more than 400% from 2000 to 2014. China has traditionally awarded a large proportion of its first university degrees in engineering, but the percentage declined from 43% in 2000 to 33% in 2014.
0
0.5MM
1.0MM
1.5MM
2000 2002 2004 2006 2008 2010 2012 2014
First University Degrees, Selected Countries / Economies First University
(Bachelor's Equivalent)
Doctoral
China
USA
EU Top 8
Japan
228
Artificial Intelligence Focus =
China Government Highly Focused on Developing AI
Artificial Intelligence - Next Generation Development Plan Goals
1) Build Open & Coordinated AI Innovation Systems
2) Foster a Highly Efficient Smart Economy
3) Construct Safe / Convenient Intelligent Society
4) Strengthen Military-Civilian Integration in AI
5) Build Safe & Efficient Information Infrastructure
6) Plan Next Generation AI Science & Technology Projects
Source: New America Translation of China State Council documents (7/20/17).
229
Artificial Intelligence = USA Ahead
China = Focused + Organized + Gaining
I'm assuming that [USA's] lead [in Artificial
Intelligence] will continue over the next five years,
& that China will catch up extremely quickly.
In five years we'll kind of be at the same level, possibly.
It's hard to see how China would have
passed us in that period, although their rate of
improvement is so impressively good.
- Eric Schmidt, Chairman, US Defense Innovation Advisory Board,
Keynote Address at Artificial Intelligence & Global Security Summit, 11/13/17
230
ECONOMIC GROWTH DRIVERS =
EVOLVE OVER TIME
231
Century
Economic Growth Drivers
Pre-18th
Cultivation & Extraction
19-20th
Manufacturing & Industry
21st
Compute Power & Human Potential
232
Lifelong Learning =
Crucial in Evolving
Work Environment &
Tools Getting Better +
More Accessible
233
Lifelong Learning =
33MM Learners +30% (Coursera)
Source: Coursera. Note: Course popularity based on average
daily enrollments. Graph shows learners as of 5/18.
Machine Learning
Neural Networks & Deeper Learning
Learning How to Learn: Powerful Mental Tools to
Help You Master Tough Subjects
Introduction to Mathematical Thinking
Bitcoin & Cryptocurrency Technologies
Programming for Everybody
Algorithms, Part I
English for Career Development
Neural Networks / Machine Learning
Financial Markets
Top Courses, 2017
Learners
0
10MM
20MM
30MM
40MM
2014
2015
2016
2017
Registered Learners, Global30%
28%
20%
11%
5%
0%
20%
40%
60%
80%
100%
North America
Asia
Europe
South America
Africa
Learners by Geography
Stanford
Deeplearning.ai
UC San Diego
Stanford
Princeton
University of Michigan
Princeton
University of Pennsylvania
University of Toronto
Yale
234
Lifelong Learning =
Educational Content Usage Ramping Fast (YouTube)
Selected Education
Channel Subscribers
0
3MM
6MM
9MM
Asap
SCIENCE
Crash
Course
TED-
Ed
Smarter
Every
Day
Khan
Academy
Subscribers 2013
2018
Source: YouTube (5/18).
1B
Daily Learning Video Views
70%
Viewers Use Platform to Help Solve
Work / School / Hobby Problems
+38%
Growth Y/Y (2017)
Job Search Video Views
(e.g., Resume-Writing Guides)
+6MM
+7
+6
+6
+2
235
Lifelong Learning =
Employee Re-Training Engagement High (AT&T)
Source: AT&T (4/18).
'Workforce 2020' / 'Future Ready' Programs
$1B
Allocated for web-based employee training.
Partners = Coursera / Udacity / Universities.
2.9MM
Emerging tech courses completed by employees.
Most popular courses = Cyber Security / Machine Learning /
Data-Driven Decision Making / Virtual Collaboration.
194K
Employees (77% of workforce) actively engaged in re-training.
61%
Share of promotions received by re-trained employees (2016-Q1:18)
236
Lifelong Learning =
>50% of Freelancers Updated Skills Within Past 6 Months
Source: Edelman Research / Upwork 'Freelancing In America: 2017.' Note: Survey
conducted July-August 2017 on 2,173 Freelance Employees who have received
payment for supplemental temporary, or project-oriented work in the past 12 months.
When Did You Last Participate in Skill-Related Training?
55%
30%
45%
70%
0%
25%
50%
75%
Freelancers
Non-Freelancers
% of USA RespondentsWithin Past 6 Months
>6 Months Ago / Never
237
CHINA INTERNET =
ROBUST ENTERTAINMENT +
RETAIL INNOVATION
*Disclaimer The information provided in the following slides is for informational and illustrative purposes only. No representation or warranty, express or implied, is given and no responsibility or
liability is accepted by any person with respect to the accuracy, reliability, correctness or completeness of this Information or its contents or any oral or written communication in connection with it.
Hillhouse Capital may hold equity stakes in companies mentioned in this section. A business relationship, arrangement, or contract by or among any of the businesses described herein may not
exist at all and should not be implied or assumed from the information provided. The information provided herein by Hillhouse Capital does not constitute an offer to sell or a solicitation of an offer
to buy, and may not be relied upon in connection with the purchase or sale of, any security or interest offered, sponsored, or managed by Hillhouse Capital or its affiliates.
238
China Macro Trends =
Strong
239
China Consumer Confidence = Near 4 Year High
Manufacturing Index = Expanding
48
49
50
51
52
53
54
80
90
100
110
120
130
140
2014
2015
2016
2017
2018
PMI, ChinaConsumer Confidence Index, ChinaChina Consumer Confidence Index (LHS)
China Manufacturing PMI (RHS)
Source: China National Bureau of Statistics (CNNIC), Morgan Stanley Research. Note: The Purchasing Managers Index is
Measured by China National Bureau of Statistics Based on New Orders, Inventory Levels, Production, Supplier Deliveries &
the Employment Environment. Score of 50+ Indicates an Expanding Manufacturing Sector. Consumer Confidence is a
Measure of Consumers' Sentiment About the Current / Future State of the Domestic Economy, Indexed to 100.
China Consumer Confidence Index +
Manufacturing Purchasing Managers' Index (PMI)
240
China GDP Growth = Increasingly Driven by Domestic Consumption
@ 62% vs. 35% of GDP Growth (2003
35%
62%
0%
20%
40%
60%
80%
2003
2006
2009
2012
2015
2018E
% Contribution to GDP Growth, ChinaSource: China National Bureau of Statistics, Morgan Stanley Research. Note: Domestic
Consumption Includes Household and Government Consumption. Other Drivers of GDP
Growth Include Investments (Gross Capital Formation) and Net Export of Goods and Services.
China Domestic Consumption Contribution to GDP Growth
241
China Internet Usage =
Accelerating
242
China Mobile Internet Users vs. Y/Y Growth
Source: China Internet Network Information Center (CNNIC).
Note: Mobile Internet User Data is as of Year-End.
0%
20%
40%
60%
80%
0
200MM
400MM
600MM
800MM
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Growth, Y/YMobile Internet Users, ChinaChina Mobile Internet Users
Y/Y Growth
China Mobile Internet Users =
753MM+8% vs. 12% Y/Y
243
China Mobile Internet (Data) Usage =
Accelerating+162% vs. +124% Y/Y
China Cellular Internet Data Usage & Growth Y/Y
0%
60%
120%
180%
0
10EB
20EB
30EB
2012
2013
2014
2015
2016
2017
Growth Y/YMobile Data Usage, China (EB = Exabyte) China Mobile Data Consumption
Y/Y Growth
Source: China Ministry of Industry and Information Technology.
Note: Cellular Internet Refers to 3G/4G Network data.
244
China Online Entertainment =
Long + Short-Form Video &
Team-Based Multiplayer Mobile Games
Growing Quickly
245
China Mobile Media / Entertainment Time Spent =
+22% Y/YMobile Video Growing Fastest
Source: QuestMobile (3/18).
Social
Networking
60%
Video
13%
Game
13%
News
7%
Audio
4%
Reading
3%
March 2016
2.0B Hours
China Mobile Media / Entertainment Daily Time Spent
Social
Networking
47%
Video
22%
Game
13%
News
11%
Audio
4%
Reading
3%
March 2018
3.2B Hours, +22% Y/Y
246
China Short-Form Video =
Usage Growing Rapidly
0
100MM
200MM
300MM
400MM
500MM
2016
2017
2018
Daily Mobile Media Hours China Daily Mobile Media Time Spent
Long Form Video
Short Form Video
Live & Game Streaming
Source: QuestMobile (3/18). Note: Short-Form Videos are typically <5 Minute in
Length and include companies such as Kuaishou, Douyin, Xigua. Long-Form
Videos include companies such as iQiyi, Tencent Video, Youku, Bilibili.
Long-Form Video
Short-Form Video
247
China Short-Form Video Leaders = 100MM+ DAU
Massive Growth + High Engagement (50 Minute Daily Average)
Source: QuestMobile (4/18).
Douyin (Tik-Tok)
AI-Augmented Mobile Video Creation
/ Personalized Feed
DAU = 95MM +78x Y/Y
Daily Time Spent = 52 Minutes
DAU / MAU Ratio = 57%
Kuaishou
De-Centralized / Personalized /
Location-Based Mobile Video Discovery
DAU = 104MM +2x Y/Y
Daily Time Spent = 52 Minutes
DAU / MAU Ratio = 46%
248
China Online Long-Form Video Content Budgets =
Exceeded TV Networks (2017)
$0
$2B
$4B
$6B
$8B
2009 2010 2011 2012 2013 2014 2015 2016 2017
Annual Content BudgetTV Networks*
Online Video Platforms**
China TV Networks* vs. Online Video Platform** Content Budget
Source: Public disclosures, Goldman Sachs, Bank of America, Hillhouse
estimates. *Includes estimates from CCTV, provincial satellite TV channels
and major local TV networks. **Includes iQiyi + Tencent Video + Youku.
249
China Online Long-Form Video Original / Exclusive Content =
Driving Industry-Wide Paying Subscriber Growth
Source: Subscriber data per iQiyi (3/18). Tencent Video and Youku are not standalone
publicly listed companies hence do not provide regular disclosure on paying
subscribers. Tencent Video last announced more than 62MM subscribers in 2/18.
0
25MM
50MM
75MM
2015
2016
2017
2018
Paying SubscribersOriginal / Exclusive Content
iQiyi Paying Subscribers
250
China Team-Based Multiplayer Mobile Games =
Lead Game Time Spent in China
Source: Questmobile (3/18). *MOBA is Multiplayer Online Battle Arena; **FPS is First Person
Shooting, FPS / Survival games include Tencent's PUBG Mobile and NetEase's Rules of
Survival. ***Other genre includes RPG, action, racing, strategy, card battle, and other games.
0
80MM
160MM
240MM
320MM
2016
2017
2018
Daily Mobile Hours Spent, ChinaMOBA / FPS / Survival
Casual
Other***
Honor of Kings
80MM+ China DAU
PUBG Mobile
50MM+ China DAU
China Mobile Games Daily Hours
251
$0
$10
$20
$30
2012
2013
2014
2015
2016
2017
Video Game (excl. Hardware) Revenue ($B)China
USA
EMEA
Asia (ex. China)
Source: Newzoo. *Excludes console / gaming PC hardware revenue.
Global Interactive Game Revenue =
China #1 Market in World* > USA (2017)
Interactive Game Software Revenue
252
China Retail Innovation =
Spreading from Online to Offline
253
Worldwide E-Commerce Share Gains Continue
China @ 20% = Highest Penetration Rate + Fastest Growing
0%
5%
10%
15%
20%
25%
2003
2005
2007
2009
2011
2013
2015
2017
ShareKorea
UK
China
USA
Germany
Japan
France
Brazil
Source: Euromonitor. Note data excludes certain
consumer-to-consumer (C2C) transactions.
E-Commerce % of Retail Sales
254
China E-Commerce = Strong Growth +28% Y/Y
Mobile = 73% of GMV
0%
100%
200%
300%
$0
$200B
$400B
$600B
2013
2014
2015
2016
2017
Mobile Growth Y/YB2CE-CommerceGMV, ChinaDesktop
Mobile
Mobile Growth Y/Y
Source: iResearch. Note: Assumes constant USD / RMB rate = 6.9.
China B2C E-Commerce Gross Merchandise Value
255
Hema Stores = Re-Imagining Grocery Retail Experience
High Quality + Convenience + Digital
Digital Grocery Store
SKU Selection =
Based on Customer Data..
Alipay Membership To Pay
Real-Time E-Commerce
Ceiling-Conveyor System /
In-Store Fulfillment /
30-Minute Delivery
Restaurant
Cook To Order Chefs /
Eat-in-Shop
Source: Hillhouse. Note: As of 4/18, there are 37 Hema stores in China.
256
Hema Stores = Material Portion of Orders Online
Driving Higher Sales Productivity vs. Offline Peers
Source: Bernstein Research. Note: Hema data points in chart came from stores in Shanghai and
Hangzhou in 11/17. In Q1:18, more than 50% of Hema store orders were placed online for home delivery.
0
4,000
8,000
12,000
Carrefour China
Yonghui
Hema
Daily Transactions per StoreOffline
Online
Daily Retail Transactions per Store, 11/17
257
Belle =
Re-Imagining Offline Retail Experience with Online Analytics
Traffic Heat Map
RFID in Shoes /
Floor Mat
3D Foot Scan
Source: Belle, Hillhouse Capital.
Optimize Layout
Personalization
138 fittings / 37 sales
27% conversion
168 fittings / 5 sales
3% conversion
Conversion Analysis
258
China
Online Payments / Advertising /
On-Demand Transportation =
Growing Rapidly
259
China Mobile Payment Volume =
+209% vs. +116% Y/Y Led by Alipay + WeChat Pay
Source: Analysys (Q1:18, 3/18). *Excludes certain P2P and
transfer payments. Assumes constant USD / RMB rate = 6.9.
$0
$4T
$8T
$12T
$16T
2012 2013 2014 2015 2016 2017
Mobile Payment Volume, ChinaAliPay
54%
Pay
38%
Others
8%
China Mobile Payment Volume
China Mobile Payment Share*
260
0%
10%
20%
30%
40%
50%
$0
$10B
$20B
$30B
$40B
$50B
2012
2013
2014
2015
2016
2017
Growth Y/YOnlineAdvertising, ChinaOnline Advertising
Y/Y Growth
China Online Advertising Revenue =
+29% vs. 29% Y/Y
Source: iResearch. Note: Assumes constant USD / RMB rate = 6.9.
.
China Online Advertising Revenue
261
China On-Demand Transportation (Cars + Bikes) = +96%...
68% Global Share & Rising
Source: Hillhouse Capital estimates. Note: Includes on-demand taxi, private for-hire vehicles,
as well as on-demand for-hire motorbike and bike trips booked through smartphone apps.
On-Demand Transportation Trip Volume by Region
0
2B
4B
6B
8B
Q1:13
Q1:14
Q1:15
Q1:16
Q1:17
Q1:18
Quarterly Completed Trips, GlobalROW
SE Asia
India
EMEA
N. America
China Bike
China Car
ia ike
262
ENTERPRISE SOFTWARE =
USABILITY / USAGE IMPROVING
263
Consumer-Like Apps =
Changed Enterprise Computing
264
Dropbox (2007) = Pioneered
Consumer-Grade Product With Enterprise Appeal
Dropbox synchronizes files across your / your team's
computersfiles are securely backed up to Amazon S3.
It takes concepts that are proven winners from the
dev community & puts them in a package that my
little sister can figure out
Competing products force the user to
constantly think & do things
With Dropbox, you hit "Save," as you
normally would & everything just works.
- Drew Houston, Founder, Y Combinator Application, Summer 2007
265
Users
...Dropbox = Pioneered...
Consumerization of Enterprise Software Business Model
1.7%
2.2%
0%
2%
4%
0
250MM
500MM
2015
2016
2017
Paying ShareUsersPaying Users
Other Registered Users
33%
67%
0%
40%
80%
$0
$0.5B
$1.0B
$1.5B
2015
2016
2017
Gross MarginRevenueRevenue
Gross Margin
Revenue & Gross Margin
Source: Ilya Fushman @ Kleiner Perkins. Dropbox, Techcrunch, JMP Securities estimates of Dropbox
public releases of registered users. *Major products = Paper, Showcase, & Smart Sync.
Inflection Points
2008 = Consumer / Individual
Free Premium Features for Referral Launch
8 Months to 1MM Users
2013 = Enterprise / Team
Dropbox for Business Launch
30% = Dropbox Business Share of Paid Users (2018)
2015 = Revenue / Sales Efficiency
Free-to-Pay User Conversion Launch
90% = Revenue From Self-Serve Channels (2018)
>40% = New Teams with Former Individual Paid User (2018)
2018 = Platform
Integrated Product Suite Launch
3 = Major Product Launches Since 2017*
Paying Share
266
Slack (2013) = Pioneered
Enterprise-Grade Product With Consumer Look & Feel...
When you want something really bad,
you will put up with a lot of flaws.
But if you do not yet know you want something,
your tolerance will be much lower.
That's why it is especially important for us to build a
beautiful, elegant and considerate piece of software.
Every bit of grace, refinement, & thoughtfulness
on our part will pull people along.
Every petty irritation will stop them &
give the impression that it is not worth it.
- Stewart Butterfield, Slack Founder / CEO (2013)
267
...Slack = Pioneered
Consumerization of Enterprise Software Business Model
26%
34%
20%
30%
40%
0
2MM
4MM
6MM
8MM
2014 2015 2016 2017
Daily Active UsersPaying Users
Other Active Users
Paying Share
Paying Users as % of DAUsSlack Daily Active Users
Source: Slack.
2013 = Small Teams
Consumer-Like Onboarding Launch
128K Users 6 Months Post-Launch (2014)
2015 = Platform
3rd-Party App Directory Launch
>1.5K Apps in Slack App Directory (2018)
>200K Developers on Slack Platform (2018)
2015 = Revenue / Sales Efficiency
Free-to-Pay User Conversion Launch
>400% = 2015 Y/Y Paid Subscription Growth
2017 = Enterprise / Large Teams
Enterprise Features Plan Launch
>70K = Paid Teams (2018)
>500K = Organizations Using Slack (2018)
>150 = Large Enterprises Using Slack Grid (2018)
Slack Inflection Points
268
Enterprise Software Success Formula
Build Amazing Consumer-Grade Product
Leverage Virality Across Individual Users To Grow
Personal + Professional Adoption @ Low Cost
Harvest Individual Users for Enterprise Go-to-Market With
Dedicated Product + Inside / Outbound Sales
Build Enterprise-Grade Platform + Ecosystem
Net = Low Cost Product-Driven Customer Acquisition +
Strong / Sticky Business Model
- Ilya Fushman @ Kleiner Perkins
Source: Ilya Fushman @ Kleiner Perkins.
269
Messaging Threads =
Transforming Collaboration...
Distributing + Increasing Productivity
270
Messaging Threads =
Increasingly Foundational for Consumers + Enterprises
Consumer Services
Snapchat
Social
Square Cash
Payments
Strava
Workouts
Enterprise Services
Slack
Communication
Dropbox
File Management
Intercom
Customer Interactions
Source: Snapchat, Square, Strava, Dropbox, Slack, Intercom.
271
Google Set Out to
'Organize the World's Information &
Make It Universally Accessible & Useful'
Now Apps...
Organize Business Information &
Make It Accessible & Useful
Within Enterprises
272
Enterprise Messaging Threads =
Organizing Information + Teams
Providing Context + History...
273
Slack = Communication Threads...
Organizing Information by Channel Topic
32% Decline in Email Usage
24% Reduction in Employee Onboarding
Time
23% Faster Time to Market For
Development Teams
23% Decline in Meetings
10% Rise in Employee Satisfaction
Slack Benefits
Source: Slack (5/18), IDC "The Business Value of Slack" research report (2017).
Slack Daily Active Users
0
2MM
4MM
6MM
8MM
2013
2014
2015
2016
2017
Daily Active Users
274
...Dropbox = File Management Threads...
Organizing Data by File + Version
Teams % of Paid Users
6x Rise in Employees on
Multi-Department Teams
31% Decline in IT Time Spent
Supporting Collaboration
3.7K Hours Saved Annually Per
Organization in Document Management
6% Rise in Sales Team Productivity
Dropbox Benefits
Source: Dropbox. Piper Jaffray (4/18, Teams % of paid users). Dropbox + IDC commissioned
study for Dropbox on effects of enterprises using Dropbox (Dropbox benefits, 2016).
20%
22%
24%
26%
28%
30%
32%
2014 2015
2016
2017
2018
Teams, % of Total Paid Users
275
...Zoom = Visual Communication / Meeting Threads...
Distributing + Increasing Productivity
0
10B
20B
30B
40B
2015
2016
2017
2018
85% Improved Collaboration
71% Improved Productivity
62% Supported Flexible Work Schedule
58% Built Trust Among Remote Workers
58% Reduced Meeting Times
48% Removed Company Silos
72 Net Promoter Score
Annualized Meeting Minutes
Annualized Minutes, GlobalZoom Benefits
Source: Survey conducted by Zoom Video Communications
of Zoom customers +700 responses (2/18).
276
Intercom Benefits
Source: Intercom.
82% Rise in Conversion For Customers
Chatting In Intercom
36% Rise in Conversion For Customers
Assisted by 'Operator' Chatbot
13% Rise in Order Value for Customers
Chatting in Intercom
...Intercom = Customer Transaction Threads...
Organizing Customer Dialog
277
Enterprise Messaging Threads =
Helping Improve Productivity + Collaboration
278
USA INC.* =
WHERE YOUR TAX DOLLARS GO
* USA, Inc. Full Report: http://www.kleinerperkins.com/blog/2011-usa-inc-full-report
279
USA Income Statement =
-19% Average Net Margin Over 30 Years
USA Income Statement
Source: Congressional Budget Office, White House Office of Management and Budget. *Individual & corporate
income taxes include capital gains taxes. Note: USA federal fiscal year ends in September. Non-defense
discretionary includes federal spending on education, infrastructure, law enforcement, judiciary functions.
F1987
F1992
F1997
F2002
F2007
F2012
F2017
Comments
Revenue ($B)
$854
$1,091
$1,579
$1,853
$2,568
$2,449
$3,316
+5% Y/Y average over 25 years
Y/Y Growth
11%
3%
9%
-7%
7%
6%
2%
Individual Income Taxes*
$393
$476
$737
$858
$1,163
$1,132
$1,587
Largest Driver of Revenue
% of Revenue
46%
44%
47%
46%
45%
46%
48%
Social Insurance Taxes
$303
$414
$539
$701
$870
$845
$1,162
Social Security & Medicare Payroll Tax
% of Revenue
36%
38%
34%
38%
34%
35%
35%
Corporate Income Taxes*
$84
$100
$182
$148
$370
$242
$297
Fluctuates with Economic Conditions
% of Revenue
10%
9%
12%
8%
14%
10%
9%
Other
$74
$101
$120
$146
$165
$229
$270
Estate & Gift Taxes / Duties / Fees / etc.
% of Revenue
9%
9%
8%
8%
6%
9%
8%
Expense ($B)
$1,004
$1,382
$1,601
$2,011
$2,729
$3,537
$3,982
Y/Y Growth
1%
4%
3%
8%
3%
-2%
3%
Entitlement / Mandatory
$421
$648
$810
$1,106
$1,450
$2,030
$2,519
Risen Owing to Rising Healthcare Costs +
% of Expense
42%
47%
51%
55%
53%
57%
63%
Aging Population
Non-Defense Discretionary
$162
$231
$275
$385
$494
$616
$610
Education / Law Enforcement /
% of Expense
16%
17%
17%
19%
18%
17%
15%
Transportation / Government Administration
Defense
$283
$303
$272
$349
$548
$671
$590
2007 increase driven by War on Terror
% of Expense
28%
22%
17%
17%
20%
19%
15%
Net Interest on Public Debt
$139
$199
$244
$171
$237
$220
$263
Has Benefitted from Declining Interest
% of Expense
14%
14%
15%
9%
9%
6%
7%
Rates Since Early 1980s
Surplus / Deficit ($B)
-$150
-$290
-$22
-$158
-$161
-$1,088
-$666
-19% Average Net Margin, 1987-2017
Net Margin (%)
-18%
-27%
-1%
-9%
-6%
-44%
-20%
280
USA Income Statement =
Net Loses in 45 of 50 Years
USA Annual Profits & Losses
-$1,500B
-$1,000B
-$500B
$0
$500B
1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016
USA Annual Net Profit / LossSource: Congressional Budget Office, White House Office of Management
and Budget. Note: USA federal fiscal year ends in September.
281
Real GDP Growth @ 2.3% (Q1)...
1988-2003 @ 3.0%...2003-2018 @ 2.0% Average
Source: Bureau of Economic Analysis (BEA). Note: Real GDP based on chained 2009
dollars. Growth defined as growth over preceding period, seasonally adjusted annual rate.
Real GDP Growth Y/Y
-12%
-8%
-4%
0%
4%
8%
12%
1988
1993
1998
2003
2008
2013
2018
Q1:18 = 2.3%
Growth, USA3.0% Average
1988-2003
2.0% Average
2003-2018
282
USA Rising
Debt Commitments =
Non-Trivial Challenge
283
Net Debt / GDP Ratio =
Highest Level Since WWII
USA Net Debt / GDP Ratio
0%
20%
40%
60%
80%
100%
120%
1790
1815
1840
1865
1890
1915
1940
1965
1990
2015
USA Net Debt / GDPHistorical Net Debt / GDP
Source: Congressional Budget Office Long-Term Outlook (3/18).
Civil War =
~30%
World War I =
~30%
World War II =
~105%
284
USA Public Debt / GDP Level =
7th Highest vs. Major Economies
Source: IMF 2017 Estimates Note: Ranking excludes countries with public debt less than $10B in 2015. Public
debt includes federal, state and local government debt but excludes unfunded pension liabilities from government
defined-benefit pension plans and debt from public enterprises and central banks. FX rates as of 3/28/18.
Government Debt
Country
% of GDP
2017 ($B)
1) Japan
240%
$12,317
2) Greece
180
403
3) Lebanon
152
80
4) Italy
133
2,798
5) Portugal
126
301
6) Singapore
111
362
7) USA
108
20,939
8) Jamaica
107
16
9) Cyprus
106
24
10) Belgium
104
561
Government Debt
Country
% of GDP
2017 ($B)
11) Egypt
101%
$199
12) Spain
99
1,412
13) France
97
2,730
14) Jordan
96
39
15) Bahrain
91
31
16) Canada
90
1,482
17) UK
89
2,532
18) Mozambique
88
12
19) Ukraine
86
92
20) Yemen
83
30
285
USA Rising
Debt Drivers =
Spending on
Healthcare Entitlements
(Medicare + Medicaid)
286
42%
63%
28%
15%
16%
15%
14%
7%
0%
20%
40%
60%
80%
100%
1987
2017
US ExpensesEntitlements / Mandatory
Defense
Non-Defense Discretionary
Net Interest Cost
USA Entitlements =
63% vs. 42% of Government Spending Thirty Years Ago
USA Expenses by Category
$1.0T
$4.0T
10%
8%
6%
4%
2%
0%
USA 10Y Treasury Yield1987 2017
Change
Debt*
+$13T (+650%)
Entitlements
+$2.1T (+498%)
Non-Defense
Discretionary
+$448B (+277%)
Defense
+$308B (+109%)
Net Interest Cost:
+$124B (+89%)
10 Year Treasury Yield
Source: Congressional Budget Office, White House Office of Management and Budget, USA Treasury
*Debt reflects net debt (i.e. excludes debt issued by The Treasury and owned by other Government accounts)
Note: Yellow line represents yield on 10-year USA Treasury bill from 12/31/86 to 12/31/17.
287
USA Entitlements =
Medicare + Medicaid Driving Most Spending Growth
Source: Congressional Budget Office, White House Office of Management and Budget. *1987 Income Security programs
defined as Food Stamps + SSI + Family Support + Child Nutrition + Earned Income Tax Credit + Other. 2017 Income
Security defined as Earned Income Tax Credit + SNAP + SSI + Unemployment + Family Support + Child Nutrition. In
2017, there was an additional ~$200MM in mandatory spending, including Veterans' pensions & ~$73MM in 1987.
20%
7%
3%
4%
24%
15%
9%
8%
0%
10%
20%
30%
Social Security
Medicare
Medicaid
Income Security
USA Mandatory Entitlementsiare
i
1987
2017
USA Entitlements by Category
1987 Entitlements* =
$349B / 35% of Expenses
2017 Entitlements* =
$2.2T / 56% of Expenses
288
2016
$59K =
Median USA Household Income
$20K =
Average Entitlement Payout per Household from Federal Government
Scale = Equivalent to 34% of Household Income
1986
$25K =
Median USA Household Income
$5K =
Average Entitlement Payout per Household from Federal Government
Scale = Equivalent to 19% of Household Income
USA Entitlements Growth Over 30 Years =
Looking @ NumbersCloser to Home
Source: Congressional Budget Office, White House Office of Management and Budget, US Census Bureau
289
IMMIGRATION =
IMPORTANT FOR USA TECHNOLOGY
JOB CREATION
290
USA = 56% of Most Highly Valued Tech Companies Founded By
1st or 2nd Generation Americans1.7MM Employees, 2017
Immigrant Founders / Co-Founders of Top 25 USA Valued Public Tech
Companies, Ranked by Market Capitalization
Source: CapIQ as of 4/16/18. "The 'New American' Fortune 500" (2011), a report by the Partnership for a New American Economy, as well as "Reason
for Reform: Entrepreneurship" (10/16), "American Made, The Impact of Immigrant Founders & Professionals on U.S. Corporations." *While Andy Grove
(from Hungary) is not a co-founder of Intel, he joined as COO on the day it was incorporated. **Francisco D'Souza is a person of Indian origin born in
Kenya. ***Max Levchin / Luke Nosek / Peter Thiel's startup Confinity merged with Elon Musk's startup X.com to form PayPal in 3/00.
Rank
Company
Mkt Cap
($MM)
LTM Rev
($MM)
Employees
Founder / Co-Founder
(1st / 2nd Gen Immigrant )
Generation
1
Apple
$923,554
$239,176
123,000
Steve Jobs
2nd Syria
4
Amazon.com
782,608
177,866
566,000
Jeff Bezos
2nd Cuba
3
Microsoft
753,030
95,652
124,000
--
--
2
Alphabet / Google
739,122
110,855
80,110
Sergey Brin
1st Russia
5
537,648
40,653
25,105
Eduardo Saverin
1st Brazil
6
Intel
257,791
62,761
102,700
--*
--
7
Cisco
202,083
48,096
72,900
--
--
8
Oracle
188,848
39,472
138,000
Larry Ellison /
Bob Miner
2nd Russia /
2nd Iran
11
Netflix
152,025
11,693
4,850
--
--
10
NVIDIA
150,894
9,714
10,299
Jensen Huang
1st Taiwan
9
IBM
129,635
79,139
366,600
Herman Hollerith
2nd Germany
12
Adobe Systems
119,271
7,699
17,973
--
--
13
Booking.com
100,013
12,681
22,900
--
--
14
Texas Instruments
108,912
14,961
29,714
Cecil Green /
J. Erik Jonsson
1st UK /
2nd Sweden
15
PayPal
95,858
13,094
18,700
Max Levchin /
Luke Nosek /
Peter Thiel /
Elon Musk***
1st Ukraine /
1st Poland /
1st Germany /
1st South Africa
16
Salesforce.com
94,260
10,480
25,000
--
--
17
Qualcomm
86,333
22,360
33,800
Andrew Viterbi
1st Italy
19
Automatic Data Processing
57,237
12,790
58,000
Henry Taub
2nd Poland
21
VMware
55,282
7,922
20,615
Edouard Bugnion
1st Switzerland
20
Activision Blizzard
53,772
7,017
9,625
--
--
18
Applied Materials
52,439
15,463
18,400
--
--
23
Intuit
50,471
5,434
8,200
--
--
22
Cognizant Technology
43,597
14,810
260,000
Francisco D'Souza /
Kumar Mahadeva
1st India** /
1st Sri Lanka
24
eBay
37,304
9,567
14,100
Pierre Omidyar
1st France
25
Electronic Arts
34,763
4,845
8,800
--
--
291
USA = Many Highly Valued Private Tech Companies Founded By
1st Generation Immigrants
Source for Valuation and Founders Backgrounds: Based on analysis by the Wall Street Journal, CB Insights, Forbes and Business Insider
Note: Due to varying definitions of unicorns, may not align with various unicorn lists. As of April 2018 there are 105 US-based, venture-backed
unicorns (including rumored valuations). *UiPath is headquartered in New York, NY but was originally founded in Romania.
Company
Immigrant
Founder / Co-Founder
Country
of Origin
Market Value
($B)
Uber
Garrett Camp
Canada
$72
SpaceX
Elon Musk
South Africa
25
Palantir
Peter Thiel
Germany
21
WeWork
Adam Neumann
Israel
21
Stripe
John Collison,
Patrick Collison
Ireland
9
Wish
(ContextLogic)
Peter Szulczewski,
Danny Zhang
Canada
9
Moderna
Therapeutics
Noubar Afeyan,
Derrick Rossi
Armenia /
Canada
8
Robinhood
Baiju Bhatt,
Vlad Tenev
India /
Bulgaria
6
Slack
Stewart Butterfield,
Serguei Mourachov,
Cal Henderson
Canada /
Russia / UK
5
Tanium
David Hindawi
Iraq
5
Credit Karma
Kenneth Lin
China
4
Houzz
Adi Tatarko, Alon Cohen
Israel
4
Instacart
Apoorva Mehta
India
4
Bloom Energy
KR Sridhar
India
3
Oscar Health
Mario Schlosser
Germany
3
Unity
Technologies
David Helgason
Iceland
3
Avant
Al Goldstein,
John Sun, Paul Zhang
Uzbekistan /
China / China
2
Zenefits
Laks Srini
India
2
AppNexus
Mike Nolet
Holland
2
ZocDoc
Oliver Kharraz
Germany
2
Sprinklr
Ragy Thomas
India
2
Compass
Ori Allon
Israel
2
Company
Immigrant
Founder / Co-Founder
Country
of Origin
Market Value
($B)
JetSmarter
Sergey Petrossov
Russia
$2
Warby Parker
Dave Gilboa
Sweden
2
Carbon3D
Alex Ermoshkin
Russia
2
Infinidat
Moshe Yanai
Israel
2
Tango
Uri Raz, Eric Setton
Israel / France
2
Quanergy
Louay Eldada,
Tianyue Yu
Lebanon /
China
2
Zoox
Tim Kentley-Klay
Australia
2
Eventbrite
Renaud Visage
France
2
Apttus
Kirk Krappe
UK
2
Cloudflare
Michelle Zatlyn
Canada
2
Proteus Digital
Health
Andrew Thompson
UK
2
Anaplan
Guy Haddleton,
Michael Gould
New Zealand /
UK
1
Rubrik
Bipul Sinha
India
1
OfferUp
Arean Van Veelen
Netherlands
1
Actifio
Ash Ashutosh
India
1
Gusto
Tomer London
Israel
1
Medallia
Borge Hald
Norway
1
FanDuel
Nigel Eccles,
Tom Griffiths,
Lesley Eccles
UK
1
AppDirect
Daniel Saks,
Nicolas Desmarais
Canada
1
Evernote
Stepan Pachikov,
Phil Libin
Azerbaijan /
Russia
1
Udacity
Sebastian Thrun
Germany
1
UiPath*
Daniel Dines, Marius
Tirca
Romania
1
Zoom Video
Eric Yuan
China
1
292
APPENDIX
293
Source: MSCI, S&P 500
Global Industry Classification System (GICS)
(Slides 39 / 41 / 42)
GICS is a four-tiered, hierarchical industry classification system. It consists of 11 sectors, 24 industry groups, 68 industries and 157 sub-industries.
The GICS methodology is widely accepted as an industry analytical framework for investment research, portfolio management and asset allocation.
Companies are classified quantitatively and qualitatively. Each company is assigned a single GICS classification at the sub-industry level according to
its principal business activity. MSCI and S&P Global use revenues as a key factor in determining a firm's principal business activity. Earnings and
market, however, are also recognized as important and relevant
information for classification purposes.
Global industry coverage is comprehensive and precise. The classification system is comprised of over 50,000 trading securities across 125 countries,
covering approximately 95% of the world's equity market capitalization.
Company classifications are regularly reviewed and maintained. Specialized teams from two major index providers MSCI and S&P Global have
defined review procedures, refined over nearly 15 years.
Each sector includes the following industries:
Energy = Energy Equipment & Services, Oil, Gas & Consumables Fuels
Materials = Chemicals, Construction Materials, Containers & Packaging, Metals & Mining, Paper & Forest Products
Industrials = Aerospace & Defense, Building Products, Construction & Engineering, Electrical Equipment, Industrial Conglomerates, Machinery,
Trading Companies & Distributors, Commercial Services & Suppliers, Professional Services, Air Freight & Logistics, Airlines, Marine, Road & rail,
Transportation Infrastructure
Consumer Discretionary = Auto Components, Automobiles, Household Durables, Leisure Products, Textiles, Apparel & Luxury Goods, Hotels,
Restaurants & Leisure, Diversified Consumer Services, Media, Distributors, Internet & Direct Marketing Retail, Multiline Retail, Specialty Retail
Consumer Staples =Food & Staples Retailing, Beverages, Food Products, Tobacco, Household Products, Personal Products
Healthcare = Healthcare Equipment & Supplies, Healthcare Providers & Services, Healthcare Technology, Biotechnology, Pharmaceuticals, Life
Sciences Tools & Services
Financials = Commercial Banks, Thrifts & Mortgage Finance, Diversified Financial Services, Consumer Finance, Capital Markets, Mortgage Real
Estate Investment Trusts (REITs), Insurance
Information Technology = Internet Software & Services, IT Services, Software, Communications Equipment, Computers & Peripherals, Electronic
Equipment & Instruments, Semiconductors & Semiconductors Equipment
Telecommunication Services = Diversified Telecommunication Services, Wireless Telecommunication Services
Utilities = Electric Utilities, Gas Utilities, Multi-Utilities, Water Utilities, Independent Power & Renewable Electricity Producers
Real Estate = Equity Real Estate Investment Trusts (REITs), Real Estate Management & Development
294
This presentation has been compiled for informational purposes only and should not be
construed as a solicitation or an offer to buy or sell securities in any entity, or to invest in
any Kleiner Perkins (KP) entity or affiliated fund.
The presentation relies on data + insights from a wide range of sources, including public
+ private companies, market research firms + government agencies. We cite specific
sources where data are public; the presentation is also informed by non-public
information + insights. We disclaim any + all warranties, express or implied, with respect
to the presentation. No presentation content should be construed as professional advice
of any kind (including legal or investment advice).
We publish the Internet Trends report on an annual basis, but on occasion will highlight
new insights. We may post updates, revisions, or clarifications on the KP website.
KP is a venture capital firm that owns significant equity positions in certain of the
companies referenced in this presentation, including those at
http://www.kleinerperkins.com/companies.
Any trademarks or service marks used in this report are the marks of their respective
owners, who are not participating partners or sponsors of the presentation or of KP or its
affiliated funds + such owners do not endorse the presentation or any statements made
herein. All rights in such marks are reserved by their respective owners.
Disclaimer