mary meeker kpcb internet trends 2016 code conference jun 01 2016
TRANSCRIPT
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Mary MeekerJune 1, 2016
kpcb.com/InternetTrends
INTERNET TRENDS 2016 –CODE CONFERENCE
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KPCB INTERNET TRENDS 2016 | PAGE 2
Outline
1) Global Internet Trends2) Global Macro Trends
3) Advertising / Commerce + Brand Trends
4) Re-Imagining Communication – Video / Image / Messaging5) Re-Imagining Human-Computer Interfaces – Voice / Transportation
6) China = Internet Leader on Many Metrics(Provided by Hillhouse Capital)
7) Public / Private Company Data
8) Data as a Platform / Data Privacy
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KPCB INTERNET TRENDS 2016 | PAGE 3
Thanks...
KPCB Partners
Especially Alex Tran / Dino Becirovic / Alexander Krey / Cindy Chengwho helped develop the ideas / presentation we hope you find useful...
Hillhouse CapitalEspecially Liang Wu...his / their contribution of the China section ofInternet Trends provides an especially thoughtful overview of thelargest market of Internet users in the world...
Participants in Evolution of Internet Connectivity
From creators to consumers who keep us on our toes 24x7...and thepeople who directly help us prepare this presentation...
Kara & WaltFor continuing to do what you do so well...
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GLOBAL INTERNET TRENDS
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KPCB INTERNET TRENDS 2016 | PAGE 5
Global Internet Users @ 3B
Growth Flat =+9% vs. +9% Y/Y...
+7% Y/Y (Excluding India)
Source: United Nations / International Telecommunications Union, US Census Bureau. Internet user data is as of mid-year. Internet user data for: China f rom CNNIC, Iran from Islamic Republic NewsAgency, citing data released by the National Internet Development Center, India from IAMAI, Indonesia from APJII / eMarketer.
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KPCB INTERNET TRENDS 2016 | PAGE 6Source: United Nations / International Telecommunications Union, US Census Bureau. Internet user data is as of mid-year. Internet user data for: China f rom CNNIC, Iran from Islamic Republic NewsAgency, citing data released by the National Internet Development Center, India from IAMAI, Indonesia from APJII / eMarketer.
Global Internet Users = 3B @ 42% Penetration...+9% vs. +9% Y/Y...+7% (Excluding India)
0%
5%
10%
15%
20%
25%
30%
35%
0
500
1,000
1,500
2,000
2,500
3,000
3,500
2008 2009 2010 2011 2012 2013 2014 2015
Y / Y % G r o w
t h
G l o b a l I n t e r n e
t U s e r s
( M M )
Global Internet Users Y/Y Growth (%)
Global Internet Users, 2008 – 2015
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India Internet UserGrowth Accelerating =+40% vs. +33% Y/Y...
@ 277MM Users...India Passed USA to Become
#2 Global User MarketBehind China
Source: United Nations / International Telecommunications Union, US Census Bureau. Internet user data is as of mid-year. Internet user data for: China f rom CNNIC, India from IAMAI. India users as of10/2015 was 317MM per IAMAI; USA total population at 12/2015 (inclusive of all ages) was 323MM per US Census.
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India Internet Users = 277MM @ 22% Penetration...+40% vs. +33% Y/Y
Source: IAMAI. Uses mid-year figures.
India Internet Users, 2008 – 2015
0%
10%
20%
30%
40%
50%
60%
0
50
100
150
200
250
300
2008 2009 2010 2011 2012 2013 2014 2015
Y / Y % G r o w
t h
I n d i a
I n t e r n e t
U s e r s
( M M )
India Internet Users Y/Y Growth (%)
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KPCB INTERNET TRENDS 2016 | PAGE 9
Global SmartphoneUsers Slowing =
+21% vs. +31% Y/Y
Global SmartphoneUnit Shipments Slowing
Dramatically =+10% vs. +28% Y/Y
Source: Nakono Research (2/16), Morgan Stanley Research (5/16).“Smartphone Users” represented by installed base.
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Global Smartphone User Growth Slowing...Largest Market (Asia-Pacific) = +23% vs. +35% Y/Y
Source: Nakono Research (2/16).* “Smartphone Users” represented by installed base.
Smartphone Users, Global, 2005 – 2015
0
500
1,000
1,500
2,000
2,500
3,000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
North America Western Europe Eastern Europe Asia-Pacific Latin America MEA
2015: Asia-Pacific = 52%
2008: Asia-Pacific = 34% G
l o b a l S m a r
t p h o n e
U s e r s
( M M )
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Global Smartphone Units Slowing Dramatically...After 5 Years of High Growth @ +10% vs. +28% Y/Y
Source: Morgan Stanley Research, 5/16.
Smartphone Unit Shipments by Operating System, Global, 2007 – 2015
0%
20%
40%
60%
80%
100%
0
300
600
900
1,200
1,500
2007 2008 2009 2010 2011 2012 2013 2014 2015
Y / Y G r o w
t h ( % )
G l o b a l S m a r
t p h o n e
U n i
t S h i p m e n
t s ( M M )
Android iOS Other Y/Y Growth
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Android Smartphone Share Gains Continue vs. iOS...Android ASP Declines Continue...Delta to iOS @ ~3x
Source: Morgan Stanley Research, 5/16.
0
400
800
1,200
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016E
U n i
t S h i p m e n t s
( M M )
iOS
Android
Smartphone Unit Shipments, iOS vs. Android, Global, 2007 – 2016E
-11% Y/Y
+7% Y/Y
2009 Share:iOS = 14%
Android = 4%
2015 Share:iOS = 16%
And ro id = 81%
iOS ASP ($) $594 $621 $623 $703 $712 $686 $669 $680 $717 $651
Y/Y Growth – 4% 0% 13% 1% -4% -2% 2% 5% -9%
And roid ASP – $403 $435 $441 $380 $318 $272 $237 $216 $208
Y/Y Growth – – 8% 1% -14% -16% -15% -13% -8% -4%
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New Internet Users =
Continue to be Harder toGarner Owing to High Penetration
in Developed Markets
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KPCB INTERNET TRENDS 2016 | PAGE 14
0
20
40
60
80
100
Source: World Bank; McKinsey analysis from Internet Barriers Index
Performance on InternetBarriers IndexAverage scoreMinimum - 0Maximum -100
Group 1Group 2Group 3
Countries: Bangladesh, Ethiopia, Nigeria, Pakistan, TanzaniaOffline po pulation, 2014: 548 millionInternet penetration, 2014: 18%
Group 1: High barriers across the board; off line population s that are young,rural, and have low li teracy
Countries: Egypt, India, Indonesia, Philippines, ThailandOffline popu lation, 2014: 1,438 millionInternet penetration, 2014: 20%
Group 2: Medium to high barriers wi th larger challenges in incentives andinfrastructure; mixed demographics
Countries: China, Sri Lanka, VietnamOffline popu lation, 2014: 753 millionInternet penetration, 2014: 49%
Group 3: Medium barriers with greatest challenge in incentives; rural andliterate offline populations
Incentives
Low incomes
and affordability
User capability
Infrastructure
3
Group 4Group 5
Countries: Brazil, Colombia, Mexico, South Africa, Turkey
Offline popu lation, 2014: 244 millionInternet penetration, 2014: 52%
Group 4: Medium barriers with greatest challenge in low in comes andaffordability; offline populations predominantly urban / literate / low income
Countries fall into on e of 5 groups based on
barriers they face to Internet adoption
Countries: Germany, Italy, Japan, Korea, Russia, USAOffline popu lation, 2014: 147 millionInternet penetration, 2014: 82%
Group 5: Low barriers across the board; offline population s that are highlyliterate and disproportio nately low income and female
With Already High Mobile Penetration in More Developed / Affluent Countries...New Users in Less Developed / Affluent Countries Harder to Garner, per McKinsey
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KPCB INTERNET TRENDS 2016 | PAGE 15
Smartphone Cost in Many Developing Markets = Material % of Per Capita Income...15% (Vietnam) / 10% (Nigeria) / 10% (India) / 6% (Indonesia), per McKinsey
Source: McKinsey, Euromonitor, (smartphone prices); World Bank, estimates (GNI p.c., Atlas method)Note: Reflects true prices as paid by the consumer at point-of-sale; includes taxes and subsidies. Excludes data plan costs.
1.0
3.8
0.8
10.3
6.1
4.7
2.7
0.9
14.8
2.5
5.8
3.7
0.9
4.7
3.3
0.6
1.8
10.1
4.8
11.4
21.5Tanzania
Ethiopia
Bangladesh
Turkey
China
Germany
Spain
South Korea
Japan
Italy
Mexico
Thailand
Egypt
South Africa
Philippines
Colombia
Nigeria
Vietnam
India
Indonesia
Brazil
Russia
47.6
$232
$216
$269
$327
$486
$244$232
$319
$522
$243
$256
$291
$273$163
$212
$195
$307
$158
$279
$123$198
$262
Aver age r etai l pr ice of a smart ph one, $USD, 2014
Developing Developed
x%Cost of smartphone as a %of GNI per capita, 2014
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GLOBAL MACRO TRENDS
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Global Economic Growth =Slowing
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Global GDP Growth Slowing =Growth in 6 of Last 8 Years @ Below 20-Year Average
Source: IMF WEO, 4/16. Stephen Roach, “A World Turned Inside Out,” Yale Jackson Institute for Global Af fairs, 5/16.Note: GDP growth based on constant prices (real GDP growth).
Global Real GDP Growth (%), 1980 – 2015
(1%)
0%
1%
2%
3%
4%
5%
6%
1 9 8 0
1 9 8 1
1 9 8 2
1 9 8 3
1 9 8 4
1 9 8 5
1 9 8 6
1 9 8 7
1 9 8 8
1 9 8 9
1 9 9 0
1 9 9 1
1 9 9 2
1 9 9 3
1 9 9 4
1 9 9 5
1 9 9 6
1 9 9 7
1 9 9 8
1 9 9 9
2 0 0 0
2 0 0 1
2 0 0 2
2 0 0 3
2 0 0 4
2 0 0 5
2 0 0 6
2 0 0 7
2 0 0 8
2 0 0 9
2 0 1 0
2 0 1 1
2 0 1 2
2 0 1 3
2 0 1 4
2 0 1 5
G l o b a l R e a
l G D P G r o w
t h ( % )
20-Year Avg= 3.8%
35-Year Avg
= 3.5%
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KPCB INTERNET TRENDS 2016 | PAGE 19
Commodity Price Trends =
In Part, Tell Tale ofSlowing Global Growth
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Commodity Prices Down = -39% Since 5/14 vs.-8% Annual Average (5/11-4/14) & +6% (1/00-4/11)
Source: Morgan Stanley, Bloomberg as of 5/25/16Note: Bloomberg Commodity Index represents 22 globally traded commodities, weighted as: 31% Energy, 23% Grains, 17% Industrial Metals, 16% Precious Metals, 7% Softs (Sugar, Coffee, Cotton), and6% Livestock.
(50%)
0%
50%
100%
150%
200%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
B l o o m
b e r g
C o m m o d
i t y I n d e x
( I n d e x e
d t o 0 @
1 / 0 0 )
Global Commodity Prices, Bloomberg Commodity Index(Indexed to 0 @ 1/00), 2000 – 2016YTD
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KPCB INTERNET TRENDS 2016 | PAGE 21
Global Growth Engines =Evolve Over Time
Global Growth Engines @ ~2/3 of Global GDP Growth
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Global Growth Engines @ ~2/3 of Global GDP Growth...1985 = N. America + Europe + Japan2015 = China + Emerging Asia
Source: IMF WEO, 4/16. GDP growth based on constant prices (real GDP growth). PPP = Purchasing Power Parity exchange rate, national currency per international dollar. GDP PPP = GDP adjusted byPPP rate. Emerging Asia includes Bangladesh, Cambodia, India, Indonesia, Lao, Malaysia, Mongolia, Myanmar, Nepal, Philippines, Sri Lanka, Thailand, Vietnam and others and excludes China.GDP growth contribution based on annual snapshots stated above and not necessarily reflective of secular trends.
22%
28%13%
11%
7%9%
9%
15%
13%
1%
37%
26%
0%9%
Europe N. America Japan China Emerging Asia (ex-China) Lat Am Middle East, Africa, Other
1985$19T = World GDP
+4% Y/Y
2015$114T = World GDP
+3% Y/Y
Real GDP Growth Contribution by Region, 1985 / 2015(Based on Purchasing Power Parity)
N. America +Europe + Japan =
63% of Total
China +Emerging Asia =
63% of Total
China +Emerging Asia =
18% of Total
N. America +Europe + Japan =
29% of Total
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KPCB INTERNET TRENDS 2016 | PAGE 23
China’sGross Capital Formation
(Capital Equipment /Roads / Buildings...)
Past 6 Years >Previous 30 Years
Chi G C i l F i Sl i
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KPCB INTERNET TRENDS 2016 | PAGE 24
China Gross Capital Formation = Slowing...Sum of Past 6 Years > Previous 30 Years
Source: China National Bureau of Statistics, 5/16. Assumes constant FX rate RMB/USD @ 6.5.Amounts are inflation adjusted to 2010 dollars based on IMF data on inflation rates (yearly average).Gross capital formation = gross fixed capital formation (majority) + changes in inventory. Gross fixed capital formation includes land improvements (fences, ditches, drains, and so on); plant, machinery, andequipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. It also includes thevalue of draught animals, breeding stock and animals for milk, for wool and for recreational purposes, and newly increased forest with economic value.
$0$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
$4,500
1 9 8 0
1 9 8 1
1 9 8 2
1 9 8 3
1 9 8 4
1 9 8 5
1 9 8 6
1 9 8 7
1 9 8 8
1 9 8 9
1 9 9 0
1 9 9 1
1 9 9 2
1 9 9 3
1 9 9 4
1 9 9 5
1 9 9 6
1 9 9 7
1 9 9 8
1 9 9 9
2 0 0 0
2 0 0 1
2 0 0 2
2 0 0 3
2 0 0 4
2 0 0 5
2 0 0 6
2 0 0 7
2 0 0 8
2 0 0 9
2 0 1 0
2 0 1 1
2 0 1 2
2 0 1 3
2 0 1 4
2 0 1 5
C h i n a
G r o s s
C a p
i t a l F o r m a t
i o n
( $ B )
China Gross Capital Formation ($B)
$21T+
China Gross Capital Formation, 1980 – 2015(In 2010 Dollars)
$20T+
Sh h i A O P 2 D d
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Shanghai Area Over Past 2+ Decades =Illustrates Magnitude of China (& Emerging Asia) Growth
Source: Reuters/Stringer, Carlos Barria, Yichen Guo.
Shanghai, China,
Pudong District
1987
2016
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Re-Imagination of ChinaOver Past 3+ Decades –
World’s Population Leader +#3 in Land Mass –
Helped Drive IncrementalGlobal Growth of Likes Which is
Difficult to Repeat
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Interest Rates Have Fallen toHistorically Low Levels =
Interest Rate Trends =Can be Indicative of
Perception for Growth Outlook
USA 10 Y T Yi ld
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USA 10-Year Treasury Yield =Low by Historical Standards
(5%)
0%
5%
10%
15%
20%
1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012
1 0 - Y e a r
Y i e l
d ( % )
Nominal Yield (%) Real Yield (%)
USA 10-Year Treasury Yields, Nominal and Real, 1962 – 2016YTD
Source: Morgan Stanley, Bloomberg, 5/16Note: Real rates based on USGGT10Y Index on Bloomberg, which measures yield to maturity (pre-tax) on Generic 10-Year USA government inflation-index bonds.
Gl b l 10 Y T Yi ld
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Global 10-Year Treasury Yields =Have Trended Down
Source: Morgan Stanley, Bloomberg, 5/16.Note: Real rates based on yield to maturity on 10-year inflation-indexed treasury security for each country.
10-Year Real Sovereign Bond Yields (%), Various Countries, 2001 – 2016YTD
(2%)
0%
2%
4%
6%
8%
2001 2003 2005 2007 2009 2011 2013 2015
1 0 - Y e a r
R e a
l S o v e r e i g n
B o n d
Y i e l
d s ( % )
USA Canada UK Japan France Germany Italy
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Total Global Debt Loads
Over 2 Decades =High & Rising Faster Than GDP
Gl b l G t D bt @ 66% A D bt / GDP (2015) & U
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Global Government Debt @ 66% Average Debt / GDP (2015) & Up...+9% Annually Over 8 Years vs. +2% GDP Growth* for 50 Major Countries
Source: McKinsey Global Institute (3/16), IMF.*GDP growth rate based on constant prices and calculated as average of average growth rates across 50 countries from 2000-2007 and 2008-2015.
250 274 299Total debt as
% of GDP
Compound annualgrowth rate (%)
8.5
5.7
5.9
9.6
2000–2007 2007–Q2:15
3.0
6.4
8.7
3.7
+70T
$208
Financial
Government
Corporate
Household
Q2:15
$37
$138
$21
$37$59
$20
$33$59
Q4:00
$19$84
Q4:07
$32$25
$49
$41
Global Debt By Type ($T, Constant 2014 FX), Q4:00 – Q2:15
4.1 2.2GDPGrowth*:
Tot l Debt to GDP R tios High & Up in Most M jor Co ntries
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Total Debt-to-GDP Ratios = High & Up in Most Major Countries...@ 202% Average vs. 147% (2000)*
Source: McKinsey Global Institute (3/16). Debt includes that owed by households, non-financial corporates, and governments (i.e. excludes fi nancial sector debt).*Country inclusion per McKinsey; includes top developed countries by GDP and representative geographic selection of emerging countries.
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420
130
140
60
120
70
-10
90
80
10
30
0
-20
40
20
50
India
HungaryPhilippines
Peru
Nigeria
Egypt
Colombia
Chile
Singapore
United States
SlovakiaItaly
CanadaNetherlands
United KingdomKorea
France
Japan
Ireland
Hong Kong
Czech RepublicDenmark
Portugal
Norway
Switzerland
Germany
Finland
Greece
Spain
Belgium
Austria
Australia
China
Morocco
RussiaSweden
South Africa
ThailandBrazil
Saudi ArabiaVietnam
Turkey
Mexico
Argentina
Indonesia
Poland
Malaysia
Romania
Increasing leverage
Deleveraging
Leveraging
Deleveraging
Developed Emerging
Change in Real Economy Debt / GDP (%), 2007 – Q2:15
C h a n g e
i n R e a
l E c o n o m y
D e b t / G D P ( % )
Q2:15 Real Econ omy Debt / GDP (%)
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KPCB INTERNET TRENDS 2016 | PAGE 33
Demographic Trends =Slowing Population Growth...
Slowing Birthrates +Rising Lifespans
World Population Growth Rate Slowing =
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World Population Growth Rate Slowing =+1.2% vs. +2.0% (1975)
Source: U.N. Population DivisionNote: Growth Rates based on CAGRs over 5 Year Periods.
Global Population and Y/Y % Growth, 1950 – 2050E
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
0
2
4
6
8
10
Y / Y G r o w
t h R a t e ( % )
G l o b a l P o p u
l a t i o n
( B )
Global Population (B) Y/Y Growth (%)
Global Birth Rates =
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Global Birth Rates =Down 39% Since 1960 (1% Annual Average Decline)
Source: World Bank W orld Development IndicatorsNote: Represents birth rates per 1,000 people per year.
0
10
20
30
40
50
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2014
B i r t h R a t e p e r
1 , 0 0 0 P e o p l e , p e r
Y e a r
World USA China
India Europe / Central Asia East Asia / Pacific
Midd le East / No rth Afr ica Su b-Sah aran Af ri ca
Birth Rates per 1,000 People per Year, By Region, 1960 – 2014
Global Life Expectancy @ 72 Years =
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Global Life Expectancy @ 72 Years =Up 36% Since 1960 (0.6% Annual Average Increase)
Source: World Bank W orld Development Indicators
30
40
50
60
70
80
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2014
L i f e E x p e c
t a n c y
( Y e a r s
)
World USA ChinaIndia Europe / Central Asia East Asia / PacificMidd le East / No rth Afr ica Su b-Sah aran Afr ica
Life Expectancy (Years, Both Genders), By Region, 1960 – 2014
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Net, Net,
Economic Growth Slowing +Margins for Error Declining =
Easy Growth Behind Us
5 Epic Growth Drivers Over Past 2 Decades =
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5 Epic Growth Drivers Over Past 2 Decades =Losing Mojo
Source: US Census, ITU, IMF, Stephen Roach, McKinsey, Bloomberg, US Bureau of Labor Statistics, UN Population Division
1) Connectivity Growth Slowing –
Internet Users rose to 3B from 35MM (1995) 2) Emerging Country Growth Slowing –
Underdeveloped regions developed – including China / Emerging Asia /Middle East which rose to 69% of global GDP growth from 43%...
3) Government Debt Rising (& High) –Spending rose to help support growth...Government debt-to-GDProse to 66% from 51% (2000) for 50 major economies
4) Interest Rates Have Declined –Helped fuel borrowing – USA 10-Year Nominal Treasury Yield fell to1.9% (2016) from 6.6% (1995)
5) Population Growth Rate Slowing & Population Aging –Higher birth rates helped drive labor force growth – population growth ratecontinued to fall – to 1.2% from 1.6% (1995)
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Adjusting to Slower Growth +Higher Debt + Aging Population
Creates Rising Risks...
Creates Opportunities for Businesses thatInnovate / Increase Efficiency /Lower Prices / Create Jobs –
Internet Can Be @ Core of This...
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ADVERTISING /COMMERCE + BRAND TRENDS
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KPCB INTERNET TRENDS 2016 | PAGE 42
Online Advertising =
Mobile + Majors + NewcomersContinue to Crank Away
USA Internet Advertising Growth = Accelerating +20% vs +16% Y/Y
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USA Internet Advertising Growth Accelerating, +20% vs. +16% Y/Y...Owing to Mobile (+66%) vs. Desktop (+5%)
Source: 2015 IAB / PWC Internet Advertising Report.
USA Internet Advertising, 2009 – 2015
$23$26
$32
$37
$43
$50
$60
0%
5%
10%
15%
20%
25%
30%
35%
$0
$10
$20
$30
$40
$50
$60
$70
2009 2010 2011 2012 2013 2014 2015
Y / Y G r o w
t h ( % )
U S A I n t e r n e t
A d v e r
t i s i n g
( $ B )
Desktop Advertising Mobile Advertising Y/Y Growth
Google + Facebook =
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$
$5
$10
$15
$20
$25
$30
$35
U S A A d v e r
t i s i n g
R e v e n u e
( $ B )
Google Facebook 76% (& Rising) Share of Internet Advertising Growth, USA
Source: IAB / PWC 2015 Advertising Report, Facebook, Morgan Stanley ResearchNote: Facebook revenue include Canada. Google USA ad revenue per Morgan Stanley estimates as company only discloses total ad revenue and total USA revenue. “Others” includes all other USAinternet (mobile + desktop) advertising revenue ex-Google / Facebook.
Adver ti sing Revenue and Growth Rates (%) of Google vs . Facebook vs. Other,USA, 2014 – 2015
2014 2015 2014 2015 2014 2015Google Facebook Others
+18% Y/Y
+59% Y/Y
Others+13% Y/Y
$0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
@ Margin...
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@ Margin...Advertisers Remain Over-Indexed to Legacy Media
Source: Advertising spend based on IAB data for full year 2015. Print includes newspaper and magazine. Internet includes desktop + laptop + other connected devices. ~$22B opportunity calculatedassuming Mobile ad spend share equal its respective time spent share. Time spent share data based on eMarketer 4/16. Arrows denote Y/Y shift i n percent share.Excludes out-of-home, video game, and cinema advertising.
% of Time Spent in Media vs. % of Advertising Spending, USA, 2015
4%
13%
36%
22%25%
16%
10%
39%
23%
12%
0%
10%
20%
30%
40%
50%
Print Radio TV Internet Mobile
%
o f T o t a l M e d
i a C o n s u m p t
i o n
T i m e
o r A d v e r
t i s i n g
S p e n
d i n g
Time Spent Ad Spend
TotalInternet Ad
= $60B
Of WhichMobile Ad
= $21B
~$22BOpportunity
in USA
Online Advertising Efficacy =
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KPCB INTERNET TRENDS 2016 | PAGE 46Source: CapitalIQ as of 5/31/16, Unruly Future Video Survey, July 2015. N = 3,200 internet users surveyed from the US, UK, Germany, Australia, Sweden, France, Indonesia and Japan.
Google Has Proven Effective Online Advertising Works...
Google = $75B Revenue (2015), +14% Y/Y / $510B Market Value (5/31/16)
...But Many Online (Video) Ads are Ineffective, per Unruly... 81% = Mute Video Ads62% = Annoyed with / Put Off by Brand Forcing Pre-Roll Viewing93% = Consider Using Ad Blocking Software
...But There are Ways Video Ads Can Work, per Unruly 1) Authentic2) Entertaining3) Evoke Emotion4) Personal / Relatable5) Useful6) Viewer Control7) Work with Sound Off8) Non-Interruptive Ad Format
Online Advertising Efficacy Still Has Long Way to Go
Adblocking @ ~220MM Desktop Users (+16% Y/Y)...~420MM+ Mobile (+94%)...
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KPCB INTERNET TRENDS 2016 | PAGE 47Source: PageFair, 5/16. Dotted line represents estimated data. These two data sets have not been de-duplicated. The number of desktop adblockers after 6/15 are estimates based on the observed trend indesktop adblocking and provided by PageFair. Note that mobile adblocking refers to web / browser-based adblocking and not in-app adblocking.Desktop adblocking estimates are for global monthly active users of desktop adblocking software between 4/09 – 6/15, as calculated in the PageFair & Adobe 2015 Adblocking Report. Mobile adblockingestimates are for global monthly active users of mobile browsers that block ads by default between 9/14 – 3/16, including the number of Digicel subscribers in the Caribbean (added 10/15), as calculated inthe PageFair & Priori Data 2016 Adblocking Report.
0
100
200
300
400
500
2009 2010 2011 2012 2013 2014 2015
G l o b a l A d b l o c k
i n g U s e r s
( M M )
Desktop Adblocking Software Users Mobile Adblocking Browser Users
Adblocking @ 220MM Desktop Users ( 16% Y/Y)... 420MM Mobile ( 94%)...Majority in China / India / Indonesia = Call-to-Arms to Create Better Ads, per PageFair
Global Adblocking Users on Web (Mobile + Desktop), 4/09 – 3/16
Video Ads that Work =
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KPCB INTERNET TRENDS 2016 | PAGE 48Source: Snapchat
Authentic / Entertaining / In-Context / Often Brief
Snapchat’s 3V Advertising
Vertical (Made for Mobiles) / Video (Great Way to Tell Story) / Viewing (Always Full Screen)
+30% Lift in Subscription Intent,2x More Effective Than
Typical Mobile Channels
Spotify (10-Second Ad) in...Snapchat Live Stories + Discover
26MM+ Views, 12/15
+3x Attend ance Among Target Demo forSnapchatters vs. Non-Snapchatters
= Opening Weekend Box Office
Furious 7 (10-Second Ad) in...Ultra Music Festival Miami Live Story
14MM+ Views, 3/15
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Commerce + Brands =
Evolving Rapidly By / ForThis Generation
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Each Generation HasSlightly Different Core Values +
Expectations...
Shaped by Events thatOccur in Their Lifetimes
Consumer Preference / Value Evolution by Generation, USA...
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Silent Baby Boomers Gen X MillennialsBirth Years 1928 – 1945 1946 – 1964 1965 – 1980 1981 – 1996
Year Most of Generation18-33 Years Old 1963 1980 1998 2014
Summary • Grew up during GreatDepression
• Fought 2nd “war to end allwars”
• Went to college on G.I. Bill• Raised “nuclear” families in time
of great prosperity + Cold War
• Grew up during time of idealismwith TV + car for every suburbanhome
• Apollo, Civil Rights, Women’sLiberation
• Disillusionment set in withassassination of JFK, VietnamWar, Watergate + increase indivorce rates
• Grew up during time of changepolitically, socially + economically
• Experienced end of the Cold War,Reaganomics, shift frommanufacturing to serviceseconomy, + AIDS epidemic
• Rise of cable TV + PCs
• Grew up during digital era withinternet, mobile computing, socialmedia + streaming media oniPhones
• Experiencing time of risingglobalization, diversity in race +lifestyle, 9/11, war on terror, massmurder in schools + the GreatRecession
Core Values • Discipline• Dedication• Family focus• Patriotism
• Anyt hing is poss ib le• Equal opportunity• Question authority• Personal gratification
• Independent• Pragmatic• Entrepreneurial• Self reliance
• Globally minded• Optimistic• Tolerant
Work / Life Balance • Work hard for job security • Climb corporate ladder• Family time not first on list
• Work / life balance important• Don’t want to repeat Boomer
parents’ workaholic lifestyles
• Expanded view on work / lifebalance including time forcommunity service + self-development
Technology • Have assimilated in order tokeep in touch and stay informed
• Use technology as needed forwork + increasingly to stay intouch through social media suchas Facebook
• Technology assimilatedseamlessly into day-to-day life
• Technology is integral• Early adopters who move
technology forward
Financial Approach • Save, save, save • Buy now, pay later • Cautious, conservative • Earn to spend
y ,Millennials = More Global / Optimistic / Tolerant..., per Acosta
Source: Acosta Inc., Pew ResearchImage: Doomsteaddiner.net, Billboard.com, Metro.co.ukNote: Data from Acosta as of 7/13. Pew Research Center tabulations of the March Current Population Surveys (1963, 1980, 1998, and 2014). Pew Research defines each generation and may differ fromother sources as there are varying opinions on what years each generation begin and end.
Characteristic Evolution by Generation @ Peak Adult Years (18-33), USA...
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y @ ( ),Millennials = More Urban / Diverse / Single...
Silent Baby Boomers Gen X MillennialsBirth Years 1928 – 1945 1946 – 1964 1965 – 1980 1981 – 1996
Year Most of Generation18-33 Years Old 1963 1980 1998 2014
Location
When Ages 18-33Metropolitan as % Total
64% 68% 83% 86%
DiversityWhen Ages 18-33White as % Total
84% 77% 66% 57%
Marital StatusWhen Ages 18-33Married as % Total
64% 49% 38% 28%
Education by GenderWhen Ages 18-33% with Bachelor’s Degree
12% Male / 7% Female 17% Male / 14% Female 18% Male / 20% Female 21% Male / 27% Female
Employment Status byGenderWhen Ages 18-33 Employed as % Total*
78% Male / 38% Female 78% Male / 60% Female 78% Male / 69% Female 68% Male / 63% Female
Median HouseholdIncome **When Ages 18-33
N/A $61,115 $64,469 $62,066
Population of GenerationWhen Ages 18-33 35MM 61MM 60MM 68MM
Source: Pew ResearchImage: Doomsteaddiner.net, Billboard.com, Metro.co.ukNote: *Only shows those that were civilian employed (i.e. excludes armed forces, unemployed civilians, and those not in l abor force). **Median household income shown in 2015 dollars. Pew ResearchCenter tabulations of the March Current Population Surveys (1963, 1980, 1998, and 2014). Pew Research defines each generation and may differ from other sources as there are varying opinions on whatyears each generation begin and end.
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Marketing ChannelsEvolve With Time...
Shaped by Evolution ofTechnology + Media
Each New Marketing Channel = Grew Faster...
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gInternet > TV > Radio
Source: McCann Erickson (1926-1979); Morgan Stanley Research, Magna, RAB, OAAA, IAB, NAA, PIB (1980-2015)Note: Data adjusted for inflation and shown in 2015 U.S. dollars. Television consists of cable and broadcast television advertising. Radio consists of network, national spot, local spot, and streaming audioadvertising. Internet consists of mobile and desktop advertising.
$0
$10
$20
$30
$40
$50
$60
$70
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
A d v e r
t i s i n g
E x p e n d
i t u r e s
( $ B )
Years
Internet
Television
Radio
Adver tising Expenditure Ramp by Channel , First 20 Years , USA, 1926 – 2015(In 2015 Dollars)
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Retailing ChannelsEvolve With Time...
Shaped by Evolution of
Technology + Distribution
Evolution of Commerce Over Past ~2 Centuries, USA =
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Stores More Stores Malls E-Commerce
Source: McKinseyImage: Wi kipedia.org, Barnumlanding.com, Cbsd.org, Dwell.com, Rediff.com, Freep.com, Corporate.walmart.com, Zdnet.comNote: Millennials defined as those born between 1980 and 2000. In 2015, they are ages 15-35. Gen X defined as those born between 1965 and 1979. In 2015, they are ages 36-50. Boomers defined asthose born between 1946-1964. In 2015, they are ages 51-70. Silents defined as those born between 1925 – 1945. In 2015, they are ages 71 – 90. Note there are varying opinions on what years eachgeneration begin and end.
Department StoresMid-1800s
Shopping Malls1950s
Corner / General Stores1800s
Supermarkets1930s
Discount Chains1950-60s
Wholesale Clubs1970-80s
Superstores1960-80s
E-Commerce1990s
Illustrative Generational Overlap
Silent Generation
Baby Boomers
Generation X
Millennials
New / Emerging Retailers Optimize for Generational Change =
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J.C. Penney Meijer Walmart Costco Amazon Casper
1900s 1910s 1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s 2010s
`
Retail Companies Founded by Decade (Illus trative Example), USA, 1900 – 2015Generational Overlap
Silent Generation
Baby Boomers
Generation X
Millennials
GI Generation
Generation Z
Source: KPCB, Retailindustry.about.com (1900s – 1980s), Ranker (1990s), Internet Retailer “2016 Top 500 Guide” (2000s – 2010s)Note: Companies shown above in chronological order by founding year by decade. Companies from 2000s onwards selected as diverse set of fast-growing companies based on web sales data from theInternet Retailer “2016 Top 500 Guide.” Gen Z defined as those born after 2000. In 2015, they are ages 0-15. Millennials defined as those born between 1980 and 2000. In 2015, they are ages 15-35. Gen Xdefined as those born between 1965 and 1979. In 2015, they are ages 36-50. Boomers defined as those born between 1946-1964. In 2015, they are ages 51-70. Silents defined as those born between 1925
– 1945. In 2015, they are ages 71 – 90. GI Generation defined as those born between 1900 – 1924. In 2015, they are age 91 – 115. Note there are varying opinions on what years each generation beginand end.
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Millennials =Impacting + Evolving Retail...
Millennials @ 27% of Population = Largest Generation, USA...
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Spending Power Should Rise Significantly in Next 10-20 Years
Source: U.S. Census Bureau “2010-2014 American Community Survey 5-Year Estimates”, Bureau of Labor Statistics “Consumer Expenditure Survey 2014”Note: Millennials defined as persons born between 1980 – 2000. There are varying opinions on what years each generation begin and end.
Population by Age Range, USA, 2014
0
10
20
30
40
50
60
70
< 1 5
1 5 t o 2 4
2 5 t o 3 4
3 5 t o 4 4
4 5 t o 5 4
5 5 t o 6 4
6 5 t o 7 4 > 7
5
U S A P o p u l a t
i o n
( M M
)
$0
$10
$20
$30
$40
$50
$60
$70
< 2 5
2 5 t o 3 4
3 5 t o 4 4
4 5 t o 5 4
5 5 t o 6 4
6 5 t o 7 4 > 7
5
A n n u a
l E x p e n
d i t u r e
( $ K )
Household Expenditure, Annual Average, by
Age of Reference Person, USA, 2014
Millennials
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Retail =Technology + Media + Distribution
Increasingly Intertwined
Retail – The New Normal = Drive Transaction Volume Collect / Use Data L h N P d / P i L b l
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Launch New Products / Private Labels...
OutdoorFurniture
Strathwood2004
% Total Amazon PurchasersWhich Purchased Home &
Garden Products:11%
HomeGoods
Pinzon2008
% Total Amazon PurchasersWhich Purchased Household
Products:10%
Electronic Accessor ies
AmazonBasics 2009
% Total Amazon PurchasersWhich Purchased
Electronics (
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Retailers Become Products / Brands...Retailers Come Into Homes...Less differentiation between products / brands / retailers as single products evolve into brands + consumers shop directly from
brands + retailers leverage insights to develop own vertically-integrated brands...New distribution models emerging enablingdirect-to-consumer commerce in the home...
Brands Retailers
(Warby Parker)
Retailers Products / Brands
(Thrive Market)
New DTCDistribution Models
(Stitch Fix)
Products Brands(Casper)
Image: Myjane.ru, CNBC.com, Vanityfair.com, Insidebusinessnyc.com, Funandfit.org, Thrivemarket.com, Thedustyrosestyle.com, Stitchfix.tumblr.com
...Physical Retailers Become Digital Retailers...Di i l R il B D O i i d Ph i l R il
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Digital Retailers Become Data-Optimized Physical Retailers...
Offline Online(Neiman Marcus)
Online Offline(Warby Parker)
Physical Retailers Evolving & Increasing E-Commerce Presence...New Products / Brands / Retailers Launching Physical Stores /Showrooms / Retail Channels...Omni-Channel is Key...Warby Parker @ $3K Annual Sales per Square Foot = One of Top
Grossing Physical Retailers per Square Foot in USA
31 locations (5/16),up from 10 locations
(12/14)
$1,466
$1,560
$2,951
$3,000
$5,546
Michael Kors
Lululemon Athletica
Tiffany & Co.
Warby Parker
Apple
Top 5 Physical Retailersby Sales / Sq. Ft., USA, 2015*
26% of F2015 Sales on Intern et, +24% Y/Y
Source: Company filings, Fast Company, Time, eMarketerImage: Pursuitist.com, Digiday.com, Warbyparker.comNote: *Excludes gas stations. Based on sales figures from trailing 12 months. Warby Parker fi gures as of February 2015.
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Internet-EnabledRetailers / Products / BrandsOn Rise =
Bolstered by Always-On Connectivity +
Hyper-Targeted Marketing +Images + Personalization
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Stitch Fix User Experience = Micro Data-Driven Engagement & Satisfaction...Data Collection + Personalization / Curation + Feedback
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Data Collection + Personalization / Curation + Feedback...
Stitch Fix = Applying Netflix / Spotify-Like Content Discovery to Fashion... Each Customer = Differentiated Experience...99.99% of Fixes Shipped = Unique
Data-Driven Onboarding Process= Mix of Art + Science
Collect data points on customer preferences /style / activities. 46% of active clients provide
Pinterest profiles. Stylists use Pinterest boards +access to algorithms to help improve product
selection
Ship ‘Fixes’ wi th Curated ItemsBased on Preferences / Style
Allows clients to try products selected by stylistsin comfort of home / return items they don’t like
Customer Preferences &Feedback
Collect information on customer experience todrive future product selection
Source: Stitch FixImage: Forbes.com
...Stitch Fix Back-End Experience = 39% of Clients Purchase Majorityof Clothing from Stitch Fi s 30% of Clients Y/Y
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of Clothing from Stitch Fix vs. ~30% of Clients Y/Y
Stitch Fix = Data On Users + Data on Items + Constantly Improving Algorithms =Drive High Customer Satisfaction...100% of Purchases from Recommendations
Data Collection onItem-by-Item Basis Coupled with
User Insights
Stitch Fix captures 50-150 attributes on eachitem, uses algorithms + feedback to determine
probability of success (i.e. item will bepurchased) for specific demographics, allows
stylists to better select items for clients
Data Network ing Effect ...Helps Stylis t Predict Success of
Items with Specific Client
The more information collected, the better theprobability of success. Stitch Fix showing 1:1
correlation between probability of purchase peritem and observed purchase rate over time
0%
20%
40%
60%
80%
100%
0% 20% 40% 60% 80%100%
A c t u a
l P r o p o r t
i o n
P u r c h a s e d
Probability of Purchase
Strong Consumer Engagement / Ant ic ipati on...Inc reased Wallet
Share...
39% of Stitch Fix clients get majority ofclothing from Stitch Fix, up
from ~30% of clients a year ago
Example of Product SuccessProbability by Age
Example of Product SuccessProbability by Sizing
Source: Stitch FixImage: Cheapmamachick.com
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RE-IMAGINING COMMUNICATIONVIA SOCIAL PLATFORMS –
– VIDEO – IMAGE – MESSAGING
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Visual(Video + Image)
Usage Continues to Rise
Millennial Social Network Engagement Leaders = Visual...Facebook / Snapchat / Instagram
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KPCB INTERNET TRENDS 2016 | PAGE 73Source: ComScore Media Metrix Multi-Platform, 12/15.
Facebook / Snapchat / Instagram...
Age 18-34 Digi tal Audience Penetration vs.Engagement of Leading Social Networks, USA, 12/15
0
200
400
600
800
1,000
1,200
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
A v e r a g e
M o n
t h l y M i n u t e s
p e r
V i s i t o r
% Reach Among Age 18-34
Snapchat
Instagram
Twitter
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Video Viewing Evolution
Over Past Century =
Live On-Demand Semi-Live Real-Live
Video Evolution = AcceleratingLive (Linear) On Demand Semi Live Real Live
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Live (Linear) On-Demand Semi-Live Real-Live
Images: Facebook, Twitter, Snapchat, Netflix, TiVopedia, BT.com1926 - First television introduced by John Baird to members of the Royal Institution. 1999 - First DVR released by Tivo. 2013 – Snapchat Stories launched.
Live (Linear)
Traditional TV1926
Tune-In orMiss Out
Mass Concurrent
Audience
Real-Time Buzz
On-Demand
DVR / Streaming1999
Watch onOwn Terms
Mass Disparate
Audience
Anytime Buzz
Semi-Live
Snapchat Stories2013
Tune-In Within 24Hours or Miss Out
Mostly Personal
Audience
Anytime Buzz
Real-Live
Perisco pe + Facebook Live2015 / 2016
Tune-In / Watchon Own Terms
Mass Audience,
yet Personal
Real Time + Anytime Buzz
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VideoUsage / Sophistication / Relevance
Continues to Grow Rapidly
User-Shared Video Views on Snapchat & Facebook =Growing Fast
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KPCB INTERNET TRENDS 2016 | PAGE 78Source: Facebook, Snapchat. Q2:15 Facebook video views data based on KPCB estimate.Facebook video views represent any v ideo shown onscreen for >3 seconds (including autoplay). Snapchat video views counted instantaneously on load.
0
2
4
6
8
10
Q4:14 Q1:15 Q2:15 Q3:15 Q4:15 Q1:16
V i d e o
V i e w s p e r
D a y
( B )
0
2
4
6
8
10
Q3:14 Q4:14 Q1:15 Q2:15* Q3:15
V i d e o
V i e w s p e r
D a y
( B )
Facebook Daily Video Views,
Global, Q3:14 – Q3:15
Snapchat Daily Video Views,
Global, Q4:14 – Q1:16
Growing Fast
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Smartphone Usage Increasingly =Camera + Storytelling + Creativity +
Messaging / Sharing
Snapchat Trifecta = Communications + Video + Platform...Stories (Personal) Live (Personal + Pro Curation) Discover (Pro)
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KPCB INTERNET TRENDS 2016 | PAGE 80Source: Snapchat
Stories (Personal)10/13 Launch
Live (Personal + Pro Curation) 6/14
Discover (Professional)1/15
10–20MM Snapchatters ViewLive Stor ies Each Day
More Users Watched CollegeFootball and MTV Music Awards onSnapchat than Watched the Events
on TV
70MM+ Snapchatters ViewDiscover Each Month
Top Performing Channels Average6 – 7 minutes per Snapchatter per
Day
Stories (Personal) Live (Personal + Pro Curation) Discover (Pro)
Alex
Alexan der
Dino
Cindy
Anjn ey
Ariel le
Aviv
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Advertisers / Brands =Finding Ways Into...
Camera-BasedStorytelling + Creativity +
Messaging / Sharing
Brand Filters Integrated into Snapchat Snaps by Users...Often Geo-Fenced, in Venue
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KPCB INTERNET TRENDS 2016 | PAGE 82Source: Snapchat
+23% Visitation Lift Within 7 Daysof Seeing Friend’s Geofilter
+90% Higher Likelihood of Donating to (RED) Among Those Who Saw Geof il ter
Often Geo Fenced, in Venue
‘Love at Firs t Bite’by KFC
9MM+ Views Geofilter offered @ 900+ KFCsin UK and applied 200K+ times,
12/15 – 2/16
‘World AIDS Day – Join the Fight’by (RED)
76MM+ ViewsEach time a geofilter was sent, Bill & Melinda GatesFoundation donated $3 to (RED)’s fight against AIDS
12/15
Branded Snapchat Lenses & Facebook Filters...Increasingly Applied by Users
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KPCB INTERNET TRENDS 2016 | PAGE 83Source: Snapchat, FacebookTime on sponsored lens excludes time taking and uploading image / video.
Increasingly Applied by Users
Average Snapchatter Plays With Sponsored Lens for20 Seconds
Taco Bell Cinco de Mayo Lens
224MM Views on Snapchat5/5/16
Gatorade Super Bowl Lens
165MM Views on Snapchat2/7/16
Iron Man Filter from MSQRD
8MM+ Views on Facebook3/9/16
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Real-Live =Facebook Live...
New Paradigm forLive Broadcasting
UGC (User Generated Content) @ New Orders of Viewing Magnitude...Facebook Live = Raw / Authentic / Accessible for Creators & Consumers
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KPCB INTERNET TRENDS 2016 | PAGE 85Source: Facebook
Candace Payne in Chewbacca Mask on Facebook Live
Most Viewed Live Video @ 153MM+ Views, 5/16Kohl’s = Mentioned 2 Times in Video
Kohl’s = Became Leading App in USA iOS App StoreChewbacca Mask Demand Rose Dramatically
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2016E = Milestone Year for ‘Traditional’ Live Streaming on Social Networks...NFL Live Broadcast TV of Thursday Night Football on Twitter (Fall 2016)
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KPCB INTERNET TRENDS 2016 | PAGE 88Source: KPCB Hypothetical Mock-Up.Design provided by Brian Tran (KPCB Edge)
Tune-In Notifications,Game Reminders,Breaking Actions
Scoreboard Allows Fans toFollow Play-by-Play
ProfessionalCommentary and
Analysis
Vertical View =Live Broadcast + Tweets
Dashboard for SocialMedia Engagement
Hypothetical Mock-UpComplete Sports Viewing Platform =Live Broadcast + Analysi s + Scores + Replays + Noti fications + Social Media Tools
Toggle Between Tweetsfrom Stadium / Nearby / All
Tweets Engage Fans inReal-Time Conversation
Horizontal View =Unencumbered, Full-
Screen, TV-like ViewingExperience
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Image Growth Remains Strong
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0
500
1,000
1,500
2,000
2,500
3,000
3,500
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
# o f
P h o t o s
S h a r e
d p e r
D a y
( M M )
SnapchatFacebook Messenger
InstagramWhatsAppFacebook
Daily Number of Photos Shared on Select Platforms, Global, 2005 – 2015
(2013 onward only)
(2015 only)
Facebook-ownedproperties
Source: Snapchat, Company disclosed information, KPCB estimatesNote: Snapchat data includes images and video. Snapchat stories are a compilation of images and video. WhatsApp data estimated based on average of photos shared disclosed in Q1:15 and Q1:16.Instagram data per Instagram press release. Messenger data per Facebook (~9.5B photos per month). Facebook shares ~2B photos per day across Facebook, Instagram, Messenger, and WhatsApp(2015).
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Image-Based Platforms Like Pinterest =Often Used for Finding Products / Shopping...
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KPCB INTERNET TRENDS 2016 | PAGE 92Source: Cowen & Company ”ShopTalk Conference Takeaways: A Glimpse Into The Future of Retail & eCommerce” (05/16)Note: Based on Cowen & Company proprietary Consumer Internet Survey from April / May 2016 of 2,500 US consumers, 30% of which where Pinterest MAUs as of 4/16.
% of Users on Each Platform Who Utilize toFind / Shop for Products, USA, 4/16
‘What Do You Use Pinterest For?’(% of Respondents), USA, 4/16
55%
12% 12%9%
5%3%
0%
10%
20%
30%
40%
50%
60%
Pi nt er es t Fac eb oo k In st ag ram Tw it ter L in ked In Sn ap ch at
10%
10%
15%
24%
55%
60%
Networking / promotion
News
Watching videos
Sharing photos / videos/personal messages
Finding / shopping for products
Viewing photo s
g pp g
...Image-Based Platforms Like OfferUp =High (& Rising) Engagement Levels & Used for Commerce...
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KPCB INTERNET TRENDS 2016 | PAGE 93Source: OfferUp, Cowen & Company “Twitter/Social User Survey 2.0: W hat’s changed?”
Note: Based on SurveyMonkey survey conducted in June 2015 on 2,000 US persons aged 18+
Average Daily Time Spent per User, USA, 11/14 & 6/15
42
21
13
17
21
17
41
25 25 2521 21
0
10
20
30
40
50
Facebook Instagram OfferUp Snapchat Pinterest Twitter
M i n u t e s p e r
D a y
11/14
6/15
g ( g) g g
...Image-Based Peer-to-Peer (P2P) Marketplace OfferUp =Ramping Faster than eBay @ Same Stage...
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KPCB INTERNET TRENDS 2016 | PAGE 94Source: OfferUp, company filings, and KPCB estimates.
Note: Shown on a calendar year basis and in nominal dollars. eBay was launched in 1995 and Off erUp in 2011.
$0
$4
$8
$12
$16
0 1 2 3 4 5 6 7 8
G M V ( $ B )
Year Since Inception
eBay
OfferUp
OfferUp vs. eBay GMV Growth, First 8 Years Since Inception
p g y @ g
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Messaging =
Evolving Rapidly
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Messaging Leaders =
Strong User (+ Use) Growth
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Messaging =
Evolving fromSimple Social Conversations to
More ExpressiveCommunication...
Messaging Platform Evolution =More Tools for Simple Self-Expression
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Global Electronic Messaging Platforms –Evolution of Simple Self-Expression
Source: Wired, Company Statements, Press Releases.
Japanese Cell Phones – Type-Based Emoji
1990s
AOL Instant Messenger –Convert Text Emoticon to
Graphical Smiley1997
NTT DoCoMo-Emoji1999
Apple iOS 5 –Native Emoji
2011
Line –Stickers
2011
Bitstrips – BitmojiPersonalized Emoji
2014
Facebook Messenger –GIF Keyboard
2015
Snapchat –Lenses
2015
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Asia-Based Messaging Leaders =Continue to Expand Uses / Services Beyond Social Messaging
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Source: Company websites, press releases, Morgan Stanley Research.
*Blue shading denotes that at least one of the platforms listed has added new features since 2015. Some features for other platforms may have been added in prior yearsNote: Enterprise denotes product made specifically for messaging or social networking within the enterprise, which is distinct from B2C messaging where businesses engage with current or potentialcustomers.
Name KakaoTalk WeChat LINE
Launch March 2010 January 2011 June 2011Primary Country Korea China Japan
Banking / Financial Services Kakao Bank (11/15) WeBank (1/15) Debit Card (2016)
Enterprise Enterprise WeChat (3/16)
Online-To-Offline (O2O) Kakao Hairshop (1H:16E)Kakao Driver (1H:16E) Grocery Delivery (2015)
TV Kakao TV (6/15) Line Live & Line TV (2015)
Video Calls / Chat (6/15)
Taxi Services Kakao Taxi
(3/15)
Messaging
Group Messaging
Voice Calls Free VoIP calls (2012) WeChat Phonebook(2014)
Payments KakaoPay (2014) (2013) Line Pay (2014)
Stickers (2012) Sticker shop(2013) (2011)
Games Game Center (2012) (2014) (2011)
Commerce Kakao Page (2013) Delivery supportw / Yixun (2013) Line Mall (2013)
Media Kakao Topic (2014)
QR Codes QR codeidentity (2012)
User Stories / Moments Kakao Story (2012) WeChat Moment s Line Home (2012)
Developer Platform KakaoDevelop ers WeChat API Line Partner (2012)
New Services Added 2015 -16*
Previous ExistingServices
Messaging Secret Sauce = Magic of the Thread = Conversational...Remembers Identity / Time / Specifics / Preferences / Context
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Source: “Digital Transformation for Telecom Operators,” by Deloitte, 2016. Wired.
The Commissioner for Complaints for Telecommunications Services (CCTS) reported a 65 per cent decrease in customer complaints between 8/15 and 1/16 compared to the previous six months
Rogers Communications Ask Questions / Update Account / Set Up New Plan
HyattCheck Availability / Reservations / Order Room Service
Started Offering Customer Service onFacebook Messenger in 12/15
65% Increase in Customer Satisfaction65% Decrease in Customer Complaints
Started Offering Customer Service onFacebook Messenger in 11/15
+20x Increase in Messages Receivedby Hyatt Within ~1 Month
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Average Global Mobile User = ~33 Apps...12 Apps Used Daily...80% of Time Spent in 3 Apps
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Source: SimilarWeb, 5/16.*Apps installed does not include pre-installed apps. Most commonly used apps includes preloads.
Day in Life of a Mobile User, 2016
Average # AppsInstalled on
Device*
Average Numberof Apps Used
Daily
Average Number of Apps Accounting fo r
80%+ of App Usage
Time Spent onPhone (per Day)
Most CommonlyUsed Apps
USA 37 12 3 5 Hours
Facebook
ChromeYouTube
Worldwide 33 12 3 4 HoursFacebookWhatsApp
Chrome
Messaging Apps = Increasingly Becoming Second Home Screen...
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Facebook MessengerInbox
iOSHome Screen
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RE-IMAGINING HUMAN / COMPUTERINTERFACES –
– VOICE – TRANSPORTATION
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Evolution of BasicHuman-Computer Interaction
Over ~2 Centuries =
Innovations Every DecadeOver Past 75 Years
Human-Computer Interaction (1830s – 2015), USA =Touch 1.0 Touch 2.0 Touch 3.0 Voice
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Source: University of Calgary, “History of Computer Interfaces” (Saul Greenberg)
Trackball1952
Mainframe Computers(IBM SSEC)
1948
Joystick1967
Microcomputers(IBM Mark-8)
1974
Commercial Useof Mouse
(Apple Lisa)1983
Commercial Use ofWindow-Based GUI
(Xerox Star)1981
Commercial Useof Mobile
Computing(PalmPilot)
1996
Touch + Camera -based Mobile
Computing(iPhone 2G)
2007
Punch Cards forInformatics
1832
QWERTYKeyboard
1872
ElectromechanicalComputer (Z3)
1941
Electronic Computer(ENIAC)
1943
Paper Tape Reader(Harvard Mark I)
1944
Portable Computer(IBM 5100)
1975
Voice on Mobile(Siri)2011
Voice on Connected /Ambient Devices(Amazon Echo)
2014
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Voice as
Computing Interface =
Why Now?
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Machine Speech Recognition @ Human Level Recognition for...Voice Search in Low Noise Environment, per Google
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Source: Johan Schalkwyk, Voice Technology and Research Lead, GoogleNote: For the English language.
Next Fron tier = Recognition in heavy background noise in far-field &across diverse speaker characteristics (accents, pitch...)
Words Recognized by Machine (per Google), 1970 – 2016
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
1970 1980 1990 2000 2010
W o r
d s R e c o g n i z e
d b y M
a c h i n e
2016
@ ~70%accuracy
@ ~90%accuracy
Voice Word Accuracy Rates Improving Rapidly...+90% Accuracy for Major Platforms
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Source: Baidu, Google, VentureBeat, SoundHoundNote: *Word Error Rate (W ER) definitions are specific to each company. Word accuracy rate = 1 - W ER. (1) Data shown is word accuracy rate on Mandarin speech recognition on one of Baidu's speechtasks. Real world mobile phone speech data is very noisy and hard for humans to transcribe. A 3.5% WER is better than what most native speakers can accomplish on this task. WER across different
datasets and languages are generally not comparable. (2) Data as of 5/15 and refers to recognition accuracy for English language. Word error rate is evaluated using real world search data which isextremely diverse and more error prone than typical human dialogue. (3) Data as of 1/16 and refers to recognition accuracy for English language. Word accuracy rate based on data collected fromthousands of speakers and real world queries with noise and accents.
Word Accuracy Rates by Platform*, 2012 – 2016
0%
10%
20%
30%
40%
50%60%
70%
80%
90%
100%
Baidu(2012 - 2016)
Google(2013 - 2015)
Hound Voice Search& Assistant App
(2012 - 2016)
W o r
d A c c u r a c y R a t e
( % )
*Word accuracy rate definitions are unique to each company...see footnotes for more details
1 2 3
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Computing Interface...
Evolving from Keyboards toMicrophones & Keyboards =
Still Early Innings
Mobile Voice Assistant Usage = Rising Quickly...Primarily Driven By Technology Improvements
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Source: Thrive Analytics, “Local Search Reports” 2013-2015Note: Results highlighted in these charts are from the 2013, 2014, and/or 2015 Local Search surveys. These surveys were conducted via an online panel with representative sample sizes for the nationalpopulation in the US. There were 1,102, 2,058, and 2,125 US smartphone owners that completed the surveys in 2013, 2014 and 2015 respectively.
% of Smartphone Owners Using Voice Assis tants Annually, USA, 2013 – 2015
30%
56%
65%
0%
20%
40%
60%
80%
2013 2014 2015
% o f
T o t a l R e s p o n d e n t s
2%
4%
9%
20%
30%
35%
1%
3%
9%
23%
32%
32%
Other (Please Specify)
Don't know why
More relevant services to meetneeds
Need to us e more because of lifestyle / schedule
More aware of products viaadvertising / friends / family /
other ways
Software / technology hasimproved
20152014
Voice Assistant Usage – Primary Reason forChange, % of Respondents, USA, 2014 – 2015
Google Voice Search Queries =Up >35x Since 2008 & >7x Since 2010, per Google Trends
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Source: Google TrendsNote: Assume command-based queries are voice searches given lack of relevance for keyword-based search. Aggregate growth values determined using growth in Google Trends for three queries l istedabove.
Google Trends imply queries associated with voice-related commands haverisen >35x since 2008 after launch of iPhone & Google Voice Search
008 2009 2010 2011 2012 2013 2014 2015 2016
Navigate Home
Call Mom
Call Dad
Google Trends, Worldwide, 2008 – 2016
Baidu Voice =Input Growth >4x...Output >26x, Since Q2:14
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Source: BaiduNote: (1) Data shown is growth of speech recognition at Baidu, as measured by the number of API calls to Baidu's speech recognition system across time, from multiple products. Most of these API callswere for Mandarin speech recognition. (2) Data shown is growth of TTS (text to speech) at Baidu, in terms of the total number of API calls to Baidu's TTS system across time, from multiple products. Most ofthese API calls were for Mandarin TTS.
Q2:14 Q3:14 Q4:14 Q1:15 Q2:15 Q3:15 Q4:15 Q1:16
A P I C a l
l s
Baidu Speech Recognition Daily Usage by API Calls,Global, 2014 – 2016 1
Baidu Text to Speech (TTS) Daily Usage by API Calls ,Glob al, 2014 – 2016 2
Q2:14 Q3:14 Q4:14 Q1:15 Q2:15 Q3:15 Q4:15 Q1:16
A P I C a l
l s
Usage across all Baidu products growing rapidly...typing Chinese on small cellphone keyboardeven more difficult than typing English...Text-to-Speech supplements speech recognition &
key component of man-machine communications using voice
Hound Voice Search & Assistant App =6-8 Queries Across 4 Categories per User per Day
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Source: SoundHoundNote: Based on most recent 30-days of user activity. Local information refers to queries about weather, restaurants, hotels, maps and navigation. Fun & entertainment refers to queries about music, movies,games, etc. General information refers to queries about facts, dictionary, sports, stocks, mortgages, nutrition, etc. Personal assistant refers to queries and commands about phone / communications, Uberand transportation, flight status, calendars, timers, alarms, etc.
Seeing 6-8 queries per active user per day among 100+ domains across 4 categories...Users most care about speed / accuracy / ability to follow up / ability to understand complex
queries...
Fun &Entertainment
21%
GeneralInformation
30%
Personal Assistant
27%
Local
Information22%
Voice Query Breakdown –Observed Data on Hound App, USA, 2016
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Voice asComputing Interface...
Hands & Vision-Free =Expands Concept of ‘Always On’
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Amazon Alexa Voice Platform Goal =Voice-Enable Devices = Mics for Home / Car / Mobiles...
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Alexa ‘Skills’ Kit Developers = ~950 Skills (5/16) vs . 14 Skills (9/15)
Alexa Voice Service – OEM / Developer Integrations (10+ integrations...)
Source: TechCrunch, Amazon Alexa, AFTVnewsImage: Geekwire.com, Heylexi.com
Note: Amazon launched the Alexa Skills Kit for third-party developers in 6/15.
Home(Various OEMs)
Car(Ford Sync)
On Go(Lexi app)
Ring Invoxia Philips Hue Ecobee
LumaToyMailScout Security
...Amazon Alexa Voice Platform Goal =Faster / Easier Shopping on Amazon
Leveraging proliferation of microphones throughout house to reduce friction for making purchases
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Leveraging proliferation of microphones throughout house to reduce friction for making purchases...3x faster to shop using microphone than to navigate menus in mobile apps *...
Amazon Echo
AmazonEcho Dot
AmazonEcho Tap
Amazon Prime(~44MM USA Subscribers)
Evolution of Shoppingwith Echo
1. Shopping Lists (2014)
2. Reorder past purchases by voice (2015)3. Order new items – assuming you are finewith Amazon selecting exact item (2015)
Source: Cowen & Company Internet Retail Tracker (3/16), Recode, MindMeldImage: Amazon.com, Gadgets-and-tech.com, Tomaltman.com, Techtimes.com, Venturebeat.com
Note: *Per MindMeld study comparing voice-enabled commerce to mobile commerce for the foll owing task, “show me men’s black Adidas shoes for under $75” – takes ~7 seconds using voice compared to~3x longer navigating menus in an app.
~5% of Amazon USA Customers Own an Echo vs. 2% Y/Y...~4MM Units Sold Since Launch (11/14), per CIRP
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Source: Consumer Intelligence Research Partners (CIRP)Note: Amazon Echo limited launch occurred in 11/14 and wide-release occurred in 6/15.
Amazon Customer Awareness of AmazonEcho, USA, Q1:15 – Q1:16
20%
30%
40%
47%
61%
0%
10%
20%
30%
40%
50%
60%
70%
Q1:15 Q2:15 Q3:15 Q4:15 Q1:16
%
o f C u s
t o m e r
B a s e
Amazon Customer Ownership of AmazonDevices, USA, Q1:16
51%
34%
22%
6% 5%
26%
0%
10%
20%
30%
40%
50%
60%
Pr ime Ki ndleFire
KindleReader
FireTV
Echo None
%
o f C u s
t o m e r
B a s e
~4MM Amazon Echo devices have been sold in USA as of 3/16, with ~1MM sold in Q1:16, per CIRP estimates
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Computing IndustryInflection Points =
Typically Only ObviousWith Hindsight
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Re-ImaginingTransportation =
Another New Paradigm inHuman-Computer Interaction...Cars
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...One Can...Lock / Monitor / Summon One’s Tesla from One’s Wrist
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Source: Tesla, The Verge, Redmond Pie
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Car Industry Evolution =
Computerization Accelerating
Car Computing Evolution Since Pre-1980s =Mechanical / Electrical Simple Processors Computers
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Source: KPCB Green Investing Team, Darren Liccardo (DJI); Reilly Brennan (Stanford); Tom Denton, “Automobile Electrical and Electronics Systems, 3 rd Edition,” Oxford, UK: Tom Denton, 2004; SamuelDaCosta, Popular Mechanics, Techmor, US EPA, Elec-Intro.com, Autoweb, General Motors, Garmin, Evaluation Engineering, Digi-Key Electronics, Renesas, Jason Aldag and Jhaan E lker / Washington
Post, James Brooks / Richard Bone, Shareable
Pre-1980s Anal og / Mechan ical
Used switches / wiring to routefeature controls to driver
1980s (to Present)CAN Bus
(Integrated Network)New regulatory standards drove
need to monitor emissions inreal time, hence central
computer
1990s-2010sFeature-Built Computing
+ Early ConnectivityAutomatic cruise control...
Infotainment...Telematics... GPS/ Mapping...
Today = Smart /Connected Cars
Embedded / tetheredconnectivity...
Big Tech = New Tier 1 autosupplier
(CarPlay / Android Auto)...
Tomorrow = ComputersGo Mobil e?...
Central hub / decentralizedsystems?LIDAR...
Vehicle-to-Vehicle (V2V) /Vehicle-to-Infrastructure (V2I) /
5G...Security software...
1990s (to Present)OBD (On-BoardDiagnostics) II
Monitor / report engineperformance; Required in all
USA cars post-1996
Today = ComplexComputing
Up to 100 Electronic ControlUnits / car...
Multiple bus networksper car (CAN / LIN /FlexRay / MOST)...
Drive by Wire...
“The Box”(Brooks & Bone)
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Early Autonomous / ADAS Features Continue to Improve =Miles Driven Continue to Rise
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Source: Google, Tesla, Steve Jurvetson, EmTech Conference, The Verge
Tesla (Level 2 Autonomy)Google (Level 3 / 4 Autonomy)
Primary Approaches to Autonomous Vehicle Rollouts =All New or Assimilation...Traditional OEMs Taking Combined Approach
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Source: Google, Tesla, Morgan Stanley Research, Reilly Brennan (Stanford)
• Roll out / upgrade autonomous featuresin current automotive context
• Solves issue of integrating autonomy intoexisting asset base
• Real-time, in-field updates &improvements (Tesla over-the-airsoftware updates)...real-world learnings
• Semi-autonomous stages requirepotentially dangerous resumption ofdriver control
• OEM production cycles sometimes long,which could cause innovation to remainslow
• Key Example:
• Design & build vehicles from day one withgoal of full autonomy
• Craft architect