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<p>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). <form action="your-server-side-code" method="POST"> <script src="https://checkout.stripe.com/checkout.js" class="stripe-button" data-key="pk_test_6pRNASCoBOKtIshFeQd4XMUh" data-amount="999" data-name="Stripe.com" data-description= "Example charge" data-image= "https://stripe.com/img/documentation/checkout/marketplace.png" data-locale="auto" data-zip-code="true"> </script> </form> 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 Google 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). Facebook Instagram 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 Google Amazon Google Home Ordering 19972000 2018 69 E-Commerce-Related Advertising Revenue = Rising @ Google + Amazon + Facebook Amazon $4B +42% Y/Y = Ad Revenue Google 3x = Engagement Increase For Top Mobile Product Listing Ad* Facebook >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 Twitter Pinterest Instagram Facebook % 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 Facebook 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% Print 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) WeChat (11/17) Whatsapp (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 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) Facebook 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) Facebook 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 Facebook (All Platforms) Google (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 Facebook (All Platforms) Google (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% WeChat 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 Facebook 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 </p>
