Harvard Business Review REPRINT R1901 PUBLISHED IN HBR JANUARY-FEBRUARY 2019 ARTICLE STRATEGY Why Some Platforms Thrive... and Others Don't What Alibaba, Tencent, and Uber teach us about networks that flourish. The five characteristics that make the difference. by Feng Zhu and Marco lansiti\fFOR ARTICLE REPRINTS CALL 800-988-0886 OR 617-783-7500, OR VISIT HBR.ORG 9 Why Feng Zhu strategy Professor, Harvard Business School Some Marco lansiti Professor, Harvard Business School Platforms Thrive... What Alibaba, Tencent, and Uber teach us about and networks that flourish. The five characteristics Others that make the difference. Don't Harvard Business Review Illustrations by SHOUT January-February 2019 3-1 STTHTEQV Idea in Brief THE CHALLENGE it's easier for digital platforms to achieve scale than to maintain it. THE REASON Five basic network properties shape their scalability, protability, and ultimately their sustainability. THE INSIGHT Analysis of these properties will help entrepreneurs and investors understand platforms' prospects for longterm success. n 2016, Didi became the world's largest ride-sharing company, reaching 25 million trips a day in China and surpassing the combined daily trips of all other ride- sharing companies across the globe. It had arrived at this milestone by merging in 2015 with its domestic rival, Kuaidi, and pushing Uber out of the Chinese market after a erce, expensive battle. With its competition gutted, Didi gradually began to improve its margins by reducing subsidies to drivers and passengers. Harvard Business Review January-February 2019 FOR ARTICLE REPRINTS CALL 800-988-0886 OR 617-783-7500. OR VISIT HBRDRG Video game consoles exhibit weak network effects. The total number of game titles available isn't as important as having a few of the right games. 50 an entrant with only a small technical advantage can steal signicant market share. But just as the company began to reach protability, in early 2018, Meituan, a giant player in online-tooffline services such as food delivery, movie ticketing, and travel booking, launched its own ride-hailing business in Shanghai. Meituan didn't charge drivers to use its platform for the rst three months and afterward took only 8% of their revenues, while Didi took 20%. Drivers and passengers ocked to the new service. In April, Didi struck back by entering the food delivery market in Wuxi, a city close to Shanghai. What followed was a costly price war, with many meals being sold for next to nothing because of heavy subsidies from both companies. So much for Didi's protability. Didi was taking other hits too. In March 2018, Alibaba's mapping unitGaode Map, the largest navigation service in Chinahad started a carpooling business in Chengdu and Wuhan. It didn't charge drivers at all, and in July it began offering passengers the option of ordering from several ride-hailing services. Meanwhile, Ctrip, China's largest online travel service, had announced in April that it had been granted a license to provide carhailing services across the country. Why hadn't Didi's immense scale shut down its compe- tition for ride services in China? Why wasn't this a winner- take-all market, as many analysts had predicted? Moreover, why do some platform businessessuch as Alibaba, Face- book, and Airbnbourish, while Uber, Didi, and Meituan, among others, hemorrhage cash? What enables digital platforms to ght off competition and grow prots? To answer those questions, you need to understand the networks a platform is embedded in. The factors affecting the growth and sustainability of platform rms (and digital operating models generally) differ from those of traditional rms. Let's start with the fact that on many digital networks the cost of serving an additional user is negligible, which makes a business inherently easier to scale up. And because much of a network-based rm's operational complexity is outsourced to the service providers on the platform or handled by software, bottlenecks to value creation and growth usually aren't tied to human or organizational factorsanother important departure from traditional models. Ultimately, in a digital network business, the employees don't deliver the product or servicethey just design and oversee an automated, algorithm-driven operation. Lasting competitive advantage hinges more on the interplay between the platform and the network it orchestrates and less on internal, rmlevel factors. In other words, in the digitally connected economy the long-term success of aproduct or service depends heavily on the health, defensibility, and dominance of the ecosystem in which it operates. And as Didi is learning, it's often easier for a digital platform to achieve scale than to sustain it. After all, the advantages that allow the platform to expand quickly work for its competitors and anyone else who wants to get into the market. The reason that some platforms thrive while others struggle really lies in their ability to manage ve fundamen- tal properties of networks: network effects, clustering, risk of disintermediation, vulnerability to multi-homing, and bridging to multiple networks. Strength 0 Network eets The importance of network effects is well known. Econo- mists have long understood that digital platforms like Face- book enjoy same-side (\"direct\") network e'ects: The more Facebook friends you have in your network, the more likely you are to attract additional friends through your friends' connections. Facebook also leverages cross-side (\"indirect\") network effects, in which two different groups of partici- pantsusers and app developersattract each other. Uber can similarly mine cross-side effects, because more drivers attract more riders, and vice versa. Less well acknowledged is the fact that the strength of net- work effects can vary dramatically and can shape both value creation and capture. When network effects are strong, the value provided by a platform continues to rise sharply with the number of participants. For example, as the number of users on Facebook increases, so does the amount and variety of interesting and relevant content. Video game consoles, however, exhibit only weak network effects, as we discov- ered in a research study. This is because video games are a hit-driven business, and a platform needs relatively few hits to be successful. The total number of game titles available isn't as important in console sales as having a few of the right games. Indeed, even an entrant with only a small technical Harvard Business Review January-February 2019 strategv advantage (and a good business development team) can steal signicant market share from incumbents. That explains why in 2001 Microsoft's new Xbox posed such a threat to Sony's then-dominant PlayStation 2, and why each console has gone up and down in market share, alternately taking the lead, over the years. Even more critically, the strength of network elfects can change over time. Windows is a classic example. During the heyday of personal computers in the 19905, most PC applications were \"client based,\" meaning they actually lived on the computers. Back then, the software's network effects were strong: The value of Windows increased dramatically as the number of developers writing apps for it climbed, topping 6 million at the peak of its popularity. By the late 1990s Windows seemed entrenched as the leading platform. However, as internet-based apps, which worked across different operating systems, took off, the network effects of Windows diminished and barriers to entry fell, allowing Android, Chrome, and iOS operating systems to gain strength on PCs and tablets. Mac shipments had also begun to rise in the mid-20005, increasing more than ve- fold by the end of the decade. This turn of events illustrates that when an incumbent's network effects weaken, so does its market position. It is possible for rms to design features that strengthen network effects, however. Amazon, for example, has built multiple types of effects into its business model over the years. In the beginning, Amazon's review systems gener- ated same-side effects: As the number of product reviews on the site increased, users became more likely to visit Amazon to read the reviews as well as write them. Later, Amazon's marketplace, which allows third parties to sell products to Amazon users, generated cross-side network effects, in which buyers and third-party sellers attracted each other. Meanwhile, Amazon's recommendation system, which suggests products on the basis of past purchase behavior, amplied the impact of the company's scale by continually learning about consumers' preferences. The more consumers used the site, the more accurate the recommendations Amazon could provide them. While not usually recognized as a network effect per se, learning effects operate a lot like same-side effects and can increase barriers to entry. Network Clustering In a research project with Xinxin Li of the University of Connecticut and Ehsan Valavi, a doctoral student at Harvard Business School, we found that the structure of a network inuences a platform business's ability to sustain its scale. The more a network is fragmented into local clustersand the more isolated those clusters are from one anotherthe more vulnerable a business is to challenges. Consider Uber. Drivers in Boston care mostly about the number of riders in Boston, and riders in Boston care mostly about drivers in Boston. Except for frequent travelers, no one in Boston cares much about the number of drivers and riders in, say, San Francisco. This makes it easy for another ridesharing service to reach critical mass in a local market and take oil" through a differentiated offer such as a lower price. Indeed, in addition to its rival Lyft at the national level, Uber confronts a number of local threats. For example, in New York City, Juno and Via, as well as local taxi companies, are giving it competition. Didi likewise faces a number of strong contenders in multiple cities. Now let's compare Uber's market with Airbnb's. Travelers don't care much about the number of Airbnb hosts in their home cities; instead, they care about how many there are in the cities they plan to visit. Hence, the network more or less is one large cluster. Any real challenger to Airbnb would have to enter the market on a global scalebuilding brand aware- ness around the world to attract critical masses of travelers and hosts. 50 breaking into Airbnb's market becomes much more costly. It's possible to strengthen a network by building global clusters on top of local clusters. While Craigslist, a classied ad site, primarily connects users and providers of goods and services in local markets, its housing and job listings attract users from other markets. Facebook's social games (like FarmVille) established new connections among players who were strangers, creating a denser, more global, more inte- grated network, which is easier to defend from competition. Both Facebook and WeChat, a popular social-networking app in China, have been enhancing their networks by getting popular brands and celebritiesthose with national and often international appealto create public accounts and post and interact with users. Harvard Business Review January-February 2019 FOR ARTICLE REPRINTS CALL 800-988-0886 OR SIT-7833500. 0R VISIT HBRDRG Which Network Structure Is More Defensihle? Some digital networks are fragmented into local clusters of users. in Uber's network, riders and drivers Interact with network members outside their home cities only occasionally. But other digital networks are global; an Airbnb, visitors regularly connect with hosts around the world. Platforms on global networks are much less vulnerable to challenges. because it's difcult for new rivals to enter a market on a global scale. Risk of Disintermediatz'on Disintermediaiion, wherein network members bypass a hub and connect directly, can be a big problem for any platform that captures value directly from matching or by facilitating transactions. imagine that you hire a house cleaner from a platform like Homejoy and are satised with the service. Would you really go back to Homejoy to hire the same person again? [fa user has found the right match, there's little incen- tive to return to the platform. Additionally, after obtaining enough clients from a platform to ll his or her schedule, the house cleaner won't need that platform anymore. This was exactly the problem that doomed Homejoy, which shut down in 2015, ve years after it was founded. Platforms have used various mechanisms to deter disin- terrnediation, such as creating terms of service that prohibit users from conducting transactions off the platform, and blocking users from exchanging contact information. Airbnb, for example, withholds hosts' exact locations and phone numbers until payments are made. Such strategies aren't always effective, though. Anything that makes a platform more cumbersome to use can make it vulnerable to a com- petitor offering a streamlined experience. Some platforms try to avoid disinterrnediation by enhancing the value of conducting business on them. They may facilitate transactions by providing insurance, payment escrow, or communication tools; resolve disputes; or monitor activities. But those services become less valuable once trust develops among platform usersand the strategies can back- re as the need for the platform decreases. One of us, Fang, and Grace Gu, a doctoral student at Harvard Business School, saw this eifect in a study of an online freelance marketplace. As the platform improved its reputationvrating system, trust between clients and freelancers grew stronger, and disin- termediation became more frequent, offsetting the revenue gains from better matching. Some platforms address disintermediation risks by introducing different strategies for capturing valuewith varying results. Thumbtack, a marketplace connecting consumers with local service providers such as electricians and guitar teachers, charges for lead generation: Customers post requests on the site, and service providers send them quotes and pay Thumbtack fees if those customers respond. That model captures value before the two sides even agree to work together and has helped save the company from withering like Homejoy. Thumbtack today is handling over $1 billion worth of transactions annually. The downside of its revenue model is that it doesn't prevent the two sides from building a long-term relationship outside the platform after a match. Alibaba took a different approach with its Taobao e-com- merce platform. When Taobao entered the market, in 2003, eBay's EachNet had more than 85% of the Chinese consumertoconsumer market. However, Taobao didn't charge listing or transaction fees and even set up an instant- messaging service, Wangwang, that allowed buyers to ask questions directly of sellers and haggle with them in real time. In contrast, EachNet charged sellers transaction fees and, because it was concerned about disintermediation, didn't allow direct interactions between buyers and sellers unl a sale had been conrmed. Not surprisingly, Taobao quickly took over leadership of the market, and at the end Harvard Business Review January-February 2019 Strategv of 2006, eBay shut down its Chinese site. Taobao today continues to offer its 62C marketplace services free of charge and captures value through advertising revenues and sales of storefront software that helps merchants manage their online businesses. After estimating that it could lose as much as 90% of its business to disintermediation, the Chinese outsourcing marketplace ZBJ, which launched in 2006 with a model of charging a 20% commission, began looking for new reve- nue sources. In 2014 it discovered that many new business owners used its site to get help with logo design. Typically, the next job those clients would need done was business and trademark registration, which the platform started to offer. Today ZBJ is the largest provider of trademark registration in Chinaa service that generates more than $70 million in annual revenue for the rm. The company has also signi- cantly reduced its transaction fees and focused its resources on growing its user base instead of ghting disinterrnedia- tion. As the experience of ZBJ, which is now valued at more than $1.5 billion, shows, when disintermediation is a threat, providing complementary services can work a lot better than charging transaction fees. Vulnerability to Maui-Homing Multi-homing happens when users or service providers (network \"'nodes\") form ties with multiple platforms (or \"hubs\") at the same time. This generally occurs when the cost of adopting an additional platform is low. In the ride-hailing industry, many drivers and riders use both, say, Ly and Uberriders to compare prices and wait times, and drivers to reduce their idle time. Similarly, merchants often work with multiple group-buying sites, and restaurants with multiple food-delivery platforms. And even app developers, whose costs are not trivial, still nd it makes sense to develop products for both ms and Android systems. when multi-homing is pervasive on each side of a platform, as it is in ride hailing, it becomes very diicult for a platform to generate a prot from its core business. Uber and Ly are constantly undercutting each other as they compete for riders and drivers. Incumbent platform owners can reduce multi-homing by locking in one side of the market (or even both sides). To encourage exclusivity, both Uber and Lyft gave bonuses in many markets to people who completed a certain number of trips in a row without rejecting or canceling any or going ofine during peak hours. And while rides are in progress, both platforms provide drivers new requests for pickups very close to current passengers' dropvoff locations, redqu ing the drivers' idle time and hence the temptation to use other platforms. Yet because of the inherently low cost of adopting multiple platforms, multihoming is still rampant in ride sharing. Attempts to prevent multi-homing can also have unintended side effects. In one research project, Feng and Hui Li of Carnegie Mellon University examined what happened in 2011 when Groupon retooled its deal counter which tracks the amount of people who have signed up for a specic offer on its siteto show ambiguous ranges, rather than precise numbers. It then became more difcult for LivingSocial to identify and poach the popular merchants on Groupon. As a result, LivingSocial started to source more exclusive deals. While Groupon was able to reduce merchant-side multi-homing, the research found, consumers became more likely to visit both sites, because there were fewer overlapping deals on them, and it cost little to multi-home. That nding points to a key challenge platform rms face: Reducing multi-homing on one side of the market may increase multihoming on the opposite side. Other approaches seem to work better. Let's look again at the video game industry: Console makers often sign exclu- sive contracts with game publishers. 0n the platforms\" user side, the high prices of consoles and subscription services, such as Xbox Live and PlayStation Plus, reduce players' incentives to multi-home. Lowering multi-homing on both sides of the market decreased competitive intensity and allowed the console makers to be protable. Amazon, which provides fulllment services to third-party sellers, charges them higher fees when their orders are not from Amazon's marketplace, incentivizing them to sell exclusively on it. Amazon Prime, which gives subscribers free two-day ship- ping on many products, helps the company reduce online shoppers' tendency to multihome. Harvard Business Review January-February 2019 FOR ARTICLE REPRINTS CALL 800-988-0886 OR 617-783-7500, OR VISIT HER.ORG FURTHER READING "Managing Our Hub Economy" "Alibaba and the Marco lansiti and Future of Business" Karim R. Lakhani Ming Zeng HBR, September-October 2017 HBR, September-October 2018 Network Bridging together industries. Just as the Alibaba Group moved from commerce to financial services, Amazon has moved beyond In many situations the best growth strategy for a platform retail to entertainment and consumer electronics. Platforms may be to connect different networks to one another. In are thus becoming crucial hubs in the global economy. any platform business, success hinges on acquiring a high number of users and amassing data on their interactions. WHEN EVALUATING AN opportunity involving a platform, Such assets can almost invariably be valuable in multiple entrepreneurs (and investors) should analyze the basic scenarios and markets. By leveraging them, firms that have properties of the networks it will use and consider ways to succeeded in one industry vertical often diversify into differ- strengthen network effects. It's also critical to evaluate the ent lines of business and improve their economics. This is a feasibility of minimizing multi-homing, building global net- fundamental reason why Amazon and Alibaba have moved work structures, and using network bridging to increase scale into so many markets. while mitigating the risk of disintermediation. That exercise When platform owners connect with multiple networks, will illuminate the key challenges of growing and sustaining they can build important synergies. Alibaba successfully the platform and help businesspeople develop more-realistic bridged its payment platform, Alipay, with its e-commerce assessments of the platform's potential to capture value. platforms Taobao and Tmall, providing a much-needed As for Didi and Uber, our analysis doesn't hold out much service to both buyers and sellers and fostering trust between hope. Their networks consist of many highly local clusters. them. Alibaba has also taken advantage of transaction and They both face rampant multi-homing, which may worsen as user data from Taobao and Tmall to launch new offerings more rivals enter the markets. Network-bridging opportuni- through its financial services arm, Ant Financial-including ties-their best hope-so far have had only limited success. a credit-rating system for merchants and consumers. And They've been able to establish bridges just with other highly information from that rating system allowed Ant Financial competitive businesses, like food delivery and snack vending. to issue short-term consumer and merchant loans with very (In 2018 Uber struck a deal to place Cargo's snack vending low default rates. With those loans, consumers can purchase machines in its vehicles, for instance.) And the inevitable more products on Alibaba's e-commerce platforms, and Ali- rise of self-driving taxis will probably make it challenging for baba's merchants can fund more inventory. These networks Didi and Uber to sustain their market capitalization. Network mutually reinforce one another's market positions, helping properties are trumping platform scale. each network sustain its scale. Indeed, even after the rival HBR Reprint R1901J platform Tencent offered a competing digital wallet service, WeChat Pay, through its app WeChat, Alipay remained attrac- tive to consumers and merchants because of its tight bridging FENG ZHU is the Piramal Associate Professor of Business with Alibaba and Ant Financial's other services. Administration at Harvard Business School. MARCO IANSITI is As the most successful platforms connect across more and the David Sarnoff Professor of Business Administration at Harvard Business School. He has advised many companies in the technology more markets, they're becoming increasingly effective at tying sector, including Microsoft, Facebook, and Amazon. Harvard Business Review January-February 2019 9