Platform technologies and network goods: insights on product launch and management

Platform technologies and network goods: insights on product launch and management Hemant K. Bhargava

Information Technology and Management ISSN 1385-951X Volume 15 Number 3 Inf Technol Manag (2014) 15:199-209 DOI 10.1007/s10799-014-0188-y

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Author's personal copy Inf Technol Manag (2014) 15:199–209 DOI 10.1007/s10799-014-0188-y

Platform technologies and network goods: insights on product launch and management Hemant K. Bhargava

Published online: 11 June 2014 Ó Springer Science+Business Media New York 2014

Abstract Entrepreneurs of technology platforms and network goods face distinctive challenges in managing customer adoption, and in trading off growth and profitability. The firm has several levers of control including managing product design and the intensity of network effects, managing the timing of product announcement versus actual product release, selecting the target market for initial product launch, and whether to sell a single version or an expanded product line. Product line expansion is especially useful under network effects. A freemium approach can help the firm manage both growth (via the free product) and profitability (via the premium higherpriced version). However, expanding the product line carries substantial fixed costs (e.g., marketing cost, cost of additional plant, managing multiple sets of inventory, increased distribution cost). Firms are deterred from incurring these fixed costs when there is uncertainty about product success. Such uncertainty is particularly relevant for multi-sided networks—where the value from joining one network (e.g., users) depends on the size of the other side (e.g., developers)—because potential participants on each side may be uncertain about participation on the other side. Despite uncertainty, product line expansion can be attractive for both startups and established firms. Established firms face lower uncertainty about developer participation, and should expand when fixed costs of expansion are low (and do so early in the product’s life cycle). In contrast, startup firms face greater uncertainty in securing participation from third-party developers, and are

H. K. Bhargava (&) Graduate School of Management, University of California Davis, GH-3108, Davis, CA 95616, USA e-mail: hemantb@gmail.com

more likely to benefit from a ‘‘wait and see’’ or deferred expansion strategy.

Keywords Product launch Network effects Two-sided markets Versioning Product design Revenue model

1 Categories of network effects and network goods Technology goods are usually subject to network effects, which arise when a consumer’s benefit from a product depends not just on product characteristics but on the network of consumers who adopt or use that product [7].

Common examples are Skype, DropBox, eBay and Facebook. Network goods have become a big and extremely visible aspect of today’s economy. Network effects alter product economics in very fundamental ways. One example is product quality, i.e., the factors that determine a user’s value for the good. Product quality is normally shaped and controlled by the producer. For a network good, however, product quality is shaped by the actions of other users (the network effect). For instance, the value of YouTube or Twitter is heavily dependent on content provided by its users. A 3D TV is worthless unless content providers create highly-differentiated 3D content. This feature is a fundamental departure because for traditional goods the value to the user is primarily shaped by the product’s features which in turn are controlled by the producer. This disruption of a very fundamental product property requires firms to rethink standard principles in product management.

A network good can provide both intrinsic (or standalone) benefits–benefits which are accrued regardless of actions of other users on the network—and extrinsic (or network) benefits, those which depend on participation or usage actions of other users [3]. The standalone benefit is

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the value component which depends primarily on product characteristics (e.g., size, speed, looks, texture, feature set).

The network benefit is the component that depends on the network size ðNÞ, and characteristics of network participants (such as their location, preferences, income level etc.). For instance, An iPhone has standalone value out of the box based on its user interface, ability to make calls, inbuilt apps for email, calendar and so on. DropBox users enjoy remote storage and the ability to synchronize across different computing devices; similarly, Skype users get attractive prices on calls to landline or mobile phone numbers. These benefits do not require other users on the network, but both products also provide network benefits (DropBox users can share files, and Skype users can call other Skype users for free). The extent to which a product is a network good rather than a standard good can then be interpreted in terms of the relative contribution of network benefits towards the total benefit from using the product.

Network effects can be direct and same-sided (for instance, Skype has a single network of users, all of whom have a similar role in the network) or indirect and crosssided (as a in two-sided network). In the latter case, there are two separate participant networks, and a participant in one network cares deeply about the size of the cross network. For instance, in a buyer-seller exchange (such as a health care exchange of patients and doctors), buyers care about the number of sellers, and sellers care about the number of buyers. An iPhone, besides providing standalone value, also gives its users a same-side network benefit from applications such as FaceTime which work within a network of compatible users, and indirect or cross-side benefits due to a large library of third-party apps. Computers, gaming consoles, digital book readers, are other examples of two-sided network goods which have users on one side and developers on the other side. Moreover, as these examples illustrate, a two-sided network can have both direct same-side and indirect cross-side network effects.

The lines between these categories of network effects are often blurred. For instance, in a one-sided network good such as YouTube, one can distinguish between two roles—content viewers and content providers (even though in principle each user can play both roles). YouTube viewers obtain value not from other viewers but from content providers. Does this make it (or any other usergenerated content product) a two-sided network? In fact, many network goods transition over time from being a single-sided network to a two-sided network, often as a way to develop a monetization strategy. In its earliest years, eBay, as a consumer-to-consumer auction site, was a network of peer individuals who interchanged between being buyers and sellers; but today, there is a distinct class of sellers, separate from ordinary buyers. Similarly, LinkedIn has transitioned from being a one-sided network of

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professionals to a two-sided network of corporations and professionals. DropBox is often considered a one-sided network good. But users also value the ability of thirdparty applications (such as PDF readers on an iPad or Android tablet) to automatically obtain content from the user’s DropBox storage. This feature makes DropBox a two-sided network good, because users now place value on the quantity of third-party applications that provide such integration.

An important category of network goods are platforms.

Platforms have become a hotbed of technological and business innovation, and critical enablers for the creation of ecosystems of users/customers and developers/partners.

Such ecosystems comprise a two-sided or multi-sided market in which market adoption on one side influences, and depends on, the desirability of adoption on the other side; the platform owner’s role is to develop a supportive infrastructure and set the rules for participation [8]. For example, video gaming consoles serve (1) gamers, by giving them technology for playing complex video games, and (2) game developers, by giving them a platform for releasing such games and reaching potential buyers; hence, a console platform that attracts more game developers becomes more valuable to gamers, and conversely, game developers are attracted to console platforms that have many gamers. Similarly, operating system platforms connect computer users with application software developers.

More recently, smartphone platforms enable formation of ecosystems that connect phone users with a variety of computational software and service applications. Facebook and Salesforce exemplify transition from network products to platforms, accomplished by opening up their APIs, enabling third-party developers both to extend the product and to form relationships with users.

2 Strategic relevance of network effects Network effects influence several key strategic decisions that entrepreneurs face when managing and launching technology products. How should they manage product pricing across time and across different user networks?

Should they announce the product much before it is available on the market (thereby shaping user expectations about network size), or surprise the market by delaying announcement until launch? Should firms be agnostic, or highly selective, regarding who their initial adopters are, i.e., who should they sell to in early stages when product inventory is scarce? Should the firm launch multiple versions or just one, and how should it time the release of additional versions? This section provides examples to explain how network effects can shape product management and pricing strategies for many technology firms and startups.

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Network effects shape strategy both for competitive and monopolistic products. A firm’s network size determines the network benefit for an existing or potential user, hence becomes a fundamental asset linked to product quality.

However, this strategic relevance exists only so long as the networks of competing firms cannot be interconnected.

When such networks are interconnected—through a process of standardization or perhaps by ‘‘converter programs’’ (with or without the permission of the firms) then the ‘‘network’’ comprises all users of all firms, and each firm’s own network size ceases to be a differentiator.

However product adoption and diffusion is still shaped by network effects, just as it is for monopolistic network goods.

2.1 Growth versus profitability Network size is a critical metric for network goods, and even more so in the early stages of the product lifecycle. But because so much of a network good’s perceived value depends on network size (and a large size requires initial adoption despite the lack of a viable network), it becomes almost essential for the firm to give away the product and sacrifice margin in order to mobilize the network. This effect is exacerbated when the cost to the user is not just the price charged by the firm but also includes any other internal costs of software installation, learning, process changes, etc.

Adoption would require the product’s perceived value to exceed these costs. The problem is even more acute for twosided networks and platforms. While many platforms have successfully drawn solid participation by both users and developers, getting there is not straightforward. At the start, both sides are tiny, making the platform of little value to any participant. For example, potential buyers of electric cars today are deterred by the absence of a large network of repair stations or battery-swapping facilities; conversely, development of such facilities is hindered by the absence of a large market of users.

To illustrate the ‘‘growth versus profitability’’ dilemma, consider a popular class of technology startups: information applications (for mobile phones, tablets, and computers) which connect two or more groups of users over the Internet. One such product aims to deliver and manage home exercise programs (HEP) on mobile phones. In contrast to the traditional print-based programs, the use of mobile phones can deliver multimedia content tailored to the patient, and it can also track and transfer information to the clinician. Despite these advantages, adoption involves a bidirectional loop between patients (as users and direct beneficiary of the program) and clinicians (who prescribe the exercise programs, customize and configure the application for the patient, and track information about compliance and effectiveness). Because clinicians have very

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thin margins and are unlikely to pay for the service, the firm intends, at least initially, to generate revenue primarily from patients. However, while charging a high price to patients will generate the revenue that is desperately needed to fund new applications, it will also restrict adoption; that, in turn, makes it difficult to entice clinicians to participate. This is the essence of the growth versus profitability dilemma for such startup firms.

2.2 Managing product design for a network good As noted earlier, a consumer’s perceived value for a network good usually has two components, standalone benefit and network benefit. Katz and Shapiro [12] proposed a simple way to mathematically model the value of a onesided network good as a sum of these two components [12].

That is, for a user x, her value from a network good of quality q (which is a proxy for all the characteristics that pffiffiffiffi affect standalone benefit) is x q þ e N , where N is the size of the network. The parameter e models the intensity of network effects, or the extent to which a user values other network participants. This is an important product design parameter for the firm, and the firm must both try to increase network effects, but also choose the extent of network effects to provide to each side in a multi-sided network [2, 6]. For instance, in a social network such as Facebook, the firm’s choice of opt-in or opt-out defaults for information sharing would impact the intensity of network benefit. More generally, e would be affected by the nature of search tools to discover, identify or communicate with other users or agents in the network.

In the above formulation, different users (different x’s) derive different benefit from the base product, but have pffiffiffiffi identical network benefit e N . Various other forms have been used in the literature since then. The network benefit pffiffiffiffi 1 N (i.e., N 2 ) could be written more generally as N a , so that a represents the elasticity of network benefit (the percentage increase in network benefit as network size increases).

The total value can be described more generally as a function hðx; q; eÞ increasing in each of these arguments.

The specific functional form would depend on the modeler’s intent and the product category. For example, the pffiffiffiffi expression x q þ eðxÞ N Þ might be a better description when users are heterogeneous in the value they place on each additional network participant. Even in this formulation, the standalone benefit is separable (or additive) from the network benefit. But, if the network-related features interact with the base quality of the product, then the total benefit might need to be modeled using an equation such as pffiffiffiffi x q e N. While network effects are often the highlight and most visible aspect of a network good, a careful design and

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combination of standalone benefits is necessary for successful adoption and revenue growth for a network good.

This is because the network benefit is zero or very low in the earliest stages of a network good’s life. Hence if there is a separate standalone benefit—and especially if potential customers are heterogeneous in their standalone value— then the firm can kickstart product adoption by achieving early sales on the basis of a standalone benefit, attracting those customers who have highest value for standalone features. Over time, the firm attracts customers because of the product’s network benefits. In contrast, when the product offers few standalone benefits, heterogeneity in network benefits can enable the firm to manage price dynamics over time. In the early stage, the firm can attract customers who place a high value on network features (i.e., they have high eðxÞ, thus perceive a high network benefit despite the low network size). As time passes and the network size grows, the firm is able to maintain its price and attract customers with a lower eðxÞ parameter.

2.3 Managing pricing on two sides of a platform market Many firms that supply products to multiple groups of buyers attempt to extract positive fees from each group in order to maximize profit. For example, many conventions charge an entry fee to visitors and also charge a stall fee to firms that put up booths at the convention. Similarly, a university charges its students a fee, and it might also charge corporate clients for whom students work on a consulting project. However, when a product is fundamentally a network good, this strategy of charging both sides can be quite risky. Since each side’s participation depends on the number of participants on the other side, the two-sided fee runs the risk of market failure: the firm fails to get substantial adoption on either side, thereby making the product unattractive to the corresponding other side.

Several platform firms have chosen to kickstart network growth by providing heavy subsidies to one side, thereby drawing participation from that side and, in turn, making participation on the other side more attractive [8]. For example, electronic procurement exchanges provided incentives to big buyers because their participation would attract sellers. Similarly, Sony’s PS3 playstation was believed to be sold below cost in early years (until economies of scale drove down costs), so that a large installed base would attract game developers to provide the content needed to drive console sales. But, how much should one side be subsidized? Should the product be sold below cost?

Should be it given free? Or, to take it further, should users on one side be paid for adoption? For instance, Nokia (contemplating an aggressive move into high-end smartphones in 2012) was reported to pay app developers to

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build for its chosen platform (Windows Phone 7), because a vibrant app store is crucial for sales of these phones.1 And, if one side, should be subsidized, which one?

Eisenmann et al. [8] develop several insightful heuristics for managing pricing and adoption in a two-sided market.

One finding is that the subsidy should be given to the side that is more quality- and price-sensitive, i.e., the side that cares more about quality rather than quantity of participants on the other side. This rule suggests that gaming console makers should subsidize gamers, because most gamers can buy only a handful of games, and therefore look for a library of high-quality games (not necessary a huge library of games). Conversely, the price that is less quality-sensitive and more quantity-sensitive should be the paying side: thus, game developers have to pay a royalty, and this fee not only brings revenue but also filters out low-quality game developers that have little hope of recouping their investments through game sales. If the subsidy were given to game developers, then that would lead to far bigger collection of games, but games that were lower quality on the average.

This would not only reduce revenues from the game developer side, but also make the platform less attractive to game players. Besides, a higher fee on game consoles would itself contribute to a smaller population of game players, and the combined effect would potentially create a vicious spiral leading to destruction of the two-sided market.

2.4 Managing product launch and price dynamics The initial launch of a technology good presents the firm with important choices regarding how to manage supply and demand, how to allocate initial supply, and how to achieve early adoption. First adopters see the least benefit from adopting the good, but success is impossible without initial adopters. One way to convince early adoption is to employ a very low early price, a penetration pricing strategy in which a later higher price is justified due to the benefits of a robust network. In reality, penetration pricing may be implemented via coupons or targeted promotions (rather than a low list price), in order to forestall a negative reaction to an increase in list price.

Seeding strategies are an important aspect of early adoption and long-term success. Firms must focus early sales on influentials, customers whose choices have positive impact on imitators, leading to quicker diffusion [20].

Aral et al. [1] emphasize the important role of homophilic relationships in designing seeding strategies, and that net gains from incentivizing early adoption are positive only for a tiny fraction of the population (with social network data on 27 million users for a mobile phone service, they

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estimated this threshold as 0.2 %). Similarly, if network effects are highly local then initial sales should be concentrated amongst customers who have a higher propensity to connect with each other, rather than be fragmented across a large base of customers who have little reason to interconnect. Such a strategy is exemplified by the launch strategy of Facebook, which started at one University (Harvard), then spread to other Ivy League schools, then to the broader university community, before becoming a general social network. Firms can also employ social media features to accelerate product adoption even when the firm lacks sufficient knowledge about customers to develop an effective seeding strategy [6], or co-opt early adopters into being product ambassadors, for example by giving them referral incentives for attracting other users.

Another approach, commonly used for technology goods, is to pre-announce the product [16]; a pejorative term is ‘‘vaporware’’ because actual product release does not occur within 3 months for approximately half such preannouncements. Pre-announcements can catalyze initial adoption of the product by creating expectations of a sufficiently robust network size. They can deter competitors, preempt customers from buying an alternative and then incurring switching costs, enable production of complements (such as accessories for the iPad), and position the firm in a potential future standards war. But they also give competitors early warning and more time to respond.

Therefore, firms in dominant competitive positions derive greater advantage from this tactic, while early announcements can hurt firms in a weak competitive position by giving competitors more time to react and respond.

Whereas, on one hand, the firm wishes to create a substantial mass of demand in order to drive early adoption, it must also deal with supply constraints in the early stages of product launch. Yet, firms often set initial prices so low (relative to the market-clearing price) that there is a huge demand frenzy for the product. For instance, consumers have camped out overnight to purchase Apple’s iPhones. The Amazon Kindle sold out in 6 h after launch.

Such pricing is justified because of the role of network growth and future expectations about network size.

Another barrier to a skimming strategy is the Coase conjecture: that if consumers can predict this high-low pricing strategy or if firms cannot credibly communicate a future high price, then no one would adopt early, because everyone would rather wait for price to drop. Ramanan and Bhargava [17] show that firms can mitigate this pricecredibility problem by decentralizing the distribution structure, and allying with a single retailer to distribute the product. This vertical disintegration raises the expectations regarding future price, because the retailer now faces a higher cost; simultaneously the restriction to a single retailer reduces the specter of price drops caused by fierce

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competition between retailers. For instance, Apple initially launched the iPhone solely on the AT&T network; in contrast, Microsoft introduced the Surface tablet through multiple retailers, raising the hope that retailer competition would lead to a price reduction, thereby suppressing early adoption.

2.5 Product differentiation and the freemium model for platforms Are growth and profitability incompatible for platforms, and how should platform firms respond? Even for standard products, there is a tension between growth and profitability [13, 18]. The tension is more acute for platforms because network effects drive up the importance of a large installed base. Mired in this conflict between growth and profitability, many technology innovators initially produce only a single version of their product, often to avoid production complexity or to place their best foot forward to all their customers. For example, Apple’s iPod music player was launched in 2001 with essentially a single version available only to Mac users; it was incompatible with Windows computers which comprised over 95 % of the computing market. Similarly, Amazon’s Kindle book reader was launched as a single version and remained so for several years, despite being a relatively high-priced single purpose gadget. Facebook, Pinterest, Twitter, Google, and a host of other platforms were also launched in a single ‘‘one size fits all’’ frame.

The minimalist product line strategy pursued by many platforms amplifies their growth-profitability tension.

Because there is only a single product (or a small number, relative to market breadth) that must address two conflicting objectives (growth and profitability), it can excel at neither. Making it free can drive initial adoption, but fails to generate revenues necessary for continued innovation.

Many startups who adopted this approach have come and vanished. But, the alternative of pricing too high is also dangerous because it stunts growth and fails to ignite developer participation. For example, in the 1980s and 1990s, Apple offered relatively onerous and expensive terms to software developers, and only a small fraction of PC software was available for Apple computers. The market share of Apple computers, which were well overpriced relative to comparable PCs, declined to around 3 %.

The growth-profitability dilemma can also occur in other garbs. For instance, it could be presented as a choice between price-skimming (with high initial price) and penetration pricing (low initial price). Although the former is often used for non-network goods because it can generate higher revenue over time, it can be fatal for goods with strong network effects because the high initial price can lead to limited adoption. With the latter, not only does the

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firm forego short-term revenues, but the low price becomes an anchor against price increases in the future.

However, growth and profitability need not necessarily operate in sharp conflict. For goods with network effects, having an expanded product line with multiple versions can help resolve this conflict. Bhargava and Choudhary [3] demonstrated this in the case of products with cross-side network effects, while [11] discussed this strategy for products with same-side network effects. In both cases, the fundamental idea is to have a high-priced premium version, and a low-priced (or free) basic version. In this freemium strategy, the low-end free (or low-price) version is targeted at highly price-sensitive consumers, while a higher-priced premium product serves the more quality-sensitive segment of the market. The premium product includes some features that are valuable to the high-end users, and these features are omitted from the free version.

Two illustrations of a freemium strategy are Skype and Dropbox, both examples of one-sided networks of homogeneous users. In both cases, the majority of consumers use the free product. A free Skype permits internal voice and video between two users. A free Dropbox provides a basic level of cloud storage which can be used to share files between multiple devices (of the same user) or multiple users. But each firm’s revenue is primarily from premium users who are willing to pay for additional features (e.g., multi-user video calling, more storage). Free users provide no direct revenue, but contribute by making the network larger and raising valuations of the premium users. The mechanism also works for two-sided networks with a cross-network effect. A relatively ancient example is the AAA (American Automobile service) which offered both Basic and Premium levels of membership; the scale effect provided by the inexpensive Basic service enables AAA to provide higher quality of service and increase valuations for all customers. The freemium strategy has multiple benefits: it segments the market indirectly, expands sales via the free version, preserves a high margin for the premium product, and increases the firm’s profit. Finally, the free version can also act as a trial device and increase customers’ value and willingness to pay for the premium version, especially when they are a priori uncertain about this value [14].

3 Timing of launch for multiple versions A freemium strategy is a special case of a product line with multiple product versions. To state the product line design problem in the simplest terms, the firm can either sell just its highest-quality version H, or expand the product line with a lower-priced lower-quality variant L (e.g., fewer

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Fig. 1 Launch timing possibilities and product line design: no expansion, early expansion, delayed expansion. Further product improvements might occur in each case, but are beyond the scope of this analysis

features).2 As noted above, network effects make versioning more attractive. But this advantage from versioning does not imply that vendors of new platform technology should necessarily launch the technology with an expanded, rather than minimal, product line. This is because the advantages of an expanded product line are tempered by the additional complexity and costs, including operations costs (additional plant, managing multiple sets of inventory, increased complexity in distribution), marketing costs (data collection and price optimization, segment development and management, and advertising to multiple customer segments), and cannibalization costs due to increased competition within the product line [5, 21]. Were the product not to achieve sufficient success, the expanded costs from versioning would leave the firm with a higher loss. Hence the timing of product line expansion becomes a crucial question for platform developers and innovators.

Under a potential freemium strategy, the choices can be laid out as depicted in Fig. 1.

A critical factor relating to the role of network effects is the level of uncertainty in developer participation. Initially, the firm is uncertain about the full extent of developer participation. We call this the first-period, while the second-period is the duration where the extent of participation level A is observed by the firm and customers. Combining the question of number of versions and timing of launch, the firm’s strategic choices are (1) expand early (launch an expanded product line containing two versions fL; Hg in 2

This framing does not exclude the possibility that the firm can improve its quality over time (i.e., a low quality version at the start, followed by higher quality). However, this LH sequencing is driven by the challenges of product development, rather than a strategic choice of withholding or delaying certain quality levels that are instantly available.

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the first period itself), or (2) defer the expansion decision to the second period (launch only H at the start). In the second case, the idea is to observe the level of developer participation and then launch L only if developer participation is high. Thus, this case splits into (2-a) defer expansion to second period and expand only if developer participation is high, and (2-b) no expansion, offer only H in both periods.

How should the firm approach product line expansion under the presence of expansion costs and uncertainty about developer participation?

Bhargava et al. [4] analyzed this question via the framework of two-sided markets, where adoption on either side influences the attractiveness of the platform to the other side, and with sequential formation of the two markets [9].

Potential buyers or platform adopters arrive and exist in both periods, with the number of first-period customers being j times the number of second-period customers (which is normalized to 1). The first step (or first period) in such sequential formation is adoption of the platform device by end-users (because the device has sufficient standalone features to be of value even without the second side of the market). The second step (second period) is entry by thirdparty developers who provide additional applications to extend the utility of the platform device, and late adoption by customers who wait to observe the extent of developer participation as well as early adoption by first-period customers. This sort of formation is observed in two-sided technology platforms such as computers, gaming consoles, and personal productivity devices.

First-period customers (who can be considered early adopters or technology enthusiasts) make purchase decisions based primarily on the product’s standalone features, features which are valuable by themselves and do not depend on external third-party applications. These customers are risk-takers who adopt the platform without knowing whether it will attract developers. For example, the initial iPhone released in June 2007 was an all-Apple product, endowed with several standalone features such as voice-calling capabilities, in-built contact book, calendar, mail, and music capabilities. A software development kit (SDK), which enabled the creation of third-party applications, was released only in March 2008, and the App Store was launched in July 2008, over a year after launch of the iPhone. The level of first-period adoption D depends both the level of product quality q, price p and customers’ preferences towards quality. Higher quality may mean the inclusion of a greater number of useful features (e.g., inclusion of a camera on a phone) or a premium level of a feature (e.g., a 10 MP camera with zoom vs. a 2 MP camera). Customers have heterogeneous price/quality sensitivity v, with their marginal value for incremental quality varying from low to high. This behavior of firstperiod customers is captured via the net utility function,

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U1 ðv; qÞ ¼ ðv qÞ pðqÞ:

ð1Þ

Potential developers observe product adoption level D in the first period, which influences their decision to participate in the platform ecosystem (explained in more detail shortly). Later (second-period) customers have the benefit of having observed these signals about developer participation. Hence their purchase decisions depend on both the standalone features and third-party applications. Customers value the platform more if it has a greater number of application developer participants [8, 11, 12]. The iPhone illustrates this point well. While initial iPhone adopters purchased essentially a standalone good (as stated above), today, customer choice between the iPhone and competing smartphones (such as from HTC, Google, and Research In Motion) depends substantially on the size of the respective application library (i.e., the App Store in the case of iPhone). For instance, as of 2012, Nokia and Research In Motion (Blackberry) have made several aggressive attempts into the smartphone market but have been hobbled by the lack of a substantial third-party application library. Thus, the net utility of second-period customers is the sum of their valuation for standalone features and their benefit from the product’s developer or application network.

U2 ðv; qÞ ¼ ðv q þ kAÞ qðqÞ;

ð2Þ

where qðqÞ is the second-period price when the number of applications is A, and k represents a per-application value placed by users.

Developer participation depends on initial success of the platform, represented by first-period installed base D. Developers are more likely to sign on with the platform if it achieves high popularity in the first period, and/or if the firm offers great features, and thereby lowers the cost, for app development. This suggests writing the number of participating developers as A ¼ D/ where / is the cost of developing applications [19]. However, the extent of developer participation cannot fully be predicted or determined by early demand for the platform product. While that network effect is important, developer participation also has other, possibly idiosyncratic, factors, which make the participation level partially uncertain. Recent examples of uncertainty in developer participation at time of launch include 3D TV [15]. Developer uncertainty was also present when Apple introduced iPod (it was unclear that the music industry would release many songs for digital distribution through iTunes) and the iPhone (uncertainty that third-party application developers would accept Apple’s stringent requirements, especially given Apple’s lack of reputation as a phone provider). Uncertainty about developer participation is also believed to have impacted the

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outcome for Research In Motion’s Playbook and the Motorola Xoom tablets. Similarly, uncertainty about wide adoption by merchants has throttled the growth of new electronic payment systems that have evolved over the years. To capture this uncertainty, Bhargava et al. [4] write A as c D/ þ n, where the first component is proportional to the installed base of users (and inversely related to the cost of development for that platform) and the second component is a random offset.

3.1 Impact of product-line expansion cost The product-line expansion cost impacts the firm’s design choice in the first period when it is still uncertain about developer participation. Imagine that the firm entered the second period with just a single product H, having chosen not to incur the expansion cost in the first period. Now, because of network effects, it would derive greater profits from an expanded product line fL; Hg than with just H. Call this increment in profit, DPver 2 . But, to get this gain, it must also incur expenditures to expand the product line.Second-period expansion is therefore attractive only if DPver 2 exceeds the additional expansion cost g.

Obviously, the firm is more likely to choose an expanded product line if it achieved a more positive outcome in the first period. In fact, second-period expansion would be unambiguously desirable if the expansion cost g were low enough that DPver 2 [ g under both the optimistic and pessimistic scenarios regarding developer participation. Then, surely, it would have been better to expand in the firstperiod itself, because the early expansion would increase the first-period market, increasing the likelihood of high developer participation. Hence, if product line expansion is seen as inevitable in period 2 (i.e., launch L even if A is low), then it is optimal to version in period 1 itself.

With an expanded installed base from the expanded product line, the firm is able to leverage network effects and earn higher second-period profits than it would have had it sold just H in the first period. The indirect network effects make a first-period expansion more attractive, because the higher installed base which causes greater developer participation and creates greater value for consumers in the second period. This result contrasts the strategy for a traditional good where, under the analogous formulation, the firm would have no reason to launch L early (in the first period).

Apple’s launch of the iPhone serves as a useful illustration. At launch in June 2007, the iPhone was a high-end expensive product ($499–$599)3 compared with other handsets sold by producers who already had a high 3 See http://blogs.law.harvard.edu/dlarochelle/2010/01/14/iphoneprice-history/foriPhonepricehistory .

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Fig. 2 Sequence of iPhone and iPod launch, and level of application developer participation

reputation in the phone market. Within 3 months, Apple added an iPod Touch which was essentially an iPhone without the calling functionality, and thus implied low expansion cost. It was priced at $299, half that of the iPhone (and without a recurring monthly– cellular service fee). Why the iPod Touch mattered was that (with Apple’s huge existing footprint in the iPod market), it had the promise of massively increasing the overall installed base of devices that could run iPhone apps, which made the platform very attractive to potential application developers.

Industry estimates (around January 2010) were that the installed base of the iPhone OS platform was nearly doubled by addition of the iPod Touch, referred to as a ‘‘stealth device’’ for the platform.4 This installed base benefitted Apple immensely as it opened up the iPhone to third-party application developers, with the App Store quickly attracting hundreds of thousands of apps (see Fig. 2).

Apple’s own strategy in launching the iPod in 2001 contrasts its iPhone strategy, which further illuminates the role of developer participation uncertainty (relative to the product line expansion cost) in determining the optimal launch strategy. Once again entering a new market category, Apple launched iPod in October 2001 with a minimal product line, a Mac-only iPod in a single design (with 5GB and 10GB disks). This was at a time that Apple’s Mac computers commanded no more than 5 % of the computing market, implying that it sacrificed 95 % of the potential market for the iPod which, in turn, made the market less attractive to content providers. Why not launch both versions at the start? Because, in this case, expanding the product line to include a Windows-compatible iPod—that guaranteed the same outstanding experience to all users—was non-trivial and presented Apple with a high product line expansion cost. With high g, the firm’s expansion strategy becomes more conservative: it defers expansion in the first period, observes developer participation and then incurs expansion costs 4

See http://gigaom.com/Apple/ipod-touch-now-outselling-iphone/.

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Figure 4 visualizes the combined effect of expansion cost and uncertainty in developer participation. When there is little or no uncertainty about developer participation (n ¼ 0 or small), the firm’s optimal strategy is either to expand early if g is very low or not to expand at all if g is higher; the ‘‘wait and see’’ approach of deferring expansion has no value due to lack of uncertainty, and early expansion is always superior to deferred expansion. As uncertainty increases, the firm’s optimal policy shifts and adds a new element: defer and expand only if developer participation is high (relative expansion costs). The uncertainty effect becomes dominant in the expansion policy and leads to the use of a ‘‘wait and see’’ approach to product line expansion.

3.2 Startups versus established firms The growth versus profitability dilemma affects both startup firms and established firms that are entering new product categories (e.g., Apple with iPhone). However, the two may face quite different levels of uncertainty in application development in two-sided markets. For instance, OpenTable has created a market for connecting restaurants and diners; it provides technology to enable restaurant discovery, reservations, and other applications.

A big challenge for OpenTable was to obtain sufficient numbers of restaurants (correspondingly, diners) into the network, in order to convince diners (correspondingly, restaurants) to use the system. But this level of uncertainty is quite different from what Amazon faced (from book publishers) when it entered the ebook market with its introduction of the Kindle.

Defer, Expand

Random Offset ( )

only if A is high enough to guarantee high gains from versioning. This leads to a sequential or delayed expansion of the product line, if favorable market circumstances emerge. When Apple launched the iPod, ‘‘developer participation’’ (i.e., whether music labels would offer content on iTunes) was highly uncertain. Only after a couple of years, and after observing iTunes’ roaring success, did Apple branch into an expanded product line with a Windows version of the iPod and with additional iPod form factors such as the iPod Mini and iPod Shuffle. These moves, which involved substantial fixed costs of product line expansion, enormously increased the iPod installed base but were deferred until Apple had observed high developer participation and the consequent assurances of a successful product category.

Finally, if g is very high (or n or k are low), then versioning is just not attractive even under high levels of developer participation. For instance, Amazon’s Kindle was launched in 2007 as a single version at $399 (relatively high for a single-purpose gadget), and remained a highpriced gadget for several years. Unlike Apple (which was entering new markets with iPod and iPhone), as the largest bookseller, Amazon already had a solid relationship with publishers. It could credibly signal to Kindle adopters that they would have a large content library. Indeed, while the quantity of Kindle eBooks has grown over the years, the growth rate is quite low relative to the exponential growth of music content on the iPod or iPhone apps on the App Store. In terms of our formulation, the parameter k is rather low for the Kindle because, with the guarantee of content availability, the entire utility essentially shifts to the standalone component v q. Not surprisingly, Amazon’s single-version strategy (until competition forced a broader product line) appears more similar to what one would expect without network effects. The growth-inducing price drop (to a $139 6’’ Kindle, relative to the original $399 price, see Fig. 3) occurred only in July 2010 after Apple’s iPad presented Amazon a serious competitive threat in the eBook market.

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Early Expansion

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Expansion Cost ( )

Fig. 3 Evolution of the Kindle platform

Fig. 4 Impact of uncertainty about developer participation on expansion strategy. Source: [4]

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technology and paying only for customer reservations versus paying for reservation software and hardware as well.

This example also indicates a useful strategy that many firms can follow: create product variety and segment customers through differentiation in pricing (which is relatively less expensive to administer) rather than designing multiple physical products. For the HEP example discussed in the Introduction section, our results suggest that the firm can offer i) a low-end patient HEP with a minimal fee (or even free) that might have fewer features and ii) a high-end with a higher fee that could communicate more data, and more real-time communication, to clinicians. But the result also suggests that when product line expansion costs are higher, early expansion is less attractive to startups than to established firms. As an additional consequence, this finding also suggests that a startup should carefully build its business strategy to attract more developers—i.e., raise its c—by providing development tools or incentives.

Figure 5b illustrates the effect of a firm’s ability to attract early adoption on its expansion strategy. Bhargava et al. [4] showed that as j increases, the profit under a deferred-expansion policy grows faster than the noexpansion profit. Similarly, the early expansion profit increases with j because the first-period sacrifice (higher expansion cost) produces superior gains in the later period (higher D and A). Hence, an established firm is more likely to benefit from early expansion; and, if expansion costs are too high, it might just choose not to expand at all (this is because with higher j, A becomes more certain, reducing the benefit from a ‘‘wait and see’’ approach). In contrast, a startup is more likely to find deferred expansion attractive

No

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Expansion Cost ( )

In order to differentiate and compare startups and established firms, we analyze the effect of two parameters: c, the ability to attract developers and j, the size of early adopters. The likelihood of participation is captured via the parameter c in the participation function A ¼ c D þ n.

Recall that D is the market size after the first period, and c takes on a higher value for established firms than for startups. This is because the reputation of the firm or founder is critical to recruiting developers [22]. Thus, OpenTable faces greater challenges in obtaining participants than an Amazon might face in convincing publishers to provide e-books for the Kindle (or, e.g., Apple to convince developers to write applications for the iPad). Similarly, firms with a better reputation have a higher chance to get early adoptions of its product [10], hence established firms with a new product category have a higher j value than startups.

Figure 5a illustrates the variation in product line expansion strategies for startups versus established firms.

Startups are to the left side of the x axis, because they have lower ability to attract developers and smaller size of early adopters, while established firms are to the right side of the x axis, i.e., greater ability to attract developers and larger size of early adopters. When expansion costs are low, then even startups should consider early expansion as a way to increase the installed base and position itself better for the second period. OpenTable addressed this problem by having multiple levels of nonlinear pricing structures for small versus large restaurants, both the initial one-time costs and the continuing fees for providing customers to the restaurant. It also offers restaurants a choice (and different price levels) between using their own reservation

Defer, Expand

Early Expansion

Expansion

Ability to Attract Developers ( )

(a)

Size of Early Adopters ( )

(b)

Fig. 5 Optimal expansion strategies for startups versus established firms. Source: Bhargavaet al. [4]. j is the ratio of first-period population to second-period customer population

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because the uncertain component of A carries greater weight when j is small.

209 Vidyanand Choudhary, BC Kim, Marius Niculescu, Geoffrey Parker, Daewon Sun and Marshall Van Alstyne. Section 3 of this paper draws heavily from ‘‘Commercialization of Platform Technologies: Launch Timing and Versioning Strategy’’(by H. K. Bhargava, D. Sun, and B. C. Kim), in Production and Operations Management (2013).

4 Conclusion Technology entrepreneurs routinely face a ‘‘growth versus profitability’’ dilemma, which is worsened when the firm launches a single version of the product. This single version has to pursue the conflicting goals of growth and profitability, and hence is handicapped at excelling in either. Instead, firms can often employ a versioning or freemium strategy, offering a free or low-price version to drive mass adoption and a premium higher-price version to generate revenues. This strategy is most attractive when the product has network effects or constitutes a platform for which participation on each side depends on the level of participation on the other side. However, such product line expansion is tempered by increased complexity and product line expansion costs, due to which platform developers often delay expansion until later periods, when they can gauge developer participation before expanding the product line. This note evaluates the complex interplay— between the expansion cost, degree of network effects, and the level of uncertainty in developer participation—and identifies a number of heuristics regarding the optimal launch strategy and the timing of product line expansion for platform goods.

In general, the network effects inherent to platform products make versioning more desirable. When product line expansion costs are low enough that versioning is inevitably the optimal choice over time, then a dominant approach is to expand the product line early, in order to reap the benefits of a larger installed base and a greater level of developer participation. But when expansion costs are high, or there is substantial uncertainty about developer participation in the platform, then a ‘‘wait and see’’ attitude to expansion is more desirable. Other things being equal, this deferred expansion strategy is more suited to technology startups (which have more uncertainty), whereas established firms that can guarantee developer participation are better off with early expansion. These heuristics imply that a technology-oriented startup needs to pay special attention to the level of expansion cost, the number of early adopters (or technology enthusiasts), and the likelihood of application developers’ participation. For such products, investments that induce early customer adoption and developer participation can be synergistic with investments in product line expansion early in the life of the product.

Acknowledgments I am grateful for useful comments and insights from several colleagues, including Sangeet Paul Choudary,

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