Platforms and Networks

Tuesday, July 26, 2011

Business Model Analysis, Part 5: Virality

A product grows virally when its use spreads through direct, customer-to-customer transmission. Viral growth occurs through four different mechanisms listed below. With the exception of incentives, these mechanisms do not entail any marketing expenditures, so business models that harness strong viral growth can be very attractive.

Direct Network Effects. To function properly, some products must be used jointly by two or more parties. These products are said to exhibit direct network effects, because their users interact directly. For example, early versions of Skype required both a call originator and recipient to use Skype software. When one party who already has such a product wishes to interact with another who does not, the first party can contact the second party to suggest that they acquire the product.
Word-of-Mouth. Even if they do not enjoy direct network effects, products can spread virally when a happy customer recommends them to another party, as when a satisfied diner suggests a restaurant to a friend.
Casual Contact. Like the common cold, some products can spread virally through casual customer-to-customer contact. For example, the free, web-based email service Hotmail grew explosively in 1996 after its founders added a link at the bottom of users’ emails that simply said, “Get your free email at Hotmail.”
Incentives. Many companies structure incentives that encourage their existing customers to recruit new customers, for example, MCI’s 1990s “Friends-and-Family” plan, which offered reduced long distance rates for calls between MCI customers in a circle of up to twenty members.

Virality and network effects are often conflated and confused, so the distinction between them warrants clarification. It should be clear from the list of mechanisms above that not all products that spread virally exhibit network effects. Likewise, not all users of products with network effects are acquired through viral, customer-to-customer transmission mechanisms. Specifically:

A new user of a product with direct network effects might sign up based on press coverage or advertising, then discover parties with whom they can interact after using the product. This pattern was evident in the rapid growth of MySpace, Second Life, Twitter, and the question-and-answer service Quora.
Direct network effects are distinguished from indirect network effects in a two-sided network, in which growth in one side’s user base (e.g., Android phone owners) attracts more users to the other side (e.g., Android application developers), and vice versa. With indirect network effects, the mechanism of attraction is the aggregation of a larger base of users, rather than contact between individual users.

Many startups combine more than one viral mechanism in their go-to-market plan. Dropbox, for example: 1) harnessed a direct network effect when users employed the service to collaborate on documents; 2) benefited from word-of-mouth referrals from loyal customers; 3) acquired customers through casual contact when users emailed links that allowed recipients to download (without installing Dropbox) files stored in the sender’s public folder on Dropbox; and 4) offered a two-way “user-get-user” bonus, that gave both the inviter and recipient an additional 250MB of free storage.

Viral Coefficient

A firm’s viral coefficient is calculated as the number of additional customers subsequently acquired through viral mechanisms for every new customer initially acquired. Startups that rely heavily on viral growth should track their viral coefficient overall and by customer cohort—that is, for each “vintage” of new customers acquired during a given period through different types of marketing program employed by the firm. As shown by the table below, a viral coefficient greater that 1.0 yields self-sustaining growth from an initial “seed”—that is, a batch of new customers acquired in period 1. In the table, we assume that a seed group of 1,000 new customers each purchase one unit of a firm’s product in year 1. These seed customers do not repurchase the product, but through viral means, they attract some additional customers who purchase in year 2, who in turn attract some more customers in year 3, and so forth.

	Number of New Customers
Viral Coefficient	Year 1	Year 2	Year 3	Year 4	Year 5
0.3	1,000	300	90	27	8
1.0	1,000	1,000	1,000	1,000	1,000
1.3	1,000	1,300	1,690	2,197	2,856

When modeling viral growth dynamics for customer relationships that have a multi-year life, it is important to be specific about whether the viral coefficient should only be applied in year 1, or in each year. In some contexts, new customers are likely to quickly exhaust word-of-mouth recommendations or other viral mechanisms (e.g., opportunities to leverage “member-get-member” bonuses).

David Skok of Matrix Partners discusses viral coefficients in depth in this post, and Adam Penenberg’s book Viral Loop provides many examples of viral customer acquisition.

Monday, July 25, 2011

Business Model Analysis, Part 4: Racing for Scale

"Should we step on the gas pedal?" Based on my work with startups, this is the strategy question that entrepreneurs raise most often. It's crucial to understand whether accelerated growth is a priority, given the attributes of your business model. A flawed plan to "Get Big Fast" can be fatal for a resource-constrained startup.

A new venture has incentives to race to acquire customers if it enjoys: 1) increasing returns to scale due network effects or strong scale economies in production; 2) high customer switching costs; or 3) other first mover advantages, such as opportunities to preempt scarce assets or patents. Potentially offsetting these incentives are scalability constraints and late mover advantages. In this post, I won't address the complex question of whether to adjust a startup's growth strategy in response to a valuation bubble.

Addendum, July 26, 2011: My friend Joel West, a scholar/entrepreneur at San Jose State, makes the very important point that growth is often a choice for entrepreneurs that hinges on their personal preferences as much as on economics. I've written below with the tacit assumption that a founder will always want to grow a business if she can. Of course, there are many lifestyle companies with business models that could be profitably expanded, but also can be operated successfully at a more modest scale, consistent with the owner's goals.

Increasing Returns

Most businesses exhibit constant returns to scale, that is, their margins do not vary much over the range of sales volume that a firm might reasonably achieve. In such businesses, as output expands, scale economies may be offset by diseconomies of scale, including the additional cost of coordinating more complex operations and cost escalation for increasingly scarce inputs (e.g., skilled labor, attractive locations for facilities).

A business that enjoys significant scale economies—either due to strong, proprietary network effects or the ability to leverage high fixed costs—may exhibit increasing returns to scale: its profit margin may continue to improve as the business grows. Firms are strongly motivated to race to acquire customers when they can exploit increasing returns.When customers’ willingness to pay increases with network size or when unit costs decline with greater production volume, acquiring a new customer yields two streams of benefits: 1) profits earned over time directly from that new customer; and 2) profits earned over time from all other customers. For example, with proprietary network effects, as long as a new customer remains affiliated with a platform, all other customers should be willing to pay slightly more for access to the platform. Although this increase in willingness to pay may be infinitesimally small for any given customer, these tiny amounts add up when aggregated across the entire customer base.

Consequently, when acquiring customers in a business with increasing returns, startups can afford to spend up to the present value of future profits earned directly from the new customer (i.e., benefit #1 above) plus the present value of incremental profits from other customers (i.e., benefit #2). By contrast, in businesses with constant returns to scale, companies should spend no more on customer acquisition than the present value of future profits earned directly from a new customer (i.e., benefit #1). In short, when they enjoy increasing returns, firms have a stronger motivation to race for scale. However, when several firms simultaneously enter a new market with increasing returns, they all may pursue an aggressive growth strategy, dissipating any above-normal profits available from the market.

Switching Costs

In a new market, high customer switching costs also can motivate startups to race to acquire first-time buyers—those not yet affiliated with any vendor. After locking in these first-time buyers, a firm should be able to raise prices to them by an amount just slightly less than the switching cost they would incur by changing vendors. As with increasing returns, when several firms simultaneously enter a new market with high switching costs, intense competition for first-time buyers may dissipate profits. However, dynamics may be different if a pioneer achieves a big time-to-market lead and amasses a significant customer base before rivals enter the market. Under these circumstances, switching costs may encourage the pioneer to be a “fat cat,” pricing high to its existing customers but ceding remaining first-time buyers—and market share—to entrants, who offer a lower price.

Additional First Mover Advantages

In addition to factors that yield increasing returns to scale and switching costs, pioneers in a new market may enjoy other first mover advantages, including opportunities to preempt scarce assets, patents, or capacity:

Preemption of Scarce Assets. Through purchases or long-term contracts, first movers can lock up valuable assets, for example, component supply contracts, skilled labor, or attractive geographic locations for factories. Scarce assets become more costly to acquire after rivals enter. In some cases, entry may be impossible once a crucial asset is locked up, as with government-licensed spectrum for telecommunications services.
Preemption of Key Patents. By exploiting a head start in research and development, first movers may be able to secure patent protection for key technologies. If the pioneer decides not to license its intellectual property, prospective rivals may find it costly to “invent around” the patents and, at the extreme, may be unable to enter the market.
Preemption of Capacity. If the minimum efficient scale of production facilities is high in relation to the expected size of the mature market, the first firm to build such facilities may be able to deter prospective rivals from ever entering the market. In the early 2000s, for example, Teledesic and SkyBridge both proposed to spend billions of dollars to launch scores of low Earth orbit satellites that could provide high-speed Internet access anywhere in the world. However, it was unclear whether the market was large enough to support even one competitor, so each firm had to decide whether to offer its service at all if its rival managed to launch first.

Scalability Constraints

Startups must consider operational capacity constraints on their pace of growth. Rapid expansion is more feasible when production and customer service functions can be outsourced to vendors equipped to handle volume surges. For example, by relying on Amazon’s S3 cloud storage service rather than building its own infrastructure, Dropbox was able to acquire 25 million customers within four years of its founding.

When operations are kept in-house, capacity can be scaled more readily if general-purpose equipment is employed. Rapid growth is more challenging when companies rely on specialized production and distribution facilities (e.g., proprietary equipment for packaging Keurig’s coffee capsules; Webvan’s use of highly-automated warehouses for its online grocery service). Such companies must build capacity well ahead of sales if they plan to pursue accelerated growth—a risky proposition when demand in a new market is still uncertain. Webvan took this gamble and failed after investing $800 million.

Likewise, when customer service interactions are complex, a startup’s growth may be constrained by the pace at which it can attract and train skilled personnel. This constraint is relevant for online stock brokerages, whose call center reps must respond to customers’ questions about a wide variety of transactions, including “stop loss” instructions, options trades, and margin calls.

Late Mover Advantages

Potential late mover advantages include opportunities to:

Reduce R&D Costs Through Reverse Engineering. While some pioneers enjoy strong patent protection, others are unable to prevent rivals from copying their products. Reverse engineering usually results in significant R&D cost savings compared with amounts spent by the pioneer on the original product.
Leapfrog Leaders with Newly Invented, Superior Production Technology. Late movers may also gain an edge in terms of cost or product performance by leveraging new technology that was not available when the pioneer launched. For example, Qualcomm was a successful late mover in establishing standards for cellular telephone equipment. Qualcomm’s CDMA standard leveraged leading-edge spread-spectrum technologies that deliver superior capacity and reliability relative to competing digital standards.

Free Ride on Pioneers’ Investments in Educating Customers. In the earliest stages of a new market’s development, a large investment in “missionary” marketing may be required to educate prospective customers. The pioneer may bear 100% of the investment in education, only to find that late movers reap a return on that investment without sharing its cost.
Avoid Pioneers’ Positioning Errors. Pioneers often face great uncertainty about customer needs, so they are more likely than later entrants to need to pivot, that is, change their business model. When a company dramatically changes its value proposition, it may destroy goodwill by confusing partners and alienating existing customers who were sold on the original brand promise. If a pioneer pivots and customer attrition rates rise in response, then the value of earlier investments in accelerated growth may be negated. Late movers can avoid such positioning errors by learning from the leader’s mistakes.

If, in aggregate, late mover advantages are strong, they can encourage what might be thought of as reverse racing. Prospective pioneers may have an incentive to throttle back their marketing efforts or even delay their launch plans when they can secure significant late mover advantages through reverse engineering, by free riding on missionary investments in customer education, when a major technological breakthrough is expected, or when trailblazers are likely to get lost in terra incognita.

Sunday, July 24, 2011

Business Model Analysis, Part 3: Switching Costs

Switching costs are incremental expenditures, inconveniences, and risks incurred when a customer changes from one supplier to another. For example, when consumers switch from a Windows PC to a Macintosh computer, they must buy and install Mac-compatible application software and invest time in mastering a new interface.

Switching costs fall into three broad categories:

Redundant Relationship-Specific Investments. Because their old and new vendors may have different requirements, customers who change suppliers sometimes must invest in new software/hardware or repeat certain activities they have already completed. For example, to switch online stockbrokers, users incur the hassle of transferring funds and securities. For that reason among others, customer retention rates for online stockbrokers have been high.

Disruption Risks. When businesses outsource “mission critical” activities, changing vendors may involve considerable risk. For example, some companies reduce their need for IT staff and infrastructure by relying on cloud computing services. Switching from one cloud service to another exposes a company to significant risk if customer records are lost or corrupted during the transfer.

Contractual Penalties. Companies can impose penalties on customers if they end a contractual relationship prematurely. For example, mobile phone carriers often charge an early termination fee to customers who have signed multi-year contracts. In some cases, these contractual penalties significantly exceed the true costs incurred by a firm due to customer attrition.

When its current customers confront high switching costs, a firm is well positioned to capture a portion of the value it creates. Specifically, by raising its price just below the point where current customers are indifferent between staying with a firm’s product and switching to a rival’s, a firm should be able to earn above-normal profits equal to the sum of the switching costs confronting its customers (“above normal” implies profits in excess of a competitive return on capital; see the technical appendix below for analysis of the impact of switching costs on pricing and profitability).

Switching costs are important in securing a first mover’s position. In their absence, an entrant that offers a superior product or a deep discount might quickly usurp the market lead.

Given these economic benefits, companies often seek to deliberately increase switching costs, either through contractual or technological means. Apple, for example, has employed proprietary digital rights management software, locking in its customers by making it difficult for them to transfer iTunes Store purchases to rival media players. However, efforts to increase switching costs are not likely to escape the notice of prospective customers, who will fear hold-up in the form of a “low then high” pricing strategy. Anticipation of lock-in can represent a significant barrier to adoption for a new product. To ease customers’ concerns about lock-in, firms sometimes license their products to “second source” rival suppliers.

Technical Appendix: Switching Costs and Pricing Leverage

This appendix is only likely to be of interest to readers who want to dig deeper into the economic foundations of business models. To see how switching costs can impact firms’ pricing flexibility and their motivation to race for scale, consider the following example, adapted from my note “Racing to Acquire Customers.”

Assume that two firms, Alpha and Beta, offer identical products. A typical customer purchases one unit of either firm’s product in period 1, then requires a replacement unit in period 2. The variable cost of producing one unit is the same for both firms: $100. In a perfectly competitive market without any switching costs, Alpha and Beta would both price their products at $100 in periods 1 and 2. If either firm tried to raise its price, the other could steal all of its customers by slightly undercutting the new, higher price.

Now assume that each firm has a base of customers that it acquired in period 1, but these customers would incur a $50 cost by switching suppliers in period 2. Assume further that during period 2 the firms are able to offer one price to their existing customers and—in an effort to steal share—a different, lower price to their rival’s customers (e.g., a “special promotion”).

If Alpha wanted to steal Beta’s customers in period 2, it would have to compensate those customers—in the form of a lower promotional price—for the $50 switching cost they would incur. However, Alpha must also recover its $100 variable cost, so the lowest possible price it could offer in period 2 to Beta’s customers and still break even would be $150. If, in response, Beta set its period 2 price for existing customers at $149.99, its existing customers would not bother to switch to Alpha’s product. Beta would retain all of its existing customers and earn a period 2 profit equal to $49.99 from each of them.

Finally, imagine that a new, first-time buyer—as yet unaffiliated with either Alpha or Beta—enters the market during period 2. Like other customers, this first-time buyer will wish to purchase a replacement unit—albeit during period 3. Also, like other existing customers, the first-time buyer will face a $50 switching cost after he commits to a vendor.

Assuming once again that Alpha and Beta can offer different prices to existing versus new customers, what price would they offer to this new, first-time buyer during period 2? Following the logic above (and ignoring the time value of money), we can see that the lowest period 2 price that each firm could afford to offer would be $50.01. Whoever secures this new customer would then raise their period 3 price for this customer to $149.99. Total revenue across the two periods would equal $200, as would total cost, and net profit across the two periods would be zero.

In this manner, switching costs allow firms to raise prices to their existing customers, but the resulting profit opportunity also motivates them to race to acquire first-time buyers. In a competitive market, deep discounts or heavy marketing spending to attract these first-time buyers will dissipate any above-normal profits that otherwise would accrue due to switching costs.

Saturday, July 23, 2011

Business Model Analysis, Part 2: Platforms and Network Effects

This post is part of a series on business model analysis for entrepreneurs. The first post in the series presents a comprehensive list of issues (available as a downloadable PDF) entrepreneurs should consider when designing a business model. Other posts delve deeper into specific issues; this one provides an overview of platforms and network effects.

Network effects are evident when any given customer’s willingness to pay (WTP) for a product depends on the number of other customers with whom they can interact by using the product. In the classic example, the first fax machine sold was worthless until someone purchased a second machine. By providing another potential destination for messages, the subsequent arrival of each new fax machine increased the value of every existing machine and likewise increased the WTP of prospects who had not yet acquired a fax machine. (See the technical appendix below for more on how network effects impact customers’ willingness to pay.)

Network effects arise in platform-mediated networks (PMNs), which include networks of customers—often called users—who wish to interact with each other, along with one or more intermediaries who provide a platform, encompassing infrastructure and rules to facilitate users’ interactions. PMNs form the heart of the computer, telecommunications, media, and Internet sectors. However, PMNs are not limited to information industries. They can also be found in financial services (e.g., stock exchanges, credit cards, ATMs), health care (e.g., HMOs), energy (e.g., the power grid), transportation (e.g., airlines, container shipping, gasoline stations), and retailing (e.g., shopping malls, barcodes). A diverse array of matchmaking businesses mediate network transactions, including auctioneers, executive recruiters, realtors, and travel agencies. Ranked by market value, sixty of the world’s 100 largest companies earn >50% of their revenue from platform-mediated networks, including American Express, Cisco, Citigroup, Time Warner, UPS, and Vodafone. In short, PMNs are essential features of modern economies.

Many startups aspire to either build new platforms (e.g., Facebook) or to profit by offering complements that leverage platforms (e.g., Zynga, which relies on Facebook to host its social games). Platform business models can be extraordinarily complex, and design mistakes are common. Consider Sony’s defeat in the Betamax/VHS standards battle. Or the billion dollars that IBM invested in the failed OS/2 operating system. Or the hundreds of millions of dollars squandered when Yahoo! and Amazon launched full frontal assaults on eBay’s dominant U.S. auction franchise. Or eBay’s own failure to capture auction markets in Japan and China when it imported its U.S. pricing model into those markets, where its rivals initially offered free transactions.

It’s especially difficult to get pricing right in platform-mediated networks, so I’ll focus on that topic here. Specifically, I’ll explain why a platform must be proprietary—controlled by a single company—for its provider to capture value created through network growth. I’ll also give a brief overview of rules for setting prices in a “two-sided” network—one with two distinct user groups.

But getting your pricing right is just the tip of the iceberg when designing business models that leverage network effects. It would take a series of long posts to cover all of the relevant topics, so I’ll just mention a few of them below and point to some articles and notes that should be helpful to an entrepreneur grappling with these topics.

(Links marked below with an asterisk are available as PDFs for $7 apiece from HBS Publishing; others are available as free downloads. Sorry for not being able to provide all of my work for free, but we fund our research at HBS through the sales of cases and notes.)

Proprietary Platforms and Pricing Leverage

When a platform-mediated network’s user base expands, the resulting increase in users’ WTP for platform access does not automatically translate into higher prices for the platform. Providers only gain pricing leverage with network growth when they keep their platforms proprietary. A proprietary platform has a single provider who exclusively controls its technology, for example, eBay, Federal Express, or Google. With a shared platform such as barcodes, DVD, or Wi-Fi, multiple firms collaborate in developing the platform’s technology, and then compete with each other to provide differentiated but compatible versions of the platform. Compatibility ensures that users can switch between products offered by a shared platform’s various providers without incurring significant switching costs. For example, when switching from one Windows-compatible PC to another—say, from Dell to Compaq—a user need not invest in new application software. By contrast, switching to a rival platform—for example, from Windows to Macintosh—would be much more costly.

For a shared platform’s providers, lower customer switching costs limit pricing leverage. By contrast, a proprietary platform’s sole provider can raise its price when user base growth increases users’ WTP, up to the point where a user is indifferent between sticking with the platform and switching to a rival platform, if one exists. With such pricing leverage, startups can capture more of the value they create through network effects. Consequently, entrepreneurs will normally prefer proprietary platforms when designing business models. However, as I explain in this article*, there are circumstances when it may be advantageous to share platforms with rivals. In particular, when many equally matched rivals simultaneously enter a new market that is destined to be dominated by a single platform due to very strong network effects, the firms may be better off sharing the market than conducting an expensive winner-take-all battle*. Even though they offer competing versions of a compatible platform, firms may still be able to profit by contributing their technology to the shared platform, perhaps earning licensing fees or gaining a time-to-market edge.

Sharing a platform implies an open architecture. As I’ve written elsewhere on this blog, the concept of an “open platform” can lead to confusion, because platform-mediated networks encompass several ecosystem layers—each of which can be open or closed. In this paper, Geoff Parker, Marshall Van Alstyne and I describe circumstances under which platforms may find it attractive to open or close as they mature. In a more technical paper, we describe a strategy we call platform envelopment, which entails one platform owner moving into another’s market by offering a multi-platform bundle. Through envelopment, firms can surmount the high entry barriers that often protect incumbents who harness strong network effects. Examples include moves by Microsoft (with Explorer, Windows Media Player, Silverlight, etc.), Apple (with the iPhone/iPad, which has enveloped MP3 players, feature phones, PDAs, handheld gaming devices, standalone GPS units, eBook readers, etc.), and Google (with Checkout, Docs, Chrome, Android, etc.).

Pricing in Two-Sided Networks

Platform-mediated networks can be categorized according to the number of distinct user groups they include. In some PMNs, users are fairly homogenous in terms of the platform functionality they require. For example, although a given stock trade has a buyer and seller, these roles are transient; almost all traders play both roles at different times. PMNs with homogenous users are called one-sided to distinguish them from two-sided networks, which have two distinct user groups whose respective members consistently play a single role in transactions (e.g., cardholders and merchants in credit card networks).

Platform providers who seek to mobilize new two-sided networks face a Catch-22: each side will refuse to join until the other side is on board. To avoid this impasse, platform providers often subsidize users on one side, that is, they price below marginal cost to that side or even give its users free access to the platform, as with Adobe’s PDF reader software or Google’s search engine. Due to network effects, attracting users to this “subsidy side” boosts users’ WTP on the network’s “money side,” as with customers for Adobe’s PDF creation software or Google’s advertisers. Generally, it makes sense for platform providers to permanently subsidize the network’s more price sensitive side and charge the side that increases its demand more strongly in response to the other side’s growth.

Parker, Van Alsytne and I discuss pricing rules for two-sided networks in this article.* In a note* for MBAs, Andrei Hagiu and I examine staging strategies that avoid the Catch-22 in two-sided networks by first bringing on board one side in large numbers before seeking to mobilized the other side. Amazon used such a staging strategy. Amazon attracted a big base of customers using a traditional merchant business model, then later morphed into a two-sided platform by hosting third-party merchants who leveraged Amazon’s infrastructure and customer base.

Technical Appendix: Network Effects and WTP

This appendix, adapted from my note* "Racing to Acquire Customers," will only be of interest to readers who like to lift up the hood to understand how a motor works. Below, I dig into the economics of network effects and, using a stylized example, show how they can impact customer willingness to pay, product pricing, and the pace of investment in customer acquisition.

In a market that exhibits network effects, a company must consider the present and future benefits of building its customer base when making current-period pricing and investment decisions. Absent network effects, a company typically maximizes current-period profitability by setting its price and marketing spending at levels that equate current-period marginal revenue and marginal cost. With network effects, however, optimization is less straightforward. Acquiring an additional customer—say, Mary—in the current period yields revenue directly from Mary during that period and also boosts—very slightly—the willingness to pay of all other customers, who value a larger network. Other customers’ willingness to pay remains higher by this tiny increment in future periods, until Mary exits the network. This increase in other customers’ willingness to pay in current and future periods should be factored into decisions about whether to invest in accelerated growth.

These dynamics can be illustrated using an example of a fictional company—let’s call it “Blossom”—selling spreadsheet software in 1989 (before most office productivity software was sold in bundled suites). Then as now, spreadsheet software was subject to a fairly strong network effect: users valued the ability to exchange files and required software that employed a compatible standard to do so. I use this example because scholars have estimated the impact of network effects on customer willingness to pay for spreadsheet software in 1989 using an econometric technique called hedonic regression (Brynjolfsson & Camerer, 1996, “Network Externalities in Microcomputer Software,” Managment Science 42: 1627-1647) Those estimates are employed in the stylized example below to show how network effects can influence optimal marketing spending levels.

Assume that Blossom’s unit price was $360 and its variable expenses (excluding customer acquisition costs) for supplying an additional unit were $72, or 20% of revenue. To keep the analysis simple, we also assume that a new customer would use the product for four years, at which point the product would be rendered technologically obsolete and the customer would be in no way loyal to Blossom. This assumption is unrealistic, but it lets us avoid factoring the present value of future product sales into our calculations. In reality, customers might prefer to buy Blossom’s next-generation product if it provided backward file compatibility or reduced the time spent mastering a new interface.

First, consider a scenario in which spreadsheet software was not subject to a network effect. What is the largest amount Blossom should be willing to spend to acquire an additional customer in 1989? This calculation is straightforward: the company could afford to spend up to $288 ($360 minus pre-marketing variable expenses of $72) to acquire a customer. Beyond $288, the marginal cost of acquiring and supplying a new customer would exceed the marginal revenue from that customer.

How would network effects influence the maximum amount that Blossom should be willing to spend to acquire a customer? The research mentioned above provides the following formula for predicting the logarithm of a spreadsheet’s product price in 1989 (“P”), based on its publisher’s installed base share (“S”), after controlling for differences in product quality. Consistent with the concept of network effects, the formula indicates that customers would pay more for a spreadsheet used by a bigger installed base:

Log P = 5.7376 + .0075S

If we assume that Blossom had a 20.000000% share of the total installed base of spreadsheet software, then this formula would predict customer willingness to pay equal to $360.5389517 (the reason for showing so many decimal places should be apparent in a moment). Assume further that:

The total installed base for spreadsheet software was exactly 50,000,000 in 1989—about half of the actual size of the worldwide installed base of personal computers in that year.
The spreadsheet market was not growing. This is clearly not realistic, but it simplifies our analysis in ways that do not meaningfully change the conclusions.
Annual unit sales for the industry equaled one-quarter of the industry’s total installed base, corresponding to the four-year replacement cycle described above.

Based on these assumptions, how much pricing leverage would Blossom gain by boosting its installed base by exactly one user, from 10,000,000 to 10,000,001? This corresponds to a market share of 20.000002%, which yields a predicted price of $360.5389571, and hence a price increase of $0.0000054. Across Blossom’s 1989 unit sales of 2,500,001, this additional pricing leverage would be worth $13.50 (2,500,001 x $0.0000054). If Blossom maintained its new market share of 20.000002%, it would realize this tiny pricing advantage in each of the three subsequent years. The present value of $13.50 over four years at 15% is $38.54. Thus, with network effects, it would be economically rational for Blossom to invest up to $326.54 ($288 + $38.54) to acquire a new customer—13% more than the $288 maximum it could spend under the “no network effects” scenario.

Obviously, a company cannot raise its price in increments as small as $0.0000054, but larger market share gains may yield material pricing benefits. Specifically, based on the formula above, a 1% share increase yields a 0.75% increase in customer willingness to pay for spreadsheet software in 1989. For this reason, network effects provide an incentive for companies to invest more aggressively in accelerated growth strategies—that is, to race for scale.