How Tech Firms Use Networks to Beat Rivals

Companies like Facebook and Google benefit when more users join their network, reinforcing their competitive advantage

Brian Colello, CPA 24 October, 2017 | 10:49AM Ali Mogharabi
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Facebook has built its market value on the network effect

The network effects is when the value of a good or service increases for both new and existing users as more customers use it; the more existing users there are on a network, the more attractive it becomes for newcomers.

Networks can be direct, such as Facebook (FB) where users lead to more users, or indirect, for example Google (GOOGL) where user searches create more data, which leads to better algorithms and better future results for all users. Marketplace and platform companies such as eBay (EBAY) and Microsoft (MSFT) have also built powerful networks over the years. Amazon (AMZN) has a powerful network effect in online retail.

The network effect as a source of competitive advantage, also known as an economic moat, can only be present if the firm is able to properly monetise the network, which is not a given for many services and industries. Not only must new users see a rise in value from the network, but existing users as well.

Users include all parties in a network; not just buyers or shoppers, but also suppliers and developers. Finally, the network's value proposition must increase as network usage rises via more users, more usage per customer, or both.

Network effects are the rarest moat source among companies covered by Morningstar analysts, but the most lucrative in terms of historical returns on invested capital and operating margins. Compared with other moat sources, a greater proportion of network effect firms are wide-moat - which suggests a strong and defendable competitive advantage - again owing to winner-take-all dynamics. Also, network effects rarely peak, so we foresee sustainable competitive advantages for wide-moat industry leaders such as Google and Facebook.

The network effect addresses the good or service, not the profits of the company, which, in many cases, are not closely related. Twitter’s (TWTR) value as a service is continuing to rise as existing customers use it more often, but its value as a company is not rising in tandem because advertisers still do not see enough of a return on investment in the service to warrant ad spending on the platform.

Common Types of Network Effects

Direct vs Indirect: Direct network effects occur when existing users derive greater value from a product or service as additional users join the network. The more existing users on a network such as Facebook, the more attractive the network becomes for newcomers. Indirect network effects ultimately improve a good or service's attractiveness and often benefit from data network effects.

Marketplace Effects: Marketplace firms often benefit from direct network effects as they collect fees from buyers, sellers or both. Shoppers are attracted to eBay based on the number of sellers and products being sold, and as more buyers join the platform, more sellers have incentive to join the market or focus on selling more within the market.

Platform Network Effects: Users benefit from network effects when both sides see reason to "plug in" to the platform. Platform network effects can be monetised directly, for example, Microsoft selling the Windows operating system or indirectly. Indirect monetisation examples include Apple's (APPL) iOS as free software pre-installed on each iPhone, while Google's Android mobile operating system is given away for free, with the goal of driving mobile traffic to Google's search and other digital properties. 

Complementary Network Effects: These arise as the increase in usage of one product reinforces and increases the value of a complementary, but separate, product which in turn increases the value of the original. Although this applies at the product level and not the company level, complementary network effects can often reinforce the network effect moat source. Google is similarly benefiting today as Google search serves as a complement to strong networks around Google Maps, Gmail, and YouTube.

The 'Chicken and Egg' Problem: Sometimes where businesses will not join or try a new product because there are no users, while users won't try a new product or service because there is not enough support from various businesses. Google's search functionality may or may not have been better than Yahoo's when it was first introduced, but it was clearly built to improve with more data and page ranks to generate more accurate listings, rather than Yahoo's web pages and links which did not show much improvement in accuracy.

Winner Takes All: A few firms have emerged to dominate markets: only a handful of credit card networks such as Visa (V) and Mastercard (MA) have emerged over the past 40 years. Yet they still have room to grow usage by displacing cash within these markets.

Today, advertisers have little incentive to place the bulk of their digital advertising dollars on any other search engine besides Google or any other social network besides Facebook, and such incentive to look elsewhere may even be declining further still, as evidenced by disappointing near-term financial results at Twitter and Snap (SNAP), in particular. 

Networks Rely on Shared Standards: Technology networks would not exist without the ubiquity of the Internet and the interconnectivity among wired networks, Wi-Fi and cellular networks to link all parties. These technologies arose from a variety of companies and standards bodies agreeing to share intellectual property and conform to a certain set of rules and technologies such as wireless frequencies to ensure compatibility across products. As it relates to network effects, easy connectivity is often taken for granted, but is a critical issue.

Reliance on Data:  Network effects may be present as additional users naturally lead to more data, which creates better products and attracts more users. Data generated by these networks is a valuable intangible asset for Google and Facebook and these firms benefit from both network effects and intangible assets. Credit card companies like American Express (AXP) also collect valuable data from customers that not only benefit merchants, but also improve AmEx's risk management and credit risk analysis.

Businesses Are Highly Scalable: Technology networks, such as credit card networks, financial exchanges, ecommerce marketplaces, social media, advertising, and software, all tend to be asset-light and can benefit from rapid expansion without hefty ongoing investments. They can grow immediately without needing to raise capital to build the next factory, store, or distribution centre. These networks highly scalable and can expand quickly over time.

Networks Don’t Always Have First-Mover Advantage: While there has been rapid adoption of massive networks such as Facebook, Twitter, and Snap, large networks can also dissolve quickly, with Myspace being a prominent example. Any network that faces low barriers to entry, is usurped by better tools and does not leverage the network into other important moat sources, such as customer switching costs or a valuable intangible asset associated with data, may be subject to disruption.

Companies With Strong Network Effects

Google: Network effects are often described as the "flywheel" effect, where the benefits create a virtuous cycle that perpetuates. For example, the data network effect for Google search starts with users and/or usage. These users provide search data to Google, which allows the firm to improve its algorithms to deliver better search results. Better search results, in turn, lead to more users and/or usage, and the flywheel continues.

The simplest flywheels come from firms with direct network effects. Before it emerged as a two-sided platform network, YouTube – now owned by Google – still provided utility to early users because home videos and original content could be posted and shared with a small number of users, well before it became a destination for music videos and other types of media content. Snap offered users the convenience of ephemeral messages, providing utility over more established social networks like Facebook and Twitter.

Netflix (NLFX) benefits from a data network effect, but even without the data that allows Netflix to make better viewing recommendations and content purchase decisions today, early Netflix users could still choose within a library of early content. Further, Netflix streaming was a free add-on associated with the firm's DVD shipment business, so the company spent far less effort attracting new users than what a startup might otherwise encounter. More users give Netflix more viewing behaviour data, which allows the firm to provide better recommendations and make better content acquisition decisions, so that new and existing users all benefit from a more attractive service. 

EBay: The network effect inherent in eBay Marketplace's 25 million-plus active sellers and 169 million active buyers across the globe creates barriers to entry for prospective rivals. EBay's customer-to-customer platform still possesses a network effect within the broader e-commerce/mobile commerce landscape because of Marketplace's reasonable take rates for sellers, wide customer reach, flexible listing capabilities, and security features. However, the firm faces increasing competition from Amazon, particularly its third-party platform. 

Amazon: Network effects are one of the sources underpinning Amazon’s significant competitive advantage, or wide moat. The firm’s low prices, an expansive breadth of products, and a user-friendly interface attract millions of customers, which in return attract merchants of all kinds to, including third-party sellers on Amazon's Marketplace platform - which represented roughly 50% of total units sold in 2016 -  and wholesalers/manufacturers selling directly to Amazon.

This network effect has led to the firm increasingly becoming the starting point for online purchases, akin to a mall anchor tenant. Additionally, customer reviews, product recommendations, and wish lists increase in relevance as more consumers and products are added to the Amazon platform, enhancing its network effect. 

Microsoft: A prominent example of complementary networks is Microsoft with both Microsoft Windows and Microsoft Office. Windows benefited from a platform network effect, with software developers, chipmakers, PC makers such as IBM and HP and businesses and consumers all rallying around the operating system. From there, usage of Microsoft Windows led to increased usage of Microsoft Office. Office then benefited from direct network effects as business usage would lead to even more business users coming on board to ensure compatibility.

The increased use of Microsoft Office led to even greater usage and an expanding network for Microsoft Windows. Even in cases where users and usage may be mature, for example with Microsoft Windows, firms are often able to leverage such mature networks into new ventures and diversify their business.

Networks Boost Profitability

Network effect as a competitive advantage is most prominent in technology, industrials, financial services, and consumer cyclical companies. It drives better fundamental performance than nearly all other moat sources; companies that derive their competitive advantage from network effect have posted the highest profitability across every metric.

A company cannot benefit from network effects as a source of competitive advantage unless they allow the firm to generate excess returns on capital over time. Some companies can grow their businesses based on network effects, but firms cannot claim network effects as a source of competitive advantage as they are not able to leverage these into excess returns on capital over time.

Companies can benefit from network effects not just by acquiring more users over time, but also more usage among existing users. A strong sign of network effects is when the product or service is so attractive to its users the product or service can't help but bring in more users and/or usage.

The information contained within is for educational and informational purposes ONLY. It is not intended nor should it be considered an invitation or inducement to buy or sell a security or securities noted within nor should it be viewed as a communication intended to persuade or incite you to buy or sell security or securities noted within. Any commentary provided is the opinion of the author and should not be considered a personalised recommendation. The information contained within should not be a person's sole basis for making an investment decision. Please contact your financial professional before making an investment decision.

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Securities Mentioned in Article

Security NamePriceChange (%)Morningstar
Alphabet Inc Class A181.79 USD0.07Rating Inc186.41 USD2.11Rating
American Express Co246.90 USD0.88Rating
Apple Inc225.01 USD0.47Rating
eBay Inc53.24 USD-1.24Rating
Mastercard Inc Class A441.72 USD-1.39Rating
Meta Platforms Inc Class A488.69 USD0.26Rating
Microsoft Corp444.85 USD0.43Rating
Netflix Inc642.76 USD-0.73Rating
Snap Inc Class A14.33 USD-3.04Rating
Visa Inc Class A264.79 USD-1.09Rating

About Author

Brian Colello, CPA  is a senior stock analyst with Morningstar.

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