Editor’s note: This is a guest post by Sangeet Paul Choudary, who analyzes business models for network businesses at his blog Platformed.info. He is the author of the forthcoming book, Platform Thinking. You can follow Sangeet on Twitter at @sanguit.
Network effects are the holy grail for Internet startups looking for venture-scale returns. On a platform with network effects, the value to a user increases as more users use it. Facebook, Twitter, LinkedIn, YouTube, Skype and many others benefit from this dynamic.
2012 will go down as the year when a billion people were connected over a single platform for the first time in history. But as online networks grow to a size never seen before, many question their sustainability and believe that they are becoming too large to be useful.
To explore the future of online networks, it’s important to note how network effects correlate with value and the factors that make these network effects work in reverse.
Network effects and value
There is a strong correlation between scale and value in businesses with network effects. Greater scale leads to greater value for users, which in turn attracts other users and further increases scale. This rich-becomes-richer dynamic allows networks to scale rapidly once network effects set in.
There are three sources of value created on networks: Connection, Content and Clout.
Connection: Networks allow users to discover and/or connect with other users. As more users join the network, there is greater value for every individual user. Skype and WhatsApp become more useful as a user’s connections increase. Match.com and LinkedIn become more useful as more users come on board.
Content: Users discover and consume content created by other users on the network. As more users come on board, the corpus of content scales, leading to greater value for the user base. Content platforms like YouTube, Flickr and Quora, as well as marketplaces like AirBnB and Etsy becomes more useful as the number of creators and the volume of content increase.
Clout: Some networks have power users, who enjoy influence and clout on the network. Follower counts (Twitter), leaderboards (Foursquare) and reputation platforms (Yahoo Answers) are used to separate power users from the rest. On networks like Twitter, the larger the network, the larger is the following that a power user can develop.
Across these three drivers, a network with greater scale provides greater value in the form of:
- More prospective connections for the user
- A larger corpus of potentially relevant content
- Access to a larger base of potential followers (greater clout), for power users
On most networks, value for users is created through more than one of these three sources. Facebook, for example, started with a value proposition centered around connection, but the introduction of the news feed has made content a central driver of value. In recent times, the addition of the subscribers feature has added clout for some Facebook users as well.
Why network effects work in reverse
One would expect that the bigger the network, the more value users derive from it.
However, as networks scale, the value for users may drop for several reasons:
- Connection: New users joining the online community may lower the quality of interactions and increase noise/spam through unsolicited connection requests.
- Content: The network may fail to manage the abundance of content created on it and may fail to scale the curation of content created and the personalization of the content served to users.
- Clout: The network may get inadvertently biased towards early users and promote them over users who join later.
Just as network effects create a rich-becomes-richer cycle leading to rapid growth of the network, reverse network effects can work in the opposite direction, leading to users quitting the network in droves. Friendster, MySpace and Orkut bear testimony to the destructive power that reverse network effects wield.
Reverse network effects: Connection
Connection-first networks (dating websites like Match.com and networking communities like LinkedIn) build value by connecting people.
These networks may suffer from reverse network effects as they scale if new users joining the network lower the value for existing users. To prevent this, an appropriate level of friction needs to be created, either at the point of access or when users try to connect with other users.
On dating sites, women often complain of online stalking, as the community grows, and abandon the site. Sites like CupidCurated have tried to solve this problem by curating the men that enter the system, in a manner similar to restricted access at a singles bar.
LinkedIn creates friction by preventing users from communicating with distant connections. This ensures that users do not receive unsolicited messages. This also allows LinkedIn to offer frictionless access (OpenMail) as a premium value proposition.
ChatRoulette, in contrast, anonymously connects users over a video chat without needing to login. This lack of friction led to ChatRoulette’s stellar growth but also led to reverse network effects as anonymous naked hairy men took to the network, thus increasing noise and driving genuine users away from it.
Dating sites, as well as social networks like Orkut, have imploded in a similar manner after reaching scale, owing to noise created by fake profiles.
In general, networks of connection scale well when they create appropriate barriers to access on the network.
Reverse network effects: Content
On content networks like YouTube or Flickr, a larger network is likely to have more content creators, leading to more content for the user to consume. Networks like Facebook and Twitter, in addition to being networks of connections, are also networks of content.
Most networks of content have low friction in content creation to encourage activity from users and reach critical mass faster. To ensure that the content is relevant and valuable, the network needs strong content curation and personalization of the user experience.
Reverse network effects set in if the content curation systems don’t scale well. As more producers create more content, the relevance of the content served to consumers on the network shouldn’t decrease.
Content networks create a curation mechanism through a combination of moderation, algorithms and community-driven tools (voting, rating, reporting etc. ). Voting on YouTube, flagging a post on Facebook and rating on Yelp are examples of curation tools.
Curation mechanisms often break down as the volume of content increases. When curation algorithms and moderation processes do not scale, noise on the system increases. This leads to reverse network effects and users abandoning the system.
Quora has a very strong curation mechanism in place and benefits from a tech-savvy early user base. As Quora scales, many worry that less sophisticated users, entering the system, may increase noise leading to a rapid depletion of value for existing users. It remains to be seen whether its curation can scale as the network opens up to a broader user base.
Content networks need a personalized consumption experience for users, that serves them relevant content.
An example is the news feed on Facebook or Quora or the recommendation system on YouTube.
Inability to maintain relevance of the consumption experience, with scale, may create reverse network effects.
The user experience on Facebook is centered around the News Feed. However, Facebook’s frictionless sharing and cluttered news feed may lead to lower relevance for users as the network scales. Several factors contribute to this:
1) When a user adds friends indiscriminately, her news feed becomes cluttered with irrelevant posts.
2) Noise is further increased when marketers and app developers get access to the news feed.
3) When networks like Facebook and Twitter implement monetization models like Promoted Posts/Tweets, the signal to noise ratio suffers further as promoted content is less relevant than organic content.
Networks of content are constantly faced with the risk of reverse network effects as they scale. The poor signal-to-noise ratio in the news feed, not the size of the overall network, is Facebook’s weakest link as the network scales.
Reverse network effects: Clout
Networks of clout have a system of differentiating power users from the rest. Twitter, Quora and Quibb have baked in clout through the one-sided follower model. Active users vie for greater glory while using the network.
Networks of clout tend to be biased against early users. Clout is a consequence of content that the user creates and early users get more time to create content and develop a following.
This is, ironically, aggravated by focusing on a high signal-to-noise ratio. Twitter recommends super users to prospective followers as these users are likely to create better content. Hence, the platform itself helps separate the power users from the rest.
Users who join later find it more difficult to develop a following and may stop using the network. These networks need a mechanism to ensure new users have equal access and exposure to the community to develop network clout. 500px, for example, differentiates Top creations from Upcoming creations to expose recent activity (often from undiscovered users) to the community.
Reverse network effects often cause a large and thriving network to implode. As a network scales, it’s ability to maintain a high signal-to-noise ratio is the leading indicator of its usefulness. Networks can, in fact, scale very well and prevent reverse network effects from setting in if they have
- Appropriate level of friction in network access and usage, that prevents abuse
- A strong curation system that scales well with the size of the network
- A highly relevant and personalized user experience
- A democratic model for users to build influence
Networks that have excelled in the above have scaled well. In a world where networks are reaching unprecedented scale, a keen focus on maintaining a high signal-to-noise ratio will enable them to remain valuable and effective as they grow.
Image credit: AFP / Getty Images
Article source: TNW http://feedproxy.google.com/~r/TheNextWeb/~3/maDQm4vQTQ0/