takingpitches

You are nobody on Wall Street without a hulking Bloomberg terminal on your desk.

in an age of cost-cutting and easily accessible information, that reality is in tension with its $20,000/year annual cost per terminal

The reason for the terminal’s enduring power lies in one of the most powerful and enduring local network effects of our time in the messaging feature of the terminal:

A private network since its foundation in 1982, before email was widely used or social networks emerged, Bloomberg has used its messaging technology to turn its financial data service into the hub of a vast social network.

“Bloomberg is like a very expensive Facebook,” an executive at one rival data company observes.

In a world where many market infrastructure operators provide a cheap, often free messaging tool, Bloomberg reigns supreme. Each day, its 315,000 subscribers exchange 200m messages and have 15m to 20m chats. Rivals have tried for years to break its dominance in messaging, with little success.

Financial groups with security and compliance concerns about Facebook or Twitter like Instant Bloomberg for its security, including biometric identification, and the fact messages are archived and auditable. Users like functions allowing them to share complex data sets, integrate with Yahoo or AOL chat services, or simply see whether someone has received a message. Others have to have it simply because their customers use it.

But network effects while robust are not indestructible, and the current scandal is giving those customers that pay the bills the opportunity to reevaluate alternatives and chip away at the reliance on the terminal.

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So local network effects suggests that the cold start problem to start building network effects may not actually be a great problem.

This is particularly true for enterprise oriented transaction platforms.

Like the homecoming queen sending out invites  to join an emerging IM network 15 years ago, local network density that matters to the customer can quickly be established.  The local network density of having the homecoming queen on the network is what matters for the teenage boy even if the IM network does not yet have reach beyond that local network effect.  A big enterprise can get its suppliers onto the network if there is a real threat that business is lost because certain business is bid out through the platform.

The message that must be sent by the enterprise — “Sign up, compete, or lose the business.”  Suppliers will do funny things if you make them break a sweat.

The fact that networks overlap, customers can benefit from other customer’s suppliers, and suppliers can serve other supplier’s customers, means that local network density can translate quickly to a real global network effect.

To circle back, this is the Pollenware example., in order to have the best auction of your accounts payable, you want everyone on there in your network to which you own an accounts payable.

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Facebook has a billion members.  But if your couple hundred friends are not members, the wonders of network effects are purely theoretical.

Similarly, take an instant messaging network.  On an instant messaging network, a user wants to communicate with only a small fraction of the actual and potential user base of the network.

Here is one definition of local network effects:

For example, a good displays local network effects when rather than being influenced by an increase in the size of a product’s user base in general, each consumer is influenced directly by the decisions of only a typically small subset of other consumers, for instance those he or she is “connected” to via an underlying social or business network (instant messaging is a great example of a product that displays local network effects).

As the user benefits when his or her “local network” joins the network, that user has an incentive to get his or her network to sign up.  This provides a potential solution to the cold start problem  – how does one jump-start network effects?

Local networks lead to global network effects. Local networks interlock and overlap; they are not isolated.  Local networks of friends on Facebook or IM network interconnect with other local networks.

The key lies in nurturing that interconnection.  By empowering and encouraging the local network, the global network can rise.

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Tim O’ Reilly wrote one of the early great posts on the increasing importance of network effects in data.  Data network effects make products better as a function of more users contributing data to the product.  Here are a couple of key excerpts from O’Reilly’s post:

Ah, I say to myself: Nick only sees first order network effects, what you might call endogamous networks, those that require the user to be part of the tribe. Thus, phone networks, and networks like Facebook.  But the internet is an exogamous network; its benefits increase by the extent to which it reaches out to new groups, increases cross-breeding, and thus the total robustness and variety of the gene pool. This is why links matter, why web services matter, because they extend the reach of the network. Understanding the benefit of exogamous networks requires a more subtle calculus than Nick is applying. It’s not necessarily that you benefit directly from belonging, but the fact that you belong allows others to harvest the benefit of your participation.

…

The next layer of attractive profits will accrue to companies that build data-backed applications in which the data gets better the more people use the system. This is what I’ve called Web 2.0.

It’s network effects (perhaps more simply described as virtuous circles) in data that ultimately matter, not network effects per se. Nick probably wouldn’t think of Nuance as a network-effects driven company, but it is, because their applications and services depend on data that gets better the more people use it (or have their data harvested in one way or another.) More speech in more circumstances and more domains makes Nuance better for the next user. No user thinks that Nuance is better because of them (and because many of Nuance’s products are standalone, this is in fact true.) Yet Nuance, like Google, has figured out how to harvest data contributed by millions to build a better product.

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For defensibility of an enterprise business model and avoiding the software morality play, the key in Fred’s view is gaining network effects in data contributed by the users of the service.

Data contributed by users is key.  It is not sufficient to pull data from internal or public sources; instead each user must send back data.  Usage then potentially builds an insurmountable lead in the network, since an entrant cannot catch up merely by replicating the service and pricing it cheaper.

So what are concrete examples of data network effects?  Some examples of the data contributed by usage come from the USV platform:

• Work Market: Company customers put their entire “contingent labor” or freelance workforce onto to the platform, creating a supply platform accessible to all customers.
• Pollenware: Company customers bring their suppliers onto the platform in order to bid on accounts receivables.
• Return Path: Return Path provides email software to heavy email senders in exchange for the information as to who are good or bad senders

In a data network effects model, every new user makes the network more valuable through the contribution of some sort of data.  Every new user makes it harder for all users to get off.

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Right before the holidays, I attended an amazing presentation by Fred Wilson on software and the enterprise.

The thesis of the presentation was network effects, and it underscored to me that in developing a product a key test has to be whether there is a sustainable network effect in addition to delivering value.

This has inspired me to write an ongoing series of posts on network effects.

The first question that we will take on is why network effects are important in software businesses.

Fred demonstrated his answer in the form of something he called a software morality play in three acts:

• Act 1: A high end enterprise software company launches costing hundreds of thousands dollars in recurring fees.
• Act 2: This value is undermine by a couple of entrepreneurs funded by Paul Graham working on the software in their garage selling it on self-serve basis rather than through a large sales force.  This venture serves as an ankle-biter to the big enterprise company causing pressure on prices.
• Act 3: An open-source technology is launched further destroying the value of big enterprise company.

Network effects serve to protect the venture from the fundamental point of Fred’s morality play: software is easily duplicated.  Network effects are most importantly about defensibility.

More on what those networks effects are in future posts.  Fred’s talk is here.

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