How to build a $50M online company is a topic that Dan Mitchell at the NYT explored recently, taking his cue from Jeremy Liew of Lightspeed. A few weeks ago Jeremy wrote about the scale a business has to achieve to get to $50M in revenue. I have summarized the scenarios from Jeremy's post in the table below:
RPM = Revenue per thousand impressions (including CPM, CPC, and CPA models)
Scott Karp of Publishing 2.0 had an interesting take on Jeremy's piece, saying that it "exposes a deep flaw in the way online media is currently valued and sold to advertisers". Scott's view is that the real issue is who is viewing your site. Or as Scott put it: "10 million uniques is great, but not so much if you donÄôt know who these people are". Because of the this, Scott believes that advertisers are getting too good a deal for their advertising dollars.
I think Scott is onto something here. Take Google AdSense, which shows ads on different sites based on keywords. These keywords provide valuable context for targeting ads. But still, without understanding the users the ad-targeting can go completely off. As an example, say we have two users: one interested in football and another in politics. If each is shown a targeted ad based on the keywords "defense strategy", they will be shown similar AdSense ads. Whereas what the users were really interested in is not the same.
Now imagine a system that somehow understands the users a little bit better. In our example, such a system would be able to understand that the user is looking for football strategies in the first case and advice on warfare/geopolitical maneuvering in the second case. This system could then present each of the two users the appropriate ads, thereby dramatically increasing the effectiveness of those ads. Such an ad placement system, given reliable user information, could make a website financially viable with a lot less traffic - thereby empowering alternative voices and making the Internet a richer and more vibrant environment.
Based on the potential for financial upside, it seems like this area should be rife with innovation. But the problem is not as easy as it sounds... If the system mentioned above were to work, it would need to effectively deal with issues of privacy and user control of their data. To address this problem, one approach that is gaining some currency is the idea of user-controlled Attention data (see R/WW's analysis on this and also my take). The way user-controlled attention data works is that it provides tools to users to collect and manage data about their preferences and browsing behavior etc. Users can then allow businesses they trust to access this data. This preference data will improve the richness and quality of interaction between the user and the business, while addressing all the privacy and user control issues. In the example above related to the "defense strategy" keywords, a user's attention data will help web sites understand a user's interests in politics or football and thereby show the right ads. While this solution sounds pretty good, it hasn't yet gotten enough traction - because it relies on the users being sophisticated enough to create and manage their attention data.
Another approach being explored by some big players is the old idea of Personalization. The way this works is that large businesses collect enough information on users to understand their preferences. These businesses then use this understanding of their users to better target ads. As one would expect, Google is leading the charge here - you can read more about Google's personalization technology from Read/WriteWeb's interview with Matt Cutts of Google. So Google collects information about the users via all their interactions with Google properties - like Gmail, maps, blog, calendar etc. - and uses this information to improve Google search results and ad targeting. To revisit the example of "defense strategy" we talked about above, Google will look at the totality of all user interactions and infer a user's interest in politics or football. With this inferred data, Google will be able to show the right ads to the right person. While this approach is likely to work well for big companies that have tons of user data, what should small businesses do?
Most businesses should really work on modeling their user data for targeting as soon in the product development cycle as they can. They have to realize that understanding the users is really key to competing with the big guys and being viable.
While some of the traffic numbers required for a business to hit $50M in revenue look scary, startups should not lose hope. They should focus on really understanding their users and then utilizing this understanding to improve their RPM rates. This will help businesses get viable with a lot less traffic and ensure that they get proper value for their traffic.
What other technologies do you think can help small business become viable, without achieving massive scale?