Just finished “Stealing MySpace”
I recently got a Kindle, and one of the first books I ordered was Stealing MySpace by Julia Angwin, a journalist from the Wall Street Journal. There are lots of interesting reviews on the Amazon page, so I won’t rehash them here.
I added the book to my books list, which you can view here.
Instead, I’ll give a quick description of how I first ran into MySpace at my previous company, Revenue Science, and what I learned from it.
Why start a cost-per-click behavioral ad network?
Revenue Science is a behaviorally targeted ad network – this means that they tag users based on their browsing history, and then target ads to them based on their historical behavior. So ideally, if you knew that they were in market for a car, then you could target them with car ads and achieve better clickthrough rates and conversions. This technology has slowly permeated all corners of the ads world, and it’s a powerful story.
In 2004, we were in talks with Yahoo/Overture to experiment with behaviorally targeted ads within the Yahoo network. As with all behemoth companies, they had several internal teams already working on behavioral targeting, and there was some uneasiness due to classic NIH syndrome. As a result, some of our sponsors within the Yahoo organization encouraged us to take an XML feed of all of their ads, with some basic pricing data, and sign up publishers as a full-blown ad network. The arrangement was that we’d receive access to their thousands of cost-per-click (CPC) text ads, and we’d put together data showing that we could generate revenue using behavioral targeting in a direct response setting.
Chicken or the egg
Of course, with most ad networks, there is usually a chicken-and-egg problem of getting advertisers and publishers to the table. If you don’t have a ton of advertisers already buying, then you can’t deliver premium CPMs to the publishers. And of course the advertisers won’t buy without your network being able to deliver a significant number of ad impressions with publishers already signed up. Having this XML ad feed from Yahoo made it easy for us to bootstrap the advertiser side of our ad network quickly and easily.
As an aside, most ad networks get started because a top salesguy splinters off from an already effective network and brings a bunch of advertisers with them – that means they can do some buying quickly and easily to start the business. As with most marketplaces, the Golden Rule applies – “he who has the gold makes the rules” – and as a result, most networks tend to skew towards beyond advertiser-friendly rather than publisher-friendly.
Adventures on the open internet
Now that Revenue Science had access to a bunch of advertisers, it was time to sign up publishers. I started a small group within the company that consisted of a couple inside sales folks who could sign up publishers, fax them contracts, and quickly get ads flighted. This was truly a startup-within-a-startup experience.
One of the first thing we did was construct a big target list – essentially this meant buying the list of domains from Alexa, Nielsen, and others, and prioritizing them based on factors like:
- Is the content written in English?
- Do they already have ads placed?
- Are they selling something, or is it a content site?
- Is it a forums site? Or a social site? Or a communication site?
- Do they have at least 1 million impressions per day?
Using some of the basic criteria, we could have a team of fresh-out-of-college sales analysts go through the top 30,000 sites on Alexa and email each publisher.
The sales pitch was simple – “You know that your audience is worth a lot of money, but your ad revenues aren’t reflecting this.” This pitch was especially compelling, of course, to social networks and consumer internet sites which lacked context. That is, people would go there to “see what was going on” versus going there to buy something specific. And of course, we found that the social networks that accumulated massive amounts of profile data on their users were especially interested in doing something with that data.
We had also heard that LinkedIn and Friendster were making a ton of dough targeting ads to keywords that people had placed in their profiles. It seemed like a worthy experiment.
Finding MySpace in 2004
Right around the end of 2004, we started getting in touch with some of the larger sites on our list. At the time, there was a entry called Intermix which was grouped as a network in the Nielsen top sites listing, but we couldn’t tell which sites were driving the traffic. And so we started emailing the properties we could find, and eventually reached an executive there via one of their eCard sites. As “Stealing MySpace” discusses, they had a number of eCard sites that were old school BlueMountain.com type clones. The business model for these was to harvest email addresses from people sending eCards back and forth, which would then be used to upsell consumers to offer-based monetization.
Anyway, the Intermix guy mentioned a bunch of different ways to work together, and near the end of the call, he asked us if we had ever heard of MySpace. And of course, this being late 2004, there was really no writing about the property, so we thought it was just another random site in the Intermix portfolio.
What really got us was that he said the site was adding 50,000 new registered users a day!
After the call, I ran to my desk and pulled up the site – and was immediately disappointed. It looked like a Geocities site, and a Friendster clone. But it was clear after clicking “Browse” that the site was incredibly active, and I just didn’t “get it” yet – there were tens of thousands of active users, and almost every profile had very recent comments and was completely pimped out.
Monetizing social networks is hard
After meeting with several of the execs there, we started thinking about a custom integration with them whereby they would pass us relevant keywords about user segments.
The founding team at MySpace was superb – we were impressed by almost all folks we met, and it was clear they were a scrappy, entrepreneurial group, not the staid media executives that roam the halls of most public internet companies. Many of the folks who are now no longer there, including Jason Feffer, Steve Pearman, and others, are now starting new companies of their own.
Anyway, the entire idea for behavioral targeting on MySpace was that we would take relevant keywords about audiences and then target ads to those users. While this is a fantastic idea in theory, there were a large number of difficulties exposed from this process:
- From a technology standpoint, the keywords in user profiles is extremely free-form. There’s a lot of formatting garbage, like people saying *~ dancing ~* as an interest. Similarly, people filled out the interests in complete sentences, jargon, and many other non-trivial text parsing problems
- Similarly, there’s a lack of purchase behavior on social networks – because people are there to hang out, they aren’t putting things like “looking for a new digital camera” in their profile, nor are they searching for it. Instead, they are saying “i <3 taking pics” which is different than saying you’re in the market for a great new digital SLR camera. The search traffic was similar.
- Also, there is a ton of noise in the clickthrough data we’re getting back – we found that MySpace traffic was very noisy, had a lot of accidental clicks, and thus created problems in the backend for advertisers
- Similarly, there was just SO MUCH traffic – when we started working with MySpace, they were at about a 1 billion ad impressions per month, but quickly got up to 1 billion ad impressions PER DAY. Pretty amazing growth, and we worked with them right through the inflection point. This really compelled us to get very serious about scaling our ad servers, and we heard repeatedly from other ad networks that they couldn’t take the volume MySpace tried to give them
- Another difficulty was the completely context-free ad impressions that exist on a social networking site – people are there to hang out, not to buy stuff, and so the clickthrough rates were very low. Anywhere from 0.05% to 0.2%, but never into the >10% CTRs that you’ll see on search pages.
- Visit lengths were another issue – as I’ve written about previously, more engagement doesn’t better monetization. The first 10 ad impressions might monetize extremely well, but once you’ve exhausted those premium campaigns, your super hardcore user that generates 200 pageviews is not substantially different than your engaged user that generates 30. Once you get past a certain point, then it’s all punch-the-monkey ads.
I’ve covered these monetization issues in more depth here and in these essays here.
That said, it was clear just looking from the data that MySpace was a very special property. In particular:
- The traffic was growing incredibly fast – as I said above, they went from 10-15M registered users when we started working with them to over 100M very quickly, in just a few years
- The users were highly engaged, and were definitely spending hours on the site – this is all obvious now, but at the time, MySpace was the only site we were seeing this type of engagement
- The geographies of the users were all across the US, but also 95%+ American traffic – which is a huge premium within the advertising industry
I don’t think it needs to be said, but for the folks who are ga-ga over the monetization potential of Twitter, I’d encourage you to think about the monetization shortcomings of social networks, blogs, and email, and present an argument about why it’ll be 100X better. (That said, I love using Twitter as a service, so I hope they figure it out!)
And in comes Google
Over time, we started to find things that worked well for us to monetize MySpace traffic. Taking in-market data from outside of MySpace and then targeting those same uniques was definitely effective. We found that certain ad units and sections monetized much better than others.
Very quickly though, Google came in and threw down their $900MM deal for MySpace’s traffic. The details of this deal are in Stealing MySpace, and while I got to hear about the aftermath of the deal, Julia Angwin’s book fills out a bunch of the details from the executive point of view.
The point is, while the MySpace traffic was certainly amazing, it was clear to me (and the other ad networks folks I was in touch with), that there was no way the ads would perform at the level to justify the deal. And ultimately, I think we were proven right. The deal seemed like a crazy auction with a “winner’s curse” and it always seemed like the big bucks were going to get attached to the brand deals the FIM/MySpace team were putting together rather than making the remnant text ad inventory perform 10X better.
Conclusion
Now it’s clear that MySpace’s dominance of the internet is waning, and to me, there’s no stronger indicator of this than the search term “myspace” plateau’ing recently. This means that folks who typically type it to get back to the site via navigational search are not as interested anymore. You can fix some aspects of retention through notifications, better SEO, etc., but if your users don’t want to search for you anymore, then something is wrong.
Graph of searches for “myspace”
Anyway, only time will tell if MySpace is able to recover their strength, or if they are stuck at where they are. Certainly their monetization is bound to improve, but turning the ship on growth is always very difficult.
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