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Think Big. Move Fast.

I’ve noted in the past that the four core competences of ad networks are:

Aggregating Inventory
Aggregating Data

Better targeting has historically been one core area of competition for ad networks, especially those focused on direct response advertising. However, as Anand Rajaraman (co-founder of Kosmix, a Lightspeed portfolio company) points out, more data usually beats better algorithms. Andrew Chen recently noted that after three years of work, Netflix awarded its $1m prize to a combined team of experts for an algorithm that only improved targeting by 10.5%:

This means if you combine dozens of the best machine learning people in the world, some of the cleanest datasets, you get a measly 10.5% increase. Compare this to starting a new ad network where you end up with noisy datasets, lots of crappy traffic, and a small team looking at the problem – that’s not an easy path to disruptive change. In general, 10% is not a big enough number to counteract the other economic drivers in the ad market, which revolves around better deal terms, a larger selection of advertisers, better ad inventory, etc.

Note that this observation comes from a guy who was a co-founder of Revenue Science’s Ad Network business!

While I agree with Andrew in principle, I think that even a 10% edge in targeting can be enough to build a competitive advantage in the direct response world. Because competition for publishers is fierce, and publishers switch ad networks frequently in search of higher RPMs, a slight edge in targeting can lead to a slight edge in publisher payouts which can lead to an overwhelming win in volume.

Andrew thinks that breakout ad network performance will come from two of the other key competency areas:

I think disruptive change will come not from algorithms, but rather two other areas:

* Better ad inventory: New websites and mechanics emerge all the time, and who knows what happens when you put ads on them? It was clear, until they tried it, that with the right ads search can be >30% clickthrough rates or more, which is unheard of.
* Better data: The other big opportunity is in using specialized data to drive your algorithms – rather than basing everything off of domains, cookies, and ad impressions like everyone else, there may be ways to extend the targeting to unique datasets that no one has access to. This is what’s happening in the world of retargeting.

These are good thoughts, and well worth exploring. Better ad inventory can be difficult to defend in an age of exchanges like Right Media and the Doubleclick ad exchange. However, in some areas such as mobile, video, in-game advertising and client driven inventory, it is still possible.

Data is also improving. But because it is also becoming more of a commodity, the real question will be whether this data can be proprietary. If the proposed FTC rules on third party cookies for behavioral targeting take effect, it could give some of the big web properties access to their own proprietary targeting data that will give them advantages over third party networks. Taking offline data and using that for online targeting is also another possibility.

In the brand advertising world, I think that sales will be a real differentiator. The big brand budgets are just starting to move online. CPG, one of the core categories for brand advertising, is starting to shift online this year in a meaningful way. But brand advertisers need to be sold to the way that they want to buy. Not all online sales teams know how to do that. Facebook’s recent partnership with Nielsen to show brand lift means that now only four online media companies have the ability to show the impact of a campaigns effectiveness on brand metrics (Yahoo, AOL, Facebook and Brand.net). I expect more companies to start reporting these sorts of brand lift metrics as a matter of course if they want to take their share of brand advertising dollars as they move online.

Which new startups do you think have a breakthrough in any one of these areas of core competence?