[Thanks to AdExchanger for publishing the original of this post, which I'm now reblogging. There was a lively comment thread over on the original post.]

My work focuses on the economics of advertising, but recently I’ve been thinking about the political economy of advertising. After all, advertising dollars don’t have a mind of their own. They need industry professionals to push them around from one company to another. Trusted personal relationships have historically been the conduits through which ad dollars flow.

This relationship driven world of advertising is now being replaced by the data driven world of advertising. RTB, DSP, SSP….these acronyms have got sales leaders thinking. How do you staff up for this alphabet soup of new business models? Who do you call on and what do you tell them? What does the advertiser need and how do you win their business as a publisher?

When I was a media planner, I had the answer to the last question. I could tell you what the advertiser needed and I would decide whether or not you met that need. This is the type of arbitrary power that brings a recent college graduate towering seafood platters, custom sneakers, and a taste for fine scotch.

Working on the new demand side, I still meet with major publishers and tell them what my clients need. The needs themselves don’t change that much, in fact. Brands still want effectively priced advertising units in safe environments that will get consumers to engage with and buy their products. The key shift is that I no longer decide whether any specific publisher or web page fits that need. The algorithm is the new decision maker.

The algorithm is a better decision maker. It bids rationally by fully incorporating learnings from past performance. It can value tens of thousands of individual impressions per second based on multiple data points.

Algorithms also remove the physical constraints that limits agencies to a small list of publisher partners.  An actual media planner using phone, fax and email can evaluate proposals from a couple dozen properties at most. From a time management standpoint, it is inefficient to actually go through with buying and optimizing more than a dozen sites/networks. But with algorithmic buying, the agency can buy and optimize in real time across thousands of sites.

Advertising is the art of persuasion but personal persuasion has now been taken out of the media buying process. Is it fair to make publishers compete on raw performance and not give them any appeals process when they lose? I would argue yes. It’s certainly a better deal for the brand whose marketing dollar is now working harder. I’d also argue that it makes things more fair for publishers, since they are no longer competing on the basis of access to decision makers. The algorithm is the decisionmaker, it will evaluate all publishers in the secondary channel, and its unbiased.

The traditional sales conversation — scheduling a conversation, determining if the product is a fit, then negotiating price — still happens but it occurs between agencies and technology vendors, not between agencies and publishers.

Now, the agency-publisher conversation is less of a sales conversation, and more of a collaborative problem solving conversation. Both agency and publisher are solving for the same thing: getting as much inventory as possible in front of the true decision maker, the buyside’s bidding algorithm.  Below is the complex equation – the now infamous ecosystem slide:

Ideally, this chart would be much simpler. There would be one big pool inventory that everyone plugged into, and bidding optimization would be entirely automated. This is not the case, unfortunately. Between the brand and the publisher, there are lots of different TradingDesk+DSP+Exchange+PubOptimizer+Publisher permutations, some of which may lead buy-side actors and sell-side actors to be disconnected. Coarse-grained optimization, like eliminating entire contextual channels or entire exchanges, also removes individual publishers from consideration.  So the conversation becomes about managing the supply chain to minimize these disconnects.

To use the old media buying paradigm as a metaphor, its as though agencies and publishers are administrative assistants, working together on logistics so that the the publisher’s inventory can get in front of the ultimate decision maker, the algorithm. That doesn’t sound glamorous, but getting the supply chain right offers much greater rewards than even the biggest direct deal.

[Thanks to @adexchanger for publishing the original of this post, which I'm now reblogging.]

As our industry continues to rationalize the way brands buy advertising, we’ve seen plenty of new companies and products pop up. Some provide solutions to old advertising problems, like universal frequency capping. Others deal with fresh challenges, like how to handle tens of thousands of real time bidding requests per second.

Despite the rapid pace of innovation, I think its possible to identify 10 larger trends that will continue to operate for years. Taken together they represent not just a bunch of complementary technologies and organizational challenges, but rather a new school of thought — a new way to to think about, plan, and execute marketing campaigns.

Advertisers bidding on media inventory now have a wealth of information on individual ad impressions and the audience behind them.
Much of this data has only become available recently and there is going to be a tremendous amount of learning in 2010 about what data is most important.

One new kind of data is ad visibility, which reflects how long an ad was visible to a user, if at all. Online advertisers currently pay for ads that are placed on portions of the page the user never sees, often because the user doesn’t scroll all the way down the page. Mpire, a company working on ad visibility, reports that as many as 40% of all display ads are never seen by consumers. That’s a whole lot of ads and a whole lot of advertising dollars wasted.

Digital marketers have been aware of the ad visibility issue but I think 2010 is the year where we’ll see it enter the mainstream conversation. Here’s why:

In an environment that is increasingly driven by quantitative analysis of performance, ad visibility is the single data point that is most predictive of performance. You don’t need to perform any statistical analysis to understand that an unseen ad is totally worthless. Imperfect reporting in major adserving platforms like DART and Atlas currently allows an unseen ad to receive credit for driving a purchase. But it is only a matter of time before these systems advance and unseen ads are recognized for what they really are — a big drag on campaign performance.

More and more ad technology companies are offering on ad visibility. Tracking ad visibility requires additional javascript code, but more and more companies are beginning to report on the metric. Here are a few of the companies who have started offering ad visibility in the last year.

• Lotame’s Time Spent technology
• RealVu, a dedicated ad visibility reporting platform
  
• Adometry, a custom reporting platform which includes
  
• Eyewonder’s Ad Visibility reporting suite
  
• MPire’s adXpose reporting product
• Eyeblaster’s Dwell Time reporting [authors note: added 12/16/09]

Ad visibility is well positioned from a market perspective to achieve widespread adoption. Big advertisers have a lot to gain through ad visibility reporting. By measuring the most basic performance driver for their advertising, advertisers can better manage their media buying. In the future, advertisers might insist that they only pay for ad impressions that are actually seen by consumers. Big publishers also would stand to gain if ad visibility becomes a key metric. Since ads are more highly visible on professionally produced content pages where users spend more time, ad visibility would allow premium publishers to claw back ad dollars from the long tail. David Cohen of Universal McCann points out that MSNBC has already formed a partnership with RealVu, a company that provides ad visibility reporting. Many of the recent advances in display advertising practices, and the resulting shifts in revenue, have benefiting advertisers and the long-tail, at the expense of major publishers. Ad visibility is unique because it aligns the interests of advertisers and publishers.

The importance of ad visibility is common sense. And based on my view of the market, I think it will be recognized as such in 2010.

When someone mentions “digital media optimization”, what do you think of?  An A/B creative test? The advanced algorithms that make one ad network better than another?  Pulling a report halfway through a campaign, calling publisher partners, and sending faxes to reallocate to better performing sites/placements?

Almost all digital media campaigns involve some element of optimization, and in the next 2-3 years optimization will become more pervasive, more rapid, and more automated.  Digital media planners and account managers will still have control over their campaigns, but they’ll spend more time considering strategic issues and less time calculating and comparing the ROI of different media options.

It’s hard to say who’s going to be the leader in the optimization space: media holding company ventures like Havas’ AdNetik, Vivaki Nerve Center, and Varick Media Management; dynamic creative services like Tumri, Adisn, and SnapAds; ad networks like Turn, Tribal Fusion, and X+1; or buy-side solutions like InviteMedia and Media Math.  The list goes on and on.  There are a lot of players who increase advertising performance by optimizing better than the next guy.  But when I think about Optimization, here are the three pillars that I use to frame the issue:

1. Audience

2. Media

3. Creative

Audience:

This the individual consumer that you are reaching with the ad.  These are the traits that would be relevant to optimization:

A) Demographic profile (age, gender, DMA, HHI, education, etc.)

B) Data from previous ad interactions (first impression vs. third impression, previous visits to campaign landing page, previous visits to advertiser homepage)

C) Data from transactions with brand (current customer vs. acquisition prospect, customer data from client CRM database)

Media:

This is the environment in which you are presenting the ad.  These are the traits that would be relevant to optimization:

A) Media property (Major media brand vs. long-tail site, blog vs. social network vs. editorial site)

B) Page placement (Above the fold vs. below the fold)

C) Context (If a semantic engine scanned the page, how would it classify the content?)

3. Creative

A) Format (Standard flash vs. rich media vs. video)

B) Creative Message (What product are you promoting?)

C) Creative Details (Color of ad, call to action, intro animation, logo placement)

In my experience, media optimization is the most common but there are huge gains to be had by optimizing all three, and even greater gains for those who optimize all three in an integrated platform.

Bottom Line: More and more of digital media will be optimized in the future. Marketers should look at the three pillars of digital advertising optimization — audience, media, and creative — and ask themselves: Which pillars am I currently optimizing? Which pillar do I understand best when it comes to controlling performance? Which pillar would benefit the most from quicker, more automated optimization?