[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.

In my first post, I wrote about how advertising optimization allows marketers to increase ROI by shifting allocations between audiences, media types, and creative versions. Usually these three areas are optimized against direct response metrics: the click and the conversion. But the use of automated optimization should not be synonymous with a myopic focus on click and order counts. It is possible to optimize campaigns to do more than generate clicks and online sales. If you can measure the persuasive, memorable, emotional impact of an ad — impact which clickthrough rates and conversion rates alone does not capture — you can optimize campaigns to better build brands.

But first, let me give the click it’s due respect. For one thing it’s immediately measurable. Brand study survey results might not be available for weeks, but the click is measured instantaneously. Second, attribution is clear. The adserver knows which exactly which site delivered the click, so its straightforward to optimize to deliver more clicks. Lastly, there’s no selection bias since every click is captured.

Its difficult to find a branding metric as easy to incorporate into optimization models as a click, but here are some other metrics that agencies can use to optimize branding efforts.

1) Ad Exposure Time — One important difference between ad impressions is ad exposure time. Lotame has published a white paper, “Time Exposure by Banner Size,” that found that 300×250 ads were visible for an average of 13 seconds, compared to an average of 5 seconds for 728×90’s and 2 seconds for 160×600. In addition to creative size, ad exposure time varies greatly by site type. Banners on blogs and social network pages will have much lower exposure time than a companion banner on a video site. VideoEgg recently released their floating Twig ad unit in part to address the challenges that blog publishers face given the low ad exposure time.

All else being equal, an ad that is visible to the user for 20 seconds should have a larger impact on brand health than an ad that is visible for 2 seconds. This type of information should be incorporated into advertisers’ impression bidding strategies.

If a planner is building a branding campaign, they should be able to target sites in their planning process where the ads are visible for a longer period of time. At the very least, they should be able to ensure that if their advertiser’s name and logo only shows up in the 7th second of animation, that each impression is visible for least 7 seconds on average. In a buying platform, planners should be able to enter in time exposure rules by creative. There also needs to be a comparison between value (average ad exposure time) and price (CPM). This is where planners need to rely on the automated optimization power of a buying platform.

2) Time Spent on Landing Pages — Not all clicks are created equal but just as click traffic for DR campaigns can be differentiated by conversion rates, clicks for branding campaigns can be differentiated by time spent on the site. Optimizing audience targeting by clickthrough rate is a risky strategy when studies have shown that a small majority of users generate the majority of the clicks and that display exposure still has a significant impact on those who don’t click. For branding campaigns, time spent on the landing page is a much more meaningful metric for understanding which audience segments are interested in engaging with the brand. By incorporating time spent metric into automated buying platform, an advertiser can pursue a pay-for-engagement buying model on any type of media with any type of creative assets.

3) Visits to Advertiser Homepage — A consumer who doesn’t click on an ad may still visit the advertiser’s homepage to learn more about products. Measuring the number of impressions it takes to generate a visit to the homepage is good measure of ad effectiveness. This metric is especially important for multi-channel retailers where online advertising drives consumers to do further online research but end up buying in-store. The impact of online advertising on these particular consumers is never captured by online click and conversion metrics.

There will never be an algorithm to spit out innovative, memorable integrated marketing programs, but marketers still have the opportunity to use automation, optimization, and data to build brands.

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?