It is the Year of Optimization. The recent acquisition of TouchClarity by Omniture is yet another confirmation of an intense surge in interest in technologies that make computers sell better to people. But what in the world *is* optimization?
As a matter of disclosure I am not a PhD. My ADD is a strong inoculation against advanced scholarly pursuits.
However, I have the unique viewpoint of experience. I co-ran a company, Fort Point Partners, that was responsible for deploying a dazzling range of technology for companies like Nike, Best Buy, and about 50 other firms. We launched rules-based systems (ATG Scenario Server, e.Piphany), search systems (EasyAsk and Endeca) and more advanced segmentation and modeling software (LikeMinds, Personify, netPerceptions to name a few). We also ran a lot of tests.
Our goal was simple - make the computer capable as a salesperson. For us, optimization is a fancy word for making a selling process more relevant and engaging for your customer so that they make you more money. And the best optimization tool was one where a marketer could adapt and learn, but the machine did the work.
I see four major approaches to optimization that each have critical value for the marketer (I will use this space over the next week or so to go into more detail on each approach):
1. Experimentation - testing approaches including A/B, multivariate, Taguchi, optimal design and others. Showing different experiences to different control groups to determine a "winner" or "best recipe" based on conversion rate, revenue, or other outcome. Read more here.
2. Targeting - also referred to as "rules-based optimization". Defining explicit segments and rules for delivering content experience. These can be simple definitions like "show the iPod when our customer searches for "iPod" on Yahoo or very sophisticated behavioral segments.
3. Behavioral - applying AI or linear regression to prior data to determine predictive factors from data to drive the display of content.
4. Social - offloading the work of relevance to the community through ratings, reviews, tagging, or other forms of participation.
Take Google, for example. They are algorithm guys, right? They use a predictive model that is finely tuned to determine the elusive grail of "relevance" and their results are unbelievable. Yet they also use targeting and testing. True, they outsource the work of specifying the rules to us through keyword selection, bidding, and match type, but this is targeting at its finest. And they test regularly - evaluating different treatments of the SERPs.
So what is the best optimization approach? Optimization is just marketing with math. If your user base ratings improve the relevance of your search results, then do it! If testing helps to eliminate your CEO's bias towards acres of copy, do it! The marketing "mix" for optimization is going to take time to get right, but will yield 
tasty morsels of revenue improvement every step of the way.
We started Offermatica not because we discovered the magic algorithm that turned a computer into a selling machine, but because we found out that the keys to selling online were speed and control. Speed - because marketers had no time, so the machine was going to have to do the work. And Control, because the marketer still needed to be "in the loop", either driving new ideas or removing crazy outcomes.
And remember this: Marketing is done by marketers. Machines just help us listen and aim better.
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