Post-click marketing at its most basic is a conversion path rather than a single landing page. People who respond to your ad land on a path. As they move along this path, they make choices that give you information. Here’s an example: You advertise a health-care solution to hospitals. On the first page of the path, the respondent must choose between “Solutions for Large Hospitals” and “Solutions for Small Hospitals.” When the respondent makes this choice, he “segments” into one of these categories. Your post-click results should show you how many small hospitals are converting, how many large hospitals are converting, and how many of each are abandoning.

Take things a step further. Instead of putting up one path, you might test two or three paths at one time. Let’s say you’d like to test the effectiveness of a certain graphic look and feel. Or the effectiveness of using more text as opposed to less. You do this by putting up more than one path. A person who clicks on your ad lands on one of these paths, assigned at random. The results tell you which path is converting the most respondents.

A good post-click marketing deployment should also allow you to collect data on traffic sources. So, say your traffic comes from ads you’ve posted on Google and Yahoo! You can see the data on conversions by traffic source, and know where to optimize your buy.

Putting it All Together: The RTP Matrix

Viewing the performance of respondent segments, traffic sources and paths each on their own is helpful. But the potential for real breakthrough post-click marketing intelligence is at the intersection of these three forces. The company ion interactive calls this the RTP matrix for Respondents, Traffic sources and Paths.

RTP matrix testing addresses the ultimate question: Which respondent segments, arriving from which traffic sources, are converted most effectively by which paths? Once you uncover that answer, you can optimize your entire online direct marketing chain, not just its individual components.

We call this matrix testing because it compares every segment in every traffic source against every path, in a three-dimensional matrix. If you’re familiar with Excel, this is similar to a pivot table; in data mining applications, it’s often referred to as a cube. Success is measured according to a combination of three metrics:

•    The conversion rate, as a percentage of the total traffic;

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image of Scott Brinker

Scott Brinker is co-founder and CTO of ion interactive, a provider of landing-page management software and conversion-optimization services. He also writes a blog on marketing technology called Chief Marketing Technologist.

Twitter: @chiefmartec.