Tubes
Tubes is my [current|largest|in-progress] side project.
To check it out, have a look at the up-and-coming A4D Offer Optimizer, powered by Tubes.
The leading tracking solution for an affiliate interested in nothing-too-complicated is indisputably Tracking202. The next step up is an expensive ad server. T202 is great at what it does, which is specifically to be a click tracking solution for campaigns hosted on your own servers. Writing a self-host app means that Wes had to emphasize simple setup and configuration, and while T202 does leverage MySQL partitioning and Memcached if you configure it to do so, most affiliates do not. With properly configured servers designed to analyze click data, and a software designed for clustering and batch job processing, I knew it would be easy to provide significantly more interesting and relevant data. So I deployed my tracking solution to “The Cloud”, so to speak, instead of to a bunch of servers I don’t have any control over. One server farm collects all the hits and processes redirects with a low-latency guarantee. A separate farm analyses the data and looks for trends. A third farm handles all the database storage and pools the connections. You don’t have to manage any of that.
And instead of a 7-step configuration, where you have to tell the system which $$KEYWORD$$ to track, I decided to analyze everything, and force you to configure nothing. Every time a user hits a Tubes URL, I save an exact copy of the entire web request into the database. I keep everything, because I analyze all of it. Over one thousand lines of code powers the analysis engine behind Tube’s classification system. It chops up your http://ugly.confusing.domain.com/structure?id=12345&traffic-source=yahoo.com, and looks for the common factors that consistently lead to higher payouts — Which is really the only thing you care about, right? This is an application that’s designed to look for interesting data, and display it up front. It parses URL parameters, referrers, file names, domains, subdomains, countries, US cities and states, IP blocks, and [soon] even reverse WHOIS lookups. If any of these factors gets at least a few clicks, and seems to positively or negatively influence your EPC, Tubes will find it.
So I invite you to give it a try. Pump it full of all the data you can handle. By nature, this thing inherently split tests for you — Just create a campaign, and place the same landing page pixel on 3 of your landing pages (“/1.html”, “/2.html”, “/3.html”). Then send them all some traffic, and the three different pages will show up in the graph as 3 different lines. No configuration necessary; Tubes can tell the traffic apart by the referrer the pixel is loaded from. If your overall EPC is $1.50, and landing page #1’s EPC is exactly $1.50, that’s par for the course — pretty uninteresting. Accordingly, the graph line labelled “/1.html” will show up on the bottom. Also, if LP #2 has an EPC of $0.00, but only got 20 clicks, that’s pretty uninteresting. But if LP #3 is raking in $2.25 EPC, based on a few hundred clicks and a full days worth of data, that’s interesting. This will be the first and fattest line on the graph. If all of your landing pages are converting about-the-same, but U.K. visitors convert at 200%, well, Tubes will tell you that, too.
I’ll write more on this later, and eventually I’d like to technically detail precisely how it works. First I have to finish coding it all, though 
[worried about your private data? is the lack of self-host a deal-breaker for you? please get in personal contact with me so we can talk about your needs. i'm considering various ways to approach the issue, i'd love your feedback!]