Improving internal search should be one of your primary goals. It’s probably used by an extraordinary amount of people, searching for all sorts of things. In this post, I show how to capture the number of times people search, a breakdown of keywords and search attempts, and also demonstrate how much search is costing your organisation.

Ok, it’s been way too long since I posted an update, and loads has happened since November. I’ve been to the Omniture Summit in the US, I’ve presented again at the Education Marketing Conference in Sydney and I presented twice at the Omniture Client Briefings in Sydney and Melbourne. That was all in March.

A couple of conversations at the (incredible) Omniture summit sparked an interest and I thought I’d share with you how we implemented. Long pages. Well, pages that go beyond the fold.

Do you know if your users are scrolling down to read the content? Did you know you can get this insight?

One of the little-used nuggets in SiteCatalyst is “participation”.

It’s a given that you want to know how many sales you’ve made, or how much revenue you’ve generated, but what about which pages have helped to contribute to that conversion. Not every visitor follows the same path through the content, and it’s therefore beneficial to be able to see which pages are more likely to drive a conversion than others, thereby exposing your most valuable pages.

If you’re thinking of trying to compare web analytics results by vendor, or even by the original log file, you’re in a for a very tough time.

Genie + bottle + uncork = Long time, not good time.

The problem is that while there are basically two different methods of data collection (via server logfiles or via JavaScript tags), the variables associated with both, in a real world environment, make it almost impossible to compare results.

And therein lies your genie out of bottle, and you scrabbling around trying to justify the results. You’re best off not even trying.

In my previous post on People who liked this, also liked…, I put forward an idea how to generate “related” products of interest, based on what users were looking at, which could then be automated and re-published back to a site, based on Omniture data.

Having implemented this, we’ve made an interesting observation, which changes one our user assumptions, and I thought it was worthy of a quick posting.

One of the nice things about Omniture is the ability to export information out to other systems. We use this feature to generate tag clouds on our site, based on the most popular courses viewed over the last 30 days, segmented for different audiences.

Search is a veritable gold mine that is frequently ignored.

I’m not talking about Search Engines and Keywords, I’m talking about your internal search. Providing you track internal keyword searches, you can gain a wealth of understanding.

Internal search is generally used as a quick wayfinding method, highlighting areas of content that are well used, but are not readily available. And more often than not, it’s seasonal as well.

So, there’s lots of metrics and lots of terminology. Understanding the meaning is the first step to understanding what’s important.

This post is for those of you who want a quick primer into what’s important and what’s not. It doesn’t however talk about the “why” – that’s for a future post.