As a follow-up to my previous post on how to use Web Analytics to determine the width of your Internal Search Query box – I thought it would be suitable for me to deploy an internal search utility on my blog. Looking around for a solution (beyond just adding a link to technorati) that could provide us with quick, accurate and measurable results – I ended up deciding to use the Google AJAX Search API. First, because it is free (no money is made on this blog), second because I could “code” it to my taste… and it is actually enjoyable being geeky from time to time :-)

I caught myself in trying to code the search utility for optimized tracking and not for optimized usability – this, as in I wanted the “normal” Search Query Box functionally:

However; Internal Search utilities are changing and I finally decided to go with the potentially better “Web 2.0” solution (time will tell). A solution where instant results are shown on an unchanged page after each keystroke.

Let us try to assess the results before talking about WHAT to measure: Searching for e.g. “emetrics san francisco 2007” we get:

Search results – “Web 1.0”
1. Emetrics San Francisco 2007
2. My comments to Emetrics London 2007
3. NEW Google Analytics launched at Emetrics SF Today!
4. Notes from ad:tech San Francisco
5. 9 Ways to Make Money on Analytics

Setting up a similar and comparable list is simply not possible in a dynamic and instant web 2.0 results environment (as the one deployed on this blog) – as we have instant results after each keystroke. We can on the other hand look at the numbers of results per keystroke:

Search results – “Web 2.0”
1 results for the phrase “e”
0 results for the phrase “em”
0 results for the phrase “eme”
0 results for the phrase “emet”
0 results for the phrase “emetr”
0 results for the phrase “emetri”
0 results for the phrase “emetric”
8 results for the phrase “emetrics”
8 results for the phrase “emetrics ”
5 results for the phrase “emetrics s”
0 results for the phrase “emetrics sa”
3 results for the phrase “emetrics san”
5 results for the phrase “emetrics san ”
3 results for the phrase “emetrics san f”
0 results for the phrase “emetrics san fr”
0 results for the phrase “emetrics san fra”
0 results for the phrase “emetrics san fran”
0 results for the phrase “emetrics san franc”
0 results for the phrase “emetrics san franci”
3 results for the phrase “emetrics san francis”
3 results for the phrase “emetrics san francisc”
5 results for the phrase “emetrics san francisco”
5 results for the phrase “emetrics san francisco ”
0 results for the phrase “emetrics san francisco 2”
0 results for the phrase “emetrics san francisco 20”
0 results for the phrase “emetrics san francisco 200”
5 results for the phrase “emetrics san francisco 2007”

So dependent on how quick I am to type and how quick my internal search utility is to respond I could end up performing 27 searches. If I am semi fast in typing in the query I might end up doing a random 10 or 15 searches, furthermore I might not have the qualification to look at the screen while I type and then as a usability fact “only” see the final result. Another interesting fact is that, if I were in fact searching for “Emetrics San Francisco 2007“ and that I would type and see results at the same time, I would stop after “emetrics”, but even more challenging, this would only count as a success search phrase keyword until I write a new post about Emetrics which would then be on top and then the “Emetrics” term might not be enough to guide people to the “emetrics San Francisco 2007“ post.

This is really fascinating!? :-)

Let us try to have a look at the standard KPI’s (a off the top of my head list – so do not take this as a final list of the most used Internal Search KPI’s, however; the list still rings true and must still pretty much be expected of anybody doing decent internal search tracking)

KPI’s for Internal Search – “Web 1.0”

I have plenty of ideas, as I think it is somehow obvious that the standard static internal search Web 1.0 KPI’s are not very suitable for a Web 2.0 environment, but I have no data as we speak, so it is time to deploy (from a tracking point of view) a few of them and see how actionable the results are. And this might even give me an opportunity to consult our Oracle “Avinash” or the smart people at OX2 as well :-) – More about the subject later when I have had some time to do a bit of research. And PLEASE do leave a comment with your ideas. :-)

N.B.
If you want great input for how to evaluate static internal search performance – go look at Gary Angel’s input on the matter. It is as usual a first-class post.