This is my first post to synthesize the information I listened to or read about at Emetrics Summit, earlier this week in San Francisco:
I sat in on Alex Langshur's session - Measuring ROI when you don't sell on May 7th, and noticed a lot of the analysis was based on datamining site search query logs (which I've done at IBM and for my own clients, outside IBM). The presentation is not online yet but there's other case studies on Langshur's site.
For example - Analyzing Intent (aka alignment of intent) is Search Analytics where you can compare keywords by category over time to see what went up / down, as well as seasonal variations.
However, Rich Wiggins and Lou Rosenfeld presented Listening to the Search Box: Search Analytics in a Nutshell, pretty much at the same time Langshur was presenting (in another suite). and the content of both presentations overlapped, judging from the copies I have in the Emetrics Presentation Handbook. Rosenfeld noted, and I have noted the same in my own experience, that:
- ~45% of search referral (from Search Engines) are variants of a company's name. This is certainly true of Branded businesses, like the Architects I have dealt with, including the one Architect client I still have, www.Mascord.com.
- There's a lot of Long Tail data in local search queries
Rosenfeld suggested that you data-mine search query logs and map much of your long tail data to popular pages in your site that you think go with the query. The problem with that … you need special tools to do it as the massive amount of long tail data makes doing this manually way too tedious for humans to do (and be sane for long) - I'm talking about massive amounts of queries where there are only one or two searches internally on that phase - and it's unique - meaning only one person executed that search.
Both presentations mirror what I heard at Search Engine Strategies last month, in NYC, where I spoke with someone who data-mines Search Query Logs to determine intent of Searchers and if the content of a site matches what people are searching for - and if not, changing or improving content in specific ways to increase customer satisfaction.
As Rosenfeld pointed out - there's often little time and few useful tools to parse Search Query Logs in order to generate reports - and that the work of analysis is both hard and often boring (because the right tools have not been developed - I firmly believe that).
My takeaway from both sessions (even though I only attended one of them - I have the material from both and familiarity with the subject) is that Site Search query logs don't get the attention they deserve in any of the organizations I have worked with and most companies don't realistically know what to do with the information - even if they were able to data-mine it, segment it, and match up the relevant pages.
I'd even go so far as to suggest vendor solutions that learn and match up the right pages (or create content on the fly out of XML data) over trying to manually determine what the right content on your site is for a query that was unique and executed one time for someone's specific need at the Long Tail.
In other words, for a large site, I think you'd need an automated tool with some human oversight - but doing it manually is almost unthinkable - based on the sheer volume of data that needs to be analyzed and remapped to the right pages.
