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01.05.10
 By Gary AngelI didn't quite stop working this past week (when Semphonic is closed) but it sure feels that way as I get ready for the New Year! Now that's it time to shed the holiday mindset and immerse ourselves back into the world of work, I'm going to resume my series on tactics in web analytics visitor segmentation. Segmentation is at the heart of most real analysis – and in this series I'm focusing on analytic segmentation – not segmentation of large and significant populations for reporting purposes. In the last post, I showed how we used segmentation to isolate, study and improve the behavior of printed catalog searchers coming online to look for a specific product. Today, I'm going to show an example where visitor geography turned out to be the key to effective segmentation and personalization. Example: Geo-Based Searching Use Case: A real-estate focused site knew that geography was probably important to visitor behavior, but despite many years of operation there was significant disagreement about the online population's searching behavior and how it related to where they accessed the internet. Client Question: The client wanted to know how visitor geography and search behavior were related. Key questions included:
• Did most visitors use the site to look at properties outside or inside their current location?
• Did visitors search the same locations repeatedly? • Were there significant differences between visitors searching in their own geography vs. those searching outside their own geography? Measurement Issues: Search geography was captured as an input search string – making it difficult to consistently resolve since it had many variations. Tool: We used Omniture's Data Warehouse tool for the analysis. Methodology: One of the challenges here was that we didn't have a direct correlation between search term and visitor geography. Getting that kind of report is one of the reasons we usually recommend that companies deploy a Vista rule to copy the visitor geography into variables. However, with data warehouse, we could get at the data. It was more a question of how to do it conveniently. Let's start with how we tackled the first question – did visitors use the site to look at properties within or outside their current geography? Obviously, we knew we were going to see both behaviors, but the relative percentages were hotly debated. Unfortunately, in web analytics systems as they exist today, you can't generally create a segmentation based on the comparison of two variables. In other words, we couldn't use a query that selected all the visits where the internal search term was contained within the SiteCatalyst visitor DMA. In addition, we didn't have the ability to apply any operators to the various strings involved – so comparison would be inherently hit or miss. One alternative was to produce a giant data warehouse request that simply gave us all the combinations of visitor geo and search term along with visit count. We could then load this into Access or SQL-Server and have at it. For an analysis that required comprehensive coverage, that might have been our approach. But this was just a piece of wider site analysis and we really couldn't afford to spend more than about 8-10 hours on the whole use-case. So what we decided to do was focus on a small set of target markets that we took to be representative – markets like Bakersfield for mid-size cities and Tahoe for vacation destinations. We then built a segmentation based on search terms that contained the relevant target area. This took advantage of the segmentation builder's ability to use "Contains" and multiple rules strung together. Continue reading this article. About the Author: Gary Angel is the author of the "SEMAngel blog - Web Analytics and Search Engine Marketing practices and perspectives from a 10-year experienced guru. |
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