Posted by Gary Angel

I 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.

Using segments like this, we generated reports on visitor geography for each target area. This allowed us to answer the first question in detail. For each target area, we knew what percentage of the searchers originated in the target area, what percentage were adjacent (from nearby DMAs) and what percentage were outside the target area.

It turned out that on this particular site, for all areas except resort cities like Tahoe, a very heavy majority of searchers were local.

Note that this segmentation was visit-based – in the next step we extended it to include visitor behavior.

This was almost too easy. The most important decision was simply to focus on a set of target markets that represented different potential use-cases instead of trying to answer the question for every market and for every search.

The second case was just a little bit trickier. We wanted to understand whether the site displays could be tuned based on previous visitor search behavior. So the question was did visitors tended to search on the same places when they returned to the site.

For this analysis, we extended our original target market segmentations to look at all visitor behavior when the first visit included a search of the target market. The segmentation looked something like this:

Note that the Visit filter is now nested inside a Visitor filter. This means we’ll get all of the behavior for any Visitor that meets the Visit criteria. To the Visit criteria, we added that the Visit Number for the target market search had to equal one. This gave us a population of visitors who started by searching in one of our target areas.

Now, we generated a report that looked like this:

With this filter, we were able to see all the searches done by visitors in subsequent visits to the site when they began with a Bakersfield search. We used Excel to aggregate the results and with a little arithmetic (or another segment) we could get all the Bakersfield searches who started off by doing something else in their first visit.

It turned out that for this particular site, the vast majority of visitors in all of our target markets tended to repeat their searches identically on subsequent visits.

Conclusions and Recommendations: This analysis answered some fundamental questions about how visitors used the site – but it also suggested some extremely important personalizations. For any site, the faster you can get visitors to the stuff they want, the better and more successful the experience. The analysis strongly suggested that the site could profitably display results for visitors as soon as they arrived on the site – without even waiting for a search. It also suggested that keeping and using previous results to tune session re-entry pages would have similar benefits.

Reflections: One of these segmentations was trivial, the other only a little more complicated. But used judiciously along with the idea of isolating a representative set of target markets and they were able to answer definitively some questions that had persisted within the business for years. In addition, the answer suggested some significant personalization opportunities that the site was missing.

One other really important aspect of this analysis – and one I find repeated over and over – is the importance of visitor level segmentation. In nearly every real analysis, it turns out to be vitally important to be able to extend a segmentation to retrieve all the behavior for a visitor meeting certain behavioral criteria. As an extension of that, the ability to report on behavior by visit number is almost equally important. We use visit number in reports with startling regularity – and because of that we recommend that visit number be kept in Omniture variables (using a Vista rule or plug-in).

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This entry was posted on Tuesday, January 5th, 2010 at 8:46 am and is filed under Analytics. You can leave a response, or trackback from your own site.

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.