Lately we’ve made some pretty great state-border-agnostic USA density maps, or heat maps, by feeding WebTrends city reports to a powerful mapping software product called MapViewer by Golden Software. For us, buying the $250 mapping software was a quick decision once our end users started getting insights and discussions from the maps we made with the trial version.
(If you’re wondering, these maps are just color variations of each other (done with the Snagit screencap editor) from the original blue and red MapViewer map shown at the top.)
In our opinion, these heat maps are a lot better than the usual geographic output of analytics tools. The reports are either huge lists that are hard to visualize or monolithic colored state maps that don’t show enough detail and are, frankly, misleading. Using state borders makes the data stupid.
As you’d expect, heat maps based on pure visit counts usually resemble the underlying population distribution and there tend to be few surprises. To get more insights and start more discussions with our new heatmap approach, we’ve tried the following variations:
- Calculate visits per capita (divide localized visits by localized population). The resulting map is always quite different, sometimes nearly opposite, to raw visit counts. The high-intensity areas (per capita) may turn out to be the small towns and rural areas, for example, and there may be a big de-emphasis of the coasts. The larger cities that remain lit-up can be contrasted with the cities that disappear using this alternative calculation, possibly indicating a localized word-of-mouth situation or something else that’s geographically correlated.
- Add climate data to the feed (average temperature, sunshine days) for uncovering interesting relationships with seasons.
- Show maps over time. You can get good time lapse effects with products like FantaMorph. If your content is affected by seasons, the resulting movie can give you a better sense of timing of campaigns in different areas.
- Use an engagement stat other than visits – length of visit, visits per visitor, leads or purchases, conversion rate. Start with the supposition that conversion rate etc should be about the same anywhere. But what if this analysis shows significant hot spots? It would be worthwhile theorizing why. Is the pattern perhaps corresponding to local demographics?
- Do a different map for different referrers – Google vs Yahoo vs AOL vs Comcast Search for example. This could help your geotargeted advertising, especially because AOL and Comcast seem to be geographically different from Google and Yahoo.
- For consumer packaged goods sites, maybe calculate and map the ratio of localized visits to localized purchases if you can get access to that (expensive) kind of purchase data. Look for gaps that may indicate local interest but not enough distribution. Some CPG companies keep good records on their distribution network details, so this would be another interesting place to look for interest-supply gaps.
If you do buy MapViewer, contact us for a lookup file we’ve made that changes WebTrends cities to MapViewer counties (with populations) in Excel. It’s actually the converted counties file that goes into MapViewer. Plus we have basic instructions on how to get MapViewer to produce the maps we show here. It’s not super-simple and the whole process from the WebTrends Top Cities report to a finished map takes maybe 10 minutes (using Excel 2007), but it’s simple enough.
P.S. Our lookup file uses the spelling and abbreviations produced by WebTrends GeoTrends prior to version 8.7. We haven’t yet switched over to the latest and greatest GeoTrends version that, we’re told, uses a few different abbreviation conventions. It shouldn’t be hard to find and make the needed corrections.
P.P.S. MapViewer has worldwide maps but we haven’t tried them with WebTrends international city data and don’t know anything about possible lookup table issues.
P.P.P.S. If you attended one of our Engage dashboard sessions where we showed one of these maps, and if you left a business card, it may have been “housekept” by hotel do-gooders during the after-workshop conversations. Please get back in touch.