Last week, we introduced distance weighting to trade area analysis to show the value of understanding the internal patterns within a trade area. Here we use a similar concept and apply it to the common practice of building a customer profile using Panorama segmentation.
Recently, we were asked to help with a project to find a new location for a membership facility which was facing higher lease costs and looking to minimize disruption to their members but also to relocate to an area with a better demographic fit to their membership. Drawing a largely blue-collar type customer, the facility is currently located in an upscale area near downtown that is midway between two core demand areas.
Using a customer address list, we appended Panorama codes and ran some simple analyses. The profile is very sharp, with 60% of the customers coming from just three neighborhood types – 26 High Density Diversity, 62 Living Here in Allentown, and 40 Beltway Commuters. Indeed, the top five segments account for 74% of the total customers, although they comprise just under 22% of the total adults in the trade area.
Using standard indexing techniques to weight customer counts against the trade area, we discovered that the average distance travelled varies considerably by segment – for example the group 40 Beltway Commuters travelled on average nearly 12 miles, whereas the 26 High Density Diversity group travelled just over 6 miles on average. The patron who travels 12 miles is likely a better demographic fit than the one that travels 6 miles.
By applying a distance weight to each customer, we can eliminate some of the biases in customers which are based on the nearby population and instead take a more integrated look at who is most likely to come to a new location. We do this by weighting customers by distance, in this case each customer was weighted by Log (Distance) and then scaled to the total.
The table below shows the largest customer segments, with indexes based on unweighted and distance weighted counts by Panorama segment:
Here we see the value of using distance weighted customers – group 27, which supplies a number of current customers, is an unlikely target since its members traveled only 3 miles and they may be patrons of convenience only – so each customer from this group is given a lower weighting in the analysis. On the other hand, groups 09 and 33 are much better targets than a simple analysis would suggest, since they travel over twenty miles to the site. When looking for a new site, we can take advantage of the weighted indexes, which are more likely to indicate a true customer profile rather than the unweighted one, which reflects the locational bias of the current site.
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