Here in southern California, it has always been a source of frustration that bathing suits and shorts seem to disappear off retail shelves long before locals have abandoned the beach for the winter. Automated merchandising can certainly help, but only if the data on which it is based is sufficiently detailed to capture the abrupt changes in local climate that accompanies the rugged topography of much of the west. We have recently seen very nice time lapse maps showing the change from winter to summer temperatures, but if you look carefully, you discover that these are based on county average temperatures. If you were to use this level of coarse data as the basis of an automated merchandising program, you would be consistently stocking merchandise that may not be appropriate for the local climate.
The two maps of southern California clearly illustrate the issue. The first shows average daily high temperature for January, in which elevation changes clearly dominate. January high temperatures within Los Angeles county alone range from the mid-40’s to the low-70’s within just twenty miles.
Likewise, annual rainfall averages in southern California range from under ten inches a year in the inland deserts to well over forty inches a year in the mountains near Big Bear and Lake Arrowhead. Looking only at Los Angeles county, it is clear that an average precipitation figure for the county masks substantial differences over very short distances.
AGS has been creating climatological estimates at the block group level for two decades. Using years of available weather station data, we employ a series of sophisticated spatial models which include elevation and prevailing wind direction in order to capture the various micro-climates which occur in mountainous areas – and not just in the western states.
The annual snowfall in areas along the south and east shores of the Great Lakes can be nearly double that of areas just miles away – think Buffalo, NY and its legendary snowfalls courtesy of Lake Erie. Compare this to the relatively low snowfalls found at the west end of the lake in Akron. There are substantial differences within counties which are simply not captured without proper modeling.
A retailer using climate data for the purpose of automating merchandise selection, but is using county based data, is missing an opportunity to actually get things right. Details, especially in climate, matter.