Location, Location, Location
October 29, 2006
When Scottish hotelier Charles Forte was asked about the secret of success of his hotels, he is reputed to have said, “Location, location and location.” . This credo also seems to apply to almost all companies who do some form of brick and mortar distribution. Be it a retailer who wants to improve its productivity or companies who want to optimize the spend on distribution and associated trade marketing.
Companies have historically focused on removing inefficiencies from supply chains by optimizing inventory and logistics. Store location which falls at the intersection of operations research and marketing has only recently received some focus. The major challenge in being good at locating new stores or evaluating the future potential of existing stores, is integrating the customer segmentation strategy with the distribution data.
We recently helped a client to evaluate the retail distribution strategy and came across this problem. We solved this problem by creating a market potential index, by identifying the concentration of client’s most profitable customer segments, for every county within client’s footprint. We could then map the number of points of sale (POS) per each county to evaluate whether the client stores are in ‘desirable’ locations.
We found that 24% of client’s retail investments were made in locations that were less than desirable, and realignment of POS to desirable locations would save the company millions of dollars a year, even by conservative estimates
November 22, 2006 at 6:36 pm
I recently came across a paper (http://arxiv.org/abs/physics/0608297) that applies the physics behind inter-atomic interactions to reveal store-store interactions and the importance of “location”.
According to Pablo Jensen, author of the paper titled “A network-based prediction of retail stores commercial categories and optimal locations”, the spatial organization of retail commercial activities is organized in a network comprising “anti-links”, i.e. links of negative weight. From pure location data, network analysis leads to a community structure that closely follows the commercial classification of the US Department of Labor. The interaction network allows to build a ’quality’ index of optimal location niches for stores, which has been empirically tested.
Thanks to this quantification of retail store interactions, the author constructs a mathematical index to automatically detect promising locations for retail stores. The basic idea is that a location that resembles the average location of the actual bakeries might well be a good location for a new bakery. The quality of a location is estimated by summing up the strength of anti-links with the neighboring stores in a 100m radius. The author found that bakeries closed between these two years are located on significantly lower quality sites. Inversely, new bakeries (not present in the 2003 database) do locate preferentially on better places than a random choice would dictate. This stresses the importance of location for bakeries, and the relevance of the quality here defined to quantify the interest of each possible site. Possibly, the correlation would be less satisfactory for retail activities whose locations are not so critical for commercial success. Practical applications
of this quality index are under development together with Lyon’s (city in France) Chamber of Commerce and Industry : advice to newcomers on good locations, advice to city mayor’s on improving commercial opportunities on specific town sectors.
This study shows that, through locations, the retail world is now accessible to physicists. This opens many research directions, such as : are there optimum store distributions, whose overall quality is higher than the actual one? Can one define store-store interaction ”potentials” by analogy with those used for atomic species? Moreover, new tools are needed to describe networks containing anti-links, starting with a basic one : “how to define a node degree?”.