Clustering to Improve Merchandise Allocation, Testing, and Forecasting: An Application of the K-Medians Algorithm

Master of Economics

BACKGROUND

Localization of merchandise assortment has become popular among retailers in recent years.  The recognition that store population’s heterogeneity is an important consideration in the testing, allocating and pricing of merchandise  has boosted the profits when the theory is correctly put to practice.  The issues concerning localization revolve around what stores should be grouped together?  What variables and methodology should be used to group stores to ensure the minimization of differences for stores within a group for all groups created?

Dr. Fisher and Dr. Rajaram from the University of Pennsylvania and UCLA found that using a k-medians clustering methodology based on sales, minimized testing and stock out costs and improved the accuracy of chain-wide forecasts based on small store sample merchandise testing.   The paper they wrote appeared in the Journal of Marketing Science Vol. 19, No. 3, in the Summer of 2000 and its titled, “Accurate Retail Testing of Fashion…

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