We all know layout of products in a retail store is important. It draws customers into the store, guides them to paths and hope they buy more.
How do we plan a layout to maximize sales? It is usually a mixture of art and science. Some use gut feel base on historical sales of complementary products, some also rely on planograms to develop higher sales from pick-faces. While these are good for smaller layouts and specific shelves, options and opinions may be divided when it comes to positions of different category of products and their relative location to each other. This is where a combination of lift (a measure used in data mining) and relationship chart can come in.
Lift takes into account support and confidence measures in data mining. Support is the probability of a final outcome from all transactions. Confidence is the chance of an outcome given a supporting outcome. Categories with higher lift will take the highest Total Closeness Rating (TCR) used in relationship chart for layout. Presto! You can know locate the category with the highest TCR value first! :)
By locating products close together, trans-shipment flows through Singapore will certainly be faster.
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