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(RSS) Method for influencing a shoppers choices in the store. Disclosure Number: IPCOM000016631D
Original Publication Date: 2003-Jul-07
Included in the Prior Art Database: 2003-Jul-07
Document File: 1 page(s) / 40K

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A method for optimizing sales using weather patterns and data mining techniques in an automated fashion to predict what items whould benefit from being placed on sale in a retail environment.

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(RSS) Method for influencing a shoppers choices in the store.

      Disclosed is a method whereby the short and intermediate range forecasts are used to identify weather relevant items that a shopper may be influenced to purchase. This would be an automated process whereby past, present, and forecasted weather are used to determine what items should be placed on sale to optimize sales. For instance, when severe weather is forecast the personalized guidance given to the shopper may encourage "<inclement weather> is forecast in the next 72 hours. Do you need batteries for flashlights or your radio?". While this might be most useful for inclement/severe weather it could be used routinely. "High temps will reach 92 Saturday and 95 on Sunday. Special sale on <beverage of choice>. Stock up at today's special price.". Or, "Ice storm forecast with possible power outages. Do you need (1) batteries, (2) candles, (3) Down comforter, (4) Generator, .... ".

    In addition, data mining techniques may be used to identify non-obvious correlations between weather patterns (previous and forecast) in order to provide marketing guidance to the store (about what to place on sale) and to the shopper. For instance if it's been gray and dreary for a week and tomorrow it's going to be clear and sunny, data mining could show a spike in sales for specific items or classes of items that the store could be take advantage of. The store could place the item on sale and/or the marketing guidance given t...