SYSTEM AND METHOD FOR MODELING DEMAND AND OPTIMIZING PRICES WITH IMMUNITY TO OUT-OF-STOCK EVENTS
First Claim
1. A computer-implemented method comprising:
- receiving sales data for at least one product, in at least one store, across a plurality of time periods, wherein the sales data includes at least one time period with zero unit sales; and
generating, via a processor, a demand model based on the sales data as follows;
applying a truncated Poisson distribution to the sales data to generate a derivative vector D and a Hessian matrix H, the truncated Poisson distribution applied to non-zero unit sales in the sales data; and
applying a Newton-Raphson method using the derivative vector D and the Hessian matrix H to generate a coefficient vector V, wherein the coefficient vector V comprises coefficients for elasticity and quantity factors for product-store combinations.
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Abstract
The disclosed technology improves the process of generating recommended prices for retail products. First, the present technology makes it possible to model shopper demand when sales data includes time periods with zero unit sales without hypothesizing whether the time periods are out-of-stock events or zero sales. This can be accomplished by applying a truncated Poisson distribution and the Newton-Raphson method to the non-zero unit sales to generate a coefficient vector that maximizes the likelihood of the observations in the sales data. Second, the present technology can be used to generate recommended prices for a group of products that optimize revenue and profit while limiting the number of products that require price changes to a predefined threshold value. This can be accomplished by iteratively replacing a current best value solution with a next best value solution across a collection of product networks until an acceptable number of unchanged prices is achieved.
11 Citations
20 Claims
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1. A computer-implemented method comprising:
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receiving sales data for at least one product, in at least one store, across a plurality of time periods, wherein the sales data includes at least one time period with zero unit sales; and generating, via a processor, a demand model based on the sales data as follows; applying a truncated Poisson distribution to the sales data to generate a derivative vector D and a Hessian matrix H, the truncated Poisson distribution applied to non-zero unit sales in the sales data; and applying a Newton-Raphson method using the derivative vector D and the Hessian matrix H to generate a coefficient vector V, wherein the coefficient vector V comprises coefficients for elasticity and quantity factors for product-store combinations. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A manufacture comprising:
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a non-transitory computer-readable storage medium; and a computer executable instruction stored on the non-transitory computer-readable storage medium which, when executed by a computing device, causes the computing device to perform a method comprising; receiving sales data for at least one product, in at least one store, over a plurality of time periods, wherein the sales data includes at least one time period with zero unit sales; and generating a demand model based at least on the sales data by iteratively applying; a truncated Poisson distribution to non-zero unit sales in the sales data to generate a derivative vector D and a Hessian matrix H, and a Newton-Raphson method using the derivative vector D and the Hessian matrix H to update a coefficient vector V. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15)
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16. A system comprising:
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a processor; a computer readable storage medium storing instructions for controlling the processor to perform steps comprising; receiving sales data for at least one product, in at least one store, across a plurality of time periods, wherein the sales data includes at least one time period with unknown unit sales; and generating a demand model based at least on the sales data as follows; iteratively updating a coefficient vector V by performing a number of rounds, the number of rounds determined dynamically based on a change in the coefficient vector V, each round comprising; for each product-store combination K and time period T represented in the sales data with non-zero unit sales, applying a truncated Poisson distribution to the non-zero unit sales data to generate a derivative vector D and a Hessian matrix H; applying a Newton-Raphson method step using the derivative vector D, the Hessian matrix H, and a current coefficient vector V to generate a new coefficient vector W; and computing a difference between W and V, and copying W into V when the difference is greater than a predefined value epsilon. - View Dependent Claims (17, 18, 19, 20)
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Specification