System and Method for Tuning Demand Coefficients

• US 20100010870A1
• Filed: 06/09/2009
• Published: 01/14/2010
• Est. Priority Date: 12/20/2000
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Millar, Karl, Peale, William Barrows, Desai, Paritosh
• Application Number:
  US12/481,185
• Publication Number:
  US 20100010870A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

The present invention relates to a system and method for tuning demand coefficients. Transaction data for product categories is received from a store(s). Price elasticity and uncertainty values are selected for the product categories. This transaction data may be seeded with generic price elasticity and uncertainty values. Product categories where the transaction history is not sufficient enough to generate accurate demand coefficients may be identified. Tuning parameters for a product category are estimated using price elasticity and uncertainty values. The tuning parameters include price elasticity mean and price elasticity standard deviation. A modified likelihood function is generated by applying a normally distributed price elasticity term. The modified likelihood function may then be solved for its maxima, thereby generating tuned demand coefficients which may be output to a pricing optimization system for product price setting, and/or may be stored for later product categories. New sales data may be received from the store(s). This data may be used to retrain the tuned demand coefficients.

Claims

1. A method for tuning demand coefficients for a first product category, useful in association with a pricing optimization system, the method comprising:

receiving data from at least one store, wherein the data includes transactions associated with at least one product category;

selecting price elasticity and uncertainty values from at least one of the at least one product category;

estimating tuning parameters for the first product category, wherein the tuning parameters include price elasticity mean and price elasticity standard deviation, and wherein estimating the tuning parameters uses the selected price elasticity and uncertainty values;

generating a modified likelihood function by applying a normally distributed price elasticity term; and

generating tuned demand coefficients by maximizing the modified likelihood function.

2. The method recited by claim 1, further comprising seeding the data with generic price elasticity and uncertainty values.

3. The method recited by claim 1, further comprising identifying the first product category as a product category having deficient pricing history.

4. The method recited by claim 1, wherein the price elasticity mean is defined by the equation:

5. The method recited by claim 1, wherein the price elasticity standard deviation is defined by the equation:

6. The method recited by claim 1, wherein maximizing the modified likelihood function includes at least one of maximizing the function analytically, and passing a logarithm of the likelihood function to a multivariate numerical optimization routine.

7. The method recited by claim 1, further comprising outputting the generated tuned demand coefficients to the pricing optimization system for product price setting.

8. The method recited by claim 1, further comprising receiving new sales data from the at least one store.

9. The method recited by claim 8, further comprising retraining the tuned demand coefficients in response to the newly received sales data.

10. The method recited by claim 1, further comprising storing the tuned demand coefficients for later product categories.

11. A demand coefficient tuner for a first product category, useful in association with a pricing optimization system, the demand coefficient tuner comprising:

an input configured to receive data from at least one store, wherein the data includes transactions associated with at least one product category;

a category selector configured to select price elasticity and uncertainty values from at least one of the at least one product category;

a tuning parameter estimator configured to estimate tuning parameters for the first product category, wherein the tuning parameters include price elasticity mean and price elasticity standard deviation, and wherein estimating the tuning parameters uses the selected price elasticity and uncertainty values;

a function modifier configured to generate a modified likelihood function by applying a normally distributed price elasticity term; and

a coefficient generator configured to generate tuned demand coefficients by maximizing the modified likelihood function.

12. The demand coefficient tuner of claim 11, further comprising a data seeder configured to seed the data with generic price elasticity and uncertainty values.

13. The demand coefficient tuner of claim 11, further comprising a deficient category identifier configured to identify the first product category as a product category having deficient pricing history.

14. The demand coefficient tuner of claim 11, wherein the price elasticity mean is defined by the equation:

15. The demand coefficient tuner of claim 11, wherein the price elasticity standard deviation is defined by the equation:

16. The demand coefficient tuner of claim 11, wherein maximizing the modified likelihood function includes at least one of maximizing the function analytically, and passing a logarithm of the likelihood function to a multivariate numerical optimization routine.

17. The demand coefficient tuner of claim 11, further comprising an outputter configured to output the generated tuned demand coefficients to the pricing optimization system for product price setting.

18. The demand coefficient tuner of claim 11, wherein the input is further configured to receive new sales data from the at least one store.

19. The demand coefficient tuner of claim 18, further comprising a coefficient trainer configured to retrain the tuned demand coefficients in response to the newly received sales data.

20. The demand coefficient tuner of claim 11, further comprising a database configured to store the tuned demand coefficients for later product categories.