METHODS AND SYSTEMS FOR DETERMINING PRODUCT PRICE ELASTICITY IN A SYSTEM FOR PRICING RETAIL PRODUCTS
A data warehouse system and application which analyzes historical sales and product data contained within a data warehouse to determine the best product price for a retail product. The application automatically creates statistical models using relevant information in the database and determines price elasticity characteristics for the retail product indicating the probable change in sales volume of the product resulting from a change in the selling price for the product. Output from the application can be used operationally to set new product prices, or the application can be utilized as a decision support tool to help retailers understand the influencers on their product sales and profitability.
1 . A computer-implemented method for determining the optimal price for a retail product, the steps comprising:
establishing a database containing historical sales data for a plurality of products sold by a retail enterprise;
selecting a product for pricing analysis;
analyzing historical sales data contained in said database to determine sales volumes for said product at various sales prices of said product;
determining profit for the sales volume at each one of said various sales prices of said product; and
setting the sales price of said selected product to a value that optimizes the profit from sales of said product.
2 . The computer-implemented method for determining the optimal price for a retail product in accordance with claim 1 , further comprising the step of:
generating a price elasticity coefficient for said product, said price elasticity coefficient reflecting a change in sales volume of said product resulting from a change in sales price for said product.
3 . The computer-implemented method for determining the optimal price for a retail product in accordance with claim 2 , further comprising the step of:
identifying said selected product as having an inelastic sales volume when said product has a price elasticity coefficient indicating that the change in sales volume of said product resulting from a change in sales price for said product is negligible.
4 . The computer-implemented method for determining the optimal price for a retail product in accordance with claim 3 , wherein:
said price elasticity coefficient is generated using a linear logarithmic regression model; and
a price elasticity coefficient less than −1 indicates that said selected product has an elastic sales volume.
5 . The computer-implemented method for determining the optimal price for a retail product in accordance with claim 2 , further comprising the step of:
identifying said product as having an elastic sales volume when said product has a price elasticity coefficient indicating that the sales volume of said product changes significantly in inverse relation to a change in sales price for said product.
6 . The computer-implemented method for determining the optimal price for a retail product in accordance with claim 5 , wherein:
said price elasticity coefficient is generated using a linear logarithmic regression model; and
a price elasticity coefficient between −1 and 0 indicates that said selected product has an inelastic sales volume.
7 . The method of claim 1 , further comprising the step of displaying the results of said product pricing application.
8 . A system for determining optimal pricing for a retail product, comprising:
an electronic database containing historical sales data for a plurality of products sold by a retail enterprise; and
a computer-implemented product pricing application for:
establishing a database containing historical sales data for a plurality of products sold by a retail enterprise;
selecting a product for pricing analysis;
analyzing historical sales data contained in said database to determine sales volumes for said product at various sales prices of said product;
determining profit for the sales volume at each one of said various sales prices of said product; and
setting the sales price of said selected product to a value that optimizes the profit from sales of said product.
9 . The system in accordance with claim 8 , wherein said computer-implemented product pricing application generates a price elasticity coefficient for said product, said price elasticity coefficient reflecting a change in sales volume of said product resulting from a change in sales price for said product.
10 . The system in accordance with claim 9 , wherein said computer-implemented product pricing application identifies said selected product as having an inelastic sales volume when said product has a price elasticity coefficient indicating that the change in sales volume of said product resulting from a change in sales price for said product is negligible.
11 . The system in accordance with claim 10 , wherein:
said price elasticity coefficient is generated using a linear logarithmic regression model; and
a price elasticity coefficient less than −1 indicates that said selected product has an elastic sales volume.
12 . The system in accordance with claim 9 , wherein said computer-implemented product pricing application identifies said product as having an elastic sales volume when said product has a price elasticity coefficient indicating that the sales volume of said product changes significantly in inverse relation to a change in sales price for said product.
13 . The system in accordance with claim 12 , wherein:
said price elasticity coefficient is generated using a linear logarithmic regression model; and
a price elasticity coefficient between −1 and 0 indicates that said selected product has an inelastic sales volume.
14 . The system in accordance with claim 8 , further comprising an interface application for:
enabling a user to choose said selected product from said plurality of products sold by said retail enterprise; and
displaying the results of said product pricing application.