Claims:CLAIMS
1. A system for predicting discount-demand elasticity of one or more retail items in a portfolio, the system comprising:
a feature engineering module configured to generate a plurality of features based on historical data of a plurality of retail items in the portfolio;
an elasticity estimator configured to estimate discount-demand elasticity values for the plurality of retail items in the portfolio;
an elasticity-band generator configured to generate a set of elasticity bands for the plurality of retail items based on the estimated discount-demand elasticity values;
a training module configured to train a classification model based on the generated plurality of features and the generated set of elasticity bands; and
an elasticity prediction module configured to generate discount-demand elasticity of the one or more retail item in the portfolio from the trained classification model.

2. The system of claim 1, wherein the historical data comprises product attributes, sales data, pricing data, inventory data, and visibility data aggregated for the plurality of retail items on a daily basis.

3. The system of claim 1, wherein the elasticity band generator is configured to generate the set of elasticity bands based on the estimated discount-demand elasticity values and a median estimated discount-demand elasticity value.

4. The system of claim 1, wherein the elasticity-band generator is configured to generate the set of elasticity bands that classify the plurality of retail items as having low elasticity, medium elasticity and high elasticity.

5. The system of claim 1, further comprising a distribution adjustment module configured to adjust the distribution of the plurality of retails items across the set of elasticity bands by assigning a corresponding adjustment value to each elasticity band in the set of elasticity bands.

6. The system of claim 5, wherein the training module is further configured to train the classification model based on the assigned adjustment values.

7. The system of claim 1, wherein the elasticity prediction module is configured to generate discount-demand elasticity of the one or more retail item in the portfolio from the trained classification model, based on discount data for the one or more retail item on an hourly basis.

8. The system of claim 1, further comprising a discount recommendation module configured to recommend a discount value for the one or more retail item based on the generated discount-demand elasticity and a sales target for the portfolio.

9. The system of claim 8, wherein the sales target comprises a revenue target for the portfolio, a gain margin target for the portfolio, or both.

10. A method for predicting discount-demand elasticity of one or more retail items in a portfolio, the method comprising:
generating a plurality of features based on historical data of a plurality of retail items in the portfolio;
estimating discount-demand elasticity values for the plurality of retail items in the portfolio;
generating a set of elasticity bands for the plurality of retail items based on the estimated discount-demand elasticity values;
training a classification model based on the generated plurality of features and the generated set of elasticity bands; and
generating discount-demand elasticity of the one or more retail items in the portfolio from the trained classification model.

11. The method of claim 10, wherein the historical data comprises product attributes, sales data, pricing data, inventory data, and visibility data aggregated for the plurality of retail items on a daily basis.

12. The method of claim 10, comprising generating the set of elasticity bands based on the estimated discount-demand elasticity values and a median estimated discount-demand elasticity value.

13. The method of claim 10, further comprising classifying the plurality of retail items as having low elasticity, medium elasticity and high elasticity based on the generated set of elasticity bands.

14. The method of claim 10, further comprising adjusting the distribution of the plurality of retails items across the set of elasticity bands by assigning a corresponding adjustment value to each elasticity band in the set of elasticity bands.

15. The method of claim 14, further comprising training the classification model based on the assigned adjustment values.

16. The method of claim 10, comprising generating the discount-demand elasticity of the one or more retail item in the portfolio from the trained classification model, based on discount data for the one or more retail item on an hourly basis.

17. The method of claim 10, further comprising recommending a discount value for the one or more retail item based on the generated discount-demand elasticity and a sales target for the portfolio.

18. The method of claim 17, wherein the sales target comprises a revenue target for the portfolio, a gain margin target for the portfolio, or both.
, Description:CLAIMS
1. A system for predicting discount-demand elasticity of one or more retail items in a portfolio, the system comprising:
a feature engineering module configured to generate a plurality of features based on historical data of a plurality of retail items in the portfolio;
an elasticity estimator configured to estimate discount-demand elasticity values for the plurality of retail items in the portfolio;
an elasticity-band generator configured to generate a set of elasticity bands for the plurality of retail items based on the estimated discount-demand elasticity values;
a training module configured to train a classification model based on the generated plurality of features and the generated set of elasticity bands; and
an elasticity prediction module configured to generate discount-demand elasticity of the one or more retail item in the portfolio from the trained classification model.

2. The system of claim 1, wherein the historical data comprises product attributes, sales data, pricing data, inventory data, and visibility data aggregated for the plurality of retail items on a daily basis.

3. The system of claim 1, wherein the elasticity band generator is configured to generate the set of elasticity bands based on the estimated discount-demand elasticity values and a median estimated discount-demand elasticity value.

4. The system of claim 1, wherein the elasticity-band generator is configured to generate the set of elasticity bands that classify the plurality of retail items as having low elasticity, medium elasticity and high elasticity.

5. The system of claim 1, further comprising a distribution adjustment module configured to adjust the distribution of the plurality of retails items across the set of elasticity bands by assigning a corresponding adjustment value to each elasticity band in the set of elasticity bands.

6. The system of claim 5, wherein the training module is further configured to train the classification model based on the assigned adjustment values.

7. The system of claim 1, wherein the elasticity prediction module is configured to generate discount-demand elasticity of the one or more retail item in the portfolio from the trained classification model, based on discount data for the one or more retail item on an hourly basis.

8. The system of claim 1, further comprising a discount recommendation module configured to recommend a discount value for the one or more retail item based on the generated discount-demand elasticity and a sales target for the portfolio.

9. The system of claim 8, wherein the sales target comprises a revenue target for the portfolio, a gain margin target for the portfolio, or both.

10. A method for predicting discount-demand elasticity of one or more retail items in a portfolio, the method comprising:
generating a plurality of features based on historical data of a plurality of retail items in the portfolio;
estimating discount-demand elasticity values for the plurality of retail items in the portfolio;
generating a set of elasticity bands for the plurality of retail items based on the estimated discount-demand elasticity values;
training a classification model based on the generated plurality of features and the generated set of elasticity bands; and
generating discount-demand elasticity of the one or more retail items in the portfolio from the trained classification model.

11. The method of claim 10, wherein the historical data comprises product attributes, sales data, pricing data, inventory data, and visibility data aggregated for the plurality of retail items on a daily basis.

12. The method of claim 10, comprising generating the set of elasticity bands based on the estimated discount-demand elasticity values and a median estimated discount-demand elasticity value.

13. The method of claim 10, further comprising classifying the plurality of retail items as having low elasticity, medium elasticity and high elasticity based on the generated set of elasticity bands.

14. The method of claim 10, further comprising adjusting the distribution of the plurality of retails items across the set of elasticity bands by assigning a corresponding adjustment value to each elasticity band in the set of elasticity bands.

15. The method of claim 14, further comprising training the classification model based on the assigned adjustment values.

16. The method of claim 10, comprising generating the discount-demand elasticity of the one or more retail item in the portfolio from the trained classification model, based on discount data for the one or more retail item on an hourly basis.

17. The method of claim 10, further comprising recommending a discount value for the one or more retail item based on the generated discount-demand elasticity and a sales target for the portfolio.

18. The method of claim 17, wherein the sales target comprises a revenue target for the portfolio, a gain margin target for the portfolio, or both.