Price-demand elasticity as feature in machine learning model for demand forecasting

1. A computer-implemented method, comprising:

training, by a first iterative approach comprising cyclic boosting in multiplicative mode, a machine learning model to identify one or more external causal factors that influence demand for one or more products;

training, by a second iterative approach comprising cyclic boosting in multiplicative mode that cycles through features and parameters, the machine learning model to generate one or more price-demand elasticity causal factors to predict a target outcome for a given product demand;

predicting, with the machine learning model, a demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more price-demand elasticity causal factors, wherein the machine learning model is a first machine learning model, the one or more price-demand elasticity causal factors is one or more first price-demand elasticity causal factors the target outcome is a first target outcome and the demand is a first demand;

deconfounding historical demand data for the one or more products;

training a second machine learning model to generate, based on the deconfounded historical demand data for the one or more products, one or more second price-demand elasticity causal factors to predict a second target outcome for the given product demand;

predicting, with the second machine learning model, a second demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more second price-demand elasticity causal factors; and

shaping the predicted second demand for the one or more products by altering one or more product price variables of the one or more products.

2. The computer-implemented method of claim 1 , further comprising:

transmitting, by the computer and in response to the predicted demand for the one or more products, instructions to alter actions at one or more supply chain entities, the instructions comprising one or more of:

an instruction to increase capacity at one or more supply chain entity locations;

an instruction to alter product supply levels at the one or more supply chain entities;

an instruction to adjust product mix ratios at the one or more supply chain entities; and

an instruction to alter the configuration of packaging of one or more products sold by the one or more supply chain entities.

3. The computer-implemented method of claim 1 , further comprising:

displaying, on an output device, the predicted demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more price-demand elasticity causal factors.

4. The computer-implemented method of claim 1 , further comprising:

predicting, with the second machine learning model, a third demand for the one or more products based, at least in part, on the identified one or more external causal factors, the generated one or more second price-demand elasticity causal factors, and the shaped predicted second demand for the one or more products.

5. The computer-implemented method of claim 1 , further comprising:

deconfounding the historical demand data for the one or more products by conducting one or more randomized controlled A/B group trials to deconfound one or more cause-effect relationships of the historical demand data and corresponding causal variable data from one or more potential confounding variables.

6. A system, comprising:

a computer, comprising a processor and memory, the computer configured to:

train, by a first iterative approach comprising cyclic boosting in multiplicative mode, a machine learning model to identify one or more external causal factors that influence demand for one or more products;

train, by a second iterative approach comprising cyclic boosting in multiplicative mode that cycles through features and parameters, the machine learning model to generate one or more price-demand elasticity causal factors to predict a target outcome for a given product demand;

predict, with the machine learning model, a demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more price-demand elasticity causal factors, wherein the machine learning model is a first machine learning model, the one or more price-demand elasticity causal factors is one or more first price-demand elasticity causal factors the target outcome is a first target outcome, the demand is a first demand and the computer is further configured to:

deconfound historical demand data for the one or more products;

train a second machine learning model to generate, based on the deconfounded historical demand data for the one or more products, one or more second price-demand elasticity causal factors to predict a second target outcome for the given product demand;

predict, with the second machine learning model, a second demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more second price-demand elasticity causal factors; and

shape the predicted second demand for the one or more products by altering one or more product price variables of the one or more products.

7. The system of claim 6 , wherein the computer is further configured to transmit, in response to the predicted demand for the one or more products, instructions to alter actions at one or more supply chain entities, the instructions comprising one or more of:

an instruction to increase capacity at one or more supply chain entity locations;

an instruction to alter product supply levels at the one or more supply chain entities;

an instruction to adjust product mix ratios at the one or more supply chain entities; and

an instruction to alter the configuration of packaging of one or more products sold by the one or more supply chain entities.

8. The system of claim 6 , wherein the compute is further configured to:

display, on an output device, the predicted demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more price-demand elasticity causal factors.

9. The system of claim 6 , further comprising the computer:

predicting, with the second machine learning model, a third demand for the one or more products based, at least in part, on the identified one or more external causal factors, the generated one or more second price-demand elasticity causal factors, and the shaped predicted second demand for the one or more products.

10. The system of claim 6 , wherein the computer is further configured to:

deconfound the historical demand data for the one or more products by conducting one or more randomized controlled A/B group trials to deconfound one or more cause-effect relationships of the historical demand data and corresponding causal variable data from one or more potential confounding variables.

11. A non-transitory computer-readable storage medium embodied with software, the software when executed configured to:

train, by a first iterative approach comprising cyclic boosting in multiplicative mode, a machine learning model to identify one or more external causal factors that influence demand for one or more products;

train, by a second iterative approach comprising cyclic boosting in multiplicative mode that cycles through features and parameters, the machine learning model to generate one or more price-demand elasticity causal factors to predict a target outcome for a given product demand;

predict, with the machine learning model, a demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more price-demand elasticity causal factors, wherein the machine learning model is a first machine learning model, the one or more price-demand elasticity causal factors is one or more first price-demand elasticity causal factors the target outcome is a first target outcome, the demand is a first demand and the software when executed is further configured to:

deconfound historical demand data for the one or more products;

train a second machine learning model to generate, based on the deconfounded historical demand data for the one or more products, one or more second price-demand elasticity causal factors to predict a second target outcome for the given product demand;

predict, with the second machine learning model, a second demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more second price-demand elasticity causal factors; and

shape the predicted second demand for the one or more products by altering one or more product price variables of the one or more products.

12. The non-transitory computer-readable storage medium of claim 11 , wherein the software when executed is further configured to:

transmit, in response to the predicted demand for the one or more products, instructions to alter actions at one or more supply chain entities, the instructions comprising one or more of:

an instruction to increase capacity at one or more supply chain entity locations;

an instruction to alter product supply levels at the one or more supply chain entities;

an instruction to adjust product mix ratios at the one or more supply chain entities; and

an instruction to alter the configuration of packaging of one or more products sold by the one or more supply chain entities.

13. The non-transitory computer-readable storage medium of claim 11 , wherein the software when executed is further configured to:

display, on an output device, the predicted demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more price-demand elasticity causal factors.

14. The non-transitory computer-readable storage medium of claim 11 , wherein the software when executed is further configured to:

predict, with the second machine learning model, a third demand for the one or more products based, at least in part, on the identified one or more external causal factors, the generated one or more second price-demand elasticity causal factors, and the shaped predicted second demand for the one or more products.