System and method for generating a marketing-mix solution

1. A method comprising:

receiving, by a processor, a plurality of marketing-mix variables through a display, each of the plurality of marketing-mix variables being associated with marketing strategies for one or more products,

pre-modeling, by a processor, marketing data based on the received plurality of marketing-mix variables, the pre-modeling including,

analyzing, by a processor, the plurality of marketing mix variables by performing exploratory data analysis (EDA), the EDA being based on univariate analysis in which conditional histograms are generated using Sturges formula to determine bin sizes for a range of the marketing data,

generating, by a processor, at least one of a sales based response model and revenue based response model to identify contributory marketing-mix variables from among the plurality of marketing-mix variables that affect at least one of sales and revenue of the one or more products, and

analyzing, by a processor, the at least one of the sales based response model and the revenue based response model to determine individual contribution of each of the contributory marketing-mix variables towards at least one of the sales and the revenue of the one or more products;

receiving, by a processor, through the display one or more parameters to be optimized along with optimization constraints;

generating, by a processor, a new marketing-mix solution through multi-layered optimization using the determined individual contributions, the one or more parameters, and the optimization constraints, the multi-layered optimization being based on one or more algorithms selected from among at least one of genetic algorithms, simulated annealing, particle swarm optimization, and ant colony optimization; and

providing, by a processor, the new marketing-mix solution and a real-time tracking of the generation of the new marketing-mix solution, on the display.

2. The method of claim 1 , wherein the pre-modeling the marketing data further includes assigning each of the plurality of marketing-mix variables to one or more marketing-mix categories.

3. The method of claim 2 , wherein the EDA is further based on one of a bivariate analysis, a time series analysis, and a factor analysis.

4. The method of claim 1 , wherein the generating at least one of the sales based response model and the revenue based response model comprises transforming or adding one or more of the plurality of marketing-mix variables to the response model.

5. The method of claim 4 , wherein the one or more marketing-mix variables includes at least one of an ad-stock variable, an event variable, a lead variable, a lag variable, and combinations thereof.

6. The method of claim 4 , wherein the at least one of the sales based response model and the revenue based response model is generated using at least one of a linear regression model, a non-linear regression model and a mixed model.

7. The method of claim 1 , wherein the analyzing the at least one of the sales based response model and the revenue based response model further comprises comparing spends to the individual contribution of each of the contributory marketing-mix variables.

8. The method of claim 1 , wherein the analyzing the at least one of the sales based response model and the revenue based response model further comprises determining the individual contribution of each of the contributory marketing-mix variables towards a change in sales over a time period.

9. The method of claim 1 , wherein the analyzing the at least one of the sales based response model and the revenue based response model further comprises comparing return of investment(ROI) for each of the contributory marketing-mix variables.

10. The method of claim 9 , further comprising:

forecasting the at least one of the sales and the revenue of the one or more products.

11. The method of claim 1 , further comprising:

identifying one or more of the marketing strategies contributing to the at least one of the sales and the revenue of the one or more products.

12. A system comprising:

a user interface;

a memory having computer-readable instructions stored therein; and

a processor configured to execute the computer-readable instructions to,

receive a plurality of marketing-mix variables via the user interface, each of the plurality of marketing-mix variables being associated with marketing strategies for one or more products,

pre-model marketing data based on the received plurality of marketing-mix variables, by

analyzing the plurality of marketing mix variables by performing exploratory data analysis (EDA), the EDA being based on univariate analysis in which conditional histograms are generated using Sturges formula to determine bin sizes for a range of the marketing data,

generating at least one of a sales based response model and revenue based response model to identify contributory marketing-mix variables from among the plurality of marketing-mix variables that affect at least one of sales and revenue of the one or more products, and

analyzing the at least one of the sales based response model and the revenue based response model to determine individual contribution of each of the contributory marketing-mix variables towards at least one of the sales and the revenue of the one or more products,

receive via the user interface, one or more parameters to be optimized along with optimization constraints,

generate a new marketing-mix solution through multi-layered optimization using the determined individual contributions, the one or more parameters and the optimization constraint, the multi-layered optimization being based on one or more algorithms selected from among at least one of genetic algorithms, simulated annealing, particle swarm optimization, and ant colony optimization, and

provide the new marketing-mix solution and a real-time tracking of the generation of the new marketing-mix solution on the user display.

13. The system of claim 12 , wherein the processor is further configured to assign the plurality of marketing-mix variables to one or more marketing-mix categories.

14. The system of claim 13 , wherein the marketing-mix categories include at least one of a macro-economic category, a promotional category, a media-related category, a seasonal category, and combinations thereof.

15. The system of claim 12 , wherein the processor is further configured to determine a contribution rating of each of the marketing strategies, an estimated spending on each of the marketing strategies and an estimated schedule for introduction of the marketing strategies into the market.

16. The system of claim 12 , wherein the EDA is further based on one of a bivariate analysis, a time series analysis, and a factor analysis.

17. The system of claim 12 , the one or more marketing-mix variables includes at least one of an ad-stock variable, an event variable, a lead variable, a lag variable, and combinations thereof.

18. The system of claim 12 , wherein the processor is configured to analyze the at least one of the sales based response model and the revenue based response model by at least one of,

comparing spends to the individual contribution of each of the contributory marketing-mix variables,

determining the individual contribution of each of the contributory marketing-mix variables towards a change in sales over a time period, and

comparing return of investment (ROI) for each of the contributory marketing-mix variables.

19. A non-transitory computer readable medium comprising computer-readable instructions, which when executed by a processor, cause the processor to perform functions including:

receiving a plurality of marketing-mix variables through a display, each of the plurality of marketing-mix variables associated with marketing strategies for one or more products,

pre-modeling marketing data based on the received plurality of marketing-mix variables, the pre-modeling including,

analyzing the plurality of marketing mix variables by performing exploratory data analysis (EDA), the EDA being based on univariate analysis in which conditional histograms are generated using Sturges formula to determine bin sizes for a range of the marketing data,

generating at least one of a sales based response model and revenue based response model to identify contributory marketing-mix variables from among the plurality of marketing-mix variables that affect at least one of sales and revenue of the one or more products, and

analyzing the at least one of the sales based response model and the revenue based response model to determine individual contribution of each of the contributory marketing-mix variables towards at least one of the sales and the revenue of the one or more products;

receiving through the display one or more parameters to be optimized along with optimization constraints;

generating, by a processor, a new marketing-mix solution through multi-layered optimization using the determined individual contributions, the one or more parameters, and the optimization constraints, the multi-layered optimization by based on one or more algorithms selected from among at least one of genetic algorithms, simulated annealing, particle swarm optimization, and ant colony optimization; and

providing, by a processor, the new marketing-mix solution and a real-time tracking of the generation of the new marketing-mix solution on the display.

20. The method of claim 19 , wherein the EDA is further based on one of a bivariate analysis, a time series analysis, and a factor analysis.