System and method for optimizing cross-channel marketing

1 . A computer-implemented method, the method comprising:

establishing data-acquisition links to a plurality of data sources;

ingesting historical customer data from the data sources;

in response to identifying a gap in the historical customer data, generating synthesized customer data to fill the identified gap, wherein generating the synthesized customer data comprises executing a generative data-augmentation and training process to iteratively train a customer model based at least on the historical customer data and the generated synthesized customer data, and wherein iteratively training the customer model comprises:

applying an initial customer model to generate an initial set of synthesized customer data,

combining the historical customer data with the initial set of synthesized customer data,

updating the initial customer model based on the combined data, and

generating additional synthesized customer data using the updated customer model;

obtaining an initial marketing plan, which specifies a plan goal and one or more constraints;

performing an optimization process based on the initial marketing plan, the customer model, and the attribution model; and

generating and presenting, via a reporting user interface (UI), a report based on the optimization process, the report comprising an optimized marketing plan, thereby facilitating future marketing efforts based on the optimized marketing plan.

2 . The computer-implemented method of claim 1 , wherein training the customer model further comprises performing Maximum a Posterior (MAP) estimation or a variation of Monte Carlo sampling.

3 . The computer-implemented method of claim 1 , wherein the customer data comprises customer behavior data associated with a plurality of media channels, and wherein the plurality of data sources comprise at least a first-party data source, a data source associated with an advertisement platform, and a third-party data source.

4 . The computer-implemented method of claim 1 ,

wherein the plan goal comprises a budget goal, a conversion goal, or both; and

wherein the constraints comprise one or more of a time constraint, a budget constraint, and a performance constraint associated with one or more key performance indicators (KPIs).

5 . The computer-implemented method of claim 1 , wherein obtaining the initial marketing plan comprises presenting a scenario-planning user interface (UI) to allow a user to input the plan goal and the constraints.

6 . The computer-implemented method of claim 5 , wherein the scenario-planning UI comprises a category-selection drop-down menu to allow the user to select a planning category from a plurality of planning categories.

7 . The computer-implemented method of claim 6 , wherein the plurality of planning categories comprise one or more of:

media type;

media market;

advertisement platform;

media channel;

tactic; and

country.

8 . The computer-implemented method of claim 5 , wherein the scenario-planning UI comprises a plan detail UI element to allow the user to edit the initial marketing plan by adjusting budget allocation across a plurality of subcategories associated with the selected planning category.

9 . The computer-implemented method of claim 8 ,

wherein the optimized marketing plan comprises a re-allocation of the budget across the plurality of subcategories; and

wherein the report comprises a performance comparison between the optimized marketing plan and the initial marketing plan.

10 . A non-transitory computer-readable storage medium storing instructions that when executed by a processor cause the processor to perform a method, the method comprising:

establishing data-acquisition links to a plurality of data sources;

ingesting historical customer data from the data sources;

in response to identifying a gap in the historical customer data, generating synthesized customer data to fill the identified gap, wherein generating the synthesized customer data comprises executing a generative data-augmentation and training process to iteratively train a customer model based at least on the historical customer data and the generated synthesized customer data, and wherein iteratively training the customer model comprises:

applying an initial customer model to generate an initial set of synthesized customer data,

combining the historical customer data with the initial set of synthesized customer data,

updating the initial customer model based on the combined customer data, and

generating additional synthesized customer data using the updated customer model;

obtaining an initial marketing plan, which specifies a plan goal and one or more constraints;

performing an optimization process based on the initial marketing plan and the customer model; and

generating and presenting, via a reporting user interface (UI), a report based on the optimization process, the report comprising an optimized marketing plan, thereby facilitating future marketing efforts based on the optimized marketing plan.

11 . The non-transitory computer-readable storage medium of claim 10 , wherein training the customer model further comprises performing Maximum a Posterior (MAP) estimation or a variation of Monte Carlo sampling.

12 . The non-transitory computer-readable storage medium of claim 10 , wherein the customer data comprises customer behavior data associated with a plurality of media channels, and wherein the plurality of data sources comprise at least a first-party data source, a data source associated with an advertisement platform, and a third-party data source.

13 . The non-transitory computer-readable storage medium of claim 10 ,

wherein the plan goal comprises a budget goal, a conversion goal, or both; and

wherein the constraints comprise one or more of a time constraint, a budget constraint, and a performance constraint associated with one or more key performance indicators (KPIs).

14 . The non-transitory computer-readable storage medium of claim 10 , wherein obtaining the initial marketing plan comprises presenting a scenario-planning user interface (UI) to allow a user to input the plan goal and the constraints.

15 . The non-transitory computer-readable storage medium of claim 14 , wherein the scenario-planning UI comprises a category-selection drop-down menu to allow the user to select a planning category from a plurality of planning categories, and wherein the plurality of planning categories comprise one or more of:

media type;

media market;

advertisement platform;

media channel;

tactic; and

country.

16 . The non-transitory computer-readable storage medium of claim 14 , wherein the scenario-planning UI comprises a plan detail UI element to allow the user to edit the initial marketing plan by adjusting budget allocation across a plurality of subcategories associated with the selected planning category.

17 . The non-transitory computer-readable storage medium of claim 16 ,

wherein the optimized marketing plan comprises a re-allocation of the budget across the plurality of subcategories; and

wherein the report comprises a performance comparison between the optimized marketing plan and the initial marketing plan.

18 . A computing system, comprising:

a processor;

a memory coupled to the processor and storing instructions that when executed by the processor cause the processor to perform a method, the method comprising:

establishing data-acquisition links to a plurality of data sources;

ingesting historical customer data from the data sources;

in response to identifying a gap in the historical customer data, generating synthesized customer data to fill the identified gap, wherein generating the synthesized customer data comprises executing a generative data-augmentation and training process to iteratively train a customer model based at least on the historical customer data and the generated synthesized customer data, and wherein iteratively training the customer model comprises an iterative process comprising:

applying an initial customer model to generate an initial set of synthesized customer data,

combining the historical customer data with the initial set of synthesized customer data,

updating the customer model based on the combined customer data, and

generating additional synthesized customer data using the updated customer model;

obtaining an initial marketing plan, which specifies a plan goal and one or more constraints;

performing an optimization process based on the initial marketing plan and the customer model; and

generating and presenting, via a reporting user interface (UI), a report based on the optimization process, the report comprising an optimized marketing plan, thereby facilitating future marketing efforts based on the optimized marketing plan.