SYSTEMS AND METHODS FOR GENERATING MARKETING CONTENT ATTRIBUTION AND INSIGHTS

1 . A method for generating marketing content insights, comprising:

receiving input data including one or more advertising assets and one or more metrics corresponding to the one or more advertising assets;

using a computer vision model, identifying one or more constituent elements of the one or more advertising assets;

appending the one or more constituent elements and/or descriptions of the one or more constituent elements to the input data to generate a structured data set;

training a generative AI model using the structured data set;

training a prediction model using the structured data set;

based on the trained generative AI model and a prompt, generating an additional advertising asset not included in the one or more advertising assets; and

based on the trained prediction model, generating at least one predicted metric of the additional advertising asset.

2 . The method of claim 1 , wherein the one or more metrics corresponding to the one or more advertising assets includes at least one of:

a number of comments on each of the one or more advertising assets;

a number of times each of the one or more advertising assets was shared;

a number of impressions made by each of the one or more advertising assets;

a channel of each of the one or more advertising assets;

a number of times each of the one or more advertising assets was liked;

a number of times each of the one or more advertising assets was clicked upon;

a country corresponding to each of the one or more advertising assets;

a click-through-rate for each of the one or more advertising assets;

a media type of each of the one or more advertising assets;

a media Uniform Resource Locator (URL) of each of the one or more advertising assets; and

a start date of each of the one or more advertising assets.

3 . The method of claim 2 , wherein the structured data set further comprises at least one of:

a response field containing, for each of the one or more advertising assets, a summation of the number of comments, the number of shares, the number of likes, and the number of clicks; and

a response rate field containing, for each of the one or more advertising assets, the response divided by the number of impressions.

4 . The method of claim 1 , wherein the one or more constituent elements includes at least one of:

a call-to-action (CTA) of each of the one or more advertising assets;

a headline of each of the one or more advertising assets;

objects recognized in each of the one or more advertising assets by the computer vision model;

colors recognized in each of the one or more advertising assets by the computer vision model;

a tone recognized in each of the one or more advertising assets by the computer vision model;

a summary of each of the one or more advertising assets; and

text recognized in each of the one or more advertising assets by the computer vision model.

5 . The method of claim 4 , further comprising:

using a natural language processing (NLP) module, generating a list of Top N objects across the one or more advertising assets;

filtering the objects identified in each of the one or more advertising assets against the list of Top N objects;

generating vectors corresponding to the filtered objects for each of the one or more advertising assets; and

appending the vectors to the structured data set.

6 . The method of claim 5 , wherein the NLP module is implemented using Azure™ services, BERT, ROBERTa, or GPT™.

7 . The method of claim 1 , wherein when the one or more advertising assets includes emails, the one or more metrics corresponding to the one or more advertising assets includes at least one of:

a name of an email campaign;

a subject of the emails;

a number of emails sent;

a number of emails delivered;

a delivery rate of the emails;

a number of times the emails were opened; and

an open rate of the emails.

8 . The method of claim 1 , wherein the computer vision model identifies the one or more constituent elements in response to a series of prompts entered into the computer vision model by a user.

9 . The method of claim 1 , wherein when the one or more advertising assets includes a Graphics Interchange Format (gif) file, the method further comprises:

comparing a frame in the gif file with a successive frame; and

discarding the successive frame when the frame and the successive frame are insufficiently different.

10 . The method of claim 1 , wherein the computer vision model is implemented using GPT-4V™, DALL-E™, and/or Azure™ AI Vision.

11 . A system for generating marketing content insights, comprising:

at least one processor;

a display communicatively coupled to the at least one processor and configured to display a result based on computations performed by the at least one processor; and

a memory communicatively coupled to the at least one processor, the memory storing executable instructions, which when executed by the at least one processor, cause the at least one processor to:

receive input data including one or more advertising assets and one or more metrics corresponding to the one or more advertising assets;

using a computer vision model, identify one or more constituent elements of the one or more advertising assets;

append the one or more constituent elements and/or descriptions of the one or more constituent elements to the input data to generate a structured data set;

train a generative AI model using the structured data set;

train a prediction model using the structured data set;

based on the trained generative AI model and a prompt, generate an additional advertising asset not included in the one or more advertising assets and displaying the additional advertising asset on the display; and

based on the trained prediction model, generate at least one predicted metric of the additional advertising asset.

12 . The system of claim 11 , wherein the one or more metrics corresponding to the one or more advertising assets includes at least one of:

a number of comments on each of the one or more advertising assets;

a number of times each of the one or more advertising assets was shared;

a number of impressions made by each of the one or more advertising assets;

a channel of each of the one or more advertising assets;

a number of times each of the one or more advertising assets was liked;

a number of times each of the one or more advertising assets was clicked upon;

a country corresponding to each of the one or more advertising assets;

a click-through-rate for each of the one or more advertising assets;

a media type of each of the one or more advertising assets;

a media Uniform Resource Locator (URL) of each of the one or more advertising assets; and

a start date of each of the one or more advertising assets.

13 . The system of claim 12 , wherein the structured data set further comprises at least one of:

a response field containing, for each of the one or more advertising assets, a summation of the number of comments, the number of shares, the number of likes, and the number of clicks; and

a response rate field containing, for each of the one or more advertising assets, the response divided by the number of impressions.

14 . The system of claim 11 , wherein the one or more constituent elements includes at least one of:

a call-to-action (CTA) of each of the one or more advertising assets;

a headline of each of the one or more advertising assets;

objects recognized in each of the one or more advertising assets by the computer vision model;

colors recognized in each of the one or more advertising assets by the computer vision model;

a tone recognized in each of the one or more advertising assets by the computer vision model;

a summary of each of the one or more advertising assets; and

text recognized in each of the one or more advertising assets by the computer vision model.

15 . The system of claim 14 , wherein the memory stores further executable instructions that cause the at least one processor to:

using a natural language processing (NLP) module, generate a list of Top N objects across the one or more advertising assets;

filter the objects identified in each of the one or more advertising assets against the list of Top N objects;

generate vectors corresponding to the filtered objects for each of the one or more advertising assets; and

append the vectors to the structured data set.

16 . The system of claim 15 , wherein the NLP module is implemented using Azure™ services, BERT, ROBERTa, or GPT™.

17 . The system of claim 11 , wherein when the one or more advertising assets includes emails, the one or more metrics corresponding to the one or more advertising assets includes at least one of:

a name of an email campaign;

a subject of the emails;

a number of emails sent;

a number of emails delivered;

a delivery rate of the emails;

a number of times the emails were opened; and

an open rate of the emails.

18 . The system of claim 11 , wherein the computer vision model identifies the one or more constituent elements in response to a series of prompts entered into the computer vision model by a user.

19 . The system of claim 11 , wherein when the one or more advertising assets includes a Graphics Interchange Format (gif) file, the memory stores further executable instructions that cause the at least one processor to:

compare a frame in the gif file with a successive frame; and

discard the successive frame when the frame and the successive frame are insufficiently different.

20 . The system of claim 11 , wherein the computer vision model is implemented using GPT-4V™, DALL-E™, and/or Azure™ AI Vision.