Method and system for creating a predictive model for targeting web-page to a surfer
Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a web site, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.
1. A computer-implemented method comprising:
receiving a request to access a web page of a website associated with a content provider;
accessing a set of candidate design attributes for the web page;
generating a set of web-page permutations associated with the web page, wherein a web-page permutation defines visual characteristics of web content presented on the web page, and wherein the visual characteristics are associated with a combination of one or more candidate design attributes;
applying a trained model to the set of web-page permutations to generate a set of predictive values, wherein a predictive value identifies a probability of a user device interacting with the web content defined by a web-page permutation associated with a particular combination of candidate design attributes;
selecting, based on the set of predictive values, an optimized web-page permutation associated with the web page, wherein the optimized web-page permutation defines visual characteristics that are associated with a subset of the set of candidate design attributes;
modifying the web page using the optimized web-page permutation, wherein the modified web page is configured to present the web content having the visual characteristics associated with the subset of candidate design attributes; and
facilitating display of the modified web page on the user device.
2. The computer-implemented method of claim 1 , wherein the set of candidate design attributes are determined based on one or more characteristics of the user device, and wherein the one or more characteristics includes an indication of whether a user cookie associated with the user device has been generated.
3. The computer-implemented method of claim 1 , wherein the trained model was trained using a training dataset that includes information representative of previous user interactions with the web page.
4. The computer-implemented method of claim 1 , wherein the set of candidate design attributes are associated with their respective fonts, shapes, and/or images.
5. The computer-implemented method of claim 1 , wherein a predictive value of the modified web page is greater than the predictive value of the web page.
6. The computer-implemented method of claim 1 , wherein the web page is associated with a mobile application.
7. The computer-implemented method of claim 1 , wherein the trained model is selected from a set of trained models generated for the web page.
8. A system comprising:
one or more processors; and
memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to perform operations comprising:
receiving a request to access a web page of a website associated with a content provider;
accessing a set of candidate design attributes for the web page;
generating a set of web-page permutations associated with the web page, wherein a web-page permutation defines visual characteristics of web content presented on the web page, and wherein the visual characteristics are associated with a combination of one or more candidate design attributes;
applying a trained model to the set of web-page permutations to generate a set of predictive values, wherein a predictive value identifies a probability of a user device interacting with the web content defined by a web-page permutation associated with a particular combination of candidate design attributes;
selecting, based on the set of predictive values, an optimized web-page permutation associated with the web page, wherein the optimized web-page permutation defines visual characteristics that are associated with a subset of the set of candidate design attributes;
modifying the web page using the optimized web-page permutation, wherein the modified web page is configured to present the web content having the visual characteristics associated with the subset of candidate design attributes; and
facilitating display of the modified web page on the user device.
9. The system of claim 8 , wherein the set of candidate design attributes are determined based on one or more characteristics of the user device, and wherein the one or more characteristics includes an indication of whether a user cookie associated with the user device has been generated.
10. The system of claim 8 , wherein the trained model was trained using a training dataset that includes information representative of previous user interactions with the web page.
11. The system of claim 8 , wherein the set of candidate design attributes are associated with their respective fonts, shapes, and/or images.
12. The system of claim 8 , wherein a predictive value of the modified web page is greater than the predictive value of the web page.
13. The system of claim 8 , wherein the web page is associated with a mobile application.
14. The system of claim 8 , wherein the trained model is selected from a set of trained models generated for the web page.
15. A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to perform operations comprising:
receiving a request to access a web page of a website associated with a content provider;
accessing a set of candidate design attributes for the web page;
generating a set of web-page permutations associated with the web page, wherein a web-page permutation defines visual characteristics of web content presented on the web page, and wherein the visual characteristics are associated with a combination of one or more candidate design attributes;
applying a trained model to the set of web-page permutations to generate a set of predictive values, wherein a predictive value identifies a probability of a user device interacting with the web content defined by a web-page permutation associated with a particular combination of candidate design attributes;
selecting, based on the set of predictive values, an optimized web-page permutation associated with the web page, wherein the optimized web-page permutation defines visual characteristics that are associated with a subset of the set of candidate design attributes;
modifying the web page using the optimized web-page permutation, wherein the modified web page is configured to present the web content having the visual characteristics associated with the subset of candidate design attributes; and
facilitating display of the modified web page on the user device.
16. The non-transitory, computer-readable storage medium of claim 15 , wherein the set of candidate design attributes are determined based on one or more characteristics of the user device, and wherein the one or more characteristics includes an indication of whether a user cookie associated with the user device has been generated.
17. The non-transitory, computer-readable storage medium of claim 15 , wherein the trained model was trained using a training dataset that includes information representative of previous user interactions with the web page.
18. The non-transitory, computer-readable storage medium of claim 15 , wherein the set of candidate design attributes are associated with their respective fonts, shapes, and/or images.
19. The non-transitory, computer-readable storage medium of claim 15 , wherein a predictive value of the modified web page is greater than the predictive value of the web page.
20. The non-transitory, computer-readable storage medium of claim 15 , wherein the web page is associated with a mobile application.