Dynamic web content insertion

1 . A system, comprising:

a network interface configured to communicate with a web server and one or more server systems via a network;

a processing system in communication with the network interface; and

a memory system in communication with the processing system, the memory system storing instructions that when executed by the processing system result in:

receiving, at a service platform interface, a request and request data associated with a user from the web server;

analyzing, by the service platform interface, the request data to identify a primary offer associated with the request;

accessing a look-alike model to determine at least one secondary offer based on one or more of: the request, the request data, and the primary offer, wherein the look-alike model comprises learned associations between values of data sets to group user data based on a geographic area of the user;

identifying, by the service platform interface, one or more data gaps in the request data used to generate the at least one secondary offer;

determining one or more confidence values of the look-alike model based on a number of data values and relative age of the data values of the look-alike model;

filling the one or more data gaps, by the service platform interface, based on associated values from the look-alike model having the one or more confidence values above a threshold to generate a data set;

prompting the user, through a user interface, to answer a question set to fill the one or more data gaps based on associated values from the look-alike model having the one or more confidence values less than the threshold to generate the data set;

transmitting the data set over the network to the one or more server systems configured to generate the at least one secondary offer;

receiving the at least one secondary offer from the one or more server systems;

inserting the primary offer into existing content displayed through the user interface; and

inserting the at least one secondary offer into the existing content displayed through the user interface.

2 . The system of claim 1 , wherein the look-alike model is used to fill in one or more data gaps associated with the request for the primary offer as an updated data set and further comprising instructions that when executed by the processing system result in:

transmitting the updated data set associated with the request to a first rating system associated with a first plurality of product providers;

receiving one or more product offers from the first rating system as the primary offer based on the updated data set; and

transmitting the one or more product offers to the web server.

3 . The system of claim 2 , further comprising instructions that when executed by the processing system result in:

receiving a product offer selection result based on the one or more product offers transmitted to the web server; and

updating a machine-learning component based on the product offer selection result and the one or more product offers.

4 . The system of claim 3 , further comprising instructions that when executed by the processing system result in:

determining an accuracy based on the product offer selection result, the one or more product offers, and the updated data set; and

updating the machine-learning component based on the accuracy.

5 . The system of claim 3 , wherein transmitting the data set over the network to the one or more server systems comprises transmitting the data set associated with the request to a second rating system associated with a second plurality of product providers, receiving the at least one secondary offer from the one or more server systems comprises receiving one or more secondary product offers from the second rating system as the at least one secondary offer based on the data set, and further comprising instructions that when executed by the processing system result in:

transmitting the one or more secondary product offers to the web server.

6 . The system of claim 5 , further comprising instructions that when executed by the processing system result in:

receiving a secondary product offer selection result based on the one or more secondary product offers transmitted to the web server; and

updating the machine-learning component based on the secondary product offer selection result and the one or more secondary product offers.

7 . The system of claim 2 , further comprising instructions that when executed by the processing system result in:

calling one or more third-party services to fill at least a portion of the one or more data gaps not filled by the look-alike model.

8 . The system of claim 1 , further comprising instructions that when executed by the processing system result in:

receiving a plurality of quoting metrics associated with a plurality of user profiles;

tuning the look-alike model based on the plurality of quoting metrics;

determining a preferred order of presentation of a plurality of product offers based at least in part on the plurality of quoting metrics; and

providing the preferred order of presentation to the web server.

9 . The system of claim 8 , further comprising instructions that when executed by the processing system result in:

summarizing a selection rationale for establishing the preferred order of presentation; and

providing the selection rationale to the web server for display to the user.

10 . The system of claim 1 , further comprising instructions that when executed by the processing system result in:

determining a targeted marketing plan by a machine-learning component using the look-alike model, wherein the look-alike model comprises the learned associations between the values of the data sets to group the user data based on one or more of: an age of dwelling construction in a neighborhood, sales prices within the neighborhood, loss events due to storm damage within the neighborhood, median income, pet ownership, personal property tax records, previous quotes, and quote outcomes provided within the geographic area; and

providing content to the web server based on the targeted marketing plan.

11 . The system of claim 1 , wherein a web page provided to the user by the web server comprises one or more embedded interfaces to access an application programming interface configured to insert the question set into the web page.

12 . The system of claim 11 , wherein the one or more embedded interfaces comprise one or more scripts configured to interpret a plurality of payloads from the application programming interface and apply one or more components to process one or more user interface interactions.

13 . The system of claim 11 , further comprising instructions that when executed by the processing system result in:

interfacing with a marketplace system configured to render an experience embedded within the user interface to apply one or more rules, styles, and templates for display content of the web page; and

receiving feedback from the marketplace system to assist a machine-learning component to adapt a sequence and content of the question set.

14 . A computer program product comprising a storage medium embodied with computer program instructions that when executed by a computer cause the computer to implement:

receiving, at a service platform interface, a request and request data associated with a user from a web server and one or more server systems via a network;

analyzing, by the service platform interface, the request data to identify a primary offer associated with the request;

accessing a look-alike model to determine at least one secondary offer based on one or more of: the request, the request data, and the primary offer, wherein the look-alike model comprises learned associations between values of data sets to group user data based on a geographic area of the user;

identifying, by the service platform interface, one or more data gaps in the request data used to generate the at least one secondary offer;

determining one or more confidence values of the look-alike model based on a number of data values and relative age of the data values of the look-alike model;

filling the one or more data gaps, by the service platform interface, based on associated values from the look-alike model having the one or more confidence values above a threshold to generate a data set;

prompting the user, through a user interface, to answer a question set to fill the one or more data gaps based on associated values from the look-alike model having the one or more confidence values less than the threshold to generate the data set;

transmitting the data set over the network to the one or more server systems configured to generate the at least one secondary offer;

receiving the at least one secondary offer from the one or more server systems;

inserting the primary offer into existing content displayed through the user interface; and

inserting the at least one secondary offer into existing the content displayed through the user interface.

15 . The computer program product of claim 14 , wherein the look-alike model is used to fill in one or more data gaps associated with the request for the primary offer as an updated data set and further comprising computer program instructions that when executed by the computer cause the computer to implement:

transmitting the updated data set associated with the request to a first rating system associated with a first plurality of product providers;

receiving one or more product offers from the first rating system as the primary offer based on the updated data set; and

transmitting the one or more product offers to the web server.

16 . The computer program product of claim 15 , further comprising computer program instructions that when executed by the computer cause the computer to implement:

receiving a product offer selection result based on the one or more product offers transmitted to the web server; and

updating a machine-learning component based on the product offer selection result and the one or more product offers.

17 . The computer program product of claim 16 , further comprising computer program instructions that when executed by the computer cause the computer to implement:

determining an accuracy based on the product offer selection result, the one or more product offers, and the updated data set; and

updating the machine-learning component based on the accuracy.

18 . The computer program product of claim 16 , wherein transmitting the data set over the network to the one or more server systems comprises transmitting the data set associated with the request to a second rating system associated with a second plurality of product providers, receiving the at least one secondary offer from the one or more server systems comprises receiving one or more secondary product offers from the second rating system as the at least one secondary offer based on the data set, and further comprising computer program instructions that when executed by the computer cause the computer to implement:

transmitting the one or more secondary product offers to the web server.

19 . The computer program product of claim 18 , further comprising computer program instructions that when executed by the computer cause the computer to implement:

receiving a secondary product offer selection result based on the one or more secondary product offers transmitted to the web server; and

updating the machine-learning component based on the secondary product offer selection result and the one or more secondary product offers.

20 . The computer program product of claim 14 , further comprising computer program instructions that when executed by the computer cause the computer to implement:

receiving a plurality of quoting metrics associated with a plurality of user profiles;

tuning the look-alike model based on the plurality of quoting metrics;

determining a preferred order of presentation of a plurality of product offers based at least in part on the plurality of quoting metrics;

providing the preferred order of presentation to the web server;

summarizing a selection rationale for establishing the preferred order of presentation; and

providing the selection rationale to the web server for display to the user.