Cross-network text communication management system
A text communication management system is provided that receives, analyzes, and enforces recipient actions regarding phone-based text communications. The text communication management system can obtain recipient action data regarding a recipient's action with respect to a particular text communication, and enforce the recipient action with respect to future text communications. The management system can also or alternatively analyze the recipient action data in connection with recipient action data from multiple other recipients to generate a model for use in determining whether future phone-based text communications should be permitted, determining the likelihood that such communications will cause recipients to opt-out, and the like. Third parties, such as phone service carriers and text communication originating entities, may access the management system via an application programming interface (“API”) to submit data regarding recipient actions, initiate analysis of a potential text communication using the model, and the like.
1. A system comprising computer-readable memory and one or more computer processors programmed by executable instructions in the computer-readable memory to at least:
receive, via a phone network, a phone-based text communication, wherein the phone-based text communication is associated with a source phone number, wherein the phone-based text communication is addressed to a recipient phone number, and wherein the phone-based text communication comprises a payload of text content;
determine a degree of risk associated with the phone-based text communication based at least partly on a predictive analysis of the text content, the recipient phone number, and the source phone number using a machine learning model, wherein the degree of risk represents a probability that sending the phone-based text communication to the recipient phone number will cause an opt-out recipient action; and
determine, based on the degree of risk satisfying a criterion, not to send the phone-based text communication to the recipient phone number.
2. The system of claim 1 , wherein the one or more processors are further programmed by the executable instructions to at least provide an application programming interface (“API”) to a phone carrier service, wherein the API comprises a function by which the phone carrier service submits recipient action data regarding a second phone-based text communication sent to a recipient computing device.
3. The system of claim 2 , wherein the one or more processors are further programmed by the executable instructions to at least train the machine learning model based at least partly on the recipient action data regarding the second phone-based text communication.
4. The system of claim 1 , wherein the one or more processors are further programmed by the executable instructions to at least provide an application programming interface (“API”) to a text communication originating entity, wherein the API comprises a function by which the text communication originating entity submits recipient action data regarding a second phone-based text communication sent to a recipient computing device.
5. The system of claim 4 , wherein the one or more processors are further programmed by the executable instructions to at least train the machine learning model based at least partly on the recipient action data regarding the second phone-based text communication.
6. The system of claim 1 , wherein the one or more processors are further programmed by the executable instructions to determine a probability that the phone-based text communication is fraudulent or malicious based at least partly on a predictive analysis of the text content, the recipient phone number, and the source phone number.
7. The system of claim 1 , wherein the one or more processors are further programmed by the executable instructions to determine a probability that sending the phone-based text communication to the recipient phone number violates an opt-out recipient action based at least partly on a predictive analysis of the text content, the recipient phone number, and the source phone number.
8. The system of claim 1 , wherein the one or more processors are further programmed by the executable instructions to:
receive a second phone-based text communication, wherein the second phone-based text communication is addressed to a second recipient phone number;
determine to send the second phone-based text communication based at least partly on second recipient action data representing an opt-in action associated with the source phone number and the second recipient phone number; and
send the second phone-based text communication to a second computing device associated with the second recipient phone number.
9. The system of claim 8 , wherein the one or more processors are further programmed by the executable instructions to determine a classification of the phone-based text communication, wherein determining not to send the phone-based text communication is based further on the classification.
10. The system of claim 9 , wherein to determine the classification, the one or more processors are configured to determine that text content of the phone-based text communication is classified as one of: alert, information, promotion, or service.
11. A computer-implemented method comprising:
under control of a computing system comprising one or more computer processors configured to execute specific instructions,
receiving, via a phone network, a phone-based text communication, wherein the phone-based text communication is associated with a source phone number, wherein the phone-based text communication is addressed to a recipient phone number, and wherein the phone-based text communication comprises a payload of text content;
determining a degree of risk associated with the phone-based text communication based at least partly on a predictive analysis of the text content, the recipient phone number, and the source phone number using a machine learning model, wherein the degree of risk represents a probability that sending the phone-based text communication to the recipient phone number will cause an opt-out recipient action; and
determining, based on the degree of risk satisfying a criterion, not to send the phone-based text communication to the recipient phone number.
12. The computer-implemented method of claim 11 , further comprising providing an application programming interface (“API”) to a phone carrier service, wherein the API comprises a function by which the phone carrier service submits recipient action data regarding a second phone-based text communication sent to a recipient computing device.
13. The computer-implemented method of claim 12 , further comprising training the machine learning model based at least partly on the recipient action data regarding the second phone-based text communication.
14. The computer-implemented method of claim 11 , further comprising providing an application programming interface (“API”) to a text communication originating entity, wherein the API comprises a function by which the text communication originating entity submits recipient action data regarding a second phone-based text communication sent to a recipient computing device.
15. The computer-implemented method of claim 14 , further comprising training the machine learning model based at least partly on the recipient action data regarding the second phone-based text communication.
16. The computer-implemented method of claim 11 , further comprising determining a probability that the phone-based text communication is fraudulent or malicious based at least partly on a predictive analysis of the text content, the recipient phone number, and the source phone number.
17. The computer-implemented method of claim 11 , further comprising determining a probability that sending the phone-based text communication to the recipient phone number violates an opt-out recipient action based at least partly on a predictive analysis of the text content, the recipient phone number, and the source phone number.
18. The computer-implemented method of claim 11 , further comprising:
receiving a second phone-based text communication, wherein the second phone-based text communication is addressed to a second recipient phone number;
determining to send the second phone-based text communication based at least partly on second recipient action data representing an opt-in action associated with the source phone number and the second recipient phone number; and
sending the second phone-based text communication to a second computing device associated with the second recipient phone number.
19. The computer-implemented method of claim 18 , further comprising determining a classification of the phone-based text communication, wherein determining not to send the phone-based text communication is based further on the classification.
20. The computer-implemented method of claim 19 , wherein determining the classification comprises determining that text content of the phone-based text communication is classified as one of: alert, information, promotion, or service.