Online System Lead Generation via Intermediary Messaging
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Solution Overview
Problem
Online systems lack feedback on the success of lead generation messages, making it difficult to train models for predicting user interactions and distinguishing between good and bad third-party actors, which affects the quality of leads and user trust.
Innovation Solution
The online system receives interaction data from users on lead generation content items, sends anonymous notifications to third parties, and manages communication channels while anonymizing user information to protect privacy, enabling better lead generation and user protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the online system shares user personal information with third-party companies for lead generation, then the third party can directly contact the user, but the online system loses downstream feedback on whether the lead was successful
Solution Approach 1:
The patent introduces an intermediary communication channel through the online system's messaging infrastructure. Instead of direct third-party-to-user communication, all messages are routed through the online system, which acts as a mediator. This allows the system to capture feedback information about message interactions while still enabling lead generation, thus resolving the contradiction between productivity and information loss.
2Productivity
If the online system allows third parties to directly communicate with users, then lead conversion can occur, but the system cannot distinguish between good and bad third-party actors
Solution Approach 1:
The patent implements a feedback mechanism where the online system monitors and tracks all interactions between third parties and users through the messaging system. By capturing read receipts, response times, and communication patterns, the system generates feedback data that can be used to evaluate third-party behavior and distinguish reliable actors from unreliable ones, thereby resolving the contradiction between productivity and reliability.
3Measurement precision
If the online system collects detailed user interaction data for model training, then prediction accuracy improves, but user privacy and security risks increase
Solution Approach 1:
The patent extracts and separates personally identifiable information from interaction data. The system collects detailed interaction patterns (read receipts, response times, engagement metrics) while deliberately excluding or anonymizing direct user identifiers. This extraction approach allows the system to train accurate prediction models on behavioral patterns without exposing sensitive user privacy information, thus resolving the contradiction between measurement precision and privacy protection.
Data Source
AI summary
An online system communicates a lead generation message to a client device associated with a user. The lead generation message includes a selectable option authorizing a third-party entity to open a channel of communication with the user. If the online system receives from the client device an interaction with the selectable option, the online system sends a notification to the third-party entity indicating that the user associated with the client device interacted with the lead generation content item. The third-party entity may then send a request to the online system to send a message to the user via a messaging system controlled by the online system. The online system thus enables the third-party entity to communicate with the user via the messaging system. The online system then uses information about the communication to train a model to optimize the selection of lead generation messages to users.