Promotion Engine Mediator Architecture for Social Network Targeting
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Solution Overview
Problem
Current online systems lack effective mechanisms for sharing user shopping and social data between merchant sites and social networking platforms, limiting informed shopping experiences and exposing sensitive data to competitors when merchant sites try to promote products on social networks.
Innovation Solution
A promotion engine that analyzes merchant site data to determine product promotions and targeting criteria, allowing merchants to promote products on social networking sites without sharing sensitive information, using consumption data, demographic information, and social interactions to create targeted advertisements and sponsored content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If merchant sites share user data with social networking systems to enable targeted promotions, then product promotion effectiveness is improved, but data security and privacy are worsened due to exposure to competitors
Solution Approach 1:
The social networking system acts as an intermediary that receives aggregated promotion criteria from the merchant site, processes them to identify target users, and delivers promotions without exposing raw user data to the merchant. This mediator architecture allows effective targeted promotion while preventing direct data sharing that would compromise security and privacy.
2Adaptability or versatility
If merchant sites implement data sharing capabilities with social networking systems, then promotion targeting capability is improved, but system complexity increases due to additional data sharing mechanisms
Solution Approach 1:
The system extracts only the necessary promotion criteria information from the merchant site's user data, separates this from the full user profile, and transmits only the extracted criteria to the social networking system for promotion generation. This extraction approach enables targeted promotion capability while minimizing the complexity of data sharing mechanisms.
3Measurement precision
If merchants manually analyze user data and create promotions, then promotion precision is improved, but labor costs and time consumption increase
Solution Approach 1:
The system enables self-service promotion generation where the social networking system automatically analyzes promotion criteria, identifies target users based on their profiles and interactions, and delivers customized promotions without requiring manual analysis by merchants. This automated self-service approach achieves high promotion precision while eliminating time-consuming manual processes.
Data Source
AI summary
To promote a merchant's products on a social networking system, a promotion engine receives data from a merchant site regarding the merchant site's users' activities. The users' purchases of a promoted product are correlated with the user's activities performed in connection with a related product. An automated process running on a computer system then determines promotion criteria for the promoted product, based at least in part on the activities performed in connection with the related product. A promotion for the promoted product is generated and communicated to a social networking system, which displays the promotion to one or more users of the social networking system based on the determined promotion criteria.