Social Media Campaign Configuration via ML-Generated Tracking URLs
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Solution Overview
Problem
Marketers face difficulties in tracking consumer behavior and campaign effectiveness on social media due to the complexity of tracking URLs and varying formats used by different tracking services, making it challenging for non-technical personnel to implement effective tracking systems.
Innovation Solution
A social relationship management (SRM) service employs machine learning to configure social media campaigns and dynamically generates tracking links using user-defined templates, allowing for automatic creation of tracking URLs that can be analyzed by tracking services, simplifying the process and providing actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tracking URLs with various tracking parameters are used to track consumer behavior, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary service that automatically generates and manages tracking URLs. This service acts as a mediator between the marketer's simple post creation action and the complex tracking requirements, handling parameter appending, URL formatting, and cross-platform normalization automatically, thus resolving the contradiction between tracking precision and URL complexity
Solution Approach 2:
The system implements self-service by automatically generating tracking URLs with appropriate parameters based on post metadata, eliminating the need for manual tracking URL construction. The service also self-adapts to different tracking service formats and automatically normalizes parameters across platforms, reducing operational complexity while maintaining measurement precision
2Adaptability or versatility
If different tracking service formats are supported, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal tracking service that can handle multiple tracking service formats (Google Analytics, Facebook Pixel, Twitter Analytics, etc.) through a single standardized interface. The system performs format normalization and parameter mapping automatically, allowing one system to serve multiple tracking services without increasing apparent complexity for the user
Solution Approach 2:
The tracking service acts as an intermediary layer between diverse tracking platforms and the social media posting system. It translates various tracking service requirements into a unified internal format, manages parameter mappings, and handles format conversions automatically, thus supporting multiple formats without proportionally increasing system complexity
3Measurement precision
If manual tracking URL creation is required, then measurement precision is maintained, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically generating tracking URLs with all necessary parameters appended based on post metadata, campaign settings, and platform requirements. This automation maintains tracking accuracy while eliminating manual URL construction, resolving the contradiction between measurement precision and ease of operation
Solution Approach 2:
The system performs preliminary actions by pre-configuring tracking parameters, normalizing formats, and preparing tracking URLs before posts are published. This advance preparation ensures tracking precision is maintained while users only need to perform simple post creation actions, improving ease of operation without sacrificing measurement accuracy
Data Source
AI summary
Techniques for using machine learning to configure social media campaigns are disclosed. A social relationship management (SRM) service performs supervised machine learning to generate a learned model, at least by: generating feature vectors based on training data including campaign configuration data and one or more campaign success metrics; and performing pattern recognition on the feature vectors to determine one or more preferred campaign configurations. The SRM service publishes messages to one or more social media platforms and receives user interaction data associated with users' interactions with the messages. The SRM service performs unsupervised machine learning to update the learned model based at least in part on the user interaction data. The SRM service receives a request to configure a social media campaign, applies data associated with the request to the learned model to determine a preferred campaign configuration, and configures the social media campaign based on the preferred campaign configuration.