Social Media Campaign Efficiency Scoring System
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Solution Overview
Problem
There is a need for a system and method to effectively measure the efficiency of social media campaigns and provide recommendations to improve their efficiency, as existing methods lack comprehensive data analysis and actionable insights.
Innovation Solution
A computer-implemented method and system that collects and processes publicly available activity data from social networks to extract interaction data, assign weights to interactions, estimate campaign effort, and calculate efficiency scores, while also generating recommendations for improving visibility and efficiency based on social network ranking rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive activity data is collected and analyzed from social networks, then measurement precision of campaign efficiency is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces an intermediary system comprising web crawlers, data collection modules, and analysis engines that mediate between social network platforms and the efficiency measurement function. This intermediary layer aggregates and processes raw activity data from multiple sources (Facebook, Twitter, LinkedIn, etc.) before presenting refined efficiency metrics to users, thereby improving measurement precision while managing system complexity through modular architecture
Solution Approach 2:
The patent replaces manual efficiency assessment methods with automated computational systems that use algorithms to analyze activity data, calculate efficiency scores, and generate recommendations. The system substitutes human analysis with machine-based data processing, using computational models to transform raw social media metrics into standardized efficiency measurements across different platforms
2Productivity
If real-time efficiency scoring is provided, then productivity and responsiveness are improved, but use of energy and computational resources increase
Solution Approach 1:
The patent implements periodic data collection and analysis cycles rather than continuous real-time processing. The system collects activity data at scheduled intervals, processes batches of information, and updates efficiency scores periodically. This approach maintains productivity by providing timely feedback while reducing energy consumption by avoiding constant computational operations
Solution Approach 2:
The patent performs preliminary data collection and preprocessing operations in advance, storing raw activity data in databases for later analysis. By pre-collecting and organizing data from social networks before efficiency calculations are needed, the system reduces computational burden during actual scoring operations and enables faster real-time responses when queries are made
3Ease of operation
If detailed analysis and recommendations are generated, then ease of operation for campaign improvement is improved, but loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary analysis of activity data and pre-generates insights that can be quickly retrieved when users request efficiency scores. The system pre-processes data to identify key patterns and relationships, storing processed results in databases for rapid access. This eliminates the need to perform complete analysis from scratch each time a user queries the system
Solution Approach 2:
The patent divides the complex analysis process into separate modular components: data collection, data processing, efficiency calculation, and recommendation generation. Each module operates independently and can be executed in parallel or on-demand. This segmentation allows the system to provide quick efficiency scores using pre-processed data while still generating detailed recommendations when users request comprehensive analysis
Data Source
AI summary
A system and method for measuring the efficiency of social media campaigns. The system collects searchable activity data of members of a social network and processes this data locally to extract interaction data happening on the profile page of the social media campaign on the social network. The interaction data is then weighed in accordance with its type. The system may also determine a reach of the campaign and a responsiveness score of a user. The system may also determine an effort score representing a monetary value of the profile page based on the volume of contribution by the owners of the social media campaign. The efficiency score may then be determined based on a relationship between the number of interactions, the weight associated with each interaction, the reach, the responsiveness score, and the effort score.