Content Influencer Scoring System Using NLP and Logistic Regression
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Solution Overview
Problem
Influencer marketing faces challenges in effectively matching brands with suitable influencers, as existing systems lack efficient methods to evaluate influencer relevance and historical performance data, leading to inconsistent and low-quality influencer selections.
Innovation Solution
A content influencer scoring system that utilizes a remote server to analyze advertisement campaign data, generate scores based on term frequency, document frequency, and historical performance data, and ranks influencers for suitability, incorporating natural language processing and logistic regression to determine the probability of successful campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional influencer selection methods are used, then the process is simple and quick, but the matching accuracy and campaign success rate are low
Solution Approach 1:
The influencer evaluation system segments the assessment into multiple independent dimensions including relevance scoring based on content analysis, performance scoring based on historical campaign data, and suitability scoring combining both factors. This segmentation allows each dimension to be evaluated separately using appropriate algorithms, improving overall matching accuracy while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent introduces an intermediary scoring system that acts as a mediator between brand requirements and influencer characteristics. The relevance score and performance score serve as intermediate metrics that are combined to produce a final suitability score, enabling systematic comparison and matching without requiring direct complex many-to-many relationships between brands and influencers
2Reliability
If comprehensive influencer evaluation is performed, then the quality of influencer selection improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing influencer relevance scores based on their content and performance metrics before actual campaign matching occurs. Historical performance data is pre-processed and stored in a database, enabling rapid retrieval and comparison during campaign selection without requiring real-time comprehensive analysis, thus reducing evaluation time while maintaining selection quality
Solution Approach 2:
The patent transforms qualitative influencer characteristics into quantitative parameters that can be efficiently processed. Content relevance, audience demographics, and performance metrics are converted into numerical scores that can be quickly computed and compared. This parameter transformation enables comprehensive evaluation to be performed rapidly through mathematical operations rather than qualitative assessment
Data Source
AI summary
A content influencer scoring system may include influencer computers each associated with a respective content influencer having influencer historical performance data and legacy influencer content associated therewith. A remote server may obtain advertisement campaign data associated with an advertisement campaign and parse the advertisement campaign data for advertisement keywords. The remote server may match content influencers to the advertisement campaign data based on the advertisement keywords and, for each content influencer, generate an advertisement campaign score. The score may be generated by determining whether the content influencer is suitable for the advertisement campaign based upon a term frequency of the advertisement keywords for each document from the legacy influencer content, and frequency of the advertisement keywords across the documents, and when suitable, determining whether the advertisement campaign score based upon the historical performance data to generate the advertisement campaign score.