Influencer Scoring Model Authority Influence Segmentation
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Solution Overview
Problem
Current methods for identifying key opinion leaders (KOLs) in the pharmaceutical and medical industries are ineffective as they fail to dynamically quantify a KOL's influence and authority, which can change over time due to factors like job changes or outdated practices, making it difficult for companies to target the most impactful KOLs for marketing efforts.
Innovation Solution
An influencer scoring model that generates a score for professionals based on an authority component and an influence component, using professional and publication information to create a professional profile, and determining attribute connections, which considers the credibility and influence of each professional within their industry, with a dashboard interface for user filtering and selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to identify KOLs, then the process is simple, but the accuracy and effectiveness of identifying impactful KOLs deteriorates
Solution Approach 1:
The identification system segments the evaluation of KOLs into distinct components: authority component (based on professional information like education, position, experience) and influence component (based on publication information and attribute connections). This segmentation allows each aspect to be measured independently and combined to produce a comprehensive score, improving measurement precision while maintaining manageable system complexity.
Solution Approach 2:
The system changes the parameters of evaluation from static, subjective judgments to dynamic, quantifiable metrics. By converting professional qualifications, publication records, and network connections into measurable parameters with assigned weights, the system achieves higher accuracy in identifying impactful KOLs through objective data-driven scoring.
2Reliability
If static KOL lists are used, then the implementation is straightforward, but the reliability of KOL impact assessment deteriorates over time
Solution Approach 1:
The system transitions from static KOL lists to a dynamic scoring model that continuously updates KOL assessments. The authority and influence components are recalculated based on current professional information and publication data, allowing the system to adapt to changes in KOL status, positions, and impact over time, thereby maintaining reliability.
Solution Approach 2:
The system incorporates feedback mechanisms where new professional information and publication data continuously feed into the scoring model. This feedback loop ensures that the KOL scores reflect the most current information, maintaining assessment reliability without requiring complete manual reevaluation, thus managing the time investment efficiently.
3Measurement precision
If comprehensive professional and publication data are collected, then the scoring accuracy improves, but the data processing complexity increases
Solution Approach 1:
The data processing system segments the comprehensive dataset into distinct categories: professional information (education, position, experience) and publication information (papers, citations, collaborations). Each segment is processed independently through specific algorithms that calculate authority and influence components separately, then combine them. This segmentation reduces processing complexity while maintaining scoring precision.
Solution Approach 2:
The system applies different processing qualities and methods to different data segments based on their specific characteristics. Professional information undergoes structured validation and weighting, while publication data receives text analysis and network analysis. This localized quality approach optimizes processing efficiency for each data type while maintaining overall scoring accuracy.
Data Source
AI summary
A method for operating an influencer scoring model includes receiving, for a plurality of professionals, professional information and publication information. For each professional, the method also includes generating a professional profile that includes a plurality of attributes. The plurality of attributes are based on (i) professional information and (ii) publication information. The method also includes determining one or more attribute connections among the plurality of attributes of the professional profile. Each attribute connection is based on a common attribute shared between the respective professional and another professional. The method also includes generating a score that includes an authority component and an influence component. A scoring model is configured to receive the plurality of attributes and the attribute connections for the respective professional. The authority component represents at least one attribute corresponding to the received professional information. The influence component represents one or more attribute connections of the professional profile.