Influencer Performance and Segmentation for Marketing Campaigns
收藏Zenodo2025-07-17 更新2026-05-26 收录
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https://zenodo.org/doi/10.5281/zenodo.16042261
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资源简介：
This project analyzes the performance of 1,000 TikTok influencer accounts using key metrics such as followers, average views, likes, comments, and shares. It includes data preprocessing, exploratory data analysis (EDA), and segmentation using K-Means clustering.
The analysis provides insights for marketers and brands, revealing that high engagement often matters more than follower count. Through clustering, three influencer segments are identified: Mega Influencers, Mid-Tier Influencers with High Engagement, and Stable Micro Influencers.
The dataset is based on TikTok Influencers Dataset (September 2022) from Kaggle.
Tools: Python, pandas, matplotlib, seaborn, scikit-learn, PCA.
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Zenodo创建时间：
2025-07-17
