Data-driven precision matching decision-making system
EasyKol integrates multi-source data to build an audience profiling system, which not only contains basic gender-age geographic distribution, but also analyzes fans' comment emotional tendency, active time period and device type through machine learning. For example, a fitness equipment brand successfully screened out fitness bloggers with a bandwagon conversion rate of 12.7% through conditions such as 'percentage of men aged 25-40>70%' and 'concentration in the eastern coastal region>60%'.
The portrait data comes from: 1) macro data provided by the official API of the platform; 2) public information of fans crawled; 3) supplemented by third-party data providers. The system will mark the confidence level of each data dimension, and will trigger data update prompts when it detects a sudden change in the netroots' recent fan structure (e.g., a sudden increase of 500,000 fans), so as to avoid marketing decisions based on outdated information.
This answer comes from the articleEasyKol: A marketing tool for finding web celebrities (KOLs) and getting their mailboxesThe
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