Overview
In a landscape marked by increasing labor shortages and a shift towards an employee-driven
market, understanding employer attractiveness is vital. Our aim is to conduct a feasibility
study for a data-driven, AI-based employer attractivity benchmark tool. This study will explore
the integration of machine learning (ML) for a comprehensive analysis, combining
quantitative data with qualitative insights to enhance employer understanding in the Swiss
labor market.
The focus lies on determining the effectiveness of AI in analyzing diverse data sets, including
more quantitative data such as compensation in addition with qualitative data like company
culture. This AI based analysis should then be translated into a industry comparable
benchmark and actionable insights for both employers and employees. Especially soft factors
like culture, management style or work environment should be quantified by applying MLalgorithms
to user-generated content from employer rating platforms.
Additionally, we'll assess the potential for a self-assessment tool that could contribute to
optimized matching in the labor market.
Through this study, we aspire to pave the way for an innovative tool that assists companies in
navigating and improving their market appeal in a rapidly evolving employment landscape.
