Intelligent Branding for Less Well-Known Enterprises by Matching Culture Values of Employers and Candidates

This paper develops and tests a novel employer branding toolset by applying state-of-the-art data science and machine learning techniques to a unique dataset on candidates' culture preferences. The aim of this toolset is to intelligently brand less well-known enterprises by matching the culture values of employers and candidates. In this context, the culture correlation tool compares and ranks the culture preferences of candidates in order to correlate them for an optimized employer branding. The employer clustering tool represents a grouping mechanism, which joins similar companies and organizations regarding their intrinsic culture characteristics, and matches candidates and employers having equivalent culture values. The culture recommender refers to a machine learning based model performing culture recommendations to candidates leveraging selectable attributes, i.e. “Netflix-style” recommendation of employers to candidates. The job predictor predicts how soon a job opening will be filled within our culture recommendation framework, indicating market trends for, e.g., industry sectors and companies. The findings of this study enhance the market competitiveness of less well-known enterprises in acquiring talents with suited culture background.