• What data preprocessing techniques are essential for preparing employee-related data for turnover prediction?
• Which machine learning algorithms are most effective for predicting employee turnover, and how do we determine the best one for our organization?
• How can we incorporate qualitative factors like job satisfaction and employee engagement into a predictive model for turnover?
• What evaluation metrics should be used to assess the performance of a turnover prediction model?
• How can we address potential biases in the data to ensure the turnover prediction model is fair and accurate across different employee groups?

• How can we design analytical models that assess cultural fit alongside technical skills during the recruitment process?
• What data sources and metrics are most effective for incorporating company culture into recruitment optimization models?
• How can we ensure that analytical models used in recruitment do not introduce bias and accurately reflect the company’s cultural values?
• What role does natural language processing (NLP) play in evaluating cultural fit during the recruitment process?
• How can recruitment models be continuously updated to adapt to evolving company culture and changing organizational needs?

Two more World Cafe rounds: New challenges, solutions, and Ideas.

Find your Top 3 Key Take-Aways!

In this session, our World Cafe moderators summarize the outcome of their roundtables.