A large telecommunications company wanted to find new ways to reduce employee turnover and improve talent retention. Over 90,000 people’s sensitive data needed to be collected and analyzed to identify patterns in employee churn. However, legal regulations locked up the data, and manual anonymization of datasets for each analytics project took 6 weeks on average.
Using synthetic repositories, the analytics team:
Knowing when and why people typically quit will enable the company to take extra steps to identify and retain high-risk employees in time. Reducing turnover rates by only 0.5% could already result in double-digit million cost savings and positively impact team productivity