Businesses are struggling with employee resignation, especially during this new era of “quiet quitting.” Oftentimes, managers are surprised that individuals wanted to leave in the first place. These resignations aren’t trivial, and they’re costly. Interviewing costs, training costs, and business-specific knowledge are all lost when an employee quits; thus, knowing beforehand who is likely to leave is highly valuable, so action can be taken to retain talent.
Typically, HR data can have insights into who might leave and why, but this is often tedious to parse out as an analyst with large data sources. In contrast to traditional business intelligence methods, machine learning algorithms have the potential to quickly find patterns that are otherwise difficult for humans to discover and encode.
Smartbridge can help organizations respond to the problem of employee churn pre-emptively which can help reduce costs and increase employee retention. We’ve reduced development time by abstracting otherwise manual data science tasks with state-of-the-art machine learning packages, taking your business solutions from idea to proof-of-concept in as little as 4 weeks.
To demonstrate this, we will be delving into a use case that deals with HR data such as employee satisfaction levels, average hours worked, and tenure at the company to come to the conclusion of whether an employee will resign or not.