Case Synopsis: The Algorithm That Tells the Boss Who Might Quit

Employers want to know who is likely to leave their companies. As turnover becomes a bigger worryand expensein a tightening labor market, some companies, including Wal-Mart Stores Inc., Credit Suisse Group AG, and Box Inc,. are analyzing many data points to determine who is likely to leave a post. The idea is to give managers early warning so they can take action before employees leave.

Corporate data crunchers play with dozens of factors to identify flight risks. The data often reveal a complex picture of what motivates workers to stayand what causes them to look elsewhere. Data scientists create models to predict which workers might leave a company in the near future.

As the employment picture improves, companies are focusing more on retaining workers, largely because replacing them is costly. The big challenge for employers is what, exactly, to do with the information data scientists provide them. Some arent sure how to approach employees at risk of leaving. To help retain workers, Credit Suisse launched a global effort allowing its workers to raise their hands for internal moves. To kindle closeness among team members, Box has encouraged managers to throw more social events.

Companies also are trying to predict when workers might leave their positions within a company. Wal-Mart is trying to determine in advance which employees are likely to get promoted so that it can line up replacements more quickly.

Questions

1. How does the managerial task of leading affect employee performance?

2. As the economy continues to improve, workers will be looking to change jobs (and companies) at a faster pace. What can managers do to plan for and fill job openings quickly?

3. How else can managers use big data to help plan for human resources?

i just want short answer

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