Any company that genuinely cares about long-term growth and profitability needs to attract and retain talent. To understand where employee frustrations lie and prevent attrition, a company needs to establish processes to collect and analyze employee feedback.
"It's not a faith in technology. It's faith in people" -Steve Jobs
In a well-written article (1), Henrik Kniberg proposes a lean, useful, and easy method to find out how teams are doing. The categories are designed to be holistic; they are support, teamwork, "players or pawns", mission, the health of codebase, suitable process, delivering value, learning, speed, easy to release, fun. In a facilitated meeting, a team color-votes for each category. They use special cards that describe the good and the ugly of each category.
Management can visualize and compare overall trends of aggregated team health results by specific divisions, geographic locations, or company-wide.
With minor modifications, any manager can adopt the Spotify health checks to his/her organization regardless of level or function.
Thoughtful leaders will detect early warnings of trouble from health checks and will take corrective actions before an issue becomes a crisis.
In addition to the multiple benefits mentioned in the article, I will add that Spotify health checks provide precious leading indicators of employee retention. Exit interviews and surveys conducted by HR help with lagging indicators of employee morale. Forward-thinking companies use these data points to construct predictive models for employee engagement and retention.
HR conducts anonymous surveys of current and former employees similar to glassdoor.
What is the rate of internal referrals? How long does it take for a new employee to make a referral of a potential job candidate? What is the quality of referrals in terms of retention and performance? This could include an internal NPS question: how likely are you recommend this company to a friend looking for a job? A people analytics team in HR could collect and analyze these data points and make predictive models.