Can Analytics Reduce First Year Turnover to Zero?

Imagine you are running a retail chain. Some of your in-store staff stay at least a year; some do not. Can analytics distinguish what is different between those two groups? Could it potentially do that so well that first-year turnover could be reduced to zero?

The idea is audacious in an industry where turnover can easily run in excess of 60%. However, it’s not quite as insane as it sounds. If you had asked an American automotive executive in the 1960s how many cars would come off the assembly line with defects, he’d admit that a great many would — and it couldn’t be helped. Ask an automotive executive today and they’ll talk about striving for zero defects. We see the same thing in safety. In the bad old days of the mining industry frequent accidents were considered inevitable, now they strive for zero.

Zero turnover is not impossible. Here’s how one call center achieved that goal.

For an analytics team, tackling the audacious goal of zero first-year turnover provides a broad canvas for having a business impact. It wouldn’t be enough to focus exclusively on the recruitment process. The team would have to look at each element of the employee lifecycle and one by one fix “defects” that cause people to leave.

Beside the broad canvas, what I like about this goal is that the focus is on business action. This is not about data scientists in an ivory tower doing cluster analysis; it’s a team, bit by bit, eating away at something that matters a lot to the business.

First-year turnover, defects, and accidents are not zero and never will be, but having a goal of zero focuses the mind and inspires effort far better than simply hoping to make things a bit better. If you are building an analytics team, a goal like this could get them off to a great start.

Special thanks to our community of practice for these insights. The community is a group of leading organizations that meets monthly to discuss analytics and evidence-based decision making in the real world. If you’re interested in moving down the path towards a more agile approach to people analytics, then email me at dcreelman@creelmanresearch.com or connect to me on LinkedIn.