Where are the predictive performance measures? Well apparently they’re not in the rear-view mirror. That, apparently, is where you find the lag measures. But the rear-view mirror analogy is not entirely accurate when it comes to defining lead and lag measures. So it’s time to blow it up.
The first inaccuracy is the assumption that there are two types of performance measures: lead or lag. I think every performance measure has predictive power. It just depends on what you’re trying to predict. Technically, Average Customer Satisfaction is a lag measure because it’s after the fact. But it can be predictive of Customer Retention Rate, New Customers Via Referrals, and Profit.
The second inaccuracy is that looking in the rear-view mirror only gives you lag information. But in reality, things you see in your rear-view mirror can indeed predict outcomes like being rear-ended. For example, even though profit is considered to be a ‘rear-view mirror’ kind of measure, your historic patterns of average level and variability in profit from month to month can definitely give you some indications of what’s most likely in the future if not much changes.
We need to first focus on the performance result we want to improve, and then look at both lead and lag performance measures that relate to that performance result. Each performance result will have its own unique set of lead and lag measures. And this means that a performance measure that is lead for one result can be lag for another.
If you want to improve employee engagement, lag measures might include Average Employee Job Satisfaction Rating or Gallup’s Employee Engagement Ratio*. Some lead measures might include Days of Unplanned Leave, % Decisions Supported By Employee Consensus, Average Employee Satisfaction With Being Listened To, and % Employees Who Believe Their Job is Important. Here, Employee Engagement Ratio is a lag measure, but if we were focused on reducing loss of knowledge from the organisation, or reducing employment expenses, then it would be a lead measure, with some power to predict employees’ likelihood to leave the organisation.
So I guess I’m suggesting that you need to look in all your mirrors and through all your windows to fully prepare for what’s likely to happen in the future.
TAKE ACTION: Map your performance measures on a page to visually reveal their cause-effect relationships. Only map the strong ones, or you’ll end up with unintelligible and thusly useless tangle of spaghetti. Which performance measures have predictive power for which other performance measures? Are you reporting these measures together, so you can get the most use from them?