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Lead indicators can be powerful performance measures for predicting and influencing our lag measures. But how do you create powerful ones?

Lead indicators, to some, are the holy grail of performance measures. They are mysterious, difficult to find, and yet upheld as the ideal and most treasured of performance measures or KPIs. What exactly are they, why are they valued so highly and why are they so elusive?

A lead indicator is a measure that monitors the ’cause’ result in a cause-effect relationship between two results. And just as importantly, it’s where the ‘effect’ happens after a time delay from the cause. Lead indicators give forewarning about the ‘effect’ result.

But the approach that many people take to identifying lead indicators doesn’t work. Carlos works for a European mining company as a Lean specialist, and they use PuMP to develop their performance measures. He shared with me his concerns about lead indicators chosen by his colleagues:

“Some of our functions, like Health & Safety, rely a lot on leading indicators which most of the time are measuring activities, such as number of safety observations. They strongly believe that they have to track it that way. The reality is that they are recording very good performance in these leading indicators, but there is no impact on the strategic lag measures.”

Avoid the mistakes in choosing lead indicators…

Carlos was curious about how to select lead indicators that are still results-oriented, and not just counting how much activity is done. In PuMP, we focus on results-oriented measures, not activity-oriented. And that’s the first mistake: thinking that actions are lead indicators.

Mistake 1: Thinking that actions are lead indicators.

Actions are – obviously! – required to make changes that will improve performance. But actions are not the lead indicators. As Carlos points out, our chosen actions can easily have no effect at all. The whole idea about performance measurement is get evidence about the results of our actions so we can keep choosing better actions.

Another mistake is to assume that any logical cause-effect relationship means the cause is a lead indicator. But many performance measures on the cause side of a relationship can have an immediate effect on the performance measure on the lag side. The words ‘lead’ and ‘lag’ imply there must be a time delay between the cause and the effect. We cannot get any predictive power with that time delay.

Mistake 2: Assuming any cause in a cause-effect relationship is a lead indicator.

For example, soil moisture is a cause of crop growth. But it’s an immediate cause-effect relationship since crops will respond within hours to a change in soil moisture. Soil fertility, on the other hand, has a delayed effect on crop growth, as it can take time to deplete and also take time to improve.

Lead indicators have a delayed causal effect on their lag indicators.

A lead indicator is a measure that suggests how another measure, the lag measure, might behave in the future.

If we want to predict future Staff Turnover, we could look to other measures that have a known impact on Staff Turnover. For example, we might look to an Employee Engagement Score, or design measures of new recruit satisfaction with their work or with their manager or with their co-workers.

These results can predict Staff Turnover because usually they start turning down well before people decide to leave. There’s a time lag. So, a lead indicator has a cause-effect relationship to the lag measure we’re interested in predicting, but it’s a cause-effect relationship with a time lag. And they give us, therefore, the power to change things now so we can influence the future we want.

You can guess or hypothesise what the lead indicators might be for your lag measure. But the best way is to use data to confirm the strongest relationships, and the size of the cause-effect time lag. Here’s the process:

Step 1: Check the research for known explanatory factors.

Our first step is to research and find out if anyone else has established a list of factors that do have a relationship with our lag measure. For example, if our lag measure is Staff Turnover, we’d read articles from HR journals and magazines to find out if anyone has already tested the factors that most affect the likelihood of staff leaving an organisation.

Researching is important, because it will save lots of time, we could otherwise waste in chasing very weak potential lead indicators, like those that would result from a brainstorming session.

Step 2: Check your business processes for new potential explanatory factors.

A very useful second step is to flowchart the business process that our lag measure relates to. If our lag measure is Sales Revenue, then we might flowchart both the marketing and the sales processes to identify potential steps that have a significant impact on the level of Sales Revenue.

For each of the early process steps, we’d describe the result that has the impact. For example, in the marketing process we might decide that our content creation step impacts significantly on Sales Revenue. We might describe the result that has the impact as ‘relevance of our content to the target market’. And we could measure this result in two ways: Average Visit Time on Content Pages, or the Email Newsletter Open Rate. These two measures are potential lead indicators.

Using our business processes naturally helps us find lead indicators because the early steps in a process occur earlier in time than the lag result does. But beware, because sometimes powerful lead indicators can lie in other business processes that don’t directly produce the lag result. Imagine if the service delivery process consistently over-promised and under-delivered. Then Sales Revenue would eventually nose-dive. In this case, a lead indicator might be something like the percentage of customers that felt they got more than they expected from us.

Step 3: Choose the strongest of your potential lead indicators.

When we’ve got our list of potential lead indicators, we then gather data for these and look for the strength and time delay in their relationships with our lag measure.

Just say that we’ve listed several potential lead indicators for our lag measure of Customer Retention Rate:

• Speed of Resolving Customer Inquiries
• Frequency of Customer Contact
• % of Customer Contacts Made by Assigned Customer Relationship Manager
• Days Between Broken Promises
• Number of Customer Complaints
• Net Promoter Score

We’d gather together the data for all these potential lead indicators, and for the lag measure, and start plotting. A scatter plot will help us see the strength of each relationship, by how tightly the two measures form a pattern. And a time series plot will help us see the time delay in that relationship, by how much earlier shifts in the lead measure occur, compared to the resulting shifts in the lag measure.

Where the scatter plot shows a tight relationship AND the time series plot shows there is a time delay, we’ve got some strong lead indicators worth testing.

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While there is some science and method to finding lead indicators, there’s also a touch of art, too. It takes practice and experience to build the wisdom to find very powerful lead indicators. So, start practicing!