Correlation between Lead and Lag Indicators

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A lead indicator is a measurement that offers information on something that has an impact on an outcome. For example, customer satisfaction is a lead indicator for revenue growth.

A lag indicator is a measurement that provides information on an end result. For example, revenue growth is a lag indicator.

It is useful to look at both lead and lag indicators. Lead indicators help you identify problems so that you could fix them to bring about the ideal outcomes. Lag indicators allow you to reflect on the effectiveness of your actions.

It is important to ensure that you are looking at the right lead indicators. That means you need to verify the correlation between the lead and lag indicators.

A simple way to validate the correlation is to plot the results for lead and lag indicators on a scatter plot.

This scatter plot shows the relationship between store profit and the number of competitors. One would guess the correlation would be high. The scatter plot shows there is little correlation. You can put a trend line through the data points. When you look at the equation for y, the store profit, the value for R-squared, which represents the degree of fit, is 0.01. That says only 1% of the variations in store profit could be explained by the variations in the number of competitors. It has minimal effect on store profits.

On the contrary, the scatter plot for store profit and target customer population shows there is a high correlation between the two. The R-squared value for trend line is 0.43, which says 43% of the variations in store profits can be explained by the variations in the target customer population.

When you compare the two diagrams, it is clear that the number of competitors is not the best lead indicator to use to predict store profit. So it is important to validate your lead indicators so that you won’t be wasting time and effort on things that have little effects on the target result.

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