Leading and lagging indicators

Joca Torres
4 min readNov 22, 2021


During a mentoring session about a data concept that I think is very important, I noticed that I hadn’t written about that concept yet, so here it is. Anyone who knows me knows the importance I give to metrics. They are essential to understand what is happening and to help with decision-making. There is a way to classify metrics that helps a lot in understanding the potential impact of the metric. The terms used are leading and lagging.

Churn prediction

To explain what they are and what the difference is between leading indicators and lagging indicators, I’ll tell about a work we did both at Locaweb (web hosting and internet services provider) and Conta Azul (ERP for small business) to find ways to predict churn, that is, predict which customers were more likely to cancel the contracted service. We did a lot of analysis to find out which behaviors were most likely to indicate that a customer would cancel and we found a number of interesting behaviors like, for example, that if a website hosting customer redirects their domain to point to a website hosted elsewhere, she probably did it because she is changing the hosting service. Or if her website had high access volume and that volume has drastically reduced, chances are good that she will cancel the website hosting service as well. Or if a Conta Azul customer who used to register 50 sales per month, decreases the number of sales registered to zero, there is a high chance that this customer will cancel her account in the Azul Account.

Churn is an example of a lagging indicator as it tells what happened — customers canceled. Lagging indicators are metrics that help us assess a company’s bottom line. Examples of lagging indicators are churn, revenue, profit, number of customers, and NPS. These are metrics that must be tracked frequently, in some cases even daily or even more than once a day, but they are the consequence. They show the result, but they do not show how that result was obtained.

To understand how a result was obtained we must use leading indicators. In the above example of churn and the understanding of factors that help predict churn, customer behaviors that were detected as predictors of churn, ie, the number of visits to a website or number of sales recorded are the leading indicators. This type of indicator helps to predict an outcome, that is, it helps to predict how a lagging indicator will behave. And it is on the leading indicators that we should focus our energy so that we can see the lagging indicator hand move.

Cause and effect

For example, what should we do to decrease the churn of a product? Ensure that the product is being used and being useful, that is, solving a problem or meeting a need of its user. At Locaweb, in the website hosting product, the website has to be useful to the website owner. What does this website owner or online store expect from this website? Visits? Customers? Purchases? How to help the website owner achieve her goal? This is the way to avoid churn. At Conta Azul, what is the expected behavior of a customer who is solving their small business management problems using the product? How many times a week does she access? What does she do when she logs into the system? How can I help the customer get the most out of the product?

So whenever I see product teams setting churn or even NPS goals, I recommend including engagement goals and leaving churn and NPS not as goals but as metrics to track. If you have a product that generates engagement according to what you had planned for that product, you will most likely have low churn and good NPS.

One way to help understand these concepts is to think about cause and effect, that is, cause and effect indicators. While churn and NPS are effect indicators, engagement can be seen as a cause indicator.


Thinking about OKRs, one way to use leading indicators and lagging indicators is to think about lagging indicators, or effect indicators, as objectives and leading indicators, or cause indicators, as key results.

Using OKR’s classic losing weight example, losing 3 kilos is the goal, and it’s a lagging indicator or effect indicator. While the key results of exercising 3 times a week, not eating sweets, and limiting daily calories to a certain value are leading indicators or cause indicators.

Summing up

  • An important way to look at metrics is to understand if the metric is a lagging indicator, that is, an indicator that is a consequence such as, for example, revenue, profit, churn, number of customers and NPS, or if it is a leading indicator, an indicator that is a cause, such as the engagement of a customer with a product, is the cause of a lower churn and a higher NPS.
  • Leading indicators can be seen as the cause while lagging indicators can be seen as the effect.
  • In OKRs, objectives are the lagging indicators, while key results are the leading indicators.

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Joca Torres

Workshops, coaching, and advisory services on product management and digital transformation. Also an open water swimmer and ukulelist.