Product Practice #288: How Leading Indicators are Defined by Context


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One of the most undervalued aspects of Key Results is how detectable the changes are on a weekly bases throughout the cycle to allow the team to adapt their actions.

A lot of teams focus on easy-to-document but hard-to-influence lagging indicators when they should seek to identify and use leading indicators to define their OKRs. And even though there are some common patterns of what types of Key Results are often rather lagging or leading, another ingredient is often missing: A team’s context.

While there are universal traits of what makes a metric lagging or leading, it’s not always the same type of metric. A leading indicator is defined by its ability to predict future results through correlational behaviors. Let’s look at an oversimplified example:

At a company level, a SaaS company might use a quarterly measured Customer Effort Score across the product’s main activation areas to predict customer retention. In this case, retention is the lagging indicator (the end goal) that is only measurable in hindsight, and the Customer Effort score allows them to predict the likelihood of changing.

But the quarterly CES measurement is a lagging indicator from an individual, customer-facing, product team’s perspective in the same company. For them, it’s only measurable in hindsight and is the result of multiple efforts.

So, to find their leading indicator, this product team needs to answer the question of what actions they can influence directly and detect frequently. By focusing Discovery efforts on the behaviors and pains of low-CES respondents, they might identify more leading Outcomes that can contribute to improving the CES, like

  • Invite Co-workers without the need to connect Office365–whose success could be measured through a KR like “No. of invited co-workers within x days of onboarding
  • “Time spent with inviting co-workers.”

But these are just two dimensions of context around lagging and leading indicators. What about internal-facing or platform teams? Imagine the perspective of a growth infrastructure team in that same company. From their point of view, the “Time spent with inviting co-workers” is a lagging indicator. 

Why? Because they can’t influence it directly, and it will only change as a result of multiple teams’ efforts. So, what’s leading for the customer-facing team, becomes lagging for the growth infrastructure team. But instead of latching on to the leading indicator of another team, they have to find their own. By asking the same questions, they might identify leading indicators like

  • “No. of API calls to connect to company directory”
  • Reduce the number of Invite Service Request Call Timeouts.”

Helping them land on leading Key Results that they can influence directly.

Avoid tapping into generalized definitions when trying to land on more leading indicators for your next set of OKRs. Instead, consider the context of the metric: Can you influence it directly and measure its change continuously? If yes, it’s probably a valuable leading indicator for you. If no, reverse-engineer the lagging indicator to identify what Key Results can help you guide the way.

Speak soon,


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