Working as a product manager (PM), you hear about metrics all the time. Most PMs understand the concepts of leading and lagging indicators well. But some stop there without considering the 'input' and 'output' metrics that influence leading indicators, so I would like to explain them with an example.
1. Leading and Lagging Indicators
A leading indicator tells us in advance what actions we need to take today to reach our future goals. Simply put, it is 'a metric that predicts future performance.' Typical examples include the rate at which users add items to the cart from search results in commerce, and the number of core feature uses per session in a SaaS product.
A lagging indicator, on the other hand, confirms how successful our efforts were through what has already happened. Examples include revenue, ARR (annual recurring revenue), and retention.
2. Why Focus on Leading Indicators
When setting OKRs, people often emphasize that you should set leading indicators, not lagging ones. Leading indicators matter because they let you detect changes early and secure time to make the right improvements. Also, a leading indicator is something a single team or organization can directly control, whereas lagging indicators like revenue are affected by external variables and the combined work of many teams. That means you have less control over them, and diagnosing and fixing problems can involve long delays.
Another strength of leading indicators is that they drive behavior inside the organization. As the saying goes, "people behave according to what is measured." Focusing teams on actionable behavior is a major advantage of leading indicators. If you connect leading indicators to key goals like OKRs, teams can focus on actions they can take right now, and you can verify final outcomes with lagging indicators to prevent goal distortion.
3. The Input and Output Metrics We Tend to Miss
You might think leading indicators are enough, but it helps to also know the input and output metrics that move them. When a PM understands this structure, they can manage metrics more systematically along with the product's dynamics. An input metric represents a concrete action the team can directly control, and an output metric is what results from that action. Note that some organizations, like Amazon, use output metrics to mean final outcomes such as revenue; in this essay, an output metric means an initiative-level result metric.
The two pairs of metrics operate at different levels. Strategic goals like OKRs are set with leading indicators, while the input and output metrics that move those leading indicators are set at the level of individual initiatives or projects. This strengthens execution toward the goal and helps you find practical directions for improvement.
4. A Hypothetical Example: Improving Search-to-Purchase Conversion in Commerce
Let me walk through a hypothetical example of a commerce search and discovery team to explain the relationships and roles of the input metric, output metric, leading metric, and lagging metric.
The team's goal is to improve the purchase conversion rate of users who search. At first, they set 'the rate at which users who view search results add items to the cart (search-to-cart conversion)' as their leading indicator and tried UX improvements such as funnel optimization, but the add-to-cart rate did not rise as much as expected. Then the PM in charge realized something important.
There was no 'input metric' the team could control to directly influence the leading indicator. Since the existing search results were sorted in simple alphabetical order, the team decided to run an experiment changing this to popularity-based sorting.
The team defined 'impressions of search result pages with popularity sorting applied' as their input metric, because they could directly adjust this number by partially rolling out popularity sorting on the search results screen. The actual performance of popularity sorting was measured through an output metric: 'search result CTR (click-through rate).'
The experiment showed that CTR rose on pages with popularity sorting, which led to an increase in the leading indicator, the add-to-cart conversion rate. In turn, the increase in add-to-cart conversion led to a meaningful rise in the lagging indicator, weekly and monthly search-driven revenue.
Here is the flow in a simple form.
(Input) Impressions of search result pages with popularity sorting up
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(Output) Search result CTR up
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(Leading) Add-to-cart conversion rate up
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(Lagging) Weekly/monthly search-driven revenue up
When you clearly distinguish input, output, leading, and lagging metrics and define each one's role and influence, I believe it helps PMs set the direction for product improvement.