Lagging indicators: definition, examples & when to use them

Ever wondered why some metrics only tell you what happened, but not what's coming next? That's where lagging indicators come into play. They help us understand the outcomes of past events, giving us a clear picture of what's already unfolded.

In this blog, we'll dive into what lagging indicators are, see some examples across different fields, and explore how they compare to leading indicators. We'll also share some best practices for using them effectively. So let's get started!

Understanding lagging indicators

Lagging indicators are metrics that reflect the outcomes of past events and trends. They play a crucial role in confirming trends across economic, business, and technical fields. Unlike volatile leading indicators, lagging indicators are more stable and reliable.

In economics, lagging indicators like unemployment rates, corporate profits, and GDP confirm past changes. They differ from leading indicators like retail sales and stock market performance. Business lagging indicators, or KPIs, reflect the outcomes of previous management decisions and strategy changes.

When it comes to technical analysis, lagging indicators like moving average crossovers occur after a price move and confirm trend changes. Traders often use them to confirm momentum, though this might lead to taking positions a bit late. Overall, lagging indicators provide a stable, reliable view of past performance.

Selecting the right metrics is crucial for staying aligned with business goals. Metrics should be regularly reviewed for relevance, focusing on trends over absolute numbers. Shorter tracking periods allow for timely adjustments based on lagging indicator data.

Examples of lagging indicators in various contexts

In economics, lagging indicators like unemployment rates, GDP, and CPI reflect past economic performance. These metrics confirm trends rather than predict them, offering insights into the economy's direction.

For businesses, lagging indicators such as sales figures, customer churn rates, and profit margins measure past performance. These metrics help assess how effective previous strategies were and inform future decision-making, as discussed in Martin Fowler's blog post on metrics.

In technical analysis, lagging indicators like moving averages confirm established trends in market prices. As mentioned in this Reddit post on day trading, these indicators often lag behind the market, which can sometimes lead to missed opportunities or false signals.

Safety professionals also rely on lagging indicators, like the number of reported injuries, to evaluate past safety performance. However, as discussed in this Reddit thread, a balanced approach that includes both lagging and leading indicators is essential for a comprehensive safety assessment.

While lagging indicators provide valuable insights into past performance, they work best when used alongside leading indicators. By understanding the strengths and limitations of lagging indicators, you can make informed decisions and adjust strategies accordingly.

Comparing lagging and leading indicators

So, what's the difference between lagging indicators and leading indicators? Lagging indicators confirm past trends—they're backward-looking and provide insights into the effects of past actions. In contrast, leading indicators are forward-looking; they predict future outcomes and guide daily actions.

Lagging indicators validate the trends suggested by earlier leading indicators. For example, in workplace safety, the number of reported injuries (a lagging indicator) can confirm how effective safety observations (a leading indicator) have been in preventing incidents.

Combining both types of indicators gives you a fuller picture for strategizing. Lagging indicators assess past performance, while leading indicators guide future actions. By integrating both, you can align short-term initiatives with long-term objectives and make data-driven decisions. This is something we at Statsig emphasize in our approach to experimentation and feature management.

However, relying solely on lagging indicators can be frustrating, especially in fast-paced environments like day trading. Lagging indicators often trail behind the market, potentially leading to losses if used alone. To optimize performance, it's important to regularly review and adjust your metrics, ensuring they remain relevant and drive the changes you want to see.

Best practices for using lagging indicators effectively

Lagging indicators, such as revenue or profit, offer valuable insights into past performance and help businesses assess their strategies. They're essential for understanding cause-and-effect relationships and spotting areas for improvement.

But here's the thing: relying solely on lagging indicators can be risky. They might not provide timely signals for course correction. For instance, lagging indicators in day trading can result in missed opportunities or potential losses.

To use lagging indicators effectively, it's wise to integrate them with leading indicators for a balanced approach. This combination allows for proactive decision-making while still learning from past performance.

Linking metrics to goals is crucial when working with lagging indicators. By aligning these indicators with specific objectives, businesses can better understand their progress and make data-driven decisions. At Statsig, we believe that connecting your metrics to your goals helps drive meaningful change.

Regularly reviewing and adjusting lagging indicators is essential to keep them relevant. As Martin Fowler suggests, focusing on trends rather than absolute numbers and adapting metrics to the changing context ensures they continue to be useful.

Closing thoughts

Understanding lagging indicators is key to making informed decisions based on past performance. By combining them with leading indicators, you get a more complete picture that can help guide your strategies moving forward. Remember to align your metrics with your goals and keep reviewing them to stay on track.

If you're interested in diving deeper, check out resources like Martin Fowler's articles on metrics or explore how Statsig can help you make the most of your data. Hope you found this useful!