Leading vs Lagging Indicators: Know If You're Winning Before It's Too Late

This is one of RoadmapOne ’s articles on Key Result Tagging frameworks .

The simplest explanation: lagging indicators tell you whether you won; leading indicators tell you whether you’re likely to win.

Revenue is lagging—by the time you see it, the customer already decided to buy. Pipeline is leading—it predicts future revenue you can still influence. Churn rate is lagging—the customer already left. Engagement frequency is leading—declining engagement predicts churn before it happens.

This distinction matters enormously for OKRs and roadmap planning. If all your metrics are lagging, you won’t know whether you’re succeeding until it’s too late to change course.

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The Difference That Matters

Lagging Indicators: The Scoreboard

Lagging indicators tell you the final score. They’re outcome metrics that confirm what happened after it’s happened.

Common lagging indicators:

• Revenue / ARR / MRR
• Churn rate
• Net Promoter Score (NPS)
• Customer Lifetime Value (LTV)
• Market share

These metrics are important—they’re how you measure ultimate success. But they’re retrospective. By the time churn rate increases, customers have already left. By the time revenue drops, deals have already been lost.

Lagging indicators are like checking your bank balance. Important, but it doesn’t tell you whether tomorrow’s paycheck will arrive.

Leading Indicators: The Predictors

Leading indicators predict future outcomes while you can still influence them. They’re early signals that forecast where lagging indicators are headed.

Common leading indicators:

• Activation rate (predicts retention)
• Engagement frequency (predicts churn)
• Pipeline value (predicts revenue)
• Trial-to-paid conversion (predicts customer acquisition)
• Feature adoption (predicts engagement)
• Onboarding completion (predicts activation)

These metrics let you course-correct. If activation rate is declining, you can fix onboarding before churn increases. If pipeline is thinning, you can adjust sales strategy before revenue drops.

Leading indicators are like checking the weather forecast. You can still bring an umbrella.

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The OKR Connection

The most common mistake in OKRs: setting both Objectives and Key Results on lagging indicators.

Bad OKR (all lagging):

• Objective: Increase revenue by 25%
• Key Results:
  • Achieve £2M in new bookings
  • Reduce churn to 5%
  • Increase average deal size by 15%

Every metric here is lagging. You won’t know whether you’re on track until deals close, customers churn, and revenue appears. By then, the quarter is over.

Good OKR (lagging Objective, leading Key Results):

• Objective: Increase revenue by 25%
• Key Results:
  • Increase qualified pipeline from £4M to £6M (leading—predicts bookings)
  • Improve trial-to-paid conversion from 5% to 8% (leading—predicts customer acquisition)
  • Increase weekly active usage to 60% (leading—predicts retention)

The Objective is still lagging—revenue is the ultimate outcome you care about. But the Key Results are leading—they predict whether revenue will grow and give you time to intervene if they’re off track.

Leading and Lagging Pairs

Most metrics have a leading counterpart that predicts them:

When setting Key Results, ask: “What leading indicators predict this outcome?” Measure those alongside—or instead of—the lagging metric.

Tagging Key Results

In RoadmapOne, you can tag Key Results as Leading or Lagging to audit your OKR balance:

If all your Key Results are lagging, you’ll only know success or failure after the fact. If all are leading, you might optimise proxies without confirming they drive actual outcomes.

The ideal balance: mostly leading Key Results (so you can course-correct), with some lagging ones (to confirm the leading indicators actually predict outcomes).

Tag Key Results by indicator type in RoadmapOne

The Proxy Problem

Leading indicators are powerful but dangerous. They’re proxies—things you measure instead of the thing you actually care about. Proxies can mislead.

When Leading Indicators Fail

The proxy doesn’t actually predict the outcome. You improve activation rate, but churn doesn’t decrease. Maybe activation rate wasn’t the driver you thought it was.

The proxy gets gamed. If “onboarding completion” is a leading indicator, teams might make onboarding trivially easy to complete—even if users don’t actually learn anything.

The proxy diverges from the outcome. Engagement frequency increases, but NPS decreases. Users are engaging more but liking it less. The leading indicator went up, but it stopped predicting the lagging outcome.

Closing the Loop

The solution is regularly checking whether your leading indicators actually predict your lagging outcomes.

Every quarter, ask:

• Did the leading indicators move as expected?
• Did the lagging outcomes follow?
• If leading went up but lagging didn’t, why?

If your leading indicators consistently fail to predict lagging outcomes, you’re measuring the wrong proxies. Find better leading indicators.

Where This Matters Most

Early-Stage Products

Before product-market fit, almost everything is uncertain. Leading indicators like “users return within 7 days” are more actionable than lagging indicators like “monthly revenue”—you can run experiments and see results within a week.

Subscription Businesses

SaaS and subscription models have inherent lag. A customer who decides to churn today won’t show up in churn metrics until their subscription ends (maybe months later). Leading indicators like engagement decline give you warning while you can still intervene.

Long Sales Cycles

Enterprise B2B deals can take 6-12 months. Revenue is extremely lagging—you won’t know whether this quarter’s efforts worked until next year. Pipeline, conversion rates, and deal velocity become critical leading indicators.

Practical Implementation

Audit your current OKRs. Tag each Key Result as Leading or Lagging. What’s the balance?

Find leading indicators for lagging outcomes. For each lagging metric you care about, identify 2-3 leading indicators that predict it.

Track both. Don’t abandon lagging indicators—they confirm ultimate success. But add leading indicators so you can course-correct.

Validate the connection. Regularly check whether your leading indicators actually predict lagging outcomes. If they don’t, find better proxies.

Set expectations. Help stakeholders understand the difference. “We’re tracking pipeline (leading) because it predicts revenue (lagging). If pipeline is healthy, revenue will follow.”

The Bottom Line

Lagging indicators tell you whether you won. Leading indicators tell you whether you’re likely to win.

Good OKRs pair lagging Objectives with leading Key Results. You define success in lagging terms (revenue, retention, NPS) but measure progress in leading terms (pipeline, activation, engagement). That balance gives you time to course-correct while there’s still time to change the outcome.

Tag your Key Results in RoadmapOne as Leading or Lagging. If the balance is wrong—all lagging, or leading indicators that don’t predict outcomes—fix it before you’re stuck with a scoreboard you can’t change.

Predict success before it’s too late to influence it. That’s the power of leading indicators.

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References

• OKRs for Product Teams — Connecting Objectives and Key Results
• HEART Framework — Comprehensive UX metrics including leading/lagging
• North Star Metric — The single metric that captures core value
• Pirate Metrics — Funnel metrics from acquisition to revenue
• Key Result Tagging Frameworks — Complete guide to KR tagging