What is a Lead Indicator?
A lead indicator is a forward-looking metric that predicts a future business outcome and can be directly influenced by the team measuring it. Unlike lagging metrics, which report what already happened, lead indicators track the activities and inputs that drive results, giving teams an early signal they can still act on.
- Two-test rule: A metric qualifies as a lead indicator only when it is both predictive of a future outcome and influenceable by the team that owns it.
- Activity over outcome: Lead indicators measure inputs like sales calls, training hours, or pipeline coverage, not the revenue or churn numbers those inputs produce.
- Pair them, never replace: Lead and lag indicators work as a system, you steer with the lead measure and confirm with the lag measure.
- Harder to measure, more useful to manage: Lead indicators often rely on indirect or qualitative data, but they are the only metrics a team can move before quarter-end.
The two tests every lead indicator must pass
Chris McChesney and his coauthors at FranklinCovey distilled lead indicators down to two non-negotiable properties: a true lead indicator is predictive of the outcome you care about, and it is influenceable by the team responsible for it. If a metric fails either test, it is a vanity number, not a lead indicator.
Predictive means a change in the metric reliably moves the lag indicator in the same direction. Influenceable means the team can change the metric this week, not just hope it improves.
A weather forecast is predictive but not influenceable. A daily step count is influenceable but not predictive of revenue.
Only metrics that pass both tests belong on the dashboard.
Lead indicators in practice
The same activity can be a lead indicator in one context and irrelevant in another. The test is whether the activity meaningfully moves the outcome the team is held accountable for.
- Sales: Number of qualified discovery calls per rep predicts pipeline value, which predicts closed revenue.
- Customer success: Health-score touchpoints completed predict renewal probability, which predicts net retention.
- Product: Weekly active users completing a core action predicts feature adoption, which predicts monthly retention.
- People operations: Manager 1:1s completed predicts engagement scores, which predicts voluntary turnover.
Each chain starts with something the team controls this week. Lead indicators that sit two or three causal steps away from team behavior tend to disappoint, because too many other variables intervene.
Lead vs lag indicators
The two indicator types answer different questions. Lead indicators answer "are we on track?" while lag indicators answer "did we get there?"
Treating them as substitutes is the most common measurement mistake we see in OKR rollouts.
Dimension | Lead indicator | Lag indicator |
|---|---|---|
Timing | Real-time or weekly | Monthly, quarterly, annually |
Direction | Forward looking | Backward looking |
Influenceability | High, team controls it directly | Low, outcome of many factors |
Example | Number of sales calls booked | Quarterly revenue closed |
Ease of measurement | Often harder, sometimes qualitative | Easier, comes from finance or analytics |
Best used for | Steering and weekly course correction | Confirming results and reporting |
Behavioral effect | Drives action | Drives explanation |
The companies that get measurement right run both. Bain & Company's Management Tools and Trends survey has tracked the Balanced Scorecard, which formalized the lead/lag pairing, as one of the most used tools globally, with adoption peaking near 53% in the early 2000s and remaining a top-25 tool since (Bain, 2018).
Where lead indicator programs go wrong
Most companies do not fail to pick lead indicators. They fail at one of three downstream choices.
The first failure is picking activity that looks important but does not move the lag measure. A team tracks "demos delivered" without checking whether demos correlate with closed deals in their segment.
They are influencing something. They are not predicting anything.
The second failure is the opposite: picking a metric so predictive it is functionally another lag measure. Pipeline value is predictive of revenue, but if it only updates at quarter-end, the team has no time to act on it.
A useful lead indicator updates fast enough for a weekly check-in to change behavior.
The third failure is treating lead indicators as targets in their own right. Goodhart's law applies.
When the number of sales calls becomes the goal, reps make low-quality calls to hit the count and the predictive link to revenue breaks. Lead indicators are signals, not objectives.
Using lead indicators in your OKR cycle
Inside an OKR framework, lead indicators most often surface as key results that measure the activity expected to drive the objective. A team with the objective "Make our product the preferred choice for mid-market buyers" might pair the lag-style key result "Achieve $4M in mid-market ARR" with the lead-style key result "50 product demos delivered per week to qualified mid-market prospects."
The pairing matters more than the individual metric. Lead-only OKRs invite the activity trap, while lag-only OKRs leave the team with nothing to manage between quarterly reviews.
The pair gives the team something to influence and something to verify. Tools that distinguish lead from lag at the key result level make this discipline easier to enforce across the company.
