Problem context
Many PLG dashboards are dominated by lagging outcomes such as revenue, churn, or late stage retention.
By the time a cohort looks bad in those metrics, the users who made it that way are long gone.
Teams instrument what is easiest to count instead of the behaviors that would have predicted success or failure earlier.
What breaks if this is not solved
- You react slowly to emerging problems because you only notice them once they show up in lagging financial or retention numbers.
- Experiments are hard to interpret because you do not have stable, intermediate signals that move before long term metrics do.
- It becomes difficult to prioritize work because every decision depends on waiting for late stage outcomes.
When this playbook applies
- You already have basic event tracking but most of your regular reporting focuses on revenue and high level retention.
- You suspect there are key behaviors that predict success or failure but they are not part of your core metric stack.
- You want to make faster, more confident decisions about onboarding, pricing, or feature work.
System approach
Treat leading indicators as hypotheses: candidate signals you believe will predict outcomes, to be tested with data.
Start from your best customers and your worst cohorts, and look backwards to see what behaviors reliably differ.
Elevate a small, stable set of leading indicators into your primary dashboards and review rituals.
Execution steps
- Clarify the lagging outcomes that matter most for your business, such as activation to paid conversion, D90 retention, or expansion revenue.
- For a sample of successful and unsuccessful accounts, analyze early product behavior in the first days or weeks to identify patterns that differ.
- Propose a shortlist of candidate leading indicators, such as completion of a specific workflow, collaboration events, or depth of usage in a key feature.
- Backtest each candidate: measure how strongly it correlates with your lagging outcomes across multiple cohorts.
- Select a very small set of leading indicators to formalize, and document exactly how they are defined and calculated.
- Audit and, if needed, improve the instrumentation and data pipelines that feed these indicators so that they are reliable and timely.
- Integrate leading indicators into your core dashboards and weekly or monthly reviews, and use them to trigger early interventions or experiments.
Metrics to watch
Correlation between leading indicators and target outcomes
Remain strong or improve as definitions are refined.
Track at least roughly, for example using simple cohort splits or correlation coefficients.
Signal coverage across new accounts
Increase share of accounts for which leading indicators can be computed.
Low coverage can hide emerging issues or make signals appear better than they are.
Time between leading indicator movement and lagging outcome changes
Provide enough lead time to act (for example weeks rather than days).
If indicators move almost simultaneously with outcomes, they may not be useful for intervention.
Number of decisions or experiments explicitly keyed to leading indicators
Increase as the system matures.
Shows whether indicators are actually used or just reported.
Failure modes
- Choosing too many indicators, making it hard to know which ones to trust or focus on.
- Relying on indicators that are easy to measure but weakly predictive, such as raw login counts.
- Frequently changing definitions without backfilling history, making trend analysis impossible.
- Treating leading indicators as immutable truths instead of hypotheses to be revisited as the product evolves.