What is the Leading Indicators Analysis?
The Leading Indicators Analysis discovers metrics that move ahead of your target KPI and can therefore predict near-future changes. Because it runs inside a Board, it uses the connections you’ve already mapped between metrics, bets, channels, and outcomes to find credible, contextual early signals—not random correlations.
This analysis gives you an early warning system and a way to validate whether interventions are working before lagging KPIs respond.
What You Need to Get Started
- Target KPI – The outcome you want to anticipate (e.g., Revenue, Paid Signups, NPS)
- Candidate metrics – A broad set of connected metrics on your Board (activation, usage, funnel, channel, support)
- Historical time series – Enough observations to test time-lagged relationships (50–100+ points preferred)
Your Board’s metric connections focus the search on plausible relationships and reduce spurious findings.
What You’ll Get Back
- Ranked leading indicators – Metrics that reliably precede KPI movement
- Lead time estimates – How far in advance each indicator signals change (e.g., 7/14/28 days)
- Predictive power & direction – Strength and sign of the relationship, with significance
- Alert-ready thresholds – Suggested levels and trend changes for proactive monitoring
- Reliability scores – Historical accuracy and stability of each indicator
How the Analysis Works
Step 1: Define the Target and Scope
Pick the target KPI and the candidate metric set (often pre-filtered by your Board connections and tags).
Step 2: Test Time-Lagged Relationships
Evaluate each candidate’s lead/lag profile against the KPI to identify statistically meaningful leads (e.g., metric X leads KPI by 14 days).
Step 3: Validate Predictive Signal
Filter out spurious results using significance tests and stability checks across multiple periods; account for autocorrelation and seasonality.
Step 4: Quantify Lead Time & Direction
Produce a lead time estimate per indicator, along with direction (positive/negative) and effect size.
Step 5: Operationalise Alerts
Generate suggested thresholds and slope changes for alerts so teams get actionable early warnings rather than noisy pings.
How to Interpret the Results
Leading indicators are signals, not guarantees:
- Consistent structure → stronger reliability (stable funnels, clear definitions)
- Broader yet connected candidate set → better discovery
- Regular re-validation → sustained usefulness
Keep in mind:
- Some indicators decay or flip as systems evolve
- Seasonality and campaigns can temporarily distort lead times
- Treat indicators as guides for proactive action, not as final outcomes
When to Use the Leading Indicators Analysis
Ideal for:
- Proactive management – See KPI moves weeks early to adjust plans
- Early bet validation – Detect whether interventions are working before lagging KPIs move
- Capacity & revenue planning – Anticipate demand, support load, or pipeline shifts
- Risk detection – Spot churn or quality issues before they surface in headline metrics
Less suitable for:
- Very short histories or highly volatile, definition-drifting metrics
- Teams expecting exact forecasts rather than probabilistic heads-ups
Getting the Best Results
Use Your Board to Shape the Search
- Start from connected metrics with plausible influence paths
- Include channel, funnel, product, and support signals for breadth
Ensure Data Quality & Consistency
- Stable metric definitions and sampling frequency
- Enough history to establish repeatable lead times
Keep It Living
- Re-score indicators each cycle
- Retire stale signals, promote durable ones
- Pair alerts with clear playbooks for response
Summary
The Leading Indicators Analysis turns your Board into an early warning and foresight system. By discovering credible, time-advanced signals within your connected metrics, it helps you act sooner, validate bets earlier, and manage with foresight rather than hindsight.