Introduction

metric

What Metrics Represent

Metrics are the measurable foundation of your strategy — the signals that reveal how your system behaves over time. They connect bets (what you do) with outcomes (what happens), forming the backbone of strategic learning in Segflow.

Where dashboards show results, metrics show relationships. They help teams understand why performance moves — not just whether it did.

Each metric represents a time series that evolves as your organization acts and learns. When combined across Flow Boards, these metrics form the analytical core of your Strategy Graph — a living map of cause and effect across the system.

How Metrics Fit Into the Strategy Graph

Metrics don’t exist in isolation. Their meaning comes from how they connect — to each other, to bets, and to outcomes.

These relationships are created visually inside Flow Boards:

Bet → Metric — the bet contributes to improving the metric
Metric → Metric — one metric drives or correlates with another

Once connected, metrics become part of the Strategy Graph — the system-level network Segflow uses to:

Trace influence across metrics and bets
Detect compounding loops and bottlenecks
Power Strategic Analyses like Driver, Bet Impact, and Growth Loop Analysis

Every connection you create enriches the graph — teaching Segflow how your system really operates.

Relationships are created inside Flow Boards, ensuring every connection has clear strategic context.

Strategic Roles of Metrics

Not all metrics serve the same purpose. Segflow supports three strategic types that reflect different layers of your system:

Type	Description	Example
North Star	Represents your ultimate measure of long-term success	ARR, Retention Rate
Goal	Tracks intermediate objectives that ladder up to your North Star	Conversion Rate, Weekly Active Users
Input	Measures the actions or conditions that drive your goals	Sign-up Completion Rate, Feature Usage, Campaign Reach

This structure ensures that teams optimize for leverage, not vanity. When input metrics improve, they eventually move the goals and North Star metrics above them — forming a clear hierarchy of impact.

Metric Structure

Each metric in Segflow contains several components that reflect how data evolves over time and supports analytical precision.

1. Events (Time-Series Data)

Every metric collects events — the raw historical data points that record value over time. These values form the foundation for correlation, regression, and forecasting.

Field	Description
`date`	The point in time for the data value
`value`	The recorded numeric value
`metricId`	Reference to the metric this event belongs to

These data points allow Segflow to detect seasonality, delayed effects, and emerging trends.

2. Intervals

Intervals summarize performance over defined periods — daily, weekly, monthly — making it easier to compare trends and rates of change.

Field	Description
`total`	Aggregated metric value over the period
`change`	Difference from the previous interval
`length`	Number of days included
`period`	Period type (e.g., week, month, quarter)

This helps normalize trends across uneven datasets and supports consistent comparison between metrics.

3. Goals

Goals define the target state you want to reach for a given metric. They help translate intent into measurable progress.

Field	Description
`title`	The name of the goal
`startDate` / `endDate`	The timeframe for measurement
`targetValue`	The intended outcome
`currentValue`	Progress toward the goal
`aggregation`	How data is rolled up (sum, average, increase, etc.)

Goals help track alignment between expected impact and observed change, supporting both accountability and learning.

4. Forecasts

Forecasts use statistical models to predict future metric values based on historical patterns. They help teams anticipate trajectory and detect when performance deviates from expectation.

Field	Description
`yHat`	Predicted value
`yP10 / yP90`	Confidence intervals (10th–90th percentile)
`forecastHorizon`	How far into the future the model predicts
`quality`	Forecast accuracy (MAE, RMSE, MAPE)

Forecasts are powered by time-series models (like Prophet) and continuously improve as new data arrives. They provide early signals of potential slowdowns, accelerations, or anomalies — especially useful before bet results are visible.

Working With Metrics

Creating a Metric

Metrics can be created manually or imported from your connected data sources.

Manual

Go to Metrics
Click New Metric
Give it a name, description, and display format
Select its strategic type (Input, Goal, or North Star)
Save and start tracking data

From Integrations

Connect your data source under Integrations
Select which metrics to import
Configure frequency and roll-up method

Editing and Managing

Update names, descriptions, and strategic types as your strategy evolves
Add or modify goals and targets
Review forecasts to understand expected performance
Metrics automatically update across all boards where they appear

Connecting Metrics in Flow Boards

To make metrics part of your strategic system:

Open a Flow Board
Add metrics that represent key drivers or outcomes
Draw relationships between metrics or link them to bets
Observe correlation, regression results, and systemic connections

These connections become the foundation for analyses and insight generation across your organization.

Best Practices

Start simple — begin with 3–5 key metrics that represent your system’s core drivers
Define direction clearly — specify whether up or down means improvement
Set realistic goals — use goals as alignment tools, not performance traps
Review forecasts regularly — use predicted vs. actual performance to learn faster
Connect metrics early — relationships create meaning, not just measurement

Metrics are not static KPIs — they’re dynamic signals that evolve with your strategy. The more accurately you define and connect them, the stronger your strategic insight becomes.

Next Steps

Explore how metrics connect through Flow Boards
Learn how they power Strategic Analyses
Set up data pipelines in Integrations