Introduction
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Leading indicators are metrics that show early progress before outcomes materialize. They serve as predictive signals, allowing organizations to take action before issues develop or to capitalize on emerging opportunities.
Sub-Element | Description | Example |
|---|---|---|
| Name of the leading indicator | Early Customer Engagement Rate |
| Explanation of how this indicator signals future performance | Measures initial customer interactions that precede formal conversions |
| Value at which action should be taken | 15% decrease from baseline |
| Typical time between indicator change and outcome impact | 45 days |
| Estimated reliability of prediction (0-100%) | 85% |
| Current measurement for this leading indicator | 3.2 interactions per customer |
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Potential metrics represent new measurement categories for innovative initiatives. These are forward-looking measures that may better capture emerging aspects of performance, especially for novel business activities.
Sub-Element | Description | Example |
|---|---|---|
| Name of the potential new metric | Digital Ecosystem Engagement |
| Explanation of what this metric would measure | Measures how customers move between our digital platforms |
| How this metric would add value | Identifies cross-platform opportunities and friction points |
| Potential obstacles to implementation | Data privacy concerns, cross-platform tracking limitations |
| Data needed to calculate this metric | Device IDs, session timestamps, platform identifiers |
| Estimated timeline for implementation | 3-6 months |
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immediateOutcomes (0-3 months)
Sub-Element | Description | Example |
|---|---|---|
| Description of the immediate outcome | Increased customer engagement with new feature |
| Expected value to be achieved | 25% usage among active customers |
| Confidence in this outcome (0-100%) | 90% |
intermediateOutcomes (3-12 months)
Sub-Element | Description | Example |
|---|---|---|
| Description of the intermediate outcome | Improved customer retention rate |
| Expected value to be achieved | 15% reduction in churn |
| Confidence in this outcome (0-100%) | 75% |
longTermOutcomes (12+ months)
Sub-Element | Description | Example |
|---|---|---|
| Description of the long-term outcome | Market share growth |
| Expected value to be achieved | 3.5% increase in market share |
| Confidence in this outcome (0-100%) | 60% |
catalyticEvents
Sub-Element | Description | Example |
|---|---|---|
| Description of the catalytic event | Competitor product launch |
| Probability of this event occurring (0-100%) | 80% |
| How this event would impact outcomes | Could accelerate adoption if our solution provides clear advantages |
| Estimated date for this event | 2025-09-15 |
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The measurement object specifies objective, subjective, or proxy measurement approaches, providing methodological flexibility in how performance is assessed.
approachType
Value | Description |
|---|---|
| Based on direct, quantifiable data with minimal interpretation |
| Based on judgment, perception, or qualitative assessment |
| Uses indirect measures as substitutes for direct measurement |
| Combines multiple measurement approaches |
objectiveComponents
Sub-Element | Description | Example |
|---|---|---|
| Name of the objective component | Transaction Completion Rate |
| Method for collecting data | Automated system logging |
| Calculation used | (Completed transactions / Initiated transactions) × 100 |
| Weight in overall metric (0-100%) | 65% |
| Process to validate measurement | Monthly audit and cross-check with financial records |
subjectiveComponents
Sub-Element | Description | Example |
|---|---|---|
| Name of the subjective component | User Experience Quality |
| Method for subjective assessment | Post-interaction surveys |
| Who conducts the assessment | Customers, UX specialists |
| Weight in overall metric (0-100%) | 25% |
| Measures to control bias | Randomized sampling, normalized scoring |
proxyComponents
Sub-Element | Description | Example |
|---|---|---|
| Name of the proxy component | Digital Engagement Depth |
| What this proxy represents | Customer satisfaction and loyalty |
| Correlation between proxy and target (0-1) | 0.78 |
| Method to validate correlation | Quarterly analysis against direct satisfaction measures |
| Known limitations | Not applicable to certain customer segments |
| Weight in overall metric (0-100%) | 10% |
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See: https://github.com/Orthogramic/Orthogramic_Metamodel
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Enumeration values
thresholdType
Defines the general logic for evaluating performance values:
Value | Description |
|---|---|
| Performance improves as values increase (e.g., customer satisfaction, revenue) |
| Performance improves as values decrease (e.g., error rates, costs, complaints) |
| Performance is optimal at a specific target value (e.g., inventory levels) |
| Performance is optimal within a specified range of values (e.g., temperature, staffing) |
aggregationPeriod
Specifies the timeframe used to aggregate performance data:
Value | Description |
|---|---|
| Continuous measurement with immediate updates (e.g., system availability) |
| Data aggregated on an hourly basis (e.g., peak load monitoring) |
| Data aggregated once per day (e.g., daily sales figures) |
| Data aggregated once per week (e.g., project progress) |
| Data aggregated once per month (e.g., budget performance) |
| Data aggregated once every three months (e.g., strategic initiatives) |
| Data aggregated once per year (e.g., annual objectives) |
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