KPI Tracker

Overview

The KPI Tracker skill provides comprehensive capabilities for defining, calculating, and monitoring Key Performance Indicators. It supports the full KPI lifecycle from definition through tracking, alerting, and reporting for business intelligence and performance management.

Capabilities

•KPI formula definition and validation
•Target and threshold management
•Traffic light status calculation
•Trend analysis and forecasting
•Drill-down hierarchy configuration
•Benchmark comparison
•Variance analysis
•Automated alert generation

Used By Processes

•KPI Framework Development
•Executive Dashboard Development
•Operational Reporting System Design

Usage

KPI Definition

python

# Define KPI structure
kpi_definition = {
    "name": "Customer Acquisition Cost",
    "code": "CAC",
    "category": "Marketing",
    "description": "Total cost to acquire a new customer",
    "formula": "total_marketing_spend / new_customers_acquired",
    "unit": "currency",
    "polarity": "lower_is_better",
    "frequency": "monthly",
    "owner": "Marketing Director",
    "data_sources": [
        {"name": "marketing_spend", "source": "finance_system", "table": "expenses"},
        {"name": "new_customers", "source": "crm", "table": "customers"}
    ]
}

Target Configuration

python

# Define targets and thresholds
targets = {
    "kpi": "CAC",
    "period": "2024-Q1",
    "target": 150,
    "thresholds": {
        "green": {"max": 150},
        "yellow": {"min": 150, "max": 200},
        "red": {"min": 200}
    },
    "benchmark": {
        "industry_average": 180,
        "best_in_class": 100,
        "previous_period": 175
    }
}

Hierarchy Configuration

python

# Define drill-down hierarchy
hierarchy = {
    "kpi": "Revenue",
    "levels": [
        {"name": "Total", "aggregation": "sum"},
        {"name": "Region", "dimension": "geography", "aggregation": "sum"},
        {"name": "Product Line", "dimension": "product", "aggregation": "sum"},
        {"name": "Sales Rep", "dimension": "salesperson", "aggregation": "sum"}
    ]
}

Alert Configuration

python

# Configure automated alerts
alert_config = {
    "kpi": "CAC",
    "conditions": [
        {
            "type": "threshold_breach",
            "threshold": "red",
            "consecutive_periods": 2,
            "notification": ["email", "slack"]
        },
        {
            "type": "trend",
            "direction": "increasing",
            "periods": 3,
            "min_change_percent": 10,
            "notification": ["email"]
        },
        {
            "type": "forecast_breach",
            "horizon": 3,
            "probability": 0.8,
            "notification": ["email", "dashboard"]
        }
    ]
}

KPI Categories

Category	Example KPIs
Financial	Revenue, Profit Margin, ROI, CAC, LTV
Customer	NPS, Churn Rate, CSAT, Retention
Operational	Cycle Time, Defect Rate, Utilization
Growth	MRR Growth, User Growth, Market Share
Efficiency	Cost per Unit, Revenue per Employee

Input Schema

json

{
  "operation": "define|calculate|track|alert",
  "kpi_definition": {
    "name": "string",
    "formula": "string",
    "unit": "string",
    "polarity": "higher_is_better|lower_is_better",
    "frequency": "string"
  },
  "targets": {
    "value": "number",
    "thresholds": "object"
  },
  "data": {
    "source": "string",
    "period": "string",
    "values": "object"
  },
  "analysis_options": {
    "trend_analysis": "boolean",
    "forecast": "boolean",
    "variance_analysis": "boolean"
  }
}

Output Schema

json

{
  "kpi_values": {
    "current_value": "number",
    "previous_value": "number",
    "target": "number",
    "variance": "number",
    "variance_percent": "number",
    "status": "green|yellow|red"
  },
  "trend_analysis": {
    "direction": "improving|stable|declining",
    "change_percent": "number",
    "periods_analyzed": "number"
  },
  "forecast": {
    "next_period": "number",
    "confidence_interval": ["number", "number"],
    "will_breach_target": "boolean"
  },
  "drill_down": {
    "dimension_values": "object"
  },
  "alerts": [
    {
      "type": "string",
      "severity": "string",
      "message": "string"
    }
  ]
}

Best Practices

•Limit KPIs to 5-7 per dashboard (avoid metric overload)
•Define clear ownership for each KPI
•Set SMART targets (Specific, Measurable, Achievable, Relevant, Time-bound)
•Include leading indicators, not just lagging
•Validate formulas with business stakeholders
•Document data lineage and calculation logic
•Review and retire obsolete KPIs regularly

Data Quality

The skill validates:

•Data completeness (missing values)
•Data freshness (last update time)
•Formula validity (division by zero, null handling)
•Reasonable ranges (outlier detection)

Integration Points

•Feeds into Decision Visualization for dashboards
•Connects with Data Storytelling for narratives
•Supports Time Series Forecaster for predictions
•Integrates with Alert systems for notifications