method#Observability#Reliability#Performance Metrics
Metric Tree Analysis
A structured method for visualizing and analyzing metrics.
Metric Tree Analysis allows teams to represent metrics in a hierarchical structure to better understand our key performance indicators.
Maturity
Established
Cognitive loadMedium
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeDesign
- Organizational maturityAdvanced
Technical context
Integrations
DatabasesReporting ToolsAnalytics Platforms
Principles & goals
Metrics should be visually clear.Team-oriented analysis promotes collaboration.Data should be updated regularly.
Value stream stage
Build
Organizational level
Team, Domain, Enterprise
Use cases & scenarios
Use cases
Scenarios
Compromises
Risks
- Misinterpretation of metrics can lead to wrong decisions.
- Potential for data skewing due to incorrect inputs.
- Technological dependencies can create risks.
Best practices
- Ensure regular updates of data.
- Engage the team to broaden perspectives.
- Keep visualizations clear and simple.
I/O & resources
Inputs
- Product Data
- User System Metrics
- Target Objectives
Outputs
- Visualized Metrics
- Identified Performance Issues
- Recommendations for Improvements
Description
Metric Tree Analysis allows teams to represent metrics in a hierarchical structure to better understand our key performance indicators. This method fosters the identification of patterns and relationships among different metrics and their underlying factors.
✔Benefits
- Increased transparency in metric analysis.
- Better decision-making basis through clarity.
- Promotes data-driven work.
✖Limitations
- Can become unwieldy with large datasets.
- Requires training for effective use.
- Dependence on the currency of data.
Trade-offs
Metrics
- Customer Acquisition Cost (CAC)
The cost incurred to acquire a new user.
- Churn Rate
The percentage of customers who leave the service.
- Net Promoter Score (NPS)
A measure of customer satisfaction and loyalty.
Examples & implementations
E-Commerce Metric Tree
Implementation example of a metric tree for an e-commerce platform.
SaaS Product Analysis
Case study on analyzing metrics of a SaaS product.
Mobile App Performance
Example of applying the Metric Tree Analysis on a mobile app.
Implementation steps
1
Define goals for metric analysis.
2
Identify and integrate data sources.
3
Visualize and analyze metrics.
⚠️ Technical debt & bottlenecks
Technical debt
- Using outdated data sources.
- Lack of integration of tools.
- Poor documentation of analysis processes.
Known bottlenecks
Data IntegrityReal-time ReportingUser Acceptance
Misuse examples
- Using unvalidated data.
- Considering metrics out of context.
- Focusing on wrong metrics for decisions.
Typical traps
- Analyzing too many metrics at the same time.
- Neglecting long-term goals.
- Not listening to the team during analysis.
Required skills
Data AnalysisStatisticsVisual Communication
Architectural drivers
Integration of various data sources.Compliance with data protection regulations.User-friendly interface for data visualization.
Constraints
- • Minimum requirements for IT infrastructure.
- • Adhering to budget constraints.
- • Availability of specialists for training.