Analytics
This document defines the analytics strategy, measurement framework, event taxonomy, reporting standards, experimentation process, and privacy principles for MHMD Studio. Analytics exists to improve products through evidence-based decision making rather than collecting data for its own sake.
Document Metadata
| Property | Value |
|---|---|
| Document | Analytics |
| Version | 1.0 |
| Status | Active |
| Owner | Mohammed El Maachi |
| Last Updated | July 2026 |
Purpose
Analytics enables MHMD Studio to understand how users interact with the platform and identify opportunities for improvement.
The objectives are to:
- Measure product performance.
- Understand user behavior.
- Evaluate business outcomes.
- Detect usability issues.
- Validate hypotheses.
- Guide product decisions.
Analytics should support decisions rather than replace judgment.
Analytics Philosophy
Collect only the information necessary to improve the product.
Every metric should answer a meaningful business or product question.
Avoid collecting data without a defined purpose.
Core Principles
Analytics should be:
- Privacy-first
- Transparent
- Actionable
- Accurate
- Reliable
- Ethical
- Minimal
Success Framework
Metrics should align with measurable objectives.
Evaluate:
- Product quality
- User engagement
- Business growth
- Operational efficiency
- Content performance
Vanity metrics should not drive decisions.
Key Performance Indicators
Primary KPIs include:
- Qualified leads
- Contact form conversion rate
- Portfolio engagement
- Resource downloads
- Blog engagement
- Returning visitors
- Search visibility
- Client acquisition
KPIs should be reviewed periodically.
Event Taxonomy
Every event should follow a consistent naming convention.
Format:
text
object_actionExamples:
text
page_view
article_open
portfolio_open
service_view
contact_submit
resource_download
newsletter_subscribe
search_execute
ai_requestEvent names should remain stable over time.
Event Properties
Events may include contextual properties such as:
- Page
- Category
- Resource ID
- Project ID
- Device Type
- Referrer
- Language
- Session ID
Only collect properties that provide actionable insight.
User Journey Tracking
Measure major journeys including:
- Homepage → Contact
- Homepage → Services
- Homepage → Portfolio
- Article → Contact
- Portfolio → Contact
- Resource → Download
Funnels should reveal where users abandon tasks.
Content Analytics
Track:
- Page views
- Reading time
- Scroll depth
- Exit rate
- Internal link clicks
- Resource downloads
Evaluate whether content fulfills its intended purpose.
Portfolio Analytics
Measure:
- Project views
- Filter usage
- Case study completion
- Technology interest
- CTA interactions
These metrics should inform future portfolio development.
Service Analytics
Track:
- Service page visits
- CTA clicks
- Inquiry conversions
- Frequently viewed services
Understand which offerings generate the greatest interest.
AI Analytics
Measure:
- AI feature usage
- Completion rate
- Average response time
- User feedback
- Error rate
- Cost per request
- Recommendation acceptance
Analytics should improve both quality and efficiency.
Search Analytics
Track:
- Search frequency
- Popular queries
- Empty results
- Click-through rate
- Search refinement
Search improvements should be guided by actual user behavior.
Performance Analytics
Collect:
- Core Web Vitals
- API latency
- Error rates
- Bundle size trends
- Server response time
Performance should be monitored continuously.
Error Analytics
Track:
- JavaScript errors
- API failures
- Form validation errors
- Authentication failures
- Navigation errors
Prioritize fixes by user impact.
Conversion Tracking
Define conversions such as:
- Contact form submission
- Consultation request
- Newsletter subscription
- Resource download
- Product purchase (future)
Every conversion should correspond to a business objective.
Dashboards
Recommended dashboards:
- Executive Overview
- Product Performance
- Content Performance
- Portfolio Performance
- Technical Health
- AI Usage
- SEO Performance
Dashboards should answer specific operational questions.
Reporting
Review analytics:
- Weekly for operational metrics
- Monthly for product performance
- Quarterly for strategic trends
Reports should emphasize insights and recommended actions rather than raw numbers.
Experimentation
Major product decisions should be validated when appropriate.
Possible experiments include:
- CTA variations
- Navigation improvements
- Landing page layouts
- Form optimization
- Content presentation
Document hypotheses before experimentation.
Data Quality
Ensure:
- Event consistency
- Accurate timestamps
- Reliable attribution
- Duplicate event prevention
Poor-quality data leads to poor decisions.
Privacy
Analytics should:
- Respect user consent.
- Minimize personal data.
- Support anonymization where appropriate.
- Comply with applicable privacy regulations.
Privacy should never be sacrificed for additional metrics.
Data Retention
Define retention periods for:
- Analytics events
- Session data
- Error logs
- AI usage metrics
Retain information only as long as necessary.
Recommended Tools
Current stack:
- Vercel Analytics
- Google Analytics 4
- Google Search Console
- Sentry
Future additions may include:
- PostHog
- Plausible
- Microsoft Clarity
Tools should be selected based on measurable value.
Analytics Governance
Every new feature should define:
- Success metric
- Events
- Dashboard
- Owner
- Review schedule
Measurement should be planned before implementation.
Analytics Checklist
Before releasing a feature verify:
- Events implemented.
- Naming conventions followed.
- Dashboards updated.
- Conversions configured.
- Privacy reviewed.
- Documentation updated.
- Data validated.
Future Improvements
Potential future enhancements include:
- Product analytics warehouse
- Predictive analytics
- AI-assisted insights
- Cohort analysis
- Automated anomaly detection
- Real-time dashboards
Future capabilities should build upon reliable foundational data.
Final Principle
Analytics should reduce uncertainty.
Every metric should support better decisions, better products, and better experiences rather than simply increasing the volume of collected data.