Product Analytics Overview

YeboLearn's product analytics framework provides deep insights into how schools, teachers, and students interact with our platform. This data drives product decisions, feature prioritization, and user experience improvements.

Product Analytics Philosophy

Our product analytics approach is built on three core principles:

User-Centric: Understand behavior, not just metrics
Actionable: Every insight should inform product decisions
Holistic: Track the complete user journey from signup to power user

Analytics Framework

Data Collection Strategy

Event Tracking Architecture:

User Actions → Segment → Data Warehouse (BigQuery) → Analytics Tools
                  ↓
              Mixpanel (Real-time Product Analytics)
              Metabase (Business Intelligence)
              Custom Dashboards (Executive Views)

Event Categories:

Category	Events Tracked	Purpose	Volume/Day
Authentication	Login, logout, session	User access patterns	28,000
Feature Usage	Feature opens, interactions, completions	Adoption tracking	145,000
Content Creation	Lessons, quizzes, resources created	Value delivery	38,000
AI Interactions	AI requests, completions, ratings	AI usage patterns	52,000
Engagement	Page views, clicks, time on page	Platform navigation	285,000
Collaboration	Shares, comments, invites	Social features	12,000
Administrative	Settings changes, permissions	Platform management	8,500

Total Events Tracked Daily: ~568,500 events

User Segmentation

Primary Segments:

Segment	Definition	Size	Key Metrics
Power Users	8+ features, daily usage, high AI adoption	18 schools	98% retention, $3,200 ARPU
High Engagement	5-7 features, 4+ days/week usage	45 schools	94% retention, $2,100 ARPU
Medium Engagement	3-4 features, 2-3 days/week usage	52 schools	88% retention, $1,650 ARPU
Low Engagement	1-2 features, <2 days/week usage	22 schools	68% retention, $1,100 ARPU
At-Risk	Declining usage, <1 feature/week	8 schools	42% retention, $850 ARPU

Engagement Correlation: Direct relationship between engagement level and retention/ARPU

Secondary Segments:

By Role: Teachers, Students, Administrators, Parents
By School Type: Private, Public, Charter, Religious
By Region: Gauteng, Western Cape, KZN, Eastern Cape, Other
By Tier: Enterprise, Professional, Essentials
By Tenure: New (❤️ months), Growing (3-12 months), Mature (12+ months)

Behavior Tracking

User Journey Tracking:

Signup → Onboarding → First Value → Regular Usage → Power User
   ↓         ↓            ↓              ↓              ↓
 100%      95%          80%            65%            12%

Key Conversion Points:

Signup → Onboarding: 95% (5% never complete setup)
Onboarding → First Value: 84% (reach 5+ features in 14 days)
First Value → Regular Usage: 81% (become weekly active)
Regular Usage → Power User: 18% (adopt 8+ features, daily usage)

Journey Optimization Focus: Improve "First Value → Regular Usage" (currently 81%, target 85%)

Feature Performance Tracking

Feature Health Metrics:

Metric	Definition	Measurement
Adoption Rate	% of schools using feature monthly	Monthly calculation
Engagement Rate	% of adopters using feature weekly	Weekly calculation
Stickiness	DAU/MAU ratio for feature	Daily tracking
Retention Impact	Retention difference with/without feature	Cohort analysis
Time to Adoption	Days from signup to first use	User journey tracking
Depth of Use	Actions per session with feature	Session analysis

Feature Lifecycle Stages:

Launch (0-30 days): Track initial adoption, identify early issues
Growth (30-90 days): Monitor adoption curve, optimize onboarding
Maturity (90+ days): Measure steady-state usage, plan enhancements
Decline (usage decreasing): Identify causes, plan refresh or sunset

Analytics Tools and Platforms

Mixpanel (Product Analytics)

Primary Use Cases:

Real-time feature usage tracking
User funnel analysis
Cohort retention analysis
A/B test result tracking
User path analysis

Key Dashboards:

Daily Product Health (engagement, adoption, errors)
Feature Performance (individual feature deep-dives)
User Cohort Analysis (retention, behavior patterns)
Conversion Funnels (signup, onboarding, feature adoption)

Data Retention: 5 years of event data

Metabase (Business Intelligence)

Primary Use Cases:

Custom SQL queries on data warehouse
Business metric dashboards
Cross-functional reporting
Executive summaries
Financial analytics integration

Key Dashboards:

Executive Dashboard (high-level KPIs)
Revenue Analytics (MRR, churn, expansion)
School Health Scores (engagement + financial metrics)
Product Roadmap Impact (feature ROI analysis)

Update Frequency: Real-time for critical metrics, hourly for standard reports

Google Analytics (Website Analytics)

Primary Use Cases:

Marketing website traffic
Lead generation tracking
Content performance
SEO monitoring
Conversion rate optimization

Not Used For: In-app product analytics (handled by Mixpanel)

Custom Dashboards (Internal Tools)

Built For:

Real-time operational monitoring
Customer Success team workflows
Sales pipeline integration
Support ticket correlation with usage
Engineering performance monitoring

A/B Testing Framework

Testing Philosophy

When to A/B Test:

New feature releases (test adoption approaches)
UI/UX changes (test interface variations)
Onboarding flows (test completion rates)
Pricing changes (test conversion impact)
Marketing copy (test messaging effectiveness)

When Not to Test:

Critical bug fixes (ship immediately)
Obvious improvements (don't delay value)
Insufficient traffic (need statistical significance)
Legal/compliance requirements (no choice)

Testing Process

Standard A/B Test Workflow:

Hypothesis Definition (Day 0)
- Problem statement
- Proposed solution
- Success metrics
- Minimum detectable effect
Test Design (Days 1-2)
- Control vs variant definition
- Sample size calculation
- Test duration estimate
- Randomization strategy
Implementation (Days 3-5)
- Build variant
- Implement tracking
- QA test both versions
- Set up monitoring
Test Execution (Days 6-20)
- Launch to percentage of users
- Monitor for errors
- Track metrics daily
- Ensure sample size reached
Analysis (Days 21-22)
- Statistical significance check
- Secondary metric review
- Segment analysis
- Decision recommendation
Rollout (Days 23-30)
- Winner to 100% of users
- Monitor for issues
- Document learnings
- Archive test results

Statistical Requirements:

Minimum Sample Size: 1,000 users per variant
Confidence Level: 95%
Minimum Test Duration: 14 days (capture weekly patterns)
Maximum Test Duration: 30 days (avoid metric drift)

Active Tests and Results

Recent Test Results (Last 90 Days):

Test	Variants	Winner	Improvement	Impact
Onboarding Flow	3-step vs 5-step	3-step	+15% completion	Launched
AI Feature Prompts	In-context vs Modal	In-context	+22% adoption	Launched
Dashboard Layout	Cards vs List	Cards	+8% engagement	Launched
Quiz Generator UI	Wizard vs Form	Wizard	+18% completion	Launched
Pricing Page	Feature table vs Comparison	Comparison	+12% signups	Launched

Current Active Tests:

Parent Portal Onboarding: Email invite vs In-app notification
Lesson Planner Templates: 10 templates vs 25 templates
Upgrade CTA Placement: Dashboard banner vs Feature limit modal

User Behavior Patterns

Session Analysis

Average Session Metrics:

User Type	Sessions/Week	Avg Duration	Pages/Session	Features/Session
Teachers	8.5	24 min	12.4	3.2
Students	4.2	18 min	6.8	1.8
Admins	3.1	32 min	18.6	5.4
Parents	1.8	8 min	4.2	1.2

Session Depth Distribution:

Depth Level	% of Sessions	Avg Duration	Conversion to Next Session
Shallow (1-2 pages)	28%	3 min	42%
Medium (3-5 pages)	38%	12 min	68%
Deep (6-10 pages)	24%	24 min	85%
Very Deep (11+ pages)	10%	42 min	92%

Insight: Deeper sessions have higher return rates. Focus on encouraging exploration.

Most Common Feature Paths (Top 5):

Dashboard → Lesson Planner → Quiz Generator (28% of sessions)
- Use case: Complete lesson planning workflow
- Avg completion time: 32 minutes
- Satisfaction: 4.5/5
Dashboard → Auto-Grading → Progress Tracking (18% of sessions)
- Use case: Grading and student monitoring
- Avg completion time: 22 minutes
- Satisfaction: 4.6/5
Dashboard → Resource Library → Lesson Planner (15% of sessions)
- Use case: Resource discovery and lesson creation
- Avg completion time: 28 minutes
- Satisfaction: 4.3/5
Dashboard → Student Analytics → Parent Portal (8% of sessions)
- Use case: Student performance review and parent communication
- Avg completion time: 18 minutes
- Satisfaction: 4.2/5
Dashboard → AI Lesson Planner → Curriculum Alignment (7% of sessions)
- Use case: Standards-aligned lesson creation
- Avg completion time: 26 minutes
- Satisfaction: 4.7/5

Drop-off Points:

22% drop-off at Quiz Generator (complexity barrier)
18% drop-off at Parent Portal (unclear value)
12% drop-off at Student Analytics (information overload)

Time-Based Usage Patterns

Peak Usage Times (UTC+2):

Time Block	Teacher Usage	Student Usage	Total Events
6am-8am	High (prep)	Low	38,000
8am-10am	Peak (teaching)	Medium	95,000
10am-12pm	High (teaching)	Medium	82,000
12pm-2pm	Medium (lunch)	Peak (learning)	68,000
2pm-4pm	Medium (teaching)	Peak (learning)	112,000
4pm-6pm	High (grading)	Medium	78,000
6pm-8pm	Medium (planning)	Low	42,000
8pm-10pm	Low	Low	18,000

Infrastructure Implications:

Scale up servers 1:30pm-4:30pm (peak student usage)
Optimize for mobile during 6am-8am (teachers at home)
Schedule maintenance 10pm-6am (minimal impact)

Device and Platform Distribution

Access Methods:

Platform	% of Sessions	Avg Session Duration	Primary User
Desktop Web	58%	28 min	Teachers
Mobile Web	28%	12 min	Teachers (planning)
Tablet	12%	22 min	Students
Mobile App	2%	8 min	Parents

Browser Distribution (Desktop):

Chrome: 72%
Safari: 18%
Firefox: 7%
Edge: 3%

OS Distribution (Mobile):

Android: 78%
iOS: 22%

Platform Optimization Priorities:

Desktop web (highest usage, teachers)
Mobile web (growing, important for access)
Tablet (student learning experience)
Mobile app (lower priority, limited use case)

User Feedback Integration

In-App Feedback Collection

Feedback Mechanisms:

Mechanism	Response Rate	Monthly Responses	Quality Score
Feature rating (1-5 stars)	12%	4,200	High
NPS survey (quarterly)	38%	1,850	High
Bug report button	2%	680	Medium
Feature request form	5%	1,420	High
Live chat feedback	8%	2,240	Very High

Feedback Analysis:

Automated sentiment analysis on all text feedback
Weekly product team review of themes
Monthly executive summary of user voice
Quarterly deep-dive on feature requests

User Research Program

Research Activities:

Activity	Frequency	Participants	Purpose
User interviews	Weekly	4-6 users	Deep problem understanding
Usability testing	Bi-weekly	6-8 users	Feature validation
Survey campaigns	Monthly	200+ users	Quantitative insights
Beta testing	Per feature	15-20 schools	Early feedback
Advisory board	Quarterly	12 schools	Strategic input

Research Impact: 75% of major features informed by user research

Data Privacy and Security

Data Collection Principles

What We Track:

Feature usage and interaction patterns
Performance metrics (load times, errors)
Aggregate behavior (anonymized where possible)
User-initiated feedback

What We Don't Track:

Student content or work (POPIA/GDPR compliant)
Personal student information beyond usage
Third-party tool usage outside our platform
Teacher grading decisions or comments

Data Retention:

Usage events: 5 years
Personal data: Retained while customer active + 90 days
Aggregated analytics: Indefinite (anonymized)

Compliance

Regulatory Compliance:

POPIA (Protection of Personal Information Act, South Africa)
GDPR (for any European users)
FERPA principles (student privacy, US standard)
ISO 27001 (information security)

User Controls:

Opt-out of non-essential analytics
Data export on request
Account deletion with data purge
Usage report access for administrators

Analytics Team Structure

Roles and Responsibilities

Product Analytics Lead:

Define analytics strategy
Ensure data quality and governance
Train team on analytics tools
Present insights to executive team

Data Analysts (2):

Build dashboards and reports
Conduct ad-hoc analysis
Support A/B test design and analysis
Create weekly/monthly analytics summaries

Data Engineers (2):

Maintain data pipeline
Ensure event tracking accuracy
Optimize data warehouse performance
Build custom integrations

Product Managers (All):

Define feature success metrics
Review analytics weekly
Use data to inform roadmap
Share insights with stakeholders

Success Metrics for Analytics Function

Analytics Team Goals:

Metric	Current	Target
Dashboard uptime	99.8%	99.9%
Data freshness	<15 min	<5 min
Metric accuracy	99.2%	99.5%
Self-service usage	68%	80%
Time to insight	2.5 days	1 day
PM satisfaction	8.2/10	9/10

Impact Metrics:

Product decisions backed by data: 85% (target: 90%)
Features validated with A/B tests: 62% (target: 75%)
User research participants recruited via analytics: 78%
Revenue attributed to data-driven decisions: $1.2M ARR

Next Steps

For detailed analytics deep-dives:

Product Dashboards - Real-time monitoring and executive views
Feature Analytics - Feature-specific performance tracking
User Analytics - User segmentation and behavior analysis
Product Metrics - Core product KPIs and targets

Product Analytics Overview ​

Product Analytics Philosophy ​

Analytics Framework ​

Data Collection Strategy ​

User Segmentation ​

Behavior Tracking ​

Feature Performance Tracking ​

Analytics Tools and Platforms ​

Mixpanel (Product Analytics) ​

Metabase (Business Intelligence) ​

Google Analytics (Website Analytics) ​

Custom Dashboards (Internal Tools) ​

A/B Testing Framework ​

Testing Philosophy ​

Testing Process ​

Active Tests and Results ​

User Behavior Patterns ​

Session Analysis ​

Feature Navigation Patterns ​

Time-Based Usage Patterns ​

Device and Platform Distribution ​

User Feedback Integration ​

In-App Feedback Collection ​

User Research Program ​

Data Privacy and Security ​

Data Collection Principles ​

Compliance ​

Analytics Team Structure ​

Roles and Responsibilities ​

Success Metrics for Analytics Function ​

Next Steps ​

Product Analytics Overview

Product Analytics Philosophy

Analytics Framework

Data Collection Strategy

User Segmentation

Behavior Tracking

Feature Performance Tracking

Analytics Tools and Platforms

Mixpanel (Product Analytics)

Metabase (Business Intelligence)

Google Analytics (Website Analytics)

Custom Dashboards (Internal Tools)

A/B Testing Framework

Testing Philosophy

Testing Process

Active Tests and Results

User Behavior Patterns

Session Analysis

Feature Navigation Patterns

Time-Based Usage Patterns

Device and Platform Distribution

User Feedback Integration

In-App Feedback Collection

User Research Program

Data Privacy and Security

Data Collection Principles

Compliance

Analytics Team Structure

Roles and Responsibilities

Success Metrics for Analytics Function

Next Steps