Analytics and Insights Guide

Master GreenMonkey's analytics to optimize your products and maximize revenue.

Analytics Dashboard Overview

Key Metrics

Your seller dashboard displays real-time metrics:

┌─────────────────────────────────────────────────────────┐
│                    Dashboard Overview                     │
├───────────────┬───────────────┬───────────────┬─────────┤
│ Total Revenue │ Active Products│ Total Sales   │ Avg Rating│
│ $12,456       │ 24            │ 892           │ 4.8 ⭐   │
└───────────────┴───────────────┴───────────────┴─────────┘

Navigation

• Overview - High-level metrics and trends
• Products - Individual product performance
• Revenue - Financial analytics
• Customers - Buyer insights
• Traffic - Source and conversion data
• Experiments - A/B test results

Understanding Core Metrics

Revenue Metrics

Total Revenue

Total Revenue = Σ(Product Price × Quantity Sold) - Refunds

Track by:

• Daily/Weekly/Monthly/Yearly
• Product category
• Customer segment
• Geographic region

Average Order Value (AOV)

AOV = Total Revenue / Number of Orders

Strategies to increase AOV:

• Bundle products
• Upsell premium tiers
• Cross-sell related items
• Volume discounts

Revenue Per Visitor (RPV)

RPV = Total Revenue / Unique Visitors

Benchmark:

• Good: $0.50-$1.00
• Great: $1.00-$2.50
• Excellent: $2.50+

Conversion Metrics

Conversion Rate

Conversion Rate = (Purchases / Product Views) × 100

Industry benchmarks:

• Digital products: 2-3%
• Premium products: 1-2%
• Free to paid: 5-10%

Conversion Funnel

Views → Details → Add to Cart → Purchase
100% →  40%   →    15%     →   3%

Optimize each step:

1. Views → Details (40%)

   • Compelling titles
   • Eye-catching thumbnails
   • Clear value proposition

2. Details → Cart (37.5%)

   • Detailed descriptions
   • Social proof
   • Demo/samples

3. Cart → Purchase (20%)

   • Simple checkout
   • Trust badges
   • Urgency/scarcity

Engagement Metrics

Product View Depth

View Depth = Time on Product Page / Average Session Duration

• < 30 seconds: Poor engagement
• 30-90 seconds: Average

• 90 seconds: High interest

Feature Adoption

Track which features customers use:

• Downloads completed
• API calls made
• Support accessed
• Updates downloaded

Product Performance Analysis

Product Analytics Grid

Performance Quadrants

High Sales, High Rating ⭐ | High Sales, Low Rating ⚠️
(Stars - Maintain)         | (Cash Cows - Improve)
---------------------------|---------------------------
Low Sales, High Rating 🚀  | Low Sales, Low Rating ❌
(Potential - Promote)      | (Evaluate - Pivot/Remove)

Product Lifecycle

Introduction → Growth → Maturity → Decline
     📈           📊        💰        📉

Stage indicators:

1. Introduction (0-30 days)

   • Building reviews
   • Testing pricing
   • Refining positioning

2. Growth (1-6 months)

   • Increasing sales
   • Organic traffic
   • Word of mouth

3. Maturity (6-24 months)

   • Stable sales
   • Market saturation
   • Competition emerging

4. Decline (24+ months)

   • Decreasing sales
   • Outdated content
   • Better alternatives

Customer Insights

Buyer Personas

Analyze your customer base:

Persona: Professional Developer
- Average Spend: $89
- Products Bought: 3.2
- Categories: APIs, Templates
- Time to Purchase: 2 days
- Retention: 67%

Persona: Freelance Marketer
- Average Spend: $45
- Products Bought: 5.7
- Categories: Prompts, Workflows
- Time to Purchase: Same day
- Retention: 82%

Customer Journey Mapping

Discovery → Research → Evaluation → Purchase → Usage → Advocacy
    |          |           |           |         |         |
  Search    Reviews    Compare      Buy      Implement  Share
  Social    Demos      Pricing      Pay      Support    Refer
  Referral  Docs       Support      Use      Update     Review

Cohort Analysis

Track customer groups over time:

Traffic Analytics

Traffic Sources

Organic Search: 35% 📈
Direct: 25% 🔗
Social Media: 20% 📱
Referrals: 15% 🤝
Paid Ads: 5% 💰

Source Performance

SEO Performance

Track organic visibility:

• Keyword rankings
• Click-through rates
• Page load speed
• Mobile usability

Advanced Analytics

Predictive Analytics

Churn Prediction

# Factors indicating churn risk
churn_indicators = {
    'days_since_last_purchase': 90,
    'support_tickets': 3,
    'refund_requests': 1,
    'low_ratings_given': 2,
    'decreased_usage': -50%
}

Revenue Forecasting

Next Month Revenue =
  (Trend × 0.4) +
  (Seasonal × 0.3) +
  (Recent × 0.3)

A/B Testing Analytics

Test Setup

Test: Pricing Optimization
Variants:
  A: $49 (control)
  B: $59 (test)
Duration: 14 days
Traffic Split: 50/50

Results Analysis

Variant A:
- Visitors: 1,234
- Conversions: 45
- Rate: 3.65%
- Revenue: $2,205

Variant B:
- Visitors: 1,198
- Conversions: 38
- Rate: 3.17%
- Revenue: $2,242

Statistical Significance: 89%
Winner: Inconclusive (need more data)

Correlation Analysis

Identify what drives sales:

Using Analytics for Optimization

Price Optimization

Use price elasticity data:

Price Points Tested:
$29: 145 sales = $4,205
$39: 98 sales = $3,822
$49: 67 sales = $3,283 ← Current
$59: 45 sales = $2,655
$69: 28 sales = $1,932

Optimal: $39 (highest revenue)

Listing Optimization

Elements to test:

1. Title variations

   • Benefit-focused vs. Feature-focused
   • Length (short vs. detailed)
   • Keywords placement

2. Images

   • Screenshots vs. Diagrams
   • Before/after comparisons
   • Video thumbnails

3. Description

   • Bullet points vs. Paragraphs
   • Technical vs. Benefits
   • Length variations

Conversion Rate Optimization (CRO)

Quick Wins

• Add video demos (+32% conversion)
• Show use cases (+28% conversion)
• Include testimonials (+24% conversion)
• Display recently sold (+18% conversion)
• Add urgency/scarcity (+15% conversion)

Testing Priority Matrix

Reporting and Dashboards

Daily Dashboard

Monitor these metrics daily:

Today's Performance:
├── Revenue: $456 (↑12% vs yesterday)
├── Sales: 12 units
├── Conversion: 3.4%
├── New Reviews: 3 (avg 4.7⭐)
└── Support Tickets: 2

Weekly Report

Key weekly metrics:

Week of March 10-16:
├── Total Revenue: $3,234 (↑8% WoW)
├── Best Day: Tuesday ($678)
├── Top Product: SEO Prompt Pack (34 sales)
├── New Customers: 67
├── Retention Rate: 23%
└── NPS Score: 72

Monthly Analysis

Comprehensive monthly review:

1. Revenue trends
2. Product performance
3. Customer acquisition cost
4. Lifetime value
5. Market position
6. Competitive analysis

Analytics Tools Integration

Google Analytics 4

Track custom events:

// Purchase event
gtag('event', 'purchase', {
  currency: 'USD',
  value: 49.99,
  items: [
    {
      item_id: 'PROD_123',
      item_name: 'SEO Prompt Pack',
      item_category: 'Prompts',
      price: 49.99,
      quantity: 1,
    },
  ],
});

Hotjar/Clarity

Understand user behavior:

• Heatmaps
• Session recordings
• Conversion funnels
• Form analytics

Custom Analytics

Build your tracking:

# Track custom metrics
def track_product_metric(product_id, metric, value):
    analytics.track(
        product_id=product_id,
        event=f"product.{metric}",
        properties={
            'value': value,
            'timestamp': datetime.now(),
            'source': 'seller_dashboard'
        }
    )

Action Plans from Analytics

Low Conversion Rate

If conversion < 2%:

1. Analyze where visitors drop off
2. Review competitor listings
3. Test new headlines
4. Add social proof
5. Improve product images
6. Clarify value proposition

High Refund Rate

If refunds > 5%:

1. Survey refund reasons
2. Update product description
3. Improve onboarding
4. Add video tutorials
5. Enhance support docs
6. Set better expectations

Stagnant Growth

If growth plateaus:

1. Launch new products
2. Update existing products
3. Expand to new categories
4. Create bundles
5. Run promotions
6. Invest in marketing

Best Practices

Data-Driven Decisions

✅ Do:

• Test before making changes
• Track everything
• Set clear goals
• Review regularly
• Act on insights

❌ Don't:

• Rely on gut feelings
• Make multiple changes at once
• Ignore negative feedback
• Focus on vanity metrics
• Stop testing

Analytics Hygiene

1. Clean data - Remove test purchases
2. Consistent tracking - Same metrics over time
3. Regular reviews - Weekly minimum
4. Document changes - Track what you changed
5. Share insights - Learn from community

Next Steps

1. Set up analytics goals
2. Create first A/B test
3. Build custom dashboard
4. Join analytics workshop