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SAMPLE_DATA_GENERATOR_IMPROVEMENTS.md

@@ -0,0 +1,143 @@
+# Sample Data Generator Improvements Summary
+
+## 🎯 Objective Achieved
+Successfully improved the sample data generator to create **exactly 8 events** with **higher user connectivity** for more realistic funnel analysis.
+
+## 📊 Key Improvements
+
+### 1. **Exactly 8 Events** ✅
+**Before:** 6 main funnel events + 8 additional scattered events (14 total)
+**After:** Exactly 8 focused funnel events
+
+```python
+# NEW Event Sequence (8 events)
+event_sequence = [
+    "Sign Up",
+    "Email Verification", 
+    "First Login",
+    "Profile Setup",
+    "Product Browse",
+    "Add to Cart",
+    "Checkout Start",
+    "Purchase Complete",
+]
+```
+
+### 2. **Higher User Connectivity** 📈
+**Before:** Aggressive dropout rates (25%, 20%, 25%, 20%, 22%)
+**After:** Gradual, realistic dropout rates (12%, 15%, 18%, 20%, 22%, 25%, 28%)
+
+#### Connectivity Statistics:
+- **Average events per user:** 4.91 (significantly improved)
+- **Users completing all 8 events:** 20.6% (1,652 users)
+- **Users completing 5+ events:** 56.0% (4,490 users)
+- **Users completing only 1 event:** 7.2% (580 users)
+
+### 3. **Weighted User Retention** 🎯
+Implemented intelligent user selection based on user properties:
+- **Premium users:** 1.8x more likely to continue
+- **Basic users:** 1.3x more likely to continue  
+- **Younger users (18-35):** 1.2x more likely to continue
+- **Free users:** Standard retention rate
+
+### 4. **Realistic Timing Progression** ⏰
+**Before:** Exponential time distribution with cohort factors
+**After:** Step-specific realistic timing:
+- **Sign Up:** On registration date
+- **Email Verification:** Within 2 hours (exponential)
+- **First Login:** Within 12 hours (exponential)
+- **Profile Setup:** Within 48 hours (exponential)
+- **Shopping Events:** Spread over weeks
+
+### 5. **Enhanced Event Properties** 🔧
+Added step-specific rich properties for better analysis:
+
+#### Purchase Complete:
+- `order_value`: $30-$300 range (lognormal distribution)
+- `payment_method`: credit_card, paypal, apple_pay, google_pay
+- `product_category`: electronics, clothing, books, home
+
+#### Add to Cart:
+- `cart_value`: $25-$200 range
+- `items_count`: 1-5 items with realistic distribution
+
+#### Product Browse:
+- `pages_viewed`: 1-8 pages viewed
+- `time_spent_minutes`: Exponential distribution (avg 8 min)
+
+### 6. **Cross-Step Engagement Events** 🔄
+Added repeat interactions for 40% of users to increase connectivity:
+- **Repeat events:** Product Browse, Add to Cart
+- **Timing:** 1 week to 2 months after initial journey
+- **Enhanced properties:** Longer sessions, higher values for repeat users
+
+### 7. **Improved Data Quality** 🛠️
+- **JSON Serialization:** Fixed numpy type issues with explicit type casting
+- **Session Tracking:** Added unique session IDs for each user interaction
+- **Repeat Action Flags:** Marked repeat actions for analysis
+- **Performance:** Reduced from 10,000 to 8,000 users for optimal performance
+
+## 📈 Results Comparison
+
+### Event Distribution:
+```
+Sign Up:              8,000 events (100.0%)
+Email Verification:   7,040 events (88.0%)
+Product Browse:       6,711 events (83.9%)  ← High engagement
+First Login:          5,984 events (74.8%)
+Add to Cart:          5,812 events (72.7%)  ← High engagement
+Profile Setup:        4,906 events (61.3%)
+Checkout Start:       2,295 events (28.7%)
+Purchase Complete:    1,652 events (20.6%)
+```
+
+### User Journey Connectivity:
+```
+1 event:   580 users (7.2%)   ← Very few single-event users
+2 events:  909 users (11.4%)
+3 events: 1,025 users (12.8%)
+4 events:  996 users (12.4%)
+5 events: 1,073 users (13.4%)
+6 events: 1,122 users (14.0%)
+7 events:  643 users (8.0%)
+8 events: 1,652 users (20.6%) ← Strong complete journey rate
+```
+
+## 🎯 Business Impact
+
+### Better Analysis Capabilities:
+1. **Process Mining:** More connected user journeys for path analysis
+2. **Cohort Analysis:** Realistic user behavior patterns
+3. **Time Series:** Proper timing distributions for temporal analysis
+4. **Segmentation:** Rich properties for detailed segmentation
+
+### More Realistic Funnels:
+1. **E-commerce Focus:** Clear shopping journey from browse to purchase
+2. **Engagement Patterns:** Repeat interactions mirror real user behavior
+3. **Retention Logic:** User properties influence journey completion
+4. **Revenue Tracking:** Order values and payment methods for business analysis
+
+## 🔧 Technical Improvements
+
+### Performance:
+- **Data Size:** Optimized from 10K to 8K users
+- **Event Focus:** Reduced from 14 to 8 events for clarity
+- **JSON Handling:** Fixed serialization issues
+- **Memory Usage:** More efficient data generation
+
+### Code Quality:
+- **Type Safety:** Explicit type casting for JSON serialization
+- **Documentation:** Clear comments explaining each improvement
+- **Maintainability:** Structured approach to event generation
+- **Testing:** Verified with actual funnel analysis
+
+## ✅ Success Metrics
+
+1. **✅ Exactly 8 Events:** Achieved - no more, no less
+2. **✅ Higher Connectivity:** 4.91 avg events/user vs previous lower connectivity
+3. **✅ Realistic Patterns:** E-commerce journey with proper timing
+4. **✅ Rich Properties:** Step-specific properties for advanced analysis
+5. **✅ Performance:** Faster generation and analysis
+6. **✅ Compatibility:** Works seamlessly with existing funnel calculator
+
+The improved sample data generator now provides a much more realistic and connected dataset that better represents actual user behavior in an e-commerce funnel, enabling more meaningful analysis and testing of the funnel analytics platform. 

app.py

@@ -925,7 +925,7 @@ def main():
                 )
 
             # Segmentation в отдельной секции для лучшей видимости
-            st.markdown("### 🎯 Segmentation (Optional)")
+            st.markdown("### 🎯 Segmentation (Optional, in development)")
 
             # Segmentation controls
             selected_property = "None"
@@ -2047,6 +2047,20 @@ def format_time(minutes):
                         help="Display transition counts on visualizations",
                     )
 
+                with col4:
+                    use_funnel_events_only = st.checkbox(
+                        "Use selected events only",
+                        value=True,
+                        help="Analyze only the events selected in your funnel (recommended for focused analysis)",
+                    )
+
+            # Show warning if filtering is enabled but no funnel events selected
+            if use_funnel_events_only and not st.session_state.funnel_steps:
+                st.warning(
+                    "⚠️ 'Use selected events only' is enabled but no funnel events are selected. "
+                    "Please build your funnel first or disable this option to analyze all events."
+                )
+
             # Process Mining Analysis
             if st.button("🚀 Discover Process", type="primary", use_container_width=True):
                 with st.spinner("Analyzing user journeys..."):
@@ -2055,18 +2069,30 @@ def format_time(minutes):
                         config = FunnelConfig()
                         path_analyzer = PathAnalyzer(config)
 
+                        # Determine which events to analyze
+                        filter_events = None
+                        if use_funnel_events_only and st.session_state.funnel_steps:
+                            filter_events = st.session_state.funnel_steps
+
                         # Discover process structure
                         process_data = path_analyzer.discover_process_mining_structure(
                             st.session_state.events_data,
                             min_frequency=min_frequency,
                             include_cycles=include_cycles,
+                            filter_events=filter_events,
                         )
 
                         # Store in session state
                         st.session_state.process_mining_data = process_data
 
+                        # Create success message with filtering info
+                        if filter_events:
+                            filter_info = f" (filtered to {len(filter_events)} selected funnel events)"
+                        else:
+                            filter_info = " (analyzing all events in dataset)"
+
                         st.success(
-                            f"✅ Discovered {len(process_data.activities)} activities and {len(process_data.transitions)} transitions"
+                            f"✅ Discovered {len(process_data.activities)} activities and {len(process_data.transitions)} transitions{filter_info}"
                         )
 
                     except Exception as e: