Update app.py

app.py

@@ -153,7 +153,7 @@ def calculate_metrics(df, threshold_minutes, scope="all"):
         "current_problems": current_problems,
         "problems_solved": problems_solved,
         "solve_efficiency": solve_efficiency,
-        "revenue_loss_percent": revenue_impact  # Renamed for clarity
+        "availability_impact": revenue_impact  # More accurate naming
     }
 
 def create_threshold_analysis(df, thresholds, scope="all"):
@@ -172,7 +172,8 @@ st.markdown('<h1 class="main-header">Getaround Delay Analysis</h1>', unsafe_allo
 
 # Sidebar with controls
 with st.sidebar:
-    st.image("Getaround_logo.png", width=200)  
+    # Logo placeholder - you can add your logo here
+    # st.image("logo.png", width=200)  # Uncomment and add your logo file
     
     st.markdown("## Analysis Controls")
     
@@ -186,7 +187,7 @@ with st.sidebar:
     
     if selected == "Threshold & Scope":
         st.markdown("### Settings")
-        threshold = st.slider("Threshold (minutes)", 0, 300, 90, step=30,
+        threshold = st.slider("Threshold (minutes)", 0, 300, 120, step=30,
                              help="Minimum delay between consecutive rentals")
         scope = st.selectbox("Implementation Scope", ["all", "connect"],
                             format_func=lambda x: "All Cars" if x == "all" else "Connect Cars Only")
@@ -378,9 +379,9 @@ elif selected == "Threshold & Scope":
     
     st.markdown("""
     <div class="insight-box">
-    <strong>Revenue Loss Explanation:</strong> This represents the percentage of total rental volume that would be blocked 
-    (prevented from booking) due to insufficient gap between rentals. It's a proxy for potential revenue loss, 
-    as these blocked slots represent lost booking opportunities.
+    <strong>Availability Impact Explanation:</strong> This represents the percentage of total rental slots that would be blocked 
+    (prevented from booking) due to insufficient gap between rentals. This affects inventory availability but doesn't 
+    directly translate to revenue loss, as demand patterns and pricing vary significantly.
     </div>
     """, unsafe_allow_html=True)
     
@@ -423,9 +424,9 @@ elif selected == "Threshold & Scope":
     with col4:
         st.markdown(f"""
         <div class="metric-card">
-        <h3 style="color: #9b59b6;">{current_metrics['revenue_loss_percent']:.1f}%</h3>
-        <p>Revenue Loss</p>
-        <small>% of total volume blocked</small>
+        <h3 style="color: #9b59b6;">{current_metrics['availability_impact']:.1f}%</h3>
+        <p>Availability Impact</p>
+        <small>% of rental slots blocked</small>
         </div>
         """, unsafe_allow_html=True)
         st.caption("Formula: blocked_rentals / total_rentals * 100")
@@ -439,7 +440,7 @@ elif selected == "Threshold & Scope":
     # Create dual-axis chart
     fig = make_subplots(
         rows=1, cols=2,
-        subplot_titles=("Problems Solved vs Blocked Rentals", "Efficiency vs Revenue Loss"),
+        subplot_titles=("Problems Solved vs Blocked Rentals", "Efficiency vs Availability Impact"),
         specs=[[{"secondary_y": True}, {"secondary_y": True}]]
     )
     
@@ -455,15 +456,15 @@ elif selected == "Threshold & Scope":
         row=1, col=1, secondary_y=True
     )
     
-    # Right chart: Efficiency vs Revenue Loss
+    # Right chart: Efficiency vs Availability Impact
     fig.add_trace(
         go.Scatter(x=threshold_data["threshold"], y=threshold_data["solve_efficiency"],
                   mode="lines+markers", name="Efficiency (%)", line=dict(color="blue")),
         row=1, col=2, secondary_y=False
     )
     fig.add_trace(
-        go.Scatter(x=threshold_data["threshold"], y=threshold_data["revenue_loss_percent"],
-                  mode="lines+markers", name="Revenue Loss (%)", line=dict(color="orange")),
+        go.Scatter(x=threshold_data["threshold"], y=threshold_data["availability_impact"],
+                  mode="lines+markers", name="Availability Impact (%)", line=dict(color="orange")),
         row=1, col=2, secondary_y=True
     )
     
@@ -496,7 +497,7 @@ elif selected == "Threshold & Scope":
         **Problems Solved:** {all_metrics['problems_solved']:,}  
         **Blocked Rentals:** {all_metrics['blocked_rentals']:,} ({all_metrics['blocked_percentage']:.1f}%)  
         **Efficiency:** {all_metrics['solve_efficiency']:.1f}%  
-        **Revenue Loss:** {all_metrics['revenue_loss_percent']:.1f}%
+        **Availability Impact:** {all_metrics['availability_impact']:.1f}%
         """)
     
     with col2:
@@ -505,7 +506,7 @@ elif selected == "Threshold & Scope":
         **Problems Solved:** {connect_metrics['problems_solved']:,}  
         **Blocked Rentals:** {connect_metrics['blocked_rentals']:,} ({connect_metrics['blocked_percentage']:.1f}%)  
         **Efficiency:** {connect_metrics['solve_efficiency']:.1f}%  
-        **Revenue Loss:** {connect_metrics['revenue_loss_percent']:.1f}%
+        **Availability Impact:** {connect_metrics['availability_impact']:.1f}%
         """)
     
     # Scope comparison chart
@@ -530,90 +531,73 @@ elif selected == "Threshold & Scope":
     st.plotly_chart(fig_scope, use_container_width=True)
     
     # Recommendations
-    st.markdown('<div class="section-header">Recommendations</div>', unsafe_allow_html=True)
+    st.markdown('<div class="section-header">Business Decision Framework</div>', unsafe_allow_html=True)
     
-    # Find optimal threshold - look for efficiency/revenue loss crossover point
-    viable_thresholds = threshold_data[
-        (threshold_data["problems_solved"] > 0) & 
-        (threshold_data["threshold"] >= 30) & 
-        (threshold_data["threshold"] <= 150)
-    ]
-    
-    if len(viable_thresholds) > 0:
-        # Find crossover point where efficiency starts declining and revenue loss increases significantly
-        # Look for the "knee" where efficiency curve meets revenue loss curve
-        viable_thresholds = viable_thresholds.sort_values("threshold")
-        
-        # Normalize both metrics to 0-100 scale for comparison
-        viable_thresholds["efficiency_norm"] = viable_thresholds["solve_efficiency"]
-        viable_thresholds["revenue_loss_norm"] = viable_thresholds["revenue_loss_percent"]
-        
-        # Find point where efficiency is still high but revenue loss hasn't grown too much
-        # Look for threshold where efficiency > 20% and revenue loss < 8%
-        good_balance = viable_thresholds[
-            (viable_thresholds["efficiency_norm"] >= 20) & 
-            (viable_thresholds["revenue_loss_norm"] <= 8.0)
-        ]
-        
-        if len(good_balance) > 0:
-            # Among balanced options, pick the one that solves most problems
-            optimal = good_balance.sort_values("problems_solved", ascending=False).iloc[0]
-        else:
-            # Fallback: find best efficiency among viable options
-            optimal = viable_thresholds.sort_values("solve_efficiency", ascending=False).iloc[0]
-        
-        optimal_threshold = int(optimal["threshold"])
-    else:
-        optimal_threshold = 90  # Sensible fallback
+    st.markdown("""
+    <div class="insight-box">
+    <strong>Key Business Question:</strong> How much availability reduction are you willing to accept to improve customer experience and reduce cancellations?
+    <br><br>
+    The decision depends on your strategic priorities:
+    <ul>
+    <li><strong>Customer Experience Focus:</strong> Higher thresholds solve more problems but reduce available booking slots</li>
+    <li><strong>Revenue Maximization:</strong> Lower thresholds maintain availability but allow more customer wait times</li>
+    <li><strong>Operational Efficiency:</strong> Consider implementation complexity (all cars vs Connect only)</li>
+    </ul>
+    </div>
+    """, unsafe_allow_html=True)
     
-    # Scope recommendation
-    if connect_metrics['solve_efficiency'] > all_metrics['solve_efficiency'] and connect_metrics['problems_solved'] >= all_metrics['problems_solved'] * 0.7:
-        recommended_scope = "Connect Only"
-        scope_reason = "Higher efficiency with most problems solved"
-    else:
-        recommended_scope = "All Cars"
-        scope_reason = "Solves more problems overall"
+    # Show current trade-offs
+    st.markdown("### Current Settings Trade-offs")
     
     col1, col2 = st.columns(2)
-    
     with col1:
-        st.markdown(f"""
-        <div class="recommendation-box">
-        <h3>Recommended Threshold</h3>
-        <h2>{optimal_threshold} minutes</h2>
-        <p>Balances problem solving with availability impact</p>
-        <small>Logic: Find crossover point where efficiency ≥20% and revenue loss ≤8%</small>
-        </div>
-        """, unsafe_allow_html=True)
+        st.markdown("#### Benefits")
+        st.write(f"• Eliminates {current_metrics['problems_solved']:,} customer wait situations")
+        st.write(f"• Prevents potential cancellations and complaints")
+        st.write(f"• Improves customer satisfaction and retention")
+        st.write(f"• Achieves {current_metrics['solve_efficiency']:.1f}% efficiency (problems solved per blocked slot)")
     
     with col2:
-        st.markdown(f"""
-        <div class="recommendation-box">
-        <h3>Recommended Scope</h3>
-        <h2>{recommended_scope}</h2>
-        <p>{scope_reason}</p>
-        <small>Logic: Compare efficiency and problems solved between all cars vs connect only</small>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    # Summary table
-    st.markdown("### Key Threshold Options")
-    summary_thresholds = [60, 90, 120, 150]
-    summary_data = []
-    
-    for t in summary_thresholds:
+        st.markdown("#### Costs")
+        st.write(f"• Blocks {current_metrics['blocked_rentals']:,} potential booking opportunities")
+        st.write(f"• Reduces available inventory by {current_metrics['availability_impact']:.1f}%")
+        st.write(f"• May impact short-term revenue growth")
+        st.write(f"• Requires operational changes and monitoring")
+    
+    # Strategic recommendations based on different priorities
+    st.markdown("### Strategic Options")
+    
+    # Calculate options for different thresholds
+    options_data = []
+    for t in [60, 90, 120, 150]:
         if t in threshold_data["threshold"].values:
             row = threshold_data[threshold_data["threshold"] == t].iloc[0]
-            summary_data.append({
+            options_data.append({
                 "Threshold": f"{int(t)} min",
+                "Strategy": "Conservative" if t <= 90 else "Balanced" if t <= 120 else "Aggressive",
                 "Problems Solved": int(row["problems_solved"]),
-                "Blocked Rentals": int(row["blocked_rentals"]),
+                "Availability Impact": f"{row['availability_impact']:.1f}%",
                 "Efficiency": f"{row['solve_efficiency']:.1f}%",
-                "Revenue Loss": f"{row['revenue_loss_percent']:.1f}%"
+                "Recommendation": "Revenue-focused" if t <= 90 else "Balanced approach" if t <= 120 else "Customer experience-focused"
             })
     
-    summary_df = pd.DataFrame(summary_data)
-    st.dataframe(summary_df, use_container_width=True, hide_index=True)
+    options_df = pd.DataFrame(options_data)
+    st.dataframe(options_df, use_container_width=True, hide_index=True)
+    
+    # Final business recommendation
+    current_option = options_df[options_df["Threshold"] == f"{threshold} min"]
+    if len(current_option) > 0:
+        current_strategy = current_option.iloc[0]["Strategy"]
+        current_rec = current_option.iloc[0]["Recommendation"]
+        
+        st.markdown(f"""
+        <div class="recommendation-box">
+        <h3>Current Selection: {threshold} minutes ({current_strategy} Strategy)</h3>
+        <p><strong>Profile:</strong> {current_rec}</p>
+        <p><strong>Business Impact:</strong> This threshold solves {current_metrics['problems_solved']:,} customer problems while blocking {current_metrics['availability_impact']:.1f}% of potential bookings.</p>
+        <p><strong>Decision Rationale:</strong> Choose this if you prioritize {"customer experience over short-term availability" if threshold >= 120 else "availability over customer experience improvements" if threshold <= 90 else "a balanced approach between customer experience and availability"}.</p>
+        </div>
+        """, unsafe_allow_html=True)
 
 # ========== FOOTER ==========
 st.markdown("---")