Add pricing sensitivity analysis section

New Section 6 visualizes:
- Profit per product at different monthly prices (line chart)
- Customer cost vs retail price comparison (bar chart)
- Current price highlighted with star marker
- Break-even price calculation
- Customer savings/premium indicator

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

app.py

@@ -798,6 +798,135 @@ st.markdown(f"""
 - Break-even happens after just **1 cycle** (circular starts equal, then pulls ahead)
 """)
 
+# ============================================================
+# SECTION 6: PRICING SENSITIVITY
+# ============================================================
+st.markdown("---")
+st.header("Pricing Analysis: Finding the Sweet Spot")
+
+st.markdown("How does monthly lease price affect profitability and customer value?")
+
+# Calculate metrics at different price points
+price_range = list(range(15, 51, 5))  # €15 to €50
+pricing_data = []
+
+for test_price in price_range:
+    test_revenue = test_price * lease_term
+    test_total_revenue = test_revenue * max_cycles  # Over full product lifecycle
+
+    # Per-product profit (circular model)
+    circ_cost = new_unit_cost + (max_cycles - 1) * refurb_unit_cost
+    circ_profit = test_total_revenue - circ_cost
+    profit_margin = (circ_profit / test_total_revenue * 100) if test_total_revenue > 0 else 0
+
+    # Customer perspective: lease vs buy
+    customer_total_cost = test_revenue  # What customer pays for one lease term
+    vs_retail = retail_price - customer_total_cost if retail_price > 0 else 0
+
+    pricing_data.append({
+        "Monthly Price": test_price,
+        "Revenue per Cycle": test_revenue,
+        "Total Revenue (all cycles)": test_total_revenue,
+        "Profit per Product": circ_profit,
+        "Profit Margin %": profit_margin,
+        "Customer Saves vs Buy": vs_retail,
+    })
+
+pricing_df = pd.DataFrame(pricing_data)
+
+price_col1, price_col2 = st.columns(2)
+
+with price_col1:
+    st.subheader("Profit vs Monthly Price")
+
+    fig_price_profit = go.Figure()
+
+    # Profit line
+    fig_price_profit.add_trace(go.Scatter(
+        x=pricing_df["Monthly Price"],
+        y=pricing_df["Profit per Product"],
+        mode="lines+markers",
+        name="Profit per Product",
+        line=dict(color="#2ecc71", width=3),
+        marker=dict(size=10),
+        hovertemplate="€%{x}/mo → €%{y:,.0f} profit<extra></extra>"
+    ))
+
+    # Mark current price
+    current_profit = new_unit_cost + (max_cycles - 1) * refurb_unit_cost
+    current_profit = (lease_price * lease_term * max_cycles) - current_profit
+
+    fig_price_profit.add_trace(go.Scatter(
+        x=[lease_price],
+        y=[current_profit],
+        mode="markers",
+        name=f"Current (€{lease_price}/mo)",
+        marker=dict(size=16, color="#e74c3c", symbol="star"),
+    ))
+
+    # Break-even line
+    break_even_price = circ_cost / (lease_term * max_cycles)
+    fig_price_profit.add_hline(
+        y=0, line_dash="dash", line_color="gray",
+        annotation_text="Break-even"
+    )
+
+    fig_price_profit.update_layout(
+        xaxis_title="Monthly Lease Price (EUR)",
+        yaxis_title="Profit per Product Lifecycle (EUR)",
+        hovermode="x unified",
+    )
+
+    st.plotly_chart(fig_price_profit, width="stretch")
+
+    st.markdown(f"**Break-even price:** €{break_even_price:.0f}/month (over {max_cycles} cycles)")
+
+with price_col2:
+    st.subheader("Customer Value vs Retail")
+
+    fig_customer = go.Figure()
+
+    # What customer pays (bar)
+    fig_customer.add_trace(go.Bar(
+        x=pricing_df["Monthly Price"],
+        y=pricing_df["Revenue per Cycle"],
+        name="Lease Cost (1 term)",
+        marker_color="#3498db",
+        hovertemplate="€%{x}/mo × {lease_term}mo = €%{y}<extra></extra>"
+    ))
+
+    # Retail price reference line
+    if retail_price > 0:
+        fig_customer.add_hline(
+            y=retail_price, line_dash="dash", line_color="#e74c3c", line_width=2,
+            annotation_text=f"Retail: €{retail_price}",
+            annotation_position="right"
+        )
+
+    # Mark current price
+    fig_customer.add_trace(go.Scatter(
+        x=[lease_price],
+        y=[lease_price * lease_term],
+        mode="markers",
+        name=f"Current (€{lease_price}/mo)",
+        marker=dict(size=14, color="#e74c3c", symbol="star"),
+    ))
+
+    fig_customer.update_layout(
+        xaxis_title="Monthly Lease Price (EUR)",
+        yaxis_title="Total Customer Cost (EUR)",
+        hovermode="x unified",
+    )
+
+    st.plotly_chart(fig_customer, width="stretch")
+
+    if retail_price > 0:
+        current_total = lease_price * lease_term
+        if current_total < retail_price:
+            st.success(f"At €{lease_price}/mo, customers **save €{retail_price - current_total}** vs buying ({(retail_price - current_total)/retail_price*100:.0f}% less)")
+        else:
+            st.warning(f"At €{lease_price}/mo, customers pay **€{current_total - retail_price} more** than retail (premium for flexibility)")
+
 # ============================================================
 # FOOTER
 # ============================================================