Update app.py

app.py

@@ -116,37 +116,44 @@ class SeoAppUI:
     def _display_info_expander(self):
         with st.expander("ℹ️ How the app works [CLICK TO EXPAND]", expanded=False):
             st.markdown(
-                    """
+                """
                 <div style="background-color: #f0f2f6; padding: 20px; border-radius: 10px;">
-                  <p>1. <b>Load your GSC data</b> (we lowercase all column names on load). If no file is uploaded, we use the default sample data. If no <code>cpc</code> column is present, we simulate values between 0.50 and 3.00 USD.</p>
-                  <p>2. <b>CTR benchmarks</b> by position map an expected click-through rate for positions 1–20.</p>
-                  <p>3. <b>Incremental Clicks</b> = Projected_Clicks – Current_Clicks</p>
-                  <p>&nbsp;&nbsp;&nbsp;&nbsp;• Current_Clicks = Impressions × Current_CTR</p>
-                  <p>&nbsp;&nbsp;&nbsp;&nbsp;• Projected_Clicks = Impressions × Target_CTR</p>
-                  <p>4. <b>Financials</b></p>
-                  <p>&nbsp;&nbsp;&nbsp;&nbsp;• <b>Avoided Paid Spend</b> = Incremental_Clicks × CPC. This represents the money you <b>don't</b> have to spend on paid ads because your organic SEO efforts are now bringing in those clicks and conversions.</p>
-                  <p>&nbsp;&nbsp;&nbsp;&nbsp;• <b>Net Savings vs Paid</b> = Avoided Paid Spend – SEO Investment</p>
-                  <p>&nbsp;&nbsp;&nbsp;&nbsp;• <b>Incremental MRR</b> = Customers × MRR_per_Customer</p>
-                  <p>&nbsp;&nbsp;&nbsp;&nbsp;• <b>SEO ROI</b> = (Incremental MRR – SEO Investment) ÷ SEO Investment</p>
-                  <p><b>Understanding "Additional Ad Spend"</b></p>
-                  <p>The "Additional Ad Spend" input in the sidebar is a <b>hypothetical budget figure you provide for comparison</b>. It's <b>not</b> calculated from your GSC data or CPC. Instead, it allows you to:</p>
+                  <p><b>1. Load Your GSC Data</b><br>
+                  If no file is uploaded, a sample is used. All column names are lowercased. If no <code>cpc</code> column is found, CPC values are simulated between 0.50–3.00 USD.</p>
+            
+                  <p><b>2. CTR Benchmarks by Position</b><br>
+                  Expected click-through rates (CTR) are mapped for positions 1–20 to estimate traffic uplift.</p>
+            
+                  <p><b>3. Incremental Clicks</b><br>
+                  <code>Incremental Clicks = Projected - Current</code><br>
+                  &nbsp;&nbsp;• Current = Impressions × Current CTR<br>
+                  &nbsp;&nbsp;• Projected = Impressions × Target CTR</p>
+            
+                  <p><b>4. Financial Impact</b><br>
+                  &nbsp;&nbsp;• <b>Avoided Paid Spend</b> = Incremental Clicks × CPC (value of organic traffic replacing paid ads)<br>
+                  &nbsp;&nbsp;• <b>Net Savings</b> = Avoided Spend – SEO Investment<br>
+                  &nbsp;&nbsp;• <b>Incremental MRR</b> = Customers × MRR per Customer<br>
+                  &nbsp;&nbsp;• <b>SEO ROI</b> = (Incremental MRR – SEO Investment) ÷ SEO Investment</p>
+            
+                  <p><b>5. Understanding “Ad Spend”</b><br>
+                  This is a <b>user-defined hypothetical amount</b> used only for comparison. It is not derived from CPC or added to SEO cost. The app compares:</p>
                   <ul>
-                    <li><b>Compare SEO's revenue generation directly against a specific paid ad budget.</b> For instance, if you're considering spending an extra X dollars on Google Ads, you can see whether your SEO's projected incremental MRR is higher or lower than that same amount.</li>
-                    <li><b>Visualize the efficiency of your SEO investment.</b> If your SEO investment generates significantly more incremental MRR than a comparable additional ad spend, it highlights SEO as a potentially more effective use of marketing funds.</li>
+                    <li><b>SEO's incremental MRR</b> from your defined SEO investment</li>
+                    <li><b>Against a fixed ad spend</b> (e.g. what you might spend on paid campaigns)</li>
                   </ul>
-                  <p>The "Ad Spend" metric will be <span style="color: green; font-weight: bold;">green</span> if your projected Incremental MRR from SEO is <b>greater than</b> this additional ad spend, and <span style="color: red; font-weight: bold;">red</span> if it is not.</p>
-                  
-                  <p>5. <b>Interpreting Results & Assumptions</b></p>
+                  <p>The “Ad Spend” box turns <span style="color: green; font-weight: bold;">green</span> when SEO outperforms ads, or <span style="color: red; font-weight: bold;">red</span> otherwise.</p>
+            
+                  <p><b>6. Interpreting Results</b></p>
                   <ul>
-                    <li><b>Target SERP Position:</b> The 'Target SERP Position' is an <u>aspirational average</u> you aim for among your <u>most important and achievable keywords</u>, rather than a literal expectation for every single query. In reality, not all keywords will reach the same position due to varying competition and relevance.</li>
-                    <li><b>High-Impact Queries:</b> While the model calculates for all queries, focus your analysis on the 'Detailed Keyword Performance' table. Look for queries with a '🚀 Improvement' impact category and high 'impressions' and 'incremental_clicks'. These are often your most promising opportunities for SEO effort.</li>
+                    <li><b>Target SERP Position</b>: Acts as a uniform benchmark applied to all queries to project improvement.</li>
+                    <li><b>Focus on High-Impact Keywords</b>: In the results table, look for “🚀 Improvement” rows with high impressions and incremental clicks — these are your SEO sweet spots.</li>
                   </ul>
                 </div>
                 """,
-                    unsafe_allow_html=True,
-                )
+                unsafe_allow_html=True,
             )
 
+
     def _get_sidebar_inputs(self):
         with st.sidebar:
             st.header("🔧 Assumptions & Inputs")