Add sample scenario data and reporting updates

mockups/overview_analysis.py

@@ -42,7 +42,14 @@ def deep_analysis(conn,report,start,end,evidence):
     recent=m if report['periodDays']==1 else window_metrics(conn,report['gameId'],end-timedelta(days=1),end)
     weekday=window_metrics(conn,report['gameId'],end-timedelta(days=8),end-timedelta(days=7))
     momentum=direction(recent['active_players'],weekday['active_players'])
-    if reach==-1 and ccu==1:
+    sources_by_name={r['source']:r['players'] for r in evidence.get('acquisitionSources',[])}
+    home_share=sources_by_name.get('home_recommendation',0)/max(1,sum(sources_by_name.values()))
+    surge=p['active_players']>0 and m['active_players']>p['active_players']*2 and home_share>.5
+    if surge:
+        overall=f"{name} is in a discovery-led surge: reported Home-recommendation arrivals now dominate new acquisition. This is a large expansion in exposure, while the newest cohorts still need time to establish repeat-play habits. Judge the next phase by whether those arrivals become returning players rather than expecting acquisition-driven CCU to remain at its peak indefinitely."
+    elif reach==0:
+        overall=f"{name} has a broadly stable active audience. Treat movement in session intensity and spending as separate questions from audience growth: preserve the returning-player loop, then check whether the game is creating enough reasons for new visitors to establish the same habit."
+    elif reach==-1 and ccu==1:
         overall=f"{name} is drawing more concurrent presence from a smaller active audience. That suggests more intense use among remaining players; check longer visits and repeat use before treating the CCU rise as acquisition growth."
     elif reach==-1 and spending==1:
         overall=f"{name} is monetizing a narrower audience more heavily. Revenue resilience is useful, but it can mask weakening player renewal; the next improvement should broaden engagement rather than rely only on extracting more from existing buyers."
@@ -58,6 +65,8 @@ def deep_analysis(conn,report,start,end,evidence):
         overall+=' The latest complete day was also below the same weekday last week for active reach, so the softness is visible beyond a simple weekday/weekend comparison.'
     elif momentum==1 and reach==-1:
         overall+=' The latest complete day beat its same-weekday audience comparison, offering an early recovery signal that still needs several more days of follow-through.'
+    elif momentum==1 and reach==0:
+        overall+=' The latest complete day beat its same-weekday comparison, while the full-week audience remains broadly flat.'
     elif momentum==1:
         overall+=' The latest same-weekday comparison supports the broader reach improvement.'