Merge #979: Compare support trends by calendar periods across year boundaries

support/support-support-responder.md

@@ -182,28 +182,44 @@ class SupportAnalytics:
     
     def identify_support_trends(self):
         """
-        Identify trends and patterns in support data
+        Compare calendar periods, preserving year boundaries and missing evidence.
         """
+        dated = self.data.dropna(subset=['created_date']).set_index('created_date').sort_index()
+
+        def compare_recent_periods(values, lower_is_better=False):
+            if len(values) < 2 or values.iloc[-2:].isna().any():
+                return 'insufficient_data'
+            previous, current = values.iloc[-2], values.iloc[-1]
+            if current == previous:
+                return 'stable'
+            improving = current < previous if lower_is_better else current > previous
+            return 'improving' if improving else 'declining'
+
         trends = {}
-        
-        # Ticket volume trends
-        daily_volume = self.data.groupby(self.data['created_date'].dt.date).size()
-        trends['volume_trend'] = 'increasing' if daily_volume.iloc[-7:].mean() > daily_volume.iloc[-14:-7].mean() else 'decreasing'
-        
-        # Common issue categories
-        issue_frequency = self.data['issue_category'].value_counts()
-        trends['top_issues'] = issue_frequency.head(5).to_dict()
-        
-        # Customer satisfaction trends
-        monthly_csat = self.data.groupby(self.data['created_date'].dt.month)['csat_score'].mean()
-        trends['satisfaction_trend'] = 'improving' if monthly_csat.iloc[-1] > monthly_csat.iloc[-2] else 'declining'
-        
-        # Response time trends
-        weekly_response_time = self.data.groupby(self.data['created_date'].dt.week)['first_response_time'].mean()
-        trends['response_time_trend'] = 'improving' if weekly_response_time.iloc[-1] < weekly_response_time.iloc[-2] else 'declining'
-        
+        # Assumes collection covers each day between the first and last dates.
+        # Include zero-ticket calendar days and compare consecutive seven-day
+        # windows ending on the latest observed date, not seven active dates.
+        daily_volume = dated.resample('D').size()
+        trends['volume_trend'] = 'insufficient_data'
+        if len(daily_volume) >= 14:
+            previous = daily_volume.iloc[-14:-7].mean()
+            current = daily_volume.iloc[-7:].mean()
+            trends['volume_trend'] = (
+                'stable' if current == previous else
+                'increasing' if current > previous else 'decreasing'
+            )
+
+        trends['top_issues'] = self.data['issue_category'].value_counts().head(5).to_dict()
+        # Calendar resampling keeps December before January and inserts missing
+        # months/weeks as NaN rather than merging different years or guessing a trend.
+        monthly_csat = dated['csat_score'].resample('MS').mean()
+        trends['satisfaction_trend'] = compare_recent_periods(monthly_csat)
+        weekly_response_time = dated['first_response_time'].resample('W').mean()
+        trends['response_time_trend'] = compare_recent_periods(
+            weekly_response_time, lower_is_better=True
+        )
         return trends
-    
+
     def generate_improvement_recommendations(self):
         """
         Generate specific recommendations based on support data analysis