Improve projected revenue

dashboard-server/klaviyo-server/services/events.service.js

@@ -2022,4 +2022,160 @@ export class EventsService {
       throw error;
     }
   }
+
+  async calculateSmartProjection(params = {}) {
+    try {
+      const { timeRange, startDate, endDate } = params;
+      
+      // Get current period dates
+      let periodStart, periodEnd;
+      if (startDate && endDate) {
+        periodStart = this.timeManager.getDayStart(this.timeManager.toDateTime(startDate));
+        periodEnd = this.timeManager.getDayEnd(this.timeManager.toDateTime(endDate));
+      } else if (timeRange) {
+        const range = this.timeManager.getDateRange(timeRange);
+        periodStart = range.start;
+        periodEnd = range.end;
+      }
+
+      // Get the same day of week from the last 4 weeks for pattern matching
+      const historicalPeriods = [];
+      let historicalStart = periodStart.minus({ weeks: 4 });
+      
+      for (let i = 0; i < 4; i++) {
+        historicalPeriods.push({
+          start: historicalStart.plus({ weeks: i }),
+          end: historicalStart.plus({ weeks: i + 1 }).minus({ milliseconds: 1 })
+        });
+      }
+
+      // Fetch current period data
+      const currentEvents = await this.getEvents({
+        startDate: periodStart.toISO(),
+        endDate: periodEnd.toISO(),
+        metricId: METRIC_IDS.PLACED_ORDER
+      });
+
+      // Fetch historical data for pattern matching
+      const historicalPromises = historicalPeriods.map(period => 
+        this.getEvents({
+          startDate: period.start.toISO(),
+          endDate: period.end.toISO(),
+          metricId: METRIC_IDS.PLACED_ORDER
+        })
+      );
+
+      const historicalResults = await Promise.all(historicalPromises);
+
+      // Process current period data
+      const currentData = this._transformEvents(currentEvents.data || []);
+      const currentRevenue = currentData.reduce((sum, event) => {
+        const props = event.event_properties || {};
+        return sum + Number(props.TotalAmount || 0);
+      }, 0);
+
+      // Build hourly patterns from historical data
+      const hourlyPatterns = Array(24).fill(0).map(() => ({
+        count: 0,
+        revenue: 0,
+        percentage: 0
+      }));
+
+      let totalHistoricalRevenue = 0;
+      let totalHistoricalOrders = 0;
+
+      historicalResults.forEach(result => {
+        const events = this._transformEvents(result.data || []);
+        events.forEach(event => {
+          const datetime = this.timeManager.toDateTime(event.attributes?.datetime);
+          if (!datetime) return;
+
+          const hour = datetime.hour;
+          const props = event.event_properties || {};
+          const amount = Number(props.TotalAmount || 0);
+
+          hourlyPatterns[hour].count++;
+          hourlyPatterns[hour].revenue += amount;
+          totalHistoricalRevenue += amount;
+          totalHistoricalOrders++;
+        });
+      });
+
+      // Calculate percentages
+      hourlyPatterns.forEach(pattern => {
+        pattern.percentage = totalHistoricalRevenue > 0 ? 
+          (pattern.revenue / totalHistoricalRevenue) * 100 : 0;
+      });
+
+      // Get current hour in the period's timezone
+      const now = this.timeManager.getNow();
+      const currentHour = now.hour;
+      const currentMinute = now.minute;
+
+      // Calculate how much of the current hour has passed (0-1)
+      const hourProgress = currentMinute / 60;
+
+      // Calculate how much of the expected daily revenue we've seen so far
+      let expectedPercentageSeen = 0;
+      for (let i = 0; i < currentHour; i++) {
+        expectedPercentageSeen += hourlyPatterns[i].percentage;
+      }
+      // Add partial current hour
+      expectedPercentageSeen += hourlyPatterns[currentHour].percentage * hourProgress;
+
+      // Calculate projection based on patterns
+      let projectedRevenue = 0;
+      if (expectedPercentageSeen > 0) {
+        projectedRevenue = (currentRevenue / (expectedPercentageSeen / 100));
+      }
+
+      // Calculate confidence score (0-1) based on:
+      // 1. How much historical data we have
+      // 2. How consistent the patterns are
+      // 3. How far through the period we are
+      const patternConsistency = this._calculatePatternConsistency(hourlyPatterns);
+      const periodProgress = Math.min(100, Math.max(0, (now.diff(periodStart).milliseconds / periodEnd.diff(periodStart).milliseconds) * 100));
+      const historicalDataAmount = Math.min(totalHistoricalOrders / 1000, 1); // Normalize to 0-1, considering 1000+ orders as maximum confidence
+
+      const confidence = (
+        (patternConsistency * 0.4) + 
+        (periodProgress / 100 * 0.4) + 
+        (historicalDataAmount * 0.2)
+      );
+
+      // Return both the simple and pattern-based projections with metadata
+      return {
+        currentRevenue,
+        projectedRevenue,
+        confidence,
+        metadata: {
+          periodProgress,
+          patternConsistency,
+          historicalOrders: totalHistoricalOrders,
+          hourlyPatterns,
+          expectedPercentageSeen,
+          currentHour,
+          currentMinute
+        }
+      };
+    } catch (error) {
+      console.error('[EventsService] Error calculating smart projection:', error);
+      throw error;
+    }
+  }
+
+  _calculatePatternConsistency(hourlyPatterns) {
+    // Calculate the standard deviation of the percentage distribution
+    const mean = hourlyPatterns.reduce((sum, pattern) => sum + pattern.percentage, 0) / 24;
+    const variance = hourlyPatterns.reduce((sum, pattern) => {
+      const diff = pattern.percentage - mean;
+      return sum + (diff * diff);
+    }, 0) / 24;
+    const stdDev = Math.sqrt(variance);
+
+    // Normalize to a 0-1 scale where lower standard deviation means higher consistency
+    // Using a sigmoid function to normalize
+    const normalizedConsistency = 1 / (1 + Math.exp(stdDev / 10));
+    return normalizedConsistency;
+  }
 }