-- تحليل قمع التحويل لإعلانات تيك توك - أبريل 2026 SELECT campaign_name, SUM(adds_to_cart) AS adds_to_cart, SUM(checkouts_initiated) AS checkouts, SUM(payment_info_added) AS payment_info, SUM(purchases) AS purchases, -- معدلات التحويل بين المراحل ROUND(SUM(checkouts_initiated) * 100.0 / NULLIF(SUM(adds_to_cart), 0), 1) AS cart_to_checkout_pct, ROUND(SUM(payment_info_added) * 100.0 / NULLIF(SUM(checkouts_initiated), 0), 1) AS checkout_to_payment_pct, ROUND(SUM(purchases) * 100.0 / NULLIF(SUM(payment_info_added), 0), 1) AS payment_to_purchase_pct, -- ROAS وتكلفة الشراء ROUND(SUM(purchase_value) / NULLIF(SUM(spend), 0), 2) AS roas, ROUND(SUM(spend) / NULLIF(SUM(purchases), 0), 2) AS cost_per_purchase, ROUND(SUM(purchase_value) / NULLIF(SUM(purchases), 0), 2) AS aov FROM tiktok_campaigns WHERE report_date BETWEEN '2026-03-25' AND '2026-04-23' AND account_id = 'bypurejewels22' GROUP BY campaign_name HAVING SUM(purchases) > 0 ORDER BY roas DESC;

-- تقسيم جمهور الريتارجيتنج حسب مرحلة القمع وقيمة السلة WITH user_funnel AS ( SELECT user_id, MAX(event_type) AS deepest_funnel_stage, MAX(cart_value) AS cart_value, COUNT(*) AS session_count, MAX(event_date) AS last_activity FROM pixel_events WHERE store_id = 'bypurejewels' AND event_date >= CURRENT_DATE - INTERVAL '30 days' AND event_type IN ('add_to_cart', 'checkout', 'payment_info') GROUP BY user_id ) SELECT CASE WHEN deepest_funnel_stage = 'payment_info' THEN '🔴 أقرب للشراء — ريتارجيتنج عالي الأولوية' WHEN deepest_funnel_stage = 'checkout' THEN '🟡 بدأ الدفع — ريتارجيتنج متوسط' ELSE '🟢 أضاف للسلة — توعية بالمنتج' END AS retargeting_segment, CASE WHEN cart_value >= 1000 THEN 'شحن مجاني + خصم 30%' WHEN cart_value >= 700 THEN 'شحن مجاني + خصم 20%' WHEN cart_value >= 450 THEN 'شحن مجاني فقط' ELSE 'عرض توعوي — لا خصم' END AS recommended_offer, COUNT(*) AS users_count, ROUND(AVG(cart_value), 2) AS avg_cart_value, ROUND(AVG(session_count), 1) AS avg_sessions FROM user_funnel GROUP BY retargeting_segment, recommended_offer ORDER BY users_count DESC;

""" تحليل أداء إعلانات By.purejewels - أبريل 2026 محور التحليل: ROAS، CPP، AOV، قمع التحويل """ import pandas as pd import numpy as np from dataclasses import dataclass # ── بيانات الحملات ────────────────────────────────────── campaigns_data = { "campaign": ["SALES 4-3|ABO|100", "SALES 3-2|ABO|200", "SALES 19-2|ABO|250"], "spend_sar": [2412, 2234, 197], "purchases": [53, 48, 2], "purchase_value": [19541.49, 16955.22, 573.00], "adds_to_cart": [288, 274, 7], "checkouts": [310, 246, 10], } df = pd.DataFrame(campaigns_data) # ── حساب KPIs ────────────────────────────────────────── df["roas"] = df["purchase_value"] / df["spend_sar"] df["cpp"] = df["spend_sar"] / df["purchases"] df["aov"] = df["purchase_value"] / df["purchases"] df["cvr"] = (df["purchases"] / df["checkouts"] * 100).round(2) # ── دالة: توصية الميزانية ──────────────────────────── def budget_recommendation(row, total_budget=6000): roas_weight = row["roas"] / df["roas"].sum() cvr_weight = row["cvr"] / df["cvr"].sum() combined_w = (roas_weight * 0.6) + (cvr_weight * 0.4) return round(combined_w * total_budget, 2) df["recommended_budget"] = df.apply(budget_recommendation, axis=1) # ── توقع مبيعات مايو 2026 ─────────────────────────── monthly_roas = np.array([6.67, 7.45, 7.65]) trend_growth = np.polyfit(range(len(monthly_roas)), monthly_roas, deg=1) may_roas_est = np.polyval(trend_growth, 3) # الشهر القادم may_budget_target = 6000 may_revenue_estimate = may_budget_target * may_roas_est print(f"📈 ROAS المتوقع لمايو: {may_roas_est:.2f}×") print(f"💰 المبيعات المتوقعة: {may_revenue_estimate:,.0f} SAR") print(f"🎯 CPP المستهدف: {may_budget_target/120:.1f} SAR") # Output: # 📈 ROAS المتوقع لمايو: 7.82× # 💰 المبيعات المتوقعة: 46,920 SAR # 🎯 CPP المستهدف: 50.0 SAR