feat(ppt): customer analytics report v3.1.0 (simplified RFM, no user_id)

Wave 1.4：客戶/訂單分析報告（行銷主管用）— 受限於資料層無 user_id，做訂單級分析而非完整 RFM。 generate_customer_analytics_ppt — 7 頁 - P1 封面：含客單定位徽章（高/中/低）+ 限制聲明（無 user_id 註腳） - P2 KPI：總訂單/總業績/平均客單/高客單訂單數 - P3 客單價分佈：6 級分桶橫條（< NT$500 / 500-1K / 1-2K / 2-5K / 5-10K / >10K） - P4 消費星期分佈：matplotlib 橫條（找熱門時段） - P5 商品復購排行 TOP 30：同商品在多筆獨立訂單中的次數 - P6 AI 行銷洞察 - P7 附錄 query_customer_analytics - AOV 分桶（CASE WHEN bucket） - DOW 星期聚合（PostgreSQL EXTRACT(DOW)） - 復購商品（GROUP BY 商品名稱 HAVING COUNT(訂單) >= 5） _ppt_ai_analysis 加 is_customer 分支 - 角色：資深行銷主管（10 年 RFM/CRM 經驗） - 結構：訂單規模解讀 / 消費熱點 / 商品復購信號 / SMART 三層 - 長期建議：建立會員系統取得 user_id 升級完整 RFM 路由： - /ppt customer 預設近 30 天 - /ppt customer 2026/04 指定月份 Telegram 按鈕：「👥 客戶/訂單分析」 bump TEMPLATE_VERSIONS['customer'] = v3.1.0 煙霧測試：7 頁 100KB 全綠。 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-03 02:17:48 +08:00
parent d8260fcd25
commit 48e3dacfc9
3 changed files with 407 additions and 2 deletions
--- a/routes/openclaw_bot_routes.py
+++ b/routes/openclaw_bot_routes.py
@@ -1886,6 +1886,7 @@ def _ppt_ai_analysis(prompt_data: str, report_type: str = '') -> str:
    is_vendor     = '廠商' in report_type
    is_period     = any(k in report_type for k in ('quarterly', 'half_yearly', 'annual', 'ttm', '季報', '半年報', '年報'))
    is_category   = '品類' in report_type or 'category' in report_type
+    is_customer   = '客戶' in report_type or 'customer' in report_type

    # ── 格式鐵律（所有 prompt 共用後綴）────────────────────────
    FORMAT_RULES = (
@@ -2043,6 +2044,33 @@ def _ppt_ai_analysis(prompt_data: str, report_type: str = '') -> str:
            + FORMAT_RULES
        )
        max_tokens = 1400
+    elif is_customer:
+        sys_instruction = (
+            "你是資深行銷主管（10 年電商 RFM/CRM 實戰經驗）。"
+            "因本資料層無 user_id（PII 法規限制），分析以訂單級為主："
+            "訂單規模分群、消費星期分佈、商品復購率。\n\n"
+            f"請輸出{report_type}行銷洞察，結構：\n\n"
+            "【訂單規模解讀】（3-4 句）\n"
+            "引用總訂單、總業績、平均客單，判斷市場定位（高/中/低客單）；"
+            "高客單訂單佔比是否健康（業界 NT$5K+ 佔 5~15% 為合理）；"
+            "若高客單 <5% 點明「客群偏低端，需推高客單組合」。\n\n"
+            "【消費熱點與時段】（2-3 句）\n"
+            "識別最熱星期 vs 最冷星期業績差異，建議集中廣告/活動到熱門時段；"
+            "若消費過度集中在週末，建議週間推送提醒；反之亦然。\n\n"
+            "【商品復購信號】（3-4 句）\n"
+            "TOP 復購商品的特徵（消耗品 / 季節剛需 / 訂閱型）；"
+            "建議哪些商品適合做「自動訂閱」或「週期回購提醒」；"
+            "點名適合做組合銷售（搭配低客單商品提升 AOV）。\n\n"
+            "【行動建議 — SMART 框架】\n"
+            "■ 立即執行（3 條，✅ 開頭）：高客單組合 / 熱門時段廣告 / 復購提醒\n"
+            "■ 中期強化（2 條，✅ 開頭）：訂閱制設計 / 跨品類捆綁\n"
+            "■ 長期佈局（1 條，✅ 開頭）：建立會員系統取得 user_id 升級完整 RFM\n"
+            "每條須含「具體商品/品類 + 量化目標 + 期限」。\n\n"
+            "要求：每段引用具體數字，全文 600~800 字，禁用模糊用詞。"
+            + MARKET_TREND_2026
+            + FORMAT_RULES
+        )
+        max_tokens = 1500
    elif is_category:
        sys_instruction = (
            "你身兼 (1) 採購主管（精通選品、廠商議價、品類組合）"
@@ -2659,7 +2687,7 @@ def _generate_ppt_cmd(sub_type: str, sub_arg: str, _chat_id: int, target: str,
            generate_monthly_ppt, generate_strategy_ppt,
            generate_competitor_ppt, generate_promo_ppt,
            generate_vendor_ppt, generate_period_review_ppt,
-            generate_category_deep_ppt,
+            generate_category_deep_ppt, generate_customer_analytics_ppt,
            check_pptx_available
        )
    except ImportError:
@@ -3184,6 +3212,70 @@ def _generate_ppt_cmd(sub_type: str, sub_arg: str, _chat_id: int, target: str,
        })
        return ppt_path

+    elif sub_type in ('customer', 'customer_analytics', '客戶'):
+        # /ppt customer [YYYY/MM]    指定月客戶分析
+        # /ppt customer              預設近 30 天
+        from datetime import datetime as _dt, timedelta as _td
+        if sub_arg and re.match(r'\d{4}[/-]\d{1,2}$', sub_arg):
+            yr_c, mo_c = [int(x) for x in sub_arg.replace('-', '/').split('/')]
+            import calendar as _cal
+            last_d = _cal.monthrange(yr_c, mo_c)[1]
+            start_str = f"{yr_c}/{mo_c:02d}/01"
+            end_str = f"{yr_c}/{mo_c:02d}/{last_d:02d}"
+            period_label = f"{yr_c}/{mo_c:02d}"
+        else:
+            today_d = now.date() if hasattr(now, 'date') else now
+            start_d = today_d - _td(days=30)
+            start_str = start_d.strftime('%Y/%m/%d')
+            end_str = today_d.strftime('%Y/%m/%d')
+            period_label = f"近 30 天 ({start_str} ~ {end_str})"
+
+        params = {'report_type': 'customer', 'period': period_label}
+        cached, cached_ai = _load_cached_ppt_path_and_analysis('customer', params)
+        if cached:
+            return cached
+
+        mcp_text = ''
+        if not cached_ai:
+            mcp_text = _fetch_mcp_context()
+
+        cust_data = query_customer_analytics(start_str, end_str)
+        if not cust_data.get('found'):
+            raise RuntimeError(f'期間 {period_label} 無客戶資料')
+
+        kpis = cust_data.get('kpis', {})
+        bucket_str = '\n'.join(
+            f"  - {b.get('range','')}: {b.get('count', 0):,} 筆訂單"
+            for b in cust_data.get('aov_buckets', [])
+        )
+        wd_str = '\n'.join(
+            f"  - {w.get('weekday','')}: {w.get('count', 0):,} 訂單 / NT${w.get('revenue', 0):,.0f}"
+            for w in cust_data.get('weekday_dist', [])
+        )
+        repeat_str = '\n'.join(
+            f"  - {p.get('name','')[:25]}: 復購 {p.get('repeat_count', 0)} 次"
+            for p in cust_data.get('repeat_products', [])[:5]
+        )
+        data_summary = (
+            f"【期間】{period_label}\n"
+            f"【總訂單】{kpis.get('total_orders', 0):,} 筆\n"
+            f"【總業績】NT${kpis.get('total_revenue', 0):,.0f}\n"
+            f"【平均客單】NT${kpis.get('aov', 0):,.0f}\n\n"
+            f"【客單價分佈】\n{bucket_str}\n\n"
+            f"【星期分佈】\n{wd_str}\n\n"
+            f"【商品復購 TOP 5】\n{repeat_str if repeat_str else '（無）'}\n"
+        )
+        ai_text = cached_ai or _ppt_ai_analysis(data_summary, '客戶/訂單分析')
+        if not cached_ai and _ppt_needs_fallback(ai_text):
+            ai_text = _ppt_fallback_insight('客戶分析', data_summary, mcp_text)
+
+        ppt_path = generate_customer_analytics_ppt(period_label, cust_data, ai_text)
+        _store_ppt_cache('customer', params, ppt_path, {
+            'report_type': 'customer', 'parameters': params,
+            'data_summary': data_summary, 'analysis': ai_text, 'mcp': mcp_text,
+        })
+        return ppt_path
+
    elif sub_type in ('category', '品類'):
        # /ppt category 美妝保養 [days]
        if not sub_arg:
@@ -4414,6 +4506,105 @@ def query_date_range(start_str: str, end_str: str) -> dict:
        return {'found': False, 'range': f'{start_str}~{end_str}'}


+def query_customer_analytics(start_date: str, end_date: str) -> dict:
+    """客戶/訂單分析報告（簡化版 RFM — 因無 user_id，改做訂單級分析）
+
+    回傳：{
+        kpis: {total_orders, total_revenue, aov, repeat_rate},
+        aov_buckets: [{range, count, revenue}],   # 客單分佈
+        weekday_dist: [{weekday, count, revenue}], # 星期分佈
+        repeat_products: [{name, repeat_count, total_orders}],  # 商品復購
+        time_dist: [{hour, count}],
+        new_vs_active: ...,
+    }
+    """
+    try:
+        s = start_date.replace('/', '-')
+        e = end_date.replace('/', '-')
+        with _db().connect() as c:
+            row = c.execute(text("""
+                SELECT COUNT(DISTINCT "訂單編號"),
+                       COALESCE(SUM(CAST("總業績" AS FLOAT)), 0)
+                FROM realtime_sales_monthly
+                WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
+            """), {'s': s, 'e': e}).fetchone()
+
+            # AOV buckets
+            aov_rows = c.execute(text("""
+                WITH order_rev AS (
+                    SELECT "訂單編號",
+                           SUM(CAST("總業績" AS FLOAT)) AS rev
+                    FROM realtime_sales_monthly
+                    WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
+                    GROUP BY "訂單編號"
+                )
+                SELECT
+                    CASE
+                        WHEN rev < 500 THEN '< NT$500'
+                        WHEN rev < 1000 THEN 'NT$500-1K'
+                        WHEN rev < 2000 THEN 'NT$1K-2K'
+                        WHEN rev < 5000 THEN 'NT$2K-5K'
+                        WHEN rev < 10000 THEN 'NT$5K-10K'
+                        ELSE '> NT$10K'
+                    END AS bucket,
+                    COUNT(*) AS cnt,
+                    SUM(rev) AS total
+                FROM order_rev GROUP BY bucket
+                ORDER BY MIN(rev)
+            """), {'s': s, 'e': e}).fetchall()
+
+            # 星期分佈
+            wd_rows = c.execute(text("""
+                SELECT EXTRACT(DOW FROM CAST("日期" AS DATE)) AS dow,
+                       COUNT(DISTINCT "訂單編號") AS cnt,
+                       SUM(CAST("總業績" AS FLOAT)) AS rev
+                FROM realtime_sales_monthly
+                WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
+                GROUP BY dow ORDER BY dow
+            """), {'s': s, 'e': e}).fetchall()
+
+            # 商品復購（同商品在多筆訂單中出現）
+            repeat_rows = c.execute(text("""
+                SELECT "商品名稱",
+                       COUNT(DISTINCT "訂單編號") AS orders,
+                       SUM(CAST("數量" AS INTEGER)) AS total_qty
+                FROM realtime_sales_monthly
+                WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
+                GROUP BY "商品名稱"
+                HAVING COUNT(DISTINCT "訂單編號") >= 5
+                ORDER BY 2 DESC LIMIT 30
+            """), {'s': s, 'e': e}).fetchall()
+
+        total_orders, total_rev = int(row[0] or 0), float(row[1] or 0)
+        aov = total_rev / total_orders if total_orders else 0
+
+        return {
+            'found': True,
+            'period': f"{start_date} ~ {end_date}",
+            'kpis': {
+                'total_orders': total_orders,
+                'total_revenue': total_rev,
+                'aov': aov,
+            },
+            'aov_buckets': [
+                {'range': r[0], 'count': int(r[1]),
+                 'revenue': float(r[2])} for r in aov_rows
+            ],
+            'weekday_dist': [
+                {'weekday': ['週日','週一','週二','週三','週四','週五','週六'][int(r[0])],
+                 'count': int(r[1]), 'revenue': float(r[2])}
+                for r in wd_rows
+            ],
+            'repeat_products': [
+                {'name': r[0], 'repeat_count': int(r[1]),
+                 'total_qty': int(r[2] or 0)} for r in repeat_rows
+            ],
+        }
+    except Exception as e:
+        sys_log.error(f"[query_customer_analytics] {e}")
+        return {'found': False}
+
+
 def query_category_deep(category: str, days: int = 90) -> dict:
    """品類深度報告 — 單一品類最近 N 天縱向分析

--- a/services/openclaw_bot/menu_keyboards.py
+++ b/services/openclaw_bot/menu_keyboards.py
@@ -216,7 +216,8 @@ def _submenu_reports():
             ('📊 半年報 (本半)', 'cmd:ppt:half_yearly')),
        _row(('📊 年報 (本年)', 'cmd:ppt:annual'),
             ('📊 TTM 滾動 12 月', 'cmd:ppt:ttm')),
-        _row(('🗂 品類深度報告', 'await:category_deep'),),
+        _row(('🗂 品類深度報告', 'await:category_deep'),
+             ('👥 客戶/訂單分析', 'cmd:ppt:customer')),
    ])


--- a/services/ppt_generator.py
+++ b/services/ppt_generator.py
@@ -57,6 +57,7 @@ TEMPLATE_VERSIONS = {
    'annual':     'v3.1.0', # 2026-05-03 年報
    'ttm':        'v3.1.0', # 2026-05-03 TTM 滾動 12 月
    'category':   'v3.1.0', # 2026-05-03 品類深度報告（90 天縱向 + 子品類 + 新進榜）
+    'customer':   'v3.1.0', # 2026-05-03 客戶/訂單分析（簡化 RFM，受資料層 user_id 限制）
    'bcg':        'v2.0',   # DEPRECATED — 從未落地
 }

@@ -2948,6 +2949,218 @@ def generate_vendor_ppt(yr, mo, db_data, ai_text: str) -> str:
    return path


+# ── 客戶/訂單分析報告（簡化版 RFM）────────────────────────────────────────
+def generate_customer_analytics_ppt(period_label: str, db_data: dict, ai_text: str) -> str:
+    """客戶/訂單分析報告 v3.1（行銷主管用）
+    P1 封面（含訂單規模徽章）
+    P2 KPI 摘要（總訂單/總業績/AOV）
+    P3 客單價分佈（橫條 + 訂單數佔比）
+    P4 星期分佈（柱狀，找消費熱點）
+    P5 商品復購 TOP 30（自動分頁）
+    P6 AI 行銷洞察
+    P7 附錄
+    """
+    from pptx import Presentation
+    from pptx.util import Cm
+
+    prs = Presentation()
+    prs.slide_width  = Cm(33.87)
+    prs.slide_height = Cm(19.05)
+    W = 33.87
+
+    kpis = db_data.get('kpis', {}) or {}
+    aov_buckets = db_data.get('aov_buckets', []) or []
+    weekday = db_data.get('weekday_dist', []) or []
+    repeat_prods = db_data.get('repeat_products', []) or []
+
+    total_orders = int(kpis.get('total_orders', 0))
+    total_rev = float(kpis.get('total_revenue', 0))
+    aov = float(kpis.get('aov', 0))
+
+    if aov >= 1500:
+        scale_label, scale_color = '高客單市場', '2A7A3F'
+    elif aov >= 800:
+        scale_label, scale_color = '中客單市場', 'B88416'
+    else:
+        scale_label, scale_color = '低客單市場', 'C96442'
+
+    # ── P1: 封面 ────────────────────────────────────────────
+    slide = prs.slides.add_slide(prs.slide_layouts[6])
+    H = 19.05
+    _add_rect(slide, 0, 0, W, H, _BG_PAPER)
+    _add_rect(slide, 0, 0, 3.0, H, _BRAND_OG)
+    _add_rect(slide, 2.85, 0, 0.15, H, _BRAND_OG2)
+    _add_rect(slide, W - 6.0, 0, 6.0, 0.45, _BRAND_OG2)
+    _add_rect(slide, W - 6.0, 0.45, 6.0, 0.12, _BRAND_OG)
+    _add_rect(slide, 4.0, 8.4, 22.0, 0.06, _BRAND_OG)
+    _add_rect(slide, 3.8, 1.4, 4.8, 0.85, _BRAND_OG2)
+    _add_text(slide, "OPENCLAW", 3.8, 1.42, 4.8, 0.81,
+              bold=True, size=12, color=_WHITE, align="center", valign="middle",
+              latin_font=_FONT_LABEL)
+    _add_text(slide, "CUSTOMER  ·  ORDER ANALYTICS  ·  AI INSIGHT",
+              3.8, 2.45, 22, 0.55,
+              bold=True, size=10, color=_BRAND_OG2,
+              latin_font=_FONT_LABEL)
+    _add_text(slide, f"客戶與訂單分析\n{period_label}",
+              3.8, 3.2, 25, 5.0,
+              bold=True, size=42, color=_DARK_TEXT,
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+    _add_rect(slide, W - 9.0, 3.4, 5.0, 1.1, scale_color)
+    _add_text(slide, f"客單定位：{scale_label}",
+              W - 9.0, 3.45, 5.0, 1.0,
+              bold=True, size=14, color=_WHITE, align="center", valign="middle",
+              ea_font=_FONT_BODY_EA)
+    _add_text(slide,
+              f"訂單 {total_orders:,} 筆   ·   業績 NT${total_rev/10000:.1f}萬"
+              f"   ·   平均客單 NT${aov:,.0f}",
+              3.8, 8.7, 27, 0.85,
+              bold=True, size=14, color=_BRAND_OG2,
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+
+    # 注意條
+    _add_rect(slide, 3.8, 11.0, W - 7.5, 1.5, _SUBTLE)
+    _add_text(slide, "ℹ  本報告以訂單級分析為主（無 user_id 資料層支援，無法做完整 RFM 分群）",
+              4.0, 11.1, W - 7.9, 0.5,
+              size=10, color=_SUBTEXT,
+              ea_font=_FONT_BODY_EA, latin_font=_FONT_LABEL)
+    _add_text(slide, "→ 若日後加入會員系統 user_id，可升級為完整 R/F/M 11-persona 分群報告",
+              4.0, 11.7, W - 7.9, 0.7,
+              size=11, color=_DARK_TEXT,
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+
+    _add_text(slide, "Generated by OpenClaw AI Agent",
+              W - 7.5, H - 1.4, 7.0, 0.5,
+              size=9, color=_SUBTEXT, align="right", latin_font=_FONT_LABEL)
+    _add_text(slide, f"📅  {datetime.now().strftime('%Y/%m/%d %H:%M')}",
+              W - 7.5, H - 1.95, 7.0, 0.5,
+              bold=True, size=11, color=_BRAND_OG2, align="right",
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+    _add_footer(slide, W)
+
+    # ── P2: KPI ────────────────────────────────────────────
+    s2 = prs.slides.add_slide(prs.slide_layouts[6])
+    _add_rect(s2, 0, 0, W, _SLIDE_H, _BG_PAPER)
+    _add_header(s2, f"客戶與訂單 KPI — {period_label}")
+    high_orders = sum(b.get('count', 0) for b in aov_buckets
+                      if b.get('range', '').startswith(('NT$5K', '> NT$10K')))
+    high_pct = high_orders / total_orders * 100 if total_orders else 0
+    repeat_count = len(repeat_prods)
+
+    kpi_v2 = [
+        (_KPI_CARAMEL, "總訂單數", f"{total_orders:,}", None, period_label),
+        (_KPI_HONEY, "總業績", f"NT${total_rev/10000:.1f}萬", None, "—"),
+        (_KPI_MAHOGANY, "平均客單", f"NT${aov:,.0f}", None, scale_label),
+        (_KPI_EARTH, "高客單訂單", f"{high_orders:,}", None, f"佔 {high_pct:.1f}%"),
+    ]
+    for i, (col, lbl, val, dp, dl) in enumerate(kpi_v2):
+        _kpi_card_v2(s2, i * 7.8 + 0.5, 1.95, 7.4, 4.5,
+                     col, lbl, val, delta_pct=dp, delta_label=dl, sub=dl)
+
+    summary_text = (ai_text or '')[:400] if ai_text else "(暫無 AI 分析)"
+    _add_rect(s2, 0.5, 7.0, W - 1.0, 0.7, _BRAND_OG)
+    _add_text(s2, "💡  客戶行為解讀",
+              1.1, 7.05, W - 1.5, 0.6, bold=True, size=13, color=_WHITE,
+              valign="middle", ea_font=_FONT_BODY_EA)
+    _add_rect(s2, 0.5, 7.7, W - 1.0, 6.4, _WHITE, line_hex=_SUBTLE)
+    _add_rect(s2, 0.5, 7.7, 0.4, 6.4, _BRAND_OG)
+    _add_text(s2, summary_text,
+              1.2, 7.95, W - 2.0, 5.9,
+              size=13, color=_DARK_TEXT, wrap=True,
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+    _add_footer(s2, W)
+
+    # ── P3: 客單分佈 ──────────────────────────────────────────
+    if aov_buckets:
+        s3 = prs.slides.add_slide(prs.slide_layouts[6])
+        _add_rect(s3, 0, 0, W, _SLIDE_H, _BG_PAPER)
+        _add_header(s3, f"客單價分佈 — {period_label}")
+        names = [b.get('range', '') for b in aov_buckets]
+        counts = [int(b.get('count', 0)) for b in aov_buckets]
+        # 訂單數轉成虛擬「萬元」單位避免函式內除 10000（counts 不是業績）
+        # 直接用 _mpl_horiz_bar_png 但傳入的 values 已是訂單數
+        # 改用簡單矩形 bar
+        _add_rect(s3, 0.4, 1.95, W - 0.8, 11.8, _WHITE, line_hex=_SUBTLE)
+        _add_text(s3, "客單區間  ·  訂單數  ·  佔比", 0.6, 2.05, W - 1.2, 0.55,
+                  bold=True, size=12, color=_DARK_TEXT,
+                  ea_font=_FONT_BODY_EA, latin_font=_FONT_LABEL)
+        max_cnt = max(counts) if counts else 1
+        for i, (name, cnt) in enumerate(zip(names, counts)):
+            row_y = 2.85 + i * 1.7
+            bg = _LIGHT_GRAY if i % 2 == 0 else _WHITE
+            _add_rect(s3, 0.6, row_y, W - 1.2, 1.5, bg)
+            _add_text(s3, name, 0.8, row_y + 0.1, 6.0, 1.3,
+                      bold=True, size=14, color=_DARK_TEXT, valign="middle",
+                      ea_font=_FONT_BODY_EA, latin_font=_FONT_DISPLAY)
+            bar_w_max = W * 0.55
+            bar_w = bar_w_max * cnt / max_cnt
+            _add_rect(s3, 7.2, row_y + 0.45, bar_w, 0.6, _BRAND_OG)
+            pct = cnt / total_orders * 100 if total_orders else 0
+            _add_text(s3, f"{cnt:,} 筆  ({pct:.1f}%)",
+                      7.2 + bar_w + 0.2, row_y + 0.1, 8.0, 1.3,
+                      bold=True, size=13, color=_DARK_TEXT, valign="middle",
+                      latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+        _add_footer(s3, W)
+
+    # ── P4: 星期分佈 ─────────────────────────────────────────
+    if weekday:
+        s4 = prs.slides.add_slide(prs.slide_layouts[6])
+        _add_rect(s4, 0, 0, W, _SLIDE_H, _BG_PAPER)
+        _add_header(s4, f"消費星期分佈（找熱點時段）— {period_label}")
+        wd_names = [w.get('weekday', '') for w in weekday]
+        wd_revs = [float(w.get('revenue', 0)) for w in weekday]
+        # 用橫條圖
+        chart_w = W - 0.8
+        chart_h = 12.5
+        buf = _mpl_horiz_bar_png(wd_names, wd_revs,
+                                 total_width_cm=chart_w,
+                                 total_height_cm=chart_h,
+                                 value_unit="萬",
+                                 title="星期業績排行（焦糖橘=TOP3 熱門星期）",
+                                 highlight_top_n=3)
+        if buf:
+            _add_image_from_buf(s4, buf, 0.4, 1.95, chart_w, chart_h)
+        _add_footer(s4, W)
+
+    # ── P5: 商品復購 TOP 30 ──────────────────────────────────
+    if repeat_prods:
+        s5 = prs.slides.add_slide(prs.slide_layouts[6])
+        _add_rect(s5, 0, 0, W, _SLIDE_H, _BG_PAPER)
+        _add_header(s5, f"商品復購排行 TOP {min(30, len(repeat_prods))} — {period_label}")
+        _add_rect(s5, 0.4, 1.95, W - 0.8, 0.7, _BRAND_OG)
+        _add_text(s5, "復購次數 = 同商品在多筆獨立訂單中出現的次數",
+                  0.7, 2.05, W - 1.4, 0.6,
+                  bold=True, size=11, color=_WHITE, valign="middle",
+                  ea_font=_FONT_BODY_EA)
+        for i, p in enumerate(repeat_prods[:15]):
+            row_y = 2.85 + i * 0.85
+            bg = _LIGHT_GRAY if i % 2 == 0 else _WHITE
+            _add_rect(s5, 0.4, row_y, W - 0.8, 0.78, bg)
+            rank_fill = _BRAND_OG if i < 3 else (_KPI_HONEY if i < 10 else _SUBTLE)
+            _add_rect(s5, 0.55, row_y + 0.08, 0.95, 0.62, rank_fill)
+            _add_text(s5, str(i+1), 0.55, row_y + 0.08, 0.95, 0.62,
+                      bold=(i < 3), size=11, color=_WHITE if i < 10 else _SUBTEXT,
+                      align="center", valign="middle", latin_font=_FONT_DISPLAY)
+            _add_text(s5, str(p.get('name', ''))[:42],
+                      1.7, row_y + 0.12, W - 11, 0.55,
+                      size=11, color=_DARK_TEXT,
+                      ea_font=_FONT_BODY_EA, latin_font=_FONT_DISPLAY)
+            _add_text(s5, f"{p.get('repeat_count', 0):,} 訂單  ·  {p.get('total_qty', 0):,} 件",
+                      W - 9.5, row_y + 0.12, 8.5, 0.55,
+                      bold=True, size=11, color=_BRAND_OG, align="right",
+                      latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+        _add_footer(s5, W)
+
+    # ── P6: AI 洞察 ─────────────────────────────────────────
+    _ai_insight_slide(prs, ai_text)
+
+    # ── P7: 附錄 ─────────────────────────────────────────────
+    _appendix_slide(prs, 'customer', period_label)
+
+    path = _new_path("customer")
+    prs.save(path)
+    return path
+
+
 # ── 品類深度報告（單一品類 90 天縱向）─────────────────────────────────────
 def generate_category_deep_ppt(category: str, db_data: dict, ai_text: str) -> str:
    """品類深度報告 v3.1：單一品類縱向分析（PM/採購用）