# -*- coding: utf-8 -*- """ 同城对标检测 - 分析报告生成器 输入: 采集快照 CSV (每次采集累加, 一个商品一行) 输出: 04_输出/对标检测报告_<日期>.html 用法: python build_report.py # 用最新快照 python build_report.py --demo # 用内置模拟数据跑演示 python build_report.py --file xxx.csv # 指定快照文件 """ import csv import os import sys import random import statistics from datetime import date BASE = os.path.dirname(os.path.abspath(__file__)) OUT_DIR = os.path.join(os.path.dirname(BASE), '04_输出') SNAP_DIR = os.path.join(os.path.dirname(BASE), '03_分析') SNAP_FIELDS = ['采集日期', '门店编码', '门店名', '门店类型', 'POI链接', '商品名', '原价', '团购价', '已售', '评分', '评价数'] PRODUCTS = ['面部深层清洁', '超微小气泡', '光子嫩肤单次', '水光补水', '肩颈舒缓60min', '头皮深层清洁', '果酸焕肤', '淡斑管理', '背部疏通', '眼部护理', '小气泡3次卡', '面部抗衰套餐'] def med(xs): xs = [x for x in xs if x is not None] return statistics.median(xs) if xs else 0.0 def pct(x): return f'{x * 100:.0f}%' # ---------------------------------------------------------------- demo data def build_demo(): """构造 10 家本店 x 4~5 家竞对 的模拟快照, 用于验证报告长什么样""" rnd = random.Random(20261029) today = date.today().isoformat() rows = [] profiles = [ ('C001', 168, 1.28, 4.62, 0.55), ('C002', 198, 0.92, 4.81, 1.35), ('C003', 128, 1.05, 4.75, 1.10), ('C004', 158, 1.42, 4.51, 0.42), ('C005', 178, 0.88, 4.86, 1.62), ('C006', 148, 1.12, 4.68, 0.86), ('C007', 188, 1.35, 4.58, 0.51), ('C008', 138, 0.98, 4.79, 1.24), ('C009', 168, 1.08, 4.70, 0.95), ('C010', 208, 1.25, 4.55, 0.60), ] for code, base_price, price_k, rating, strength in profiles: n_comp = rnd.choice([4, 4, 5]) rows.append(dict(采集日期=today, 门店编码=code, 门店名=f'{code}门店', 门店类型='本店', POI链接=f'https://v.douyin.com/{code}/', 商品名='', 原价=0, 团购价=0, 已售=0, 评分=rating, 评价数=rnd.randint(300, 2400))) own_n = min(rnd.randint(6, 11), len(PRODUCTS)) own_sold_total = int(rnd.randint(600, 3000) * strength) for nm in rnd.sample(PRODUCTS, own_n): price = round(base_price * rnd.uniform(0.6, 1.6)) rows.append(dict(采集日期=today, 门店编码=code, 门店名=f'{code}门店', 门店类型='本店', POI链接=f'https://v.douyin.com/{code}/', 商品名=nm, 原价=round(price * rnd.uniform(1.6, 2.8)), 团购价=price, 已售=max(3, own_sold_total // own_n + rnd.randint(-40, 40)), 评分=rating, 评价数=0)) for i in range(n_comp): cname = f'{code}商圈·竞对{i + 1}' c_rating = round(rating + rnd.uniform(-0.35, 0.35), 1) c_n = min(rnd.randint(4, 14), len(PRODUCTS)) comp_pool = int(rnd.randint(500, 3200) / max(strength, 0.3)) for nm in rnd.sample(PRODUCTS, c_n): price = round(base_price * rnd.uniform(0.7, 1.5) / price_k) rows.append(dict(采集日期=today, 门店编码=code, 门店名=cname, 门店类型='竞对', POI链接=f'https://v.douyin.com/x{code}{i}/', 商品名=nm, 原价=round(price * rnd.uniform(1.5, 2.6)), 团购价=price, 已售=max(2, comp_pool // c_n + rnd.randint(-30, 30)), 评分=c_rating, 评价数=rnd.randint(100, 2000))) return rows # ---------------------------------------------------------------- analysis def analyse(rows): groups = {} for r in rows: groups.setdefault(r['门店编码'], []).append(r) result = [] for code, rs in groups.items(): own = [r for r in rs if r['门店类型'] == '本店' and r['商品名']] comp = [r for r in rs if r['门店类型'] == '竞对' and r['商品名']] if not own: continue comp_names = {r['门店名'] for r in comp} n = len(comp_names) or 1 own_price = med([r['团购价'] for r in own]) comp_price = med([r['团购价'] for r in comp]) own_cnt = len({r['商品名'] for r in own}) cnt_by_store = {} for r in comp: cnt_by_store.setdefault(r['门店名'], set()).add(r['商品名']) comp_cnt = med([len(v) for v in cnt_by_store.values()]) if cnt_by_store else own_cnt own_sold = sum(r['已售'] for r in own) comp_sold = sum(r['已售'] for r in comp) total = own_sold + comp_sold or 1 share = own_sold / total share_idx = share * (n + 1) price_idx = own_price / comp_price if comp_price else 1.0 own_rating = next((r['评分'] for r in own), 0) comp_rating = med([r['评分'] for r in rs if r['门店类型'] == '竞对']) gap = own_rating - (comp_rating or own_rating) composite = (0.5 * share_idx + 0.3 * (1 + gap / 0.5 * 0.2) + 0.2 * (1 + (1 - price_idx) * 0.5)) result.append(dict( 编码=code, 门店名=f'{code}门店', 竞对数=n, 本店商品数=own_cnt, 竞对商品数=comp_cnt, 本店价=own_price, 同城价=comp_price, 价格指数=price_idx, 本店销量=own_sold, 同城销量=comp_sold, 份额=share, 份额指数=share_idx, 本店评分=own_rating, 同城评分=comp_rating, 评分差=gap, 竞争力=composite, )) result.sort(key=lambda x: x['份额指数']) return result def judge(m): """检测规则 -> (标签, 等级, 归因建议)""" rules = [] if m['价格指数'] > 1.2 and m['份额指数'] < 0.8: rules.append(('定价偏高', 'danger', f"价格高于同城中位 {pct(m['价格指数'] - 1)}，销量份额仅为平均水平的 {m['份额指数']:.2f} 倍。" f"优先检查主图与团购标题，其次考虑下调入门款价格。")) if m['价格指数'] < 0.85 and m['份额指数'] < 0.8: rules.append(('性价比未被感知', 'warn', f"价格低于同城 {pct(1 - m['价格指数'])}，但份额只有平均的 {m['份额指数']:.2f} 倍。" f"不是价格问题，是曝光或评价数量不够，检查达人铺设与评价增速。")) if m['竞对商品数'] and m['本店商品数'] < m['竞对商品数'] * 0.6: rules.append(('品项不足', 'warn', f"在售 {m['本店商品数']} 个，同城中位 {m['竞对商品数']:.0f} 个。" f"补齐引流款+利润款的组合，至少到中位水平。")) if m['评分差'] < -0.15: rules.append(('口碑落后', 'danger', f"评分低于同城中位 {abs(m['评分差']):.2f} 分。差评未处理会直接压转化，先处理近 30 天差评。")) if m['份额指数'] > 1.3 and m['价格指数'] < 1.1: rules.append(('优势店', 'good', '价格不高于同城且份额领先，把这家店的商品结构和内容打法复制到同区域其他店。')) if not rules: rules.append(('与商圈持平', 'flat', '各项指标接近同城水平，暂无明显短板。')) return rules # ---------------------------------------------------------------- render CSS = """ *{box-sizing:border-box;margin:0;padding:0} body{background:#0f1115;color:#e6e8ec;font-family:-apple-system,"Segoe UI","Microsoft YaHei",sans-serif; font-size:14px;line-height:1.65;padding:36px 40px 64px} .wrap{max-width:1080px;margin:0 auto} h1{font-size:22px;font-weight:600;letter-spacing:.2px} .sub{color:#8b93a1;font-size:13px;margin-top:8px} .kpis{display:grid;grid-template-columns:repeat(4,1fr);gap:12px;margin:26px 0 30px} .kpi{background:#171a21;border:1px solid #252a34;border-radius:12px;padding:16px 18px} .kpi .v{font-size:24px;font-weight:600;font-variant-numeric:tabular-nums} .kpi .l{color:#8b93a1;font-size:12px;margin-top:2px} h2{font-size:15px;font-weight:600;margin:34px 0 14px;display:flex;align-items:center;gap:8px} h2 .bar{width:3px;height:14px;background:#6aa9ff;border-radius:2px} table{width:100%;border-collapse:collapse;font-size:13px;font-variant-numeric:tabular-nums} th{text-align:right;color:#8b93a1;font-weight:500;font-size:12px;padding:9px 10px; border-bottom:1px solid #2a2f3a;white-space:nowrap} th:first-child,th:nth-child(2){text-align:left} td{padding:10px;border-bottom:1px solid #1d212a;text-align:right;white-space:nowrap} td:first-child,td:nth-child(2){text-align:left} tr:hover td{background:#151920} .tag{display:inline-block;padding:2px 8px;border-radius:5px;font-size:11px;font-weight:500} .t-danger{background:rgba(255,107,107,.14);color:#ff8585} .t-warn{background:rgba(240,168,60,.14);color:#f0b45c} .t-good{background:rgba(78,201,160,.14);color:#5fd6ae} .t-flat{background:rgba(140,148,163,.14);color:#9aa3b2} .cards{display:grid;grid-template-columns:1fr;gap:10px} .card{background:#171a21;border:1px solid #252a34;border-left:3px solid #2f3745; border-radius:10px;padding:14px 18px} .card.danger{border-left-color:#ff6b6b} .card.warn{border-left-color:#f0a83c} .card.good{border-left-color:#4ec9a0} .card .h{display:flex;align-items:center;gap:10px;flex-wrap:wrap} .card .nm{font-weight:600;font-size:14px} .card .msg{color:#a8b0be;font-size:13px;margin-top:6px} .card .nums{color:#6f7787;font-size:12px;margin-top:6px;font-variant-numeric:tabular-nums} .note{color:#767e8c;font-size:12px;line-height:1.9;margin-top:14px} .note b{color:#9aa3b2;font-weight:500} .up{color:#ff8585}.down{color:#5fd6ae} """ def render(metrics, meta): total_comp = sum(m['竞对数'] for m in metrics) share_avg = statistics.mean([m['份额指数'] for m in metrics]) if metrics else 0 below = [m for m in metrics if m['份额指数'] < 0.8] issues = [(m, r) for m in metrics for r in judge(m) if r[1] in ('danger', 'warn')] kpis = [('试点门店', f'{len(metrics)} 家'), ('对标门店', f'{total_comp} 家'), ('份额落后门店', f'{len(below)} 家'), ('平均份额指数', f'{share_avg:.2f}')] rows_html = [] for m in metrics: j = judge(m)[0] rows_html.append(f""" {m['门店名']}{m['竞对数']} {m['本店商品数']} / {m['竞对商品数']:.0f} {m['本店价']:.0f} / {m['同城价']:.0f} {m['价格指数']:.2f} {pct(m['份额'])} {m['份额指数']:.2f} {m['本店评分']:.1f} / {m['同城评分']:.1f} {m['竞争力']:.2f} {j[0]} """) cards_html = [] for m, (label, level, msg) in issues: cards_html.append(f"""
{m['门店名']}{label}
{msg}
在售 {m['本店商品数']} 品（同城中位 {m['竞对商品数']:.0f}） · 价格指数 {m['价格指数']:.2f} · 份额指数 {m['份额指数']:.2f} · 评分 {m['本店评分']:.1f}
""") if not cards_html: cards_html.append('
本期未命中异常规则
') return f""" 同城对标检测报告

同城对标检测报告

采集日期 {meta['date']} · 口径为抖音前台公开可观测指标 · 仅用于试点验证
{''.join(f'
{v}
{k}
' for k, v in kpis)}

命中异常的 {len(issues)} 项，按优先级处理

{''.join(cards_html)}

对标明细（按份额落后排序）

{''.join(rows_html)}
门店对标店在售品(本/中位)价格中位(本/同城)价格指数 销量份额份额指数评分(本/中位)竞争力判定
指数说明　份额指数 1.00 = 与同组门店平均份额持平；价格指数 1.00 = 与同城价格中位持平；竞争力指数 = 份额 50% + 口碑 30% + 价格 20%。
口径局限　销量来自前台「已售」累计值，含历史，单次采集不能代表当日销量；要算真实日增需连续两次采集取差值。曝光、转化率、核销率、退款率属商家私有数据，外部采集拿不到，本报告不涉及。
试点性质　{'本页数据为模拟样例，用于确认字段与判定规则是否可用，不代表真实经营结论。' if meta['demo'] else '数据由实际采集生成。'}
""" def main(): args = sys.argv[1:] demo = '--demo' in args rows, src = [], '' if demo: rows = build_demo() else: path = None if '--file' in args: path = args[args.index('--file') + 1] else: files = sorted(f for f in os.listdir(SNAP_DIR) if f.endswith('.csv')) if os.path.isdir(SNAP_DIR) else [] if files: path = os.path.join(SNAP_DIR, files[-1]) if not path or not os.path.exists(path): print('未找到采集快照 CSV。可先用 --demo 查看报告形态。') sys.exit(1) src = path with open(path, encoding='utf-8-sig') as f: for r in csv.DictReader(f): r['团购价'] = float(r.get('团购价') or 0) r['原价'] = float(r.get('原价') or 0) r['已售'] = float(r.get('已售') or 0) r['评分'] = float(r.get('评分') or 0) rows.append(r) metrics = analyse(rows) if not metrics: print('快照里没有可分析的数据，检查「门店类型」是否填了「本店 / 竞对」。') sys.exit(1) meta = dict(date=rows[0]['采集日期'] if rows else date.today().isoformat(), demo=demo) os.makedirs(OUT_DIR, exist_ok=True) suffix = '示例' if demo else meta['date'].replace('-', '') out = os.path.join(OUT_DIR, f'对标检测报告_{suffix}.html') with open(out, 'w', encoding='utf-8') as f: f.write(render(metrics, meta)) print(f'报告已生成: {out}') print(f'分析门店 {len(metrics)} 家, 竞对 {sum(m["竞对数"] for m in metrics)} 家') for m in metrics[:5]: print(f" {m['门店名']} 份额指数 {m['份额指数']:.2f} 价格指数 {m['价格指数']:.2f} {judge(m)[0][0]}") if src: print(f'数据源: {src}') if __name__ == '__main__': main()