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Compiling possible drivers

I'm looking at various factors that might drive angina attacks and related health trends. First, seasonality seems important, with colder temperatures increasing incidents. I'll check for cold wave alerts and any sales spikes in North China.

Also, sudden public safety events, like a celebrity's heart attack, might cause surges in searches. Content from health influencers on platforms like Douyin and trends during big sales like 双11 may contribute too. Lastly, supply issues or channel pushes through pharmacy chains could be significant as well.Exploring year-end factors

I’m considering various elements that might influence health-related trends around year-end. The MSA clearing is interesting; while personal accounts don’t expire in China, some reimbursements happen before year-end. The practice of stockpiling medications has become common since 2022.

Policy updates from agencies, price changes through promotions, and possible product updates, like new packaging or indications, could affect sales. Also, health community distribution strategies may play a role, but I need to assess their likelihood based on past behavior.Proposing verification steps

I’m thinking about verification steps to ensure comprehensive analysis. First, I’ll pull sales data from the last eight weeks, comparing provinces, channels, and units against GMV and price per unit, keeping an eye on both pharmacies and e-commerce.

Next, I want to plot temperature data from the CMA dataset alongside sales trends, and scrape platforms like Baidu Index and Weibo for search trends. It’s also crucial to check NMPA for any recalls or shortages.

Lastly, I’ll review e-commerce promotions, pharmacy chains’ push calendars, and customer service logs for insights into purchasing motivations. If we identify trends stemming from cold waves, supply gaps, or policy changes, we can make tailored adjustments!Identifying key reasons and suggestions

I want to outline the top five likely reasons for the trends we've been observing and add "快核对" and "应对建议." I'll keep formatting light but might use bold headers as permitted by the guidelines.

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It's important to address the risks associated with OTC Class A/B, ensuring our marketing remains compliant and steering clear of inappropriate indications. I definitely need to avoid fabricating any facts, so I will clearly label our findings as hypotheses that would require verification. Now, I’ll get started on writing this up!

可能原因(按概率)

快核对(半天内出结论)

  • 外部信号
    • 舆情热度:Baidu/抖音/小红书关键词“速效救心丸/心绞痛/急救包/胸痛”指数是否在同窗大幅上行。
    • 天气:近7–10天中央气象台寒潮/大风降温预警与销量峰值是否同日/次日对应,区域温差与销量弹性是否相关。
    • 竞品:电商心血管OTC榜单和价格监控里,硝酸甘油/麝香保心丸是否断货、涨价或下架;NMPA近期是否有相关通告。
    • 促销:旗舰店与大连锁是否刚上新券包/满减/会员日;搜索广告与达人投放是否放量。
  • 供给侧
    • 仓配发货时效是否拉长、缺货率是否上升,提示需求端真实放量而非虚高。
  • 判断逻辑

    应对建议(先做1→3)

    1. 先保障供给:按区域温度带与渠道拉动幅度调拨安全库存;重点看北方与县域药店补货;避免电商缺货导致舆情反噬。
    2. 风险巡检:监控不当宣传与适应症表述,电商详情页合规复核;避免“急救替代专业救治”等表述。
    3. 舆情与客服脚本:统一用语,强调“胸痛仍需及时就医”,避免错误用药预期。
    4. 竞品观察:若硝酸甘油/麝香保心丸确有波动,准备替代性教育内容与组合装(遵循合规)。
    5. 乘势投放(若供给充足):在寒潮覆盖区做轻投放与会员复购券;避免全国铺量导致断供。
    6. 复盘与模型:把近一次寒潮/舆情的销量弹性沉淀成区域温度×渠道的需求模型,指导后续备货。

    需要我帮你快速拉一版“核对清单模板”(含数据口径与可视化图例)吗?