AI 提示词库
面向 WooCommerce 网站分析与 CRO 的 AI 提示词
12 个可复制粘贴的提示词，将您从 Statnive 导出的数据转化为 CRO 假设。每个提示词都对应一个特定报告，明示自身无法修复的局限，并适用于 ChatGPT、Claude 或 Gemini。
可直接使用、按需修改以匹配您的店铺，或将多个提示词串联起来。所有输出皆为假设——上线前请对照 Baymard 检查清单进行验证。
对每个提示词：从指定的 Statnive 报告导出 CSV，粘贴进去，再把完整提示词复制到您的 AI 助手中。这些提示词假设您正在一家独立 WooCommerce 店铺（每月 5K–50K 美元）上运行 Statnive，并希望得到可直接决策的输出，而非分析瘫痪。
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#01 每周复盘
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You are a CRO analyst for a solo WooCommerce store. Here is week-over-week data on Sessions, Bounces, and Channels: [PASTE CSV: 7-day current vs 7-day previous Overview export] Identify the 3 most important changes. Label each as 'investigate / act / ignore'. Suggest one experiment for the 'act' items. Keep it under 200 words. -
#02 落地页 CRO
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Here is Entry Count, Bounces, and Total Duration for my top 10 entry pages: [PASTE CSV from Statnive Pages report, sorted by Entry Count] Rank them by CRO opportunity. For the top 3, list 3 hypotheses each and one concrete experiment per hypothesis. Output as a table. -
#03 商品页优化
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Here is Views, Total Duration, and Exit Count for my product pages (filtered to URLs containing /product/): [PASTE CSV] Identify the 3 PDPs with the strongest signal of friction. For each, hypothesize the 3 most likely causes. Recommend the lowest-effort fix per cause. -
#04 广告系列质量审计
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Here is UTM campaign data: Source, Medium, Campaign, Sessions, Bounces, Total Duration: [PASTE CSV from Referrers report, UTM dimensions] Identify campaigns to scale, fix, or pause. For each pause/fix recommendation, give the diagnostic signal and the next step. Output as a table with reasons. Apply the channel-health rule: pass = bounces below site avg AND duration above site avg. -
#05 UTM 清洁度治理
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Here is my UTM list (Source/Medium/Campaign distinct values for the last 90 days): [PASTE CSV] Identify (1) naming inconsistencies (capitalization, duplicates, typos), (2) the most likely consolidations, and (3) propose a standardized lowercase naming scheme with examples. Flag any 'utm_medium' value that does not match Statnive's 8 channel buckets. -
#06 移动端 UX 差距检测
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Here is bounce rate by Device Type for my top 10 pages: [PASTE CSV: Pages × Device Type cross-tab from Statnive] For each page where mobile bounce exceeds desktop bounce by 15 percentage points or more, list the page and suggest 3 mobile-specific fixes (one for layout, one for speed, one for input/interaction). Skip pages where the gap is within 15pp. -
#07 本地化机会扫描
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Here is Geography data: Country, Visitors, Total Duration, Bounces: [PASTE CSV from Statnive Geography report] Identify the top 3 countries with: at least 5% share of total visitors AND total duration at least 80% of my domestic visitors' duration. For each, recommend currency-first or language-first as the cheapest first localization test, and explain why. -
#08 内容到商品归因
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Here are my top 20 blog posts by Views and Exit Count: [PASTE CSV from Statnive Pages report, filtered to blog URLs] Which posts are 'bleeding' traffic (high views + high exits + low next-page conversion)? For each, suggest 2 contextual internal links to product pages that would naturally fit the post's topic. -
#09 退出页诊断
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For this exit page: [PASTE: page URL + Exit Count + Views + Total Duration + which page type (PDP/cart/checkout)] Hypothesize 5 reasons users leave. Rank by likelihood. For the top 2, suggest one diagnostic check and one experimental fix. -
#10 实时上线监控
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During my flash sale, here is real-time visitor pattern by minute: [PASTE CSV: Real-time visitor counts in 5-min buckets from launch] Identify whether the campaign is on track vs. the expected baseline (which I supply). Flag any unusual patterns (sudden spike, abnormal source distribution). Suggest one action only if the deviation is greater than 2x baseline. -
#11 漏斗流失诊断
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Here is my Cart-to-Purchase Funnel for the last 30 days from Statnive Revenue Report: [PASTE CSV: Viewed product → Added to cart → Started checkout → Completed purchase counts + per-step conversion rate, ideally with per-channel breakdown] Identify the biggest funnel drop-off step. Suggest 3 fixes specific to that step (PDP issues for view→cart drop; cart issues for cart→checkout drop; checkout-form issues for checkout→purchase drop). Cite which fix Baymard research supports. -
#12 按渠道划分的收入——预算分配
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Here are Orders, Revenue (net), and AOV per channel from the Statnive Revenue Report, plus session counts from the Referrers report for the same period: [PASTE CSV] Calculate revenue per session (RPV) by channel. Rank channels for next-quarter budget. Flag any channel with high session volume but bottom-quartile RPV as a budget-cut candidate. Call out the AI Assistants channel specifically if its RPV beats paid channels — that's a free-acquisition signal worth investing content in.
如何最大化利用这些提示词
- 始终粘贴数据，不要描述数据。当被要求"想象一家典型店铺"时，AI 模型会产生幻觉——基于真实数字，它们的输出更靠谱。
- 先去除带个人身份识别的 URL。Statnive 不存储 PII，但您客户的订单确认 URL（含订单 ID）可能泄露唯一标识符。粘贴前请替换为 `/order-received/[id]/`。
- 把输出视作假设。AI 会自信地编造因果关系。请始终对照 Baymard、CXL 或 NN/g 等研究背书的修复方案进行交叉核对。
- 把提示词串联起来。先跑"广告系列质量审计"，再把其输出粘贴到"UTM 清洁度治理"中，处理被标记为问题的广告系列。
- 保存您的修改。最好的提示词是为您的店铺调校过的那个。请用一个 markdown 文件保留您改写过的版本。