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review-audit

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Review Audit

评论审核

This system mines positive customer reviews to extract the insights that make ad copy actually work — the real language, real pain, real moments, and real transformations that customers experienced. The output feeds directly into creative strategy and hook writing.
The goal is not to summarize reviews. The goal is to find the raw material for ads.
本系统挖掘正面客户评论，提取能让广告文案真正奏效的洞察——客户真实使用语言、真实痛点、真实时刻以及真实体验转变。输出内容可直接用于创意策略制定和广告钩子撰写。
核心目标并非总结评论，而是寻找广告创作的原始素材。

What You Need Before Starting

开始前的准备事项

Reviews can be provided in any format:
  • Pasted directly into the chat
  • CSV or spreadsheet upload
  • Copy/pasted from a document
If multiple products are present in the review set, identify them before beginning. All output is separated by product.
If the format is unclear or product attribution is ambiguous, ask before proceeding.
评论可通过任意格式提供：
  • 直接粘贴至对话中
  • 上传CSV或电子表格
  • 从文档中复制粘贴
若评论集中包含多个产品，需先明确产品归属。所有输出内容将按产品分开呈现。
若格式不清晰或产品归属模糊，请先向用户确认后再继续。

Step 1: Group by Product

步骤1：按产品分组

If the brand sells multiple products, sort all reviews by product first. Every subsequent step runs separately per product.
If all reviews are for a single product, skip grouping and proceed.
若品牌销售多款产品，需先将所有评论按产品分类。后续所有步骤均需按产品单独执行。
若所有评论均针对单一产品，可跳过分组步骤直接进行下一步。

Step 2: Score Review Quality (1–5)

步骤2：评论质量评分（1–5分）

Before analysis, score every review for quality. This determines what gets analyzed and what gets discarded.
ScoreWhat it looks like
1Garbage — gibberish, swear words, 2–3 meaningless words, zero signal ("great product", "love it", "👍")
2Low signal — very short, vague, no specific detail or emotion
3Moderate — mentions the product, some specificity, but no vivid detail or emotional depth
4High quality — specific, describes a real experience, references a before/after or a feeling
5Gold — long, emotional, vivid, paragraph-level detail; the customer was so moved they wrote an essay about it
Score 5 reviews are the priority. They contain the most usable language and the deepest insight.
分析前，需为每条评论评分，以此决定哪些评论纳入分析、哪些予以舍弃。
评分特征描述
1分无效内容——胡言乱语、脏话、2-3个无意义词汇、无有效信息（如“很棒的产品”“喜欢它”“👍”）
2分低信息密度——篇幅极短、表述模糊，无具体细节或情感表达
3分中等质量——提及产品，有一定具体性，但缺乏生动细节或情感深度
4分高质量——表述具体，描述真实体验，涉及使用前后对比或情感变化
5分优质内容——篇幅较长、情感饱满、细节生动，为段落级评论；客户因体验深刻而撰写长篇反馈
5分评论为优先分析对象，这类评论包含最具价值的语言和最深入的洞察。

Step 3: Filter

步骤3：筛选评论

Discard all reviews scored 1. Do not include them in analysis.
Analyze scores 2–5, with emphasis on 4s and 5s. Low-scoring reviews (2–3) can contribute to pattern identification but should not be the source of pulled quotes.
舍弃所有1分评论，不纳入分析范围。
分析2-5分评论，重点关注4分和5分评论。低评分（2-3分）评论可用于识别共性模式，但不可作为引用素材来源。

Step 4: Extract Insights by Bucket

步骤4：按类别提取洞察

Run this analysis separately for each product. Within each bucket, group similar insights together and write a brief summary of the pattern. Then pull the best word-for-word quotes that exemplify it.
Do not editorialize the quotes. Pull them exactly as written.
针对每个产品单独执行此分析。在每个类别下，将相似洞察分组，并简要总结模式规律。随后提取最具代表性的原文引用作为例证。
请勿对引用内容进行编辑，需完全保留原文表述。

Bucket 1: Pain Points

类别1：痛点

What problem were they experiencing before they found this product?
Look for: descriptions of the problem they had, how long they'd had it, what they'd tried before, how it affected their life, the emotional weight of living with it.
For each pain theme identified:
  • Name the theme
  • Write a 2–3 sentence summary of the pattern across reviews
  • Flag the strongest quotes for the swipe file
使用产品前，客户面临哪些问题？
关注方向：客户面临的具体问题、问题持续时长、此前尝试过的解决方案、问题对生活的影响、承受该问题的情绪负担。
针对每个识别出的痛点主题：
  • 命名主题
  • 用2-3句话总结模式规律
  • 标记最强有力的引用至素材库

Bucket 2: Trigger Moments

类别2：触发时刻

What finally made them buy?
Look for: the specific moment, event, or realization that pushed them over the edge. This is the thing that turned a maybe into an add-to-cart. It could be a life event (wedding, diagnosis, vacation), a recommendation (friend, doctor, TikTok), hitting a breaking point, or running out of patience with other solutions.
For each trigger theme identified:
  • Name the theme
  • Write a 2–3 sentence summary of the pattern
  • Flag the strongest quotes for the swipe file
是什么最终促使客户下单？
关注方向：推动客户做出购买决策的具体时刻、事件或认知转变。这是让客户从“犹豫”转向“加入购物车”的关键因素，可能是生活事件（婚礼、确诊、度假）、推荐（朋友、医生、TikTok）、达到忍耐极限，或是对其他解决方案彻底失望。
针对每个识别出的触发主题：
  • 命名主题
  • 用2-3句话总结模式规律
  • 标记最强有力的引用至素材库

Bucket 3: Objections Before Purchasing

类别3：购买前顾虑

What almost stopped them from buying?
Look for: skepticism they mention having had, comparisons to other products they'd tried, price hesitation, disbelief that this would actually work, fear of wasting money again.
Note: In positive reviews, objections are almost always mentioned in past tense — "I was skeptical but..." or "I almost didn't try it because..." These are gold for objection-handling ad copy.
For each objection theme identified:
  • Name the theme
  • Write a 2–3 sentence summary of the pattern
  • Flag the strongest quotes for the swipe file
是什么差点让客户放弃购买？
关注方向：客户提及的疑虑、与其他产品的对比、价格顾虑、对产品效果的不信任、害怕再次浪费金钱。
注意：在正面评论中，这类顾虑几乎都以过去时态表述——“我之前很怀疑，但……”或“我差点没尝试，因为……”。这类内容是撰写“打消顾虑”类广告文案的黄金素材。
针对每个识别出的顾虑主题：
  • 命名主题
  • 用2-3句话总结模式规律
  • 标记最强有力的引用至素材库

Bucket 4: Transformations

类别4：体验转变

What changed for them after using the product?
Look for: the specific result they experienced, how their life is different now, the emotional shift (confidence, relief, freedom, pride), and — most importantly — how they describe the transformation in their own words. The more specific and visceral, the better.
For each transformation theme identified:
  • Name the theme
  • Write a 2–3 sentence summary of the pattern
  • Flag the strongest quotes for the swipe file
使用产品后，客户发生了哪些变化？
关注方向：客户获得的具体结果、生活的改变、情绪转变（自信、释然、自由、自豪），最重要的是客户用自己的语言描述的转变过程。表述越具体、越有代入感，价值越高。
针对每个识别出的转变主题：
  • 命名主题
  • 用2-3句话总结模式规律
  • 标记最强有力的引用至素材库

Bucket 5: Standout Language & Ad-Ready Phrases

类别5：亮眼语言与可直接用于广告的短语

Exact language worth stealing for ads.
This bucket is different from the others. It is not organized by theme — it is a curated collection of the most vivid, emotionally charged, specific, and scroll-stopping phrases pulled from across all buckets. These are the lines that made you stop while reading. The ones that don't need to be rewritten. The ones a copywriter would highlight and build an ad around.
Pull these verbatim. Note which product they're from.
What to look for:
  • Unusually specific descriptions of pain or transformation
  • Phrases that capture an emotion in a way you couldn't have written yourself
  • Before/after language that is visceral and concrete
  • Lines that could work as a hook with zero editing
  • Anything that made you feel something while reading it
可直接借鉴用于广告的原文表述。
此类别与其他类别不同，无需按主题分类——它是从所有类别中精选出的最生动、最具情感张力、最具体、最能吸引注意力的短语集合。这些是你阅读时会停下来留意的语句，无需改写，是文案创作者会重点标记并围绕其构建广告的内容。
需完全保留原文表述，并注明所属产品。
关注方向：
  • 对痛点或转变的异常具体描述
  • 以独特方式传递情绪的短语
  • 有代入感、具体的使用前后对比语言
  • 无需修改即可作为广告钩子的语句
  • 阅读时能引发你情绪共鸣的内容

Output Format

输出格式

Produce a separate full output for each product. Structure:
─────────────────────────────────────
PRODUCT: [Product Name]
Reviews analyzed: [X] | Discarded (score 1): [X]
─────────────────────────────────────

BUCKET 1: PAIN POINTS

[Theme Name]
Summary: [2–3 sentences on the pattern]

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 2: TRIGGER MOMENTS

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 3: OBJECTIONS BEFORE PURCHASING

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 4: TRANSFORMATIONS

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 5: STANDOUT LANGUAGE & AD-READY PHRASES

"[Exact quote]"
"[Exact quote]"
"[Exact quote]"
[etc.]

─────────────────────────────────────
All word-for-word quotes are collected in Bucket 5. Do not scatter quotes throughout buckets 1–4 — keep the summaries clean and let the swipe file be the dedicated place for raw language.
针对每个产品生成独立的完整输出，结构如下：
─────────────────────────────────────
PRODUCT: [Product Name]
Reviews analyzed: [X] | Discarded (score 1): [X]
─────────────────────────────────────

BUCKET 1: PAIN POINTS

[Theme Name]
Summary: [2–3 sentences on the pattern]

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 2: TRIGGER MOMENTS

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 3: OBJECTIONS BEFORE PURCHASING

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 4: TRANSFORMATIONS

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 5: STANDOUT LANGUAGE & AD-READY PHRASES

"[Exact quote]"
"[Exact quote]"
"[Exact quote]"
[etc.]

─────────────────────────────────────
所有原文引用均需汇总至类别5中。请勿在类别1-4中分散放置引用内容——保持总结内容简洁，让素材库成为存放原始语言的专属区域。

How This Feeds the Rest of the Stack

与其他环节的关联

The output of this analysis plugs directly into creative strategy and execution:
  • Pain Points → Creative Strategy Engine — pain buckets map directly to the pain/desire anchor layer
  • Trigger Moments → Hook Writing — trigger moments are often the most powerful hook material; they capture the exact moment of emotional readiness
  • Objections → Hook Writing / Creative Mechanics — objections inform Borrowed Enemy, Reframe, and Risk Reversal mechanics and hook tactics
  • Transformations → Hook Writing — transformation language feeds aspirational and social proof hooks
  • Standout Language → Hook Voice Patterns — the best phrases can be added directly to the swipe file as native voice patterns pulled from real customers
本分析的输出内容可直接对接创意策略与执行环节：
  • 痛点 → 创意策略引擎——痛点分类直接对应“痛点/渴望锚点”层级
  • 触发时刻 → 广告钩子撰写——触发时刻通常是最具影响力的钩子素材，能精准捕捉客户的情绪决策点
  • 购买前顾虑 → 广告钩子撰写/创意机制——顾虑内容可为“借力竞品”“重构认知”“风险逆转”等创意机制和钩子策略提供依据
  • 体验转变 → 广告钩子撰写——转变类语言可用于创作励志型和社交证明型钩子
  • 亮眼语言 → 钩子风格模板——优质短语可直接加入素材库，作为源自真实客户的原生风格模板

Notes on Quality

质量注意事项

  • Score 5 reviews should be read in full and treated as primary sources
  • Score 2–3 reviews are useful for pattern confirmation but not quote sourcing
  • If the review set is small (under 20 reviews), note this — patterns may not be statistically meaningful but language is still usable
  • If a product has too few quality reviews to surface meaningful patterns, flag it rather than manufacturing themes that aren't there
  • 需完整阅读5分评论，并将其视为核心分析来源
  • 2-3分评论仅用于验证模式规律，不可作为引用素材来源
  • 若评论数量较少（不足20条），需标注说明——此时模式规律可能不具备统计意义，但语言素材仍可使用
  • 若某产品的优质评论过少，无法挖掘出有意义的模式规律，需标记说明，而非编造不存在的主题