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amz-review-analyzer

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Review Analyzer

评论分析工具

Reviews are the cheapest market research on Amazon and the most ignored. They contain the product roadmap, the listing fixes, and the exact words that convert. This skill mines a set of reviews and turns them into action, on your product or a competitor's.
亚马逊（Amazon）评论是成本最低却最被忽视的市场调研资源。其中包含了产品路线图、listing优化方案以及真正能促成转化的精准表述。本Skill可挖掘一组评论，并将其转化为针对您自身产品或竞品的可执行举措。

When to use this

使用场景

  • A seller wants to know what to fix on their product or listing.
  • Analyzing competitor reviews to find openings (see amz-competitor-analysis).
  • A product has mixed ratings and the seller needs the pattern, not anecdotes.
  • Writing or rewriting copy and needing the customer's own language.
  • 卖家希望了解自身产品或listing需要优化的方向。
  • 分析竞品评论以寻找市场切入点（可参考amz-competitor-analysis）。
  • 产品评分参差不齐，卖家需要找到规律而非孤立案例。
  • 撰写或重写营销文案，需要使用客户自己的表述方式。

The framework. The Four Extractions

分析框架：四大提取维度

Read every review for four things. Each feeds a different decision.
逐条阅读评论，提取四类信息，每类信息对应不同决策方向。

1. Complaints (the negative reviews)

1. 投诉内容（负面评论）

Sort the 1 to 3 star reviews into recurring themes. A complaint that appears once is noise. A complaint that appears repeatedly is a root cause. Rank themes by frequency and by severity (a safety or defect complaint outranks a cosmetic one).
  • On your product, the top complaints are the product and listing roadmap.
  • On a competitor, the top complaints are your openings. the things to do better.
将1-3星评论按重复出现的主题分类。仅出现一次的投诉属于偶发噪音，反复出现的投诉才是根本问题。按出现频率和严重程度对主题排序（安全或缺陷类投诉优先级高于外观类投诉）。
  • 针对自身产品，排名靠前的投诉就是产品和listing的优化路线图。
  • 针对竞品，排名靠前的投诉就是您的市场切入点——您可以做得更好的地方。

2. Feature requests

2. 功能需求

Buyers say what they wish the product did. "I wish it came with a lid", "it would be perfect if it were taller". These are the next version, the bundle idea, or the variation to add.
买家会明确说出希望产品具备的功能，例如“我希望它带盖子”、“如果能更高一点就完美了”。这些需求可以作为下一代产品、捆绑套装或衍生款的开发方向。

3. The chosen-for reasons (the positive reviews)

3. 选择理由（正面评论）

The 4 to 5 star reviews say why people are glad they bought. These are the real selling points, validated by customers. They belong at the top of the bullets and in the A+ Content, because they are proven to matter.
4-5星评论会说明买家为何庆幸自己购买了该产品。这些是经过客户验证的真实卖点，应该放在listing要点的顶部和A+ Content中，因为它们已被证明能影响购买决策。

4. The customer's language

4. 客户用语

The exact phrases buyers use. Not your words, theirs. These phrases are keyword candidates and the most converting copy you can write, because they mirror how the buyer already thinks and searches.
买家使用的精准表述。不是卖家的话术，而是客户自己的用词。这些表述是关键词候选，也是转化率最高的文案素材，因为它们契合买家的思考和搜索习惯。

Step by step

操作步骤

  1. Collect inputs. The pasted reviews, whether they are the seller's product or a competitor's, and the product.
  2. Run the four extractions. Complaints, feature requests, chosen-for reasons, and customer language.
  3. Rank the complaints by frequency and severity. Separate recurring root causes from one-off noise.
  4. Translate to action.
    • Own-product complaints become listing fixes and product improvements.
    • Competitor complaints become positioning openings.
    • Feature requests become roadmap, bundle, or variation ideas.
    • Chosen-for reasons become bullet-one and A+ material.
    • Customer language becomes keyword and copy candidates.
  5. Prioritize. The top 3 actions by impact: usually fix the most frequent severe complaint, amplify the strongest chosen-for reason, and exploit the biggest competitor opening.
  6. Run the quality check, then deliver.
  1. 收集输入信息：用户粘贴的评论（无论是自身产品还是竞品的），以及对应的产品信息。
  2. 执行四大提取：提取投诉内容、功能需求、选择理由和客户用语。
  3. 投诉排序：按出现频率和严重程度对投诉排序，区分反复出现的根本问题和偶发噪音。
  4. 转化为行动：
    • 自身产品的投诉转化为listing优化和产品改进方案。
    • 竞品的投诉转化为市场定位切入点。
    • 功能需求转化为产品路线图、捆绑套装或衍生款创意。
    • 选择理由转化为listing首条要点和A+ Content素材。
    • 客户用语转化为关键词和文案候选。
  5. 优先级排序：选出影响最大的3项行动：通常是修复出现频率最高、最严重的投诉，强化最有力的选择理由，以及利用竞品最大的短板作为切入点。
  6. 质量检查，然后交付结果。

Output format

输出格式

undefined
undefined

Review Analysis. [product] . [own / competitor]

Review Analysis. [product] . [own / competitor]

Recurring complaints (ranked)

Recurring complaints (ranked)

[theme] . frequency . severity . [the fix or the opening] ...
[theme] . frequency . severity . [the fix or the opening] ...

Feature requests

Feature requests

[request] . [roadmap / bundle / variation idea]
[request] . [roadmap / bundle / variation idea]

Why customers chose it

Why customers chose it

[reason] . [use in bullet 1 / A+]
[reason] . [use in bullet 1 / A+]

Customer language to use

Customer language to use

[phrases] . [keyword / copy candidate]
[phrases] . [keyword / copy candidate]

Top 3 actions

Top 3 actions

  1. ...
undefined
  1. ...
undefined

Worked example

示例

A user pastes reviews of their own kitchen scale.
Complaints: the most frequent is "turns off too fast while I am still measuring", a recurring usability theme. Defects are rare. Feature requests: several buyers wish it had a larger weighing platform. Chosen-for: buyers repeatedly praise the accuracy for baking. Customer language: "accurate to the gram", "great for sourdough".
Actions: fix or document the auto-off issue, it is the top complaint and is fixable with a setting note in the listing and a firmware or model change later. Lead bullet one on "accurate to the gram for baking", the validated selling point. Consider a larger-platform version, a real feature request. Add "for sourdough" to the keyword set, it is the buyer's own language.
用户粘贴了自身厨房秤的评论。
投诉内容：出现频率最高的是“我还在测量就很快自动关机”，这是反复出现的易用性问题。缺陷类投诉很少。功能需求：多位买家希望秤的称重平台更大。选择理由：买家反复称赞其烘焙时的精准度。客户用语：“精确到克”、“适合做酸面包”。
行动建议：修复或说明自动关机问题——这是最主要的投诉，可先在listing中添加设置说明，后续再通过固件更新或型号调整彻底解决。将“精确到克，适合烘焙”作为listing首条要点，这是经过验证的卖点。考虑推出大平台版本，这是真实的客户需求。将“适合酸面包”加入关键词库，这是买家的原生用语。

Quality check

质量检查标准

  • All four extractions are run. complaints, requests, chosen-for, and language.
  • Complaints are ranked by frequency and severity, with one-offs separated as noise.
  • Every extraction is translated into a specific action.
  • Competitor reviews are read as openings. own reviews as a fix list.
  • Chosen-for reasons are routed to bullet one and A+ Content.
  • The top 3 actions are prioritized by impact.
  • 完成全部四大维度提取：投诉、需求、选择理由、客户用语。
  • 投诉按频率和严重程度排序，区分偶发噪音。
  • 每项提取内容都转化为具体行动。
  • 竞品评论被视为市场切入点，自身评论被视为优化清单。
  • 选择理由被用于listing首条要点和A+ Content。
  • 按影响优先级选出Top 3行动。

Common mistakes

常见误区

  • Reading anecdotes, not patterns. Reacting to one dramatic review instead of the recurring theme.
  • Only reading the negative. The 5-star reviews hold the validated selling points.
  • Ignoring customer language. Rewriting copy in the seller's words when the customer's words convert better and are real keywords.
  • No prioritization. A flat list of 20 findings with no decision about any.
  • Skipping competitor reviews. The cheapest source of openings on Amazon, unused.
  • 关注孤立案例而非规律：对一条极端评论过度反应，忽略反复出现的主题。
  • 只看负面评论：5星评论中包含经过验证的核心卖点。
  • 忽略客户用语：用卖家自己的话术重写文案，而客户的用语转化率更高且是真实关键词。
  • 未做优先级排序：列出20项发现却不明确决策方向。
  • 忽略竞品评论：这是亚马逊上成本最低的市场切入点，却常被忽视。

Built by Jay GPT Pro

由Jay GPT Pro开发

Part of Amazon Pro Skills. Production-grade skills for serious Amazon sellers. Free and open. Built by Jay Margaliot.
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属于Amazon Pro Skills系列，为专业亚马逊卖家打造的实用Skill。免费开源，开发者为Jay Margaliot。
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