ad-angle-miner

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English

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Chinese

Ad Angle Miner

广告角度挖掘工具

Dig through customer voice data — reviews, Reddit, support tickets, competitor ads — to extract the specific language, pain points, and outcome desires that make ads convert. The output is an angle bank your team can pull from for any campaign.

Core principle: The best ad angles aren't invented in a brainstorm. They're extracted from what real people are already saying. This skill finds those angles and ranks them by strength of evidence.

深入挖掘客户声音数据——包括评论、Reddit内容、支持工单、竞品广告——提取能提升广告转化率的具体表述、痛点以及对成果的诉求。输出的角度库可供你的团队用于任何营销活动。

核心原则： 最佳广告角度不是头脑风暴出来的，而是从真实用户的实际表述中提取的。本工具能找到这些角度，并根据证据强度进行排序。

When to Use

使用场景

"What angles should we run in our ads?"
"Find pain points we can use in ad copy"
"What are people complaining about with [competitors]?"
"Mine reviews for ad messaging"
"I need fresh ad angles — not the same tired stuff"

"我们的广告应该采用哪些角度？"
"找出可用于广告文案的痛点"
"用户对[竞品]有哪些不满？"
"从评论中挖掘广告素材"
"我需要新颖的广告角度——而非陈词滥调"

Phase 0: Intake

阶段0：信息收集

Your product — Name + what it does in one sentence
Competitors — 2-5 competitor names (for review mining)
ICP — Who are you targeting? (role, company stage, pain)
Data sources to mine (pick all that apply):
- G2/Capterra/Trustpilot reviews (yours + competitors)
- Reddit threads in relevant subreddits
- Twitter/X complaints or praise
- Support tickets or NPS comments (paste or file)
- Competitor ads (Meta + Google)
Any angles you've already tested? — So we can skip those

你的产品 — 名称+一句话功能介绍
竞品 — 2-5个竞品名称（用于评论挖掘）
目标客户（ICP） — 你的目标受众是谁？（职位、企业阶段、痛点）
待挖掘的数据源（选择所有适用项）：
- G2/Capterra/Trustpilot评论（你的产品+竞品）
- 相关Reddit子版块的帖子
- Twitter/X上的投诉或好评
- 支持工单或NPS评论（粘贴或上传文件）
- 竞品广告（Meta+Google平台）
已测试过的角度？ — 我们会跳过这些内容

Phase 1: Source Collection

阶段1：数据源收集

1A: Review Mining

1A：评论挖掘

Run

review-scraper

for your product and each competitor:

bash

python3 skills/review-scraper/scripts/scrape_reviews.py \
  --product "<product_name>" \
  --platforms g2,capterra \
  --output json

Focus on:

1-2 star reviews of competitors — Pain they're failing to solve
4-5 star reviews of you — Outcomes that delight buyers
4-5 star reviews of competitors — Strengths you need to counter or match
Review language patterns — Exact phrases buyers use

为你的产品和每个竞品运行

review-scraper

：

bash

python3 skills/review-scraper/scripts/scrape_reviews.py \
  --product "<product_name>" \
  --platforms g2,capterra \
  --output json

重点关注：

竞品的1-2星评论 — 他们未能解决的痛点
你的产品的4-5星评论 — 让买家满意的成果
竞品的4-5星评论 — 你需要对标或反击的优势
评论语言模式 — 买家使用的精准表述

1B: Reddit/Community Mining

1B：Reddit/社区挖掘

Run

reddit-scraper

for relevant subreddits:

bash

python3 skills/reddit-scraper/scripts/scrape_reddit.py \
  --query "<product category> OR <competitor> OR <pain keyword>" \
  --subreddits "<relevant_subreddits>" \
  --sort relevance \
  --time month \
  --limit 50

Extract:

Questions people ask before buying
Complaints about current solutions
"I wish [product] would..." statements
Comparison threads (vs discussions)

为相关子版块运行

reddit-scraper

：

bash

python3 skills/reddit-scraper/scripts/scrape_reddit.py \
  --query "<product category> OR <competitor> OR <pain keyword>" \
  --subreddits "<relevant_subreddits>" \
  --sort relevance \
  --time month \
  --limit 50

提取内容：

买家在购买前提出的问题
对现有解决方案的投诉
"我希望[产品]能..."类表述
对比类帖子（vs讨论）

1C: Twitter/X Mining

1C：Twitter/X挖掘

Run

twitter-scraper

bash

python3 skills/twitter-scraper/scripts/scrape_twitter.py \
  --query "<competitor> (frustrating OR broken OR hate OR love OR switched)" \
  --max-results 50

运行

twitter-scraper

：

bash

python3 skills/twitter-scraper/scripts/scrape_twitter.py \
  --query "<competitor> (frustrating OR broken OR hate OR love OR switched)" \
  --max-results 50

1D: Competitor Ad Mining (Optional)

1D：竞品广告挖掘（可选）

Run

ad-creative-intelligence

to see what angles competitors are currently using. This reveals:

Angles they've validated (long-running ads = working)
Angles they're testing (new ads)
Angles nobody is running (white space)

运行

ad-creative-intelligence

查看竞品当前使用的角度。这能揭示：

他们已验证的角度（长期投放的广告=有效）
他们正在测试的角度（新广告）
无人涉足的角度（空白市场）

1E: Internal Data (Optional)

1E：内部数据（可选）

If the user provides support tickets, NPS comments, or sales call transcripts — ingest and tag with the same framework below.

如果用户提供支持工单、NPS评论或销售通话记录——请导入并使用以下框架进行标记。

Phase 2: Angle Extraction

阶段2：角度提取

Process all collected data through this extraction framework:

使用以下提取框架处理所有收集到的数据：

Angle Categories

角度类别

Category	What to Look For	Ad Power
Pain angles	Specific frustrations with status quo or competitors	High — pain motivates action
Outcome angles	Desired results buyers describe in their own words	High — positive aspiration
Identity angles	How buyers describe themselves or want to be seen	Medium — emotional resonance
Fear angles	Risks of NOT switching or acting	Medium — loss aversion
Competitive displacement	Specific reasons people switched from a competitor	Very high — direct comparison
Social proof angles	Outcomes or metrics buyers cite in reviews	High — credibility
Contrast angles	Before/after or old way/new way framings	High — clear value prop

类别	关注要点	广告效力
痛点角度	对现状或竞品的具体不满	高——痛点驱动行动
成果角度	买家用自己的话描述的期望成果	高——正向诉求
身份角度	买家如何描述自己或期望的形象	中——情感共鸣
恐惧角度	不切换或不行动的风险	中——损失厌恶
竞品替代角度	用户从竞品转投的具体原因	极高——直接对比
社交证明角度	买家在评论中提及的成果或指标	高——可信度
对比角度	前后对比或新旧方式的框架	高——清晰价值主张

For Each Angle, Extract:

针对每个角度，提取以下内容：

The angle — One-sentence framing
Proof quotes — 2-5 verbatim quotes from sources
Source count — How many independent sources mention this?
Competitor weakness? — Does this exploit a specific competitor's gap?
Emotional register — Frustration / Aspiration / Fear / Relief / Pride
Recommended format — Search ad / Meta static / Meta video / LinkedIn / Twitter

角度 — 一句话框架
佐证引用 — 2-5条来自数据源的原文引用
数据源数量 — 有多少独立数据源提到了这一点？
竞品劣势？ — 这是否利用了竞品的特定短板？
情感基调 — 沮丧/渴望/恐惧/释然/自豪
推荐形式 — 搜索广告/Meta静态广告/Meta视频广告/LinkedIn广告/Twitter广告

Phase 3: Scoring & Ranking

阶段3：评分与排序

Score each angle on:

Factor	Weight	Description
Evidence strength	30%	Number of independent sources mentioning it
Emotional intensity	25%	How strongly people feel about this (language intensity)
Competitive differentiation	20%	Does this set you apart, or could any competitor claim it?
ICP relevance	15%	How closely does this match the target buyer's world?
Freshness	10%	Is this angle already overused in competitor ads?

Total score out of 100. Rank all angles.

从以下维度对每个角度评分：

因素	权重	说明
证据强度	30%	提及该角度的独立数据源数量
情感强度	25%	用户对该点的感受强烈程度（语言强度）
差异化竞争力	20%	这是否能让你脱颖而出，还是所有竞品都能宣称？
目标客户相关性	15%	这与目标买家的契合度有多高？
新颖性	10%	该角度是否已在竞品广告中被过度使用？

总分100分，对所有角度进行排序。

Phase 4: Output Format

阶段4：输出格式

markdown

undefined

markdown

undefined

Ad Angle Bank — [Product Name] — [DATE]

广告角度库 — [产品名称] — [日期]

Sources mined: [list] Total angles extracted: [N] Top-tier angles (score 70+): [N]

已挖掘的数据源：[列表] 提取的总角度数：[N] 顶级角度（得分70+）：[N]

Tier 1: Highest-Conviction Angles (Score 70+)

第一梯队：高置信度角度（得分70+）

Angle 1: [One-sentence angle]

角度1：[一句话角度]

Category: [Pain / Outcome / Identity / Fear / Displacement / Proof / Contrast]
Score: [X/100]
Emotional register: [Frustration / Aspiration / etc.]
Proof quotes:

"[Verbatim quote 1]" — [Source: G2 review / Reddit / etc.] "[Verbatim quote 2]" — [Source] "[Verbatim quote 3]" — [Source]
Source count: [N] independent mentions
Competitor weakness exploited: [Competitor name + specific gap, or "N/A"]
Recommended formats: [Search ad headline / Meta static / Video hook / etc.]
Sample headline: "[Draft headline using this angle]"
Sample body copy: "[Draft 1-2 sentence body]"

类别： [痛点/成果/身份/恐惧/替代/证明/对比]
得分： [X/100]
情感基调： [沮丧/渴望/等]
佐证引用：

"[原文引用1]" — [来源：G2评论/Reddit/等] "[原文引用2]" — [来源] "[原文引用3]" — [来源]
数据源数量： [N]次独立提及
利用的竞品劣势： [竞品名称+具体短板，或“不适用”]
推荐形式： [搜索广告标题/Meta静态广告/视频钩子/等]
示例标题： "[使用该角度的草稿标题]"
示例正文： "[1-2句草稿正文]"

Angle 2: ...

角度2：...

Tier 2: Worth Testing (Score 50-69)

第二梯队：值得测试（得分50-69）

[Same format, briefer]

[相同格式，内容更简洁]

Tier 3: Emerging / Low-Evidence (Score < 50)

第三梯队：新兴/低证据（得分<50）

[Brief list — angles with potential but insufficient evidence]

[简短列表——有潜力但证据不足的角度]

Competitive Angle Map

竞品角度对比表

Angle	Your Product	[Comp A]	[Comp B]	[Comp C]
[Angle 1]	Can claim ✓	Weak here ✗	Also claims	Not relevant
[Angle 2]	Strong ✓	Strong	Weak ✗	Not relevant
...

角度	你的产品	[竞品A]	[竞品B]	[竞品C]
[角度1]	可宣称 ✓	此处薄弱 ✗	也可宣称	不相关
[角度2]	优势 ✓	优势	薄弱 ✗	不相关
...

Recommended Test Plan

Cost

成本

Component	Cost
Review scraper (per product)	~$0.10-0.30 (Apify)
Reddit scraper	~$0.05-0.10 (Apify)
Twitter scraper	~$0.10-0.20 (Apify)
Ad scraper (optional)	~$0.40-1.00 (Apify)
Analysis	Free (LLM reasoning)
Total	~$0.25-1.60

组件	成本
评论爬虫（每个产品）	~$0.10-0.30（Apify）
Reddit爬虫	~$0.05-0.10（Apify）
Twitter爬虫	~$0.10-0.20（Apify）
广告爬虫（可选）	~$0.40-1.00（Apify）
分析	免费（LLM推理）
总计	~$0.25-1.60

Tools Required

所需工具

Apify API token —
```
APIFY_API_TOKEN
```
env var

Upstream skills:

review-scraper

reddit-scraper

twitter-scraper

Optional:
```
ad-creative-intelligence
```
(for competitor ad angles)

Apify API令牌 —
```
APIFY_API_TOKEN
```
环境变量

上游工具：

review-scraper

reddit-scraper

twitter-scraper

可选：
```
ad-creative-intelligence
```
（用于竞品广告角度）

Trigger Phrases

触发短语

"Mine ad angles from reviews"
"What angles should we run?"
"Find pain language for our ads"
"Build an ad angle bank for [client]"
"What are people complaining about with [competitor]?"

"从评论中挖掘广告角度"
"我们应该采用哪些角度？"
"为我们的广告找出痛点表述"
"为[客户]构建广告角度库"
"用户对[竞品]有哪些不满？"

ad-angle-miner

Original

Translation

Ad Angle Miner

广告角度挖掘工具

When to Use

使用场景

Phase 0: Intake

阶段0：信息收集

Phase 1: Source Collection

阶段1：数据源收集

1A: Review Mining

1A：评论挖掘

1B: Reddit/Community Mining

1B：Reddit/社区挖掘

1C: Twitter/X Mining

1C：Twitter/X挖掘

1D: Competitor Ad Mining (Optional)

1D：竞品广告挖掘（可选）

1E: Internal Data (Optional)

1E：内部数据（可选）

Phase 2: Angle Extraction

阶段2：角度提取

Angle Categories

角度类别

For Each Angle, Extract:

针对每个角度，提取以下内容：

Phase 3: Scoring & Ranking

阶段3：评分与排序

Phase 4: Output Format

阶段4：输出格式

Ad Angle Bank — [Product Name] — [DATE]

广告角度库 — [产品名称] — [日期]

Tier 1: Highest-Conviction Angles (Score 70+)

第一梯队：高置信度角度（得分70+）

Angle 1: [One-sentence angle]

角度1：[一句话角度]

Angle 2: ...

角度2：...

Tier 2: Worth Testing (Score 50-69)

第二梯队：值得测试（得分50-69）

Tier 3: Emerging / Low-Evidence (Score < 50)

第三梯队：新兴/低证据（得分<50）

Competitive Angle Map

竞品角度对比表

Recommended Test Plan

推荐测试计划

Week 1-2: Test Tier 1 Angles

第1-2周：测试第一梯队角度

Week 3-4: Test Tier 2 Angles

第3-4周：测试第二梯队角度

Cost

成本

Tools Required

所需工具

Trigger Phrases

触发短语