# B2b Customs Data

> 海关数据分析 — 进出口记录查询、采购商筛选、市场趋势分析

- Skill: `chefroger/b2b-customs-data` (Agent Skill, multi-file: 9 files)
- Install (CLI): `npx skillmds@latest add chefroger/b2b-customs-data`
- Raw SKILL.md: https://api.skillmd.com/api/skills/chefroger/b2b-customs-data/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: chefroger (https://skillmd.com/u/chefroger)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/chefroger/b2b-customs-data

---


# 海关数据分析（Customs Data Analysis）

从进出口贸易记录中筛选高价值采购商，输出可直接用于触达的目标客户清单。

## Phase 1: Input Preparation

两种输入路径：

1. **有数据文件**（CSV/Excel 导出）→ 直接分析：逐个文件完整读取，保留原始行序与列名
2. **无数据文件** → 输出分析方法论 + 数据获取渠道建议（官方海关统计、商业贸易数据库等），不编造数据

## Phase 2: Filtering — 四层筛选

#### Filter 1: Product Match

```
Include:
- Buyers importing products that match (or complement) user's product line
- Downstream manufacturers using user's product as input

Exclude:
- Products outside user's business scope
- Raw materials that are inputs to user's product (not final product)
```

#### Filter 2: Geographic Targeting

```
Include based on user's target markets:
- Specific countries/regions (North America, Europe, Southeast Asia)
- Avoid: countries with trade restrictions or high tariffs

Exclude:
- Markets user is not targeting
- Regions with regulatory barriers
```

#### Filter 3: Volume & Frequency

```
Include:
- Buyers with regular import patterns (multiple shipments per year)
- Volume sufficient for user's MOQ

Exclude:
- One-time buyers (no repeat business potential)
- Volume below user's MOQ
```

#### Filter 4: Company Type

```
Include:
- Manufacturers (final product buyers)
- Brand owners
- Large distributors

Exclude:
- Small retailers (below threshold)
- Trading companies acting as intermediaries
```

## Phase 3: Buyer Pattern Analysis

For each qualified buyer, analyze:

### Purchase Patterns

| Metric | What It Tells You |
|--------|-------------------|
| **Frequency** | How often they buy (monthly/quarterly/annually) |
| **Volume trend** | Growing, stable, or declining purchases |
| **Seasonality** | Peak buying seasons |
| **Supplier concentration** | Do they rely on few or many suppliers? |
| **Price sensitivity** | Volume vs. price correlations |

### Example Analysis (Generic Template)

```
Company: [Buyer Name]
Country: [Country]
Products imported: [Product categories]
Annual volume: [Estimated value]
Frequency: [X] shipments/year
Typical order size: [Range]
Suppliers: [Number of suppliers] (mostly from [countries])

Patterns:
- Peak season: [Q1/Q2/Q3/Q4]
- Order cycle: [Monthly/Quarterly]
- Last shipment: [Date]

Potential approach:
- Angle: [Based on their supplier concentration/price trends]
- Timing: [Best time to reach out]
- Product focus: [Which of your products fits their pattern]
```

## Phase 4: Priority Scoring

Score each prospect based on:

### Scoring Matrix

| Criteria | Weight | Score (1-5) |
|----------|--------|-------------|
| Industry match | 25% | How well product aligns |
| Volume potential | 25% | Order size and frequency |
| Geographic fit | 20% | Your ability to serve |
| Accessibility | 15% | Ease of outreach (LinkedIn, email, etc.) |
| Growth trend | 15% | Purchase volume trend |

### Priority Classification

| Total Score | Priority | Action |
|-------------|----------|--------|
| 4.0 - 5.0 | **P1 — Hot** | Immediate outreach within 24h |
| 3.0 - 3.9 | **P2 — Warm** | Personalized outreach within 1 week |
| 2.0 - 2.9 | **P3 — Medium** | Add to nurture sequence |
| < 2.0 | **P4 — Low** | Periodic check-ins only |

## Phase 5: Output — Target Customer List

### Output Format

```
# B2B Trade Data Analysis Report

## Summary
- Total records analyzed: [X]
- Qualified prospects: [X]
- By priority: P1=[X], P2=[X], P3=[X], P4=[X]
- Geographic distribution: [Chart/Table]

## P1 Prospects (Immediate Action)

### 1. [Company Name]
| Field | Details |
|-------|---------|
| Country | [Country] |
| Products | [Product categories] |
| Est. Annual Volume | [Value] |
| Frequency | [X]x/year |
| Last Purchase | [Date] |
| Key Suppliers | [Countries] |
| Approach Angle | [How to position] |
| Recommended Action | [Specific next step] |

### 2. [Company Name]
[Same structure]

## P2 Prospects (This Week)

[Same structure]

## P3-P4 Prospects (Nurture)

[Condensed list format]

## Market Insights

1. [Key finding about the market]
2. [Key finding about competitor suppliers]
3. [Opportunity identified]

## Appendix: Full Data Table

| Company | Country | Product | Volume | Frequency | Score | Priority |
|---------|---------|---------|--------|-----------|-------|----------|
| [Name] | [Country] | [Product] | [Value] | [Freq] | [X.X] | P1 |
```

## Phase 6: Integration with Outreach

### From Data to Action

For P1 prospects, generate:

1. **Customer Brief**: 1-page summary of the prospect
2. **Customized Outreach**: Cold email referencing their specific purchase patterns
3. **Talking Points**: Based on their supplier concentration, price trends, seasonality

### Outreach Angle Examples

```
If they buy from multiple suppliers:
"We noticed you work with several [product] suppliers in [country]. 
We're a specialized manufacturer focusing on [specific product segment]. 
Would you be open to exploring if we can offer better [specific advantage]?"

If they have seasonal patterns:
"Your import data shows peak season in [Q2]. 
We're reaching out now because we'd like to discuss how we can 
support your [Q2] requirements with our [product] capabilities."

If they recently expanded volume:
"Congratulations on your growth in [product category]! 
We've helped similar companies scale their [specific need]. 
Would you be open to a brief call to explore if we're a fit?"
```

## Quality Standards

1. **Data accuracy**: Cross-check key data points (company names, volumes) against multiple rows
2. **HS code validation**: Ensure HS codes are correctly interpreted for product mapping
3. **Currency consistency**: Note currency in value fields; flag inconsistencies
4. **No assumptions**: If a field is ambiguous, note it rather than guess
5. **Source citation**: Always cite the source file and row numbers for key findings
6. **Column mapping disclosure**: 分析报告中首次引用数据时，注明对应的原始文件列名。
   例如："进口量数据来自文件中「Total Import QTY」列"。这样用户可以快速判断列解读是否正确。
7. **Completeness**: Include all relevant fields in output, even if values are missing

## Common Pitfalls

1. **Over-relying on volume**: Big buyers may already have established suppliers — look for disruption opportunities
2. **Ignoring frequency**: One-time large orders may not indicate ongoing business potential
3. **Missing seasonality**: Outreach timed wrong can kill opportunity before it starts
4. **Generic outreach**: Always customize message based on the specific buyer's patterns
5. **Not verifying data**: Company names may have typos or different spellings — verify before outreach
6. **Privacy concerns**: Customs data may have usage restrictions — ensure compliance