Metadata

name: walter-selector
version: 2.0.0
description: 亚马逊选品方向分析。帮助用户找到可执行的选品方向，基于12大选品方法论（榜单选品、竞品VOC分析、元素迁移、社交媒体趋势、新类目节点、蓝海高门槛、差异化选品等），结合三层决策逻辑（发现机会→验证可行性→产品定义）。

亚马逊选品方向分析

核心目标

帮助用户找到可执行的选品方向，基于12大选品方法论三层决策逻辑。

用户输入

品类方向或种子词（可选）
目标市场（美国/欧洲/日本）
预算范围（可选）
经验水平（新手/有经验）

12大选品方法论

第一层：发现机会

方法	说明	数据来源
榜单选品	BS / New Releases / Movers & Shakers / Most Wished For / Gift Ideas	亚马逊前台
社交趋势	TikTok/Reddit/YouTube爆款趋势	站外社媒
新类目节点	亚马逊新开的小类目/细分节点	亚马逊类目树
下拉词	亚马逊搜索框自动补全关键词	亚马逊前台
竞品店铺	监控目标竞品的新品动态	竞品店铺
站外趋势	Reddit等社区讨论热度	Reddit
冷门蓝海	高门槛品类（FDA/510K认证/医疗器械）	专业渠道

第二层：验证可行性

方法	说明	核心指标
市场容量/广告经济性	谷歌趋势+CPC/CPA/广告占比/利润率/退款率	SPR、ACOS
VOC痛点挖掘	评论分析→挖掘痛点→发现未满足需求→创新	差评/QA
差异化对标	分析竞品为何卖得好→对比自身优势→找出差异	差异化指数

第三层：产品定义

方法	说明
元素迁移	拆解爆款元素标签，像搭乐高一样重新组合
场景/人群/情绪升维	从比价格→比场景/比人群/比情绪价值

三层决策逻辑

[Layer 1] Opportunity Discovery
    │
    ├── Method: 12 selection methods
    ├── Output: 10-20 potential opportunities
    └── Filter: Market size > 10k/month
    │
    ▼
[Layer 2] Feasibility Validation
    │
    ├── Market capacity check
    ├── Competition analysis
    ├── Profit calculation
    └── Risk assessment
    │
    ▼
[Layer 3] Product Definition
    │
    ├── Differentiation design
    ├── Target positioning
    └── Go-to-market strategy
    │
    ▼
[Output] Executable Product Direction

分析流程

Phase 1: 机会发现（自动）

系统自动执行12种选品方法：

# 并行执行多种选品方法
with ThreadPoolExecutor(max_workers=6) as executor:
    futures = {
        executor.submit(analyze_bestsellers, category): "bestsellers",
        executor.submit(analyze_new_releases, category): "new_releases",
        executor.submit(analyze_social_trends, platform): "social",
        executor.submit(analyze_keyword_expansion, seed): "keywords",
        executor.submit(analyze_competitor_stores, stores): "competitors",
        executor.submit(analyze_emerging_nodes): "nodes",
    }

输出示例：

[Opportunity Discovery] 12 Methods Applied

Category-Based Opportunities:
┌─────────────────────────┬──────────┬──────────┬──────────┐
│ Category                │ Monthly  │ Growth   │ Score    │
├─────────────────────────┼──────────┼──────────┼──────────┤
│ Women Active Shorts     │ 52,847   │ +8.5%    │ 85       │
│ Yoga Leggings           │ 128,432  │ +12.3%   │ 92       │
│ Running Shorts          │ 89,234   │ +5.2%    │ 78       │
│ Biker Shorts            │ 34,567   │ +45.0%   │ 88       │
└─────────────────────────┴──────────┴──────────┴──────────┘

Keyword-Based Opportunities:
┌─────────────────────────┬──────────┬──────────┬──────────┐
│ Keyword                 │ Volume   │ Trend    │ Score    │
├─────────────────────────┼──────────┼──────────┼──────────┤
│ anti chafe shorts       │ 2,100    │ ↑↑↑      │ 75       │
│ pocket yoga shorts      │ 5,600    │ ↑↑       │ 82       │
│ reflective run shorts   │ 1,800    │ ↑↑↑      │ 78       │
└─────────────────────────┴──────────┴──────────┴──────────┘

Social Trend Opportunities:
• TikTok #gymshorts: 2.3M views, +180% growth
• Reddit r/running: "pocket shorts" trending

Phase 2: 可行性验证

对Top 5机会进行深度验证：

[Feasibility Validation] Top 5 Opportunities

Opportunity 1: Women Active Shorts
├─ Market Size: 52,847/month, $1.69M revenue
├─ Competition: CR3=56%, medium concentration
├─ Profit Potential: $2.78-4.50/unit (8-14% margin)
├─ Trend: +8.5% monthly growth
├─ Risk: Medium (seasonal, high China seller ratio)
└─ Feasibility Score: 68/100 [VALIDATED]

Opportunity 2: Biker Shorts
├─ Market Size: 34,567/month, $1.21M revenue
├─ Competition: CR3=42%, low concentration
├─ Profit Potential: $3.50-5.20/unit (10-15% margin)
├─ Trend: +45% monthly growth (TikTok viral)
├─ Risk: Low (trending, less competition)
└─ Feasibility Score: 82/100 [HIGHLY RECOMMENDED]

Opportunity 3: Anti-Chafe Shorts
├─ Market Size: 8,234/month, $289K revenue (niche)
├─ Competition: CR3=38%, low concentration
├─ Profit Potential: $5.00-7.00/unit (15-20% margin)
├─ Trend: +15% monthly growth
├─ Risk: Low (blue ocean, high margin)
└─ Feasibility Score: 75/100 [VALIDATED]

Phase 3: 产品定义

为每个验证通过的机会定义产品：

[Product Definition] 3 Product Concepts

Concept 1: "FitLife Biker Shorts"
├─ Target: Women 25-35, fitness enthusiasts
├─ Differentiation: TikTok viral style + anti-chafe fabric
├─ Price: $28-32 (mid-range)
├─ Features: High waist, 8" inseam, side pockets
├─ Launch Strategy: TikTok influencer seeding
└─ Expected ROI: 35% margin, 500 units/month

Concept 2: "ComfortMax Anti-Chafe Shorts"
├─ Target: Women 30-45, sensitive skin
├─ Differentiation: Medical-grade anti-chafe fabric
├─ Price: $35-40 (premium)
├─ Features: Seamless design, moisture-wicking
├─ Launch Strategy: Reddit community + dermatologist endorse
└─ Expected ROI: 45% margin, 200 units/month

Concept 3: "RunPro Reflective Shorts"
├─ Target: Women 25-40, night runners
├─ Differentiation: 360° reflective + phone pocket
├─ Price: $32-36 (mid-premium)
├─ Features: Lightweight, quick-dry, safety reflective
├─ Launch Strategy: Running community + safety focus
└─ Expected ROI: 30% margin, 300 units/month

最终推荐

═══════════════════════════════════════════════════════════════
FINAL RECOMMENDATION
═══════════════════════════════════════════════════════════════

[PRIMARY RECOMMENDATION]
Product: FitLife Biker Shorts
Target: Women 25-35, fitness enthusiasts
Differentiation: TikTok viral style + anti-chafe fabric
Price: $28-32
Expected ROI: 35% margin
Feasibility: 82/100

Why This Opportunity:
✓ High growth trend (+45% monthly)
✓ Low competition (CR3=42%)
✓ Good profit margin (35%)
✓ TikTok viral potential
✓ Clear differentiation path

Action Plan:
  Week 1: Contact suppliers for samples
  Week 2: Validate fabric quality
  Week 3: Design packaging & branding
  Week 4: Order 300-unit test batch

═══════════════════════════════════════════════════════════════
[ALTERNATIVES]

Alternative 1: ComfortMax Anti-Chafe Shorts
├─ Higher margin (45%) but smaller market
└─ Good for premium positioning

Alternative 2: RunPro Reflective Shorts
├─ Safety angle, loyal customer base
└─ Seasonal but high retention

═══════════════════════════════════════════════════════════════

技术实现

核心类

class ProductSelectorV2:
    def analyze(self, seed_keyword: str = None, 
                budget: str = "medium",
                experience: str = "intermediate") -> Dict:
        """
        Complete 3-layer product selection analysis
        
        Layer 1: Discover opportunities (12 methods)
        Layer 2: Validate feasibility (market/competition/profit)
        Layer 3: Define product concepts
        
        Returns: Ranked product recommendations
        """
        pass

12大选品方法实现

def analyze_bestsellers(category: str) -> List[Opportunity]:
    """Analyze Amazon Best Sellers"""
    pass

def analyze_new_releases(category: str) -> List[Opportunity]:
    """Analyze New Releases"""
    pass

def analyze_social_trends(platform: str) -> List[Opportunity]:
    """Analyze TikTok/Reddit trends"""
    pass

def analyze_keyword_expansion(seed: str) -> List[Opportunity]:
    """Expand keywords for opportunities"""
    pass

def analyze_competitor_stores(stores: List[str]) -> List[Opportunity]:
    """Monitor competitor new products"""
    pass

def analyze_emerging_nodes() -> List[Opportunity]:
    """Find emerging category nodes"""
    pass

依赖

unified_data_layer_v2.py - 统一数据层
sellersprite_mcp.py - MCP客户端
SellerSprite API access

版本

V2 - 2026-04-12

Implemented 12 product selection methods
Added 3-layer decision logic
Integrated feasibility validation
Generated executable product concepts

Walter Selector

安装技能

亚马逊选品方向分析

核心目标

用户输入

12大选品方法论

第一层：发现机会

第二层：验证可行性

第三层：产品定义

三层决策逻辑

分析流程

Phase 1: 机会发现（自动）

Phase 2: 可行性验证

Phase 3: 产品定义

最终推荐

技术实现

核心类

12大选品方法实现

依赖

版本

360 行为纵深安全审计引擎