china-3c-marketing-copilot

# China 3C Marketing Copilot Skill > Skill by [ara.so](https://ara.so) — Marketing Skills collection. An AI-powered knowledge base and copilot system for marketing consumer electronics (3C) in the Chinese market. Covers smartphones, laptops, earphones, wearables, and smart home devices with competitive analysis, creative campaign generation, risk assessment, and market insights. ## What This Project Does China 3C Marketing Copilot is a structured knowledge base designed to be used as context for AI agents to: - **Generate creative campaigns** grounded in real market data - **Analyze competitive threats** using cross-referenced product evaluations - **Assess marketing risks** including social media backlash scenarios - **Provide category insights** from expert reviews (爱否科技, 笔吧评测室, etc.) - **Simulate audience reactions** across 5 persona types - **Break into new categories** using proven playbook strategies The system enforces strict data discipline: no fabricated numbers, source attribution required, speculation must be labeled. ## Installation Clone the repository: ```bash git clone https://github.com/killsnake01/China-Marketing-Copilot-Skill.git cd China-Marketing-Copilot-Skill ``` No additional dependencies required for basic usage. The knowledge base is pure Markdown. For data preprocessing (optional): ```python pip install pandas python scripts/preprocess.py --input raw_data.csv --output knowledge-base/ ``` ## Project Structure ``` docs/ templates/ # Output formats (creative, insights, risk) references/ # KOL database, platform rules, personas ecosystem/ # Industry terminology, memes to avoid knowledge-base/ mobile/ # Phone brand matrix, pricing tiers headphones/ # Earphone reviews, clip-on comparison laptops/ # Notebook selection guide (笔吧) wearables/ # Smartwatch/band market data smart-home/ # Robot vacuum reviews, case studies scripts/ preprocess.py # Data cleaning utility ``` ## Core Capabilities ### 1. Creative Campaign Generation **Trigger phrases:** - "帮我想几个创意" (give me some creative ideas) - "写个传播方案" (write a campaign plan) - "手机发布会创意" (phone launch event ideas) **Output format:** Uses `docs/templates/creative-output.md` template. Generates 3-5 ideas per request with: - **Hook** (attention-grabbing headline) - **Core Message** (data-driven value prop) - **Execution** (platform-specific tactics) - **Risk Flag** (potential backlash points) **Example usage:** ```python # In your AI agent prompt: context = load_knowledge_base("knowledge-base/mobile/") prompt = f""" Using this data: {context} Generate 3 creative campaign ideas for a mid-range phone (2000-3000 RMB) with Snapdragon 8 Gen 3 chip, focusing on Xiaohongshu platform. Follow creative-output.md template. """ ``` **De-duplication:** Check `docs/templates/used-ideas.md` before generation. Append new ideas to prevent repetition. ### 2. Competitive Analysis **Trigger phrases:** - "XX发布了,对我们有什么威胁" (XX launched, what's the threat) - "分析竞品" (analyze competitors) - "横评对比" (cross-evaluation comparison) **Data sources in knowledge base:** - `mobile/_index.md`: 16 phone brands, chipset camps, pricing tiers - `laptops/_index.md`: 笔吧 2025 laptop guide, 8 price segments - `headphones/_index.md`: 12 clip-on earphone comparison (爱否科技) **Example code:** ```python import os import re def load_category_data(category: str) -> dict: """Load brand matrix and evaluation data for a category.""" base_path = f"knowledge-base/{category}/" index_file = os.path.join(base_path, "_index.md") with open(index_file, 'r', encoding='utf-8') as f: content = f.read() # Extract brand matrix table brands = re.findall(r'\| (.+?) \| (.+?) \| (.+?) \|', content) return { 'category': category, 'brands': brands, 'raw_content': content } # Usage laptop_data = load_category_data('laptops') # Feed to AI agent for competitive threat analysis ``` **Output format:** Uses `docs/templates/insight-output.md` with required sections: - **Market Position** (price tier, feature positioning) - **Competitive Threats** (direct overlap products) - **Differentiation Gaps** (where competitor is weak) - **Data Sources** (must cite: KOL name + platform + date) ### 3. Risk Assessment **Trigger phrases:** - "会不会翻车" (will this backfire) - "风险评估" (risk assessment) - "有没有负面" (any negative risks) **Template:** `docs/templates/risk-assessment.md` includes: - **Technical Risk** (spec exaggeration, measurement tricks) - **Social Risk** (5 persona backlash simulation) - **Platform Risk** (content guideline violations) - **Historical Precedent** (similar campaigns that failed) **Persona simulation:** Load from `docs/references/comment-personas.md`: ```python PERSONAS = { "参数党": "Spec sheet warriors - will fact-check every number", "解构找茬": "Deconstruction trolls - expose marketing tricks", "真实体验派": "Real user experience advocates - hate hype", "品牌信仰": "Brand loyalists - defend their tribe", "吃瓜群众": "Casual observers - amplify drama" } def simulate_comment_section(campaign_text: str, personas: dict) -> list: """Generate predicted negative comments from each persona.""" comments = [] for persona_type, description in personas.items(): prompt = f"As a {persona_type} ({description}), critique: {campaign_text}" # Feed to AI agent comments.append(generate_critique(prompt)) return comments ``` **Case study integration:** Knowledge base includes real翻车案例 (backfire cases): - `knowledge-base/laptops/_index.md`: Lenovo Y9000P thermal issues - `knowledge-base/smart-home/`: Robot vacuum "deceptive cleaning" scandal ### 4. New Category Playbook **Trigger phrases:** - "怎么传播新品类" (how to promote new category) - "市场教育成本高" (high market education cost) - "新品类破局" (breakthrough strategy for new category) **5 proven methods:** Located in `docs/templates/new-category-playbook.md` **Example - Method 1: 认知刷新法 (Perception Reset)** ```python def generate_perception_reset_campaign(product_data: dict) -> dict: """ Find human limit benchmark → Product breaks it → Visualize data Example: DJI ROMO cleaning 500㎡ villa in 2 hours (Human baseline: 4+ hours) """ return { "human_baseline": "Find existing perception (e.g., 'villa cleaning takes half a day')", "product_performance": product_data['key_metric'], "visualization": "Side-by-side time-lapse video", "data_source": "Must cite: test environment + conditions" } # Usage for smart home robot vacuum romo_data = { 'key_metric': '2小时清洁500㎡别墅 (2 hours for 500㎡ villa)', 'comparison': '人工清洁需4小时+ (Manual: 4+ hours)' } campaign = generate_perception_reset_campaign(romo_data) ``` **Method 5: 专业信任纪录片法 (Professional Trust Documentary)** - Recruit real professionals (not actors) - 6-month field test period - Minimalist documentary style (no hype language) - Case study: DJI agriculture drones with actual farmers ### 5. Data Import & Preprocessing **Trigger phrase:** - "处理新数据" (process new data) - "我导入了新文件" (I imported a new file) **Preprocessing script:** ```python # scripts/preprocess.py import pandas as pd import json import sys def clean_review_data(input_file: str, output_dir: str): """ Clean and validate review data before adding to knowledge base. Validation rules: - Numbers must have units - Ratings must have scale (e.g., 5/5) - Price must include currency and date - Source must include: KOL name + platform + publish date """ df = pd.read_csv(input_file) # Validation required_columns = ['product_name', 'metric', 'value', 'unit', 'source', 'date'] for col in required_columns: if col not in df.columns: raise ValueError(f"Missing required column: {col}") # Check source format for idx, row in df.iterrows(): source = row['source'] if not ('|' in source and len(source.split('|')) >= 3): print(f"Warning: Invalid source format at row {idx}: {source}") print("Expected: KOL名|平台|日期 (e.g., 爱否科技|Bilibili|2025-03)") # Export to knowledge base category = df['category'].iloc[0] output_path = f"{output_dir}/{category}/_data.json" df.to_json(output_path, orient='records', force_ascii=False, indent=2) print(f"Processed {len(df)} records to {output_path}") # Usage if __name__ == "__main__": clean_review_data(sys.argv[1], sys.argv[2]) ``` **Run preprocessing:** ```bash python scripts/preprocess.py earphone_reviews.csv knowledge-base/ ``` ## Configuration ### User Profile (Optional) Create `user-config.json` for personalized output: ```json { "brand": "your_brand_name", "category": "mobile", "target_price_range": "2000-3000", "competitors": ["Redmi", "Realme", "iQOO"], "platforms": ["Xiaohongshu", "Douyin", "Bilibili"], "risk_tolerance": "conservative", "kol_preferences": ["爱否科技", "ZEALER"] } ``` ### Environment Variables For API integrations (optional): ```bash # .env file XIAOHONGSHU_API_KEY=your_key_here BILIBILI_API_KEY=your_key_here MARKET_DATA_SOURCE=https://your-data-api.com ``` Load in Python: ```python import os from dotenv import load_dotenv load_dotenv() api_key = os.getenv('XIAOHONGSHU_API_KEY') ``` ## Real-World Workflow Example ### Complete campaign generation flow: ```python import os import json # 1. Load knowledge base def load_knowledge_base(category: str) -> str: path = f"knowledge-base/{category}/_index.md" with open(path, 'r', encoding='utf-8') as f: return f.read() # 2. Load templates def load_template(template_name: str) -> str: path = f"docs/templates/{template_name}.md" with open(path, 'r', encoding='utf-8') as f: return f.read() # 3. Check used ideas for deduplication def load_used_ideas() -> list: path = "docs/templates/used-ideas.md" with open(path, 'r', encoding='utf-8') as f: content = f.read() # Extract hooks from markdown list import re hooks = re.findall(r'- \*\*(.+?)\*\*', content) return hooks # 4. Generate campaign def generate_campaign(product_spec: dict, platform: str): kb_data = load_knowledge_base(product_spec['category']) template = load_template('creative-output') used_hooks = load_used_ideas() prompt = f""" Knowledge Base: {kb_data} Template: {template} Already Used Hooks (avoid these): {used_hooks} Product Specs: {json.dumps(product_spec, ensure_ascii=False)} Target Platform: {platform} Generate 3 creative campaign ideas following the template. Ensure: 1. Every number has a source citation 2. No AI clichés (no "值得注意的是", "首先其次") 3. Hooks are not in used_hooks list 4. Include risk assessment for each idea """ # Feed to your AI agent (Claude, GPT, etc.) return prompt # 5. Usage product = { 'category': 'mobile', 'name': 'SuperPhone X1', 'chipset': 'Snapdragon 8 Gen 3', 'price': 2499, 'key_features': ['120Hz OLED', '5000mAh battery', '67W charging'] } campaign_prompt = generate_campaign(product, 'Xiaohongshu') # Send campaign_prompt to AI agent ``` ### Risk assessment workflow: ```python def assess_campaign_risk(campaign_text: str) -> dict: """ Run multi-layer risk check on campaign content. """ # Load personas personas_content = load_template('comment-personas') # Load historical failures kb_smart_home = load_knowledge_base('smart-home') failures = extract_case_studies(kb_smart_home) prompt = f""" Campaign Text: {campaign_text} Comment Personas (simulate negative reactions): {personas_content} Historical Failures (check for similar patterns):