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1688-marketing-skill

Expert in 1688 platform marketing operations including merchant enrollment, activity registration, product pricing suggestions, and business opportunity recommendations.

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reason-machines/marketing-skills
Dernière activité de la source
28 juin 2026 à 21:54
Langue détectée de SKILL.md
anglais
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10
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1

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SKILL.md
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name
1688-marketing-skill
description
Expert in 1688 platform marketing operations including merchant enrollment, activity registration, product pricing suggestions, and business opportunity recommendations.
triggers
["How do I register for 1688 merchant activities?","Help me find business opportunities on 1688","Check suggested pricing for my 1688 products","Submit activity enrollment on 1688","Query available merchant activities on 1688","Show me business opportunity recommendations","How to use 1688 marketing tools?","Get market opportunities on 1688 platform"]
# 1688 Marketing Skill > Skill by [ara.so](https://ara.so) — Marketing Skills collection. Expert skill for helping merchants perform marketing operations on the 1688 platform, including querying merchant activities, checking product suggested pricing, submitting activity registrations, and viewing business opportunity recommendations. ## What This Project Does 1688-marketing is a marketing skill designed to help merchants on the 1688 platform (Alibaba's Chinese B2B marketplace) perform key marketing operations: - **Activity Query**: Search and browse available merchant enrollment activities - **Suggested Pricing**: Query recommended pricing for products based on market data - **Activity Registration**: Submit applications to participate in merchant activities - **Business Opportunities**: Discover and track market opportunities and recommendations ## Installation ```bash # Clone the repository git clone https://github.com/next-1688/1688-marketing.git cd 1688-marketing # Install dependencies pip install -r requirements.txt ``` ## Configuration Set up environment variables for 1688 API credentials: ```bash export ALIBABA_1688_APP_KEY=your_app_key export ALIBABA_1688_APP_SECRET=your_app_secret export ALIBABA_1688_ACCESS_TOKEN=your_access_token ``` Create a configuration file `config.py`: ```python import os CONFIG = { 'app_key': os.getenv('ALIBABA_1688_APP_KEY'), 'app_secret': os.getenv('ALIBABA_1688_APP_SECRET'), 'access_token': os.getenv('ALIBABA_1688_ACCESS_TOKEN'), 'api_base_url': 'https://gw.open.1688.com/openapi', 'timeout': 30 } ``` ## Core Components ### 1. Activity Query Module Query available merchant enrollment activities: ```python from marketing_1688.activity_query import ActivityQueryClient # Initialize client client = ActivityQueryClient( app_key=CONFIG['app_key'], app_secret=CONFIG['app_secret'], access_token=CONFIG['access_token'] ) # Query all available activities activities = client.query_activities( status='ongoing', # ongoing, upcoming, ended category='招商活动', page_size=20 ) for activity in activities['data']: print(f"Activity: {activity['name']}") print(f"Start: {activity['start_date']} - End: {activity['end_date']}") print(f"Enrollment deadline: {activity['enrollment_deadline']}") print(f"Requirements: {activity['requirements']}") print("---") ``` ### 2. Suggested Pricing Query Check market-based suggested pricing for products: ```python from marketing_1688.pricing import PricingClient pricing_client = PricingClient( app_key=CONFIG['app_key'], app_secret=CONFIG['app_secret'], access_token=CONFIG['access_token'] ) # Query suggested price for a product product_id = "123456789" price_suggestion = pricing_client.get_suggested_price( product_id=product_id, include_competitor_analysis=True ) print(f"Product ID: {price_suggestion['product_id']}") print(f"Suggested Price Range: {price_suggestion['min_price']} - {price_suggestion['max_price']}") print(f"Market Average: {price_suggestion['market_average']}") print(f"Competitor Count: {price_suggestion['competitor_count']}") # Batch query for multiple products product_ids = ["123456789", "987654321", "456789123"] batch_suggestions = pricing_client.batch_get_suggested_prices(product_ids) for suggestion in batch_suggestions: print(f"{suggestion['product_id']}: ¥{suggestion['recommended_price']}") ``` ### 3. Activity Registration Submit enrollment applications for merchant activities: ```python from marketing_1688.enrollment import EnrollmentClient enrollment_client = EnrollmentClient( app_key=CONFIG['app_key'], app_secret=CONFIG['app_secret'], access_token=CONFIG['access_token'] ) # Submit activity enrollment enrollment_data = { 'activity_id': 'ACT20260619001', 'product_ids': ['123456789', '987654321'], 'shop_id': 'SHOP123456', 'contact_name': '张三', 'contact_phone': '13800138000', 'remarks': '希望参加此次招商活动' } result = enrollment_client.submit_enrollment(enrollment_data) if result['success']: print(f"Enrollment successful! ID: {result['enrollment_id']}") print(f"Status: {result['status']}") print(f"Review time: {result['estimated_review_time']}") else: print(f"Enrollment failed: {result['error_message']}") # Check enrollment status enrollment_status = enrollment_client.check_enrollment_status( enrollment_id=result['enrollment_id'] ) print(f"Current status: {enrollment_status['status']}") print(f"Review notes: {enrollment_status.get('review_notes', 'N/A')}") ``` ### 4. Business Opportunity Recommendations Query and track business opportunities: ```python from marketing_1688.opportunities import OpportunityClient opp_client = OpportunityClient( app_key=CONFIG['app_key'], app_secret=CONFIG['app_secret'], access_token=CONFIG['access_token'] ) # Get personalized business opportunities opportunities = opp_client.get_recommendations( shop_id='SHOP123456', category='电子产品', min_score=0.7 # Relevance score threshold ) for opp in opportunities['data']: print(f"Opportunity: {opp['title']}") print(f"Category: {opp['category']}") print(f"Relevance Score: {opp['score']}") print(f"Potential Revenue: ¥{opp['estimated_revenue']}") print(f"Competition Level: {opp['competition_level']}") print(f"Description: {opp['description']}") print("---") # Track opportunity performance opp_client.track_opportunity( opportunity_id='OPP20260619001', action='viewed' # viewed, interested, applied ) ``` ## Common Patterns ### Complete Activity Enrollment Workflow ```python from marketing_1688 import Marketing1688Client # Initialize unified client client = Marketing1688Client(config=CONFIG) # Step 1: Search for relevant activities activities = client.activities.search( keywords='夏季促销', category='服装', status='ongoing' ) # Step 2: Get detailed activity information activity_detail = client.activities.get_detail( activity_id=activities[0]['id'] ) # Step 3: Check product pricing suggestions products_to_submit = ['123456789', '987654321'] pricing_check = client.pricing.batch_check(products_to_submit) # Step 4: Submit enrollment with optimal pricing enrollment = client.enrollment.submit({ 'activity_id': activity_detail['id'], 'product_ids': products_to_submit, 'pricing_strategy': pricing_check, 'auto_adjust_price': True }) # Step 5: Monitor enrollment status status = client.enrollment.monitor(enrollment['id'], auto_refresh=True) ``` ### Business Opportunity Pipeline ```python from marketing_1688 import Marketing1688Client client = Marketing1688Client(config=CONFIG) # Build opportunity pipeline pipeline = client.opportunities.create_pipeline( filters={ 'categories': ['电子产品', '家居用品'], 'min_score': 0.75, 'competition_level': ['low', 'medium'] } ) # Daily opportunity digest daily_opportunities = pipeline.get_daily_digest() for opp in daily_opportunities: # Check if products match matching_products = client.products.find_matching( opportunity_requirements=opp['requirements'] ) if matching_products: # Auto-apply if high relevance if opp['score'] > 0.9: client.opportunities.auto_apply( opportunity_id=opp['id'], product_ids=[p['id'] for p in matching_products] ) ``` ### Price Optimization Strategy ```python from marketing_1688.pricing import PricingOptimizer optimizer = PricingOptimizer(config=CONFIG) # Analyze product pricing product_id = "123456789" analysis = optimizer.analyze_product(product_id) # Get optimization recommendations recommendations = optimizer.get_recommendations( current_price=analysis['current_price'], market_data=analysis['market_data'], goals=['maximize_profit', 'increase_sales_volume'] ) # Apply dynamic pricing optimizer.apply_dynamic_pricing( product_id=product_id, strategy=recommendations['optimal_strategy'], price_range=(recommendations['min_safe_price'], recommendations['max_safe_price']), auto_adjust=True, adjustment_frequency='daily' ) ``` ## Error Handling ```python from marketing_1688.exceptions import ( AuthenticationError, EnrollmentError, QuotaExceededError ) try: result = client.enrollment.submit(enrollment_data) except AuthenticationError as e: print(f"Auth failed: {e.message}") # Refresh access token client.refresh_token() except EnrollmentError as e: print(f"Enrollment error: {e.message}") print(f"Error code: {e.code}") # Check eligibility requirements eligibility = client.activities.check_eligibility(activity_id) except QuotaExceededError as e: print(f"API quota exceeded: {e.message}") print(f"Reset time: {e.reset_time}") # Implement retry with backoff except Exception as e: print(f"Unexpected error: {str(e)}") ``` ## Troubleshooting ### Authentication Issues ```python # Verify credentials from marketing_1688.auth import verify_credentials is_valid = verify_credentials( app_key=CONFIG['app_key'], app_secret=CONFIG['app_secret'], access_token=CONFIG['access_token'] ) if not is_valid: # Regenerate access token from marketing_1688.auth import refresh_access_token new_token = refresh_access_token( app_key=CONFIG['app_key'], app_secret=CONFIG['app_secret'] ) ``` ### Rate Limiting ```python # Implement rate limiting from marketing_1688.utils import RateLimiter limiter = RateLimiter(max_requests=100, time_window=60) with limiter: activities = client.activities.query_all() ``` ### Data Validation ```python # Validate enrollment data before submission from marketing_1688.validators import EnrollmentValidator validator = EnrollmentValidator() is_valid, errors = validator.validate(enrollment_data) if not is_valid: for error in errors: print(f"Validation error: {error['field']} - {error['message']}") ``` ## Best Practices 1. **Always use environment variables** for sensitive credentials 2. **Implement retry logic** for API calls with exponential backoff 3. **Cache activity and pricing data** to reduce API calls 4. **Monitor enrollment status** regularly for timely responses 5. **Batch operations** when possible to optimize API quota usage 6. **Validate data** before submission to avoid rejection
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