Primary Use Case: Personalized Social Commerce Recommendations

Background Overview

Generic product recommendations don't convert well in social commerce. Personalized recommendations based on customer behavior and preferences significantly improve conversion. This primary use case describes recommendation engine, personalization logic, and multi-channel delivery.

Goals & Value

• Precision Recommendations: Accurate product suggestions based on customer data.
• Multi-touchpoint Formats: Deliver recommendations across all touchpoints.
• Automated Execution: Intelligent recommendation delivery without manual effort.
• Conversion Optimization: Maximize recommendation-driven sales.

Participating Roles

• Data Scientists: Build recommendation models and algorithms.
• E-commerce Manager: Configure recommendation strategies.
• Marketing: Use recommendations in campaigns.
• IT Team: Maintain recommendation infrastructure.
• Management: Review recommendation performance and ROI.

Primary Scenario User Story

As a customer, I want to see personalized product recommendations that match my interests, so that I can discover products I actually want to buy.

Sub-scenario Details

Sub-scenario A: Recommendation Engine

• Roles & Triggers: Need to build intelligent recommendations.
• Main Process:
  1. Collect customer data (purchase history, browsing, preferences).
  2. Apply machine learning algorithms.
  3. Generate personalized product recommendations.
  4. Update recommendations in real-time.
• Success Criteria: Accurate recommendations; real-time updates; relevant suggestions.
• Exceptions & Risk Control: Algorithm errors; data quality issues; recommendation relevance.
• **Metric Suggestions: Recommendation accuracy, relevance score, update timeliness.

Sub-scenario B: Personalization Logic

• Roles & Triggers: Need to personalize recommendations per customer.
• Main Process:
  1. Analyze customer behavior and preferences.
  2. Apply personalization rules and filters.
  3. Tailor recommendations to customer segments.
  4. Balance exploration vs. exploitation.
• Success Criteria: Effective personalization; segment-appropriate suggestions; balanced approach.
• Exceptions & Risk Control: Personalization errors; segment misclassification; recommendation bias.
• **Metric Suggestions: Personalization effectiveness, segment accuracy, bias reduction.

Sub-scenario C: Multi-channel Delivery

• Roles & Triggers: Deliver recommendations across channels.
• Main Process:
  1. Display recommendations in mini-program.
  2. Send recommendations via WeCom messages.
  3. Show recommendations in groups and communities.
  4. Personalize recommendations per channel context.
• Success Criteria: Consistent delivery; channel-appropriate format; effective presentation.
• Exceptions & Risk Control: Delivery failures; format issues; channel misalignment.
• **Metric Suggestions: Delivery success rate, format effectiveness, channel performance.

Sub-scenario D: Performance Optimization

• Roles & Triggers: Need to optimize recommendation performance.
• Main Process:
  1. Track recommendation click-through and conversion rates.
  2. A/B test recommendation strategies.
  3. Optimize based on performance data.
  4. Report on recommendation ROI.
• Success Criteria: Improved performance; measurable ROI; continuous optimization.
• Exceptions & Risk Control: Tracking errors; test failures; optimization issues.
• **Metric Suggestions: Conversion rate, A/B test results, ROI calculation.

Scenario-level Test Case Examples

Test Preparation: Prepare recommendation engine, personalization system, multi-channel delivery, and performance tracking.

Test Case A-1: Personalized Recommendations (Positive)

• Prerequisites: Customer with browsing history.
• Steps:
  1. Customer visits mini-program.
  2. System displays personalized recommendations.
• Expected Results:
  • Recommendations match customer interests.
  • Recommendations updated in real-time.
  • High relevance score for suggestions.

Test Case B-1: Cross-channel Recommendations (Negative)

• Prerequisites: Customer active across multiple channels.
• Steps:
  1. Customer sees recommendations in mini-program.
  2. Receives additional recommendations via WeCom.
• Expected Results:
  • Recommendations consistent across channels.
  • Channel-appropriate format and timing.
  • Seamless customer experience.

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