The reselling industry thrives on understanding consumer purchase patterns and product relationships. With MuleBuy, scalable analysis becomes possible by converting spreadsheet data into actionable knowledge graphs. This methodology enables data-driven decisions, optimizing cross-selling strategies.
Step 1: Structuring Product Relationship Data
Create a spreadsheet with the following columns:
- Product ID
- Product Name Node
- Connected Items
- Co-purchase Frequency
- Product Name Node
Use color-coding to visualize strong vs. weak connections between nodes (e.g., red high-frequency links for LV handbags + Gucci belts at 58% co-purchase rate).
Step 2: Analyzing Reddit Community Insights
Scrape and categorize data from subreddits like:
- r/RepLadies
- r/FashionReps
Key metrics to track:
- "LV+Gucci combo" mentions per month
- Brand sentiment scores (positive/negative ratios)
- Seasonal demand fluctuations
Case： MuleBuy Teams Achieve 35％ Higher AOV
Discord rep-selling groups implementing this system saw tangible results:
| Before Knowledge Graph | After Implementation (60 Days) | |
|---|---|---|
| Avg. Items per Order | 1 | 2 |
Implementation Best Practices
Dynamic edge weighting: Adjust connection strengths monthly based on latest transaction data
Taxonomy expansion: Add nodes for emerging brands (e.g., Loewe, Bottega Veneta)