1. Data Collection from Reddit Threads
Start by gathering relevant posts from subreddits like r/FashionReps, r/RepSneakers, or r/Pandabuy
Pro Tip: Use Python's PRAW library or Reddit API for large-scale data collection.
2. Structuring Data in Spreadsheets
Organize captured data in a Pandabuy-compatible spreadsheet with these columns:
| Column | Description |
|---|---|
| Item_Requested | Brand/product mentioned in posts |
| Frequency | Number of times item appears |
| User_Location | Geographic indicators from flairs/comments |
| Price_Expectation | Budget range discussions |
3. Analyzing Demand Patterns
Use spreadsheet functions to identify trends:
=COUNTIF(range,"*Dunk*") // Count frequency of Nike Dunk requests =SORT(range,2,FALSE) // Sort by most requested items =SPARKLINE(values) // Visualize monthly trend changes
Figure: Visualization of week-over-week demand changes (sample)
4. Cross-Referencing with Pandabuy Stats
Compare your findings with:
- Pandabuy's internal search analytics
- Agent order volumes for specific items
- Shipping destination distributions
Notice when Reddit demand precedes
Applying the Insights
Act on your analysis by:
- Adjusting inventory recommendations in reviews
- Creating targeted buying guides
- Predicting next popular items for sourcing
"During 2023's Dunks boom, Reddit trend analysis helped our group identify colorwave demands 3 weeks