FLUF Connect
Product Types Analytics
Understand your inventory composition and category performance with the Product Types Analytics page.
Navigate to: Dashboard → Product Data
Filters
Chart Metric
Switch between three views to analyze your products:
| Metric | Description |
|---|---|
| Inventory Count | Number of products in each category |
| Sales Count | Number of items sold per category |
| Revenue | Total revenue generated by category |
AOV Timeframe
Select the time period for Average Order Value calculations:
- 24 Hours — Today's data
- 7 Days — Last week
- 30 Days — Last month
- 90 Days — Last quarter
- All Time — Complete history
Channel Filter
Filter data by marketplace channel:
- Shopify — Products on your Shopify store
- Depop — Products listed on Depop
- eBay — Products listed on eBay
- Vinted — Products listed on Vinted
- FLUF — Products in your FLUF inventory
Summary Panel
The summary panel shows key metrics at a glance:
- Total Inventory Count — Total products across all categories
- Categories — Number of unique categories
- Chart Metric — Currently selected metric
- AOV Period — Currently selected time period
Category Breakdown Table
The detailed table shows per-category data:
| Column | Description |
|---|---|
| Category | Product category name |
| Inventory Count | Number of products in category |
| Percentage | Share of total inventory |
| AOV | Average Order Value for the selected period |
| Sell-Through Rate | Percentage of stock sold |
Category Backfilling
If your products are missing category data, use the Jump to Front of Queue button to prioritize category processing for your products.
This speeds up the AI categorization process that automatically assigns categories to your listings.
Use Cases
Inventory Planning:
- Identify overstock in certain categories
- Find gaps in your product range
- Balance inventory across categories
Performance Analysis:
- Discover your best-selling categories
- Track revenue by product type
- Identify underperforming categories
Sourcing Decisions:
- Focus on high-revenue categories
- Reduce investment in slow sellers
- Diversify based on data