Returns
Data View Returns in Data Octopus: IdoSell return analysis by product, brand and category. Columns, filters, data history and limitations.
Introduction
Data View Returns is an analytical tool in Data Octopus for comprehensive analysis of product returns.
- Identify products with high returns.
- Analyse return trends by brand and category.
- Optimise the assortment by removing frequently returned products.
- Make informed inventory decisions.
Opening the report
- 1Open the main Data Octopus workspace.
- 2Select your store.
- 3Open Data Views.
- 4Select IdoSell.
- 5Open Data View Returns.
Three report perspectives
- Product View: returns of individual products and variants.
- Brands View: brand returns with product drill-down.
- Categories View: hierarchical category analysis at levels 1, 2 and 3.
Each view uses the same metrics but groups data differently.
General features
Show Description
Shows report information, calculation methodology and metric interpretation. Useful for new users and checking definitions.
Create View
- 1Set filters (products, brands, dates).
- 2Click Create View.
- 3Name the view.
- 4Save it for future use.
Saved views eliminate repeated filter configuration.
Date Picker
The default range is the last 30 days. History covers the store’s full operating period — for example, two years for a store operating for two years.
Filters
- Product Name: product name contains the entered text.
- Brand: selected brands.
- Category: product categories.
- Net and gross return value ranges.
Product view
The most detailed perspective identifies problematic products and variants.
| Field name | Description |
|---|---|
| Product ID | Unique product variant ID (size, colour, model). |
| Product Name | Full product name including the variant. |
| Product Group ID | Base product ID (variant group). |
| Returned Product Quantity | Total quantity of returned items. |
| Return Product Net Value | Net return value (excluding VAT). |
| Return Product Gross Value | Gross return value (including VAT). |
Product ID identifies a particular variant (such as a blue shirt in XL), while Product Group ID identifies the base product shared by all variants.
Example: a shirt in four sizes has Product Group ID 12345 and Product IDs 12345-S, 12345-M, 12345-L and 12345-XL.
- Sort by Returned Product Quantity to find the most frequently returned products.
- Filter by Product Group ID to see all variants of a problematic product.
Brands view
- 1Level 1: brand return summary.
- 2Level 2: products after clicking the arrow beside the brand name.
Brand metrics sum returns of all its products. Brand filters propagate to products. Expanded rows use the product-view columns and contain only products from that brand.
| Field name | Description |
|---|---|
| Brand | Brand name. |
| Zagregowane metryki | Sum of all returns for the brand. |
- Benchmark return metrics across brands.
- Identify brands with high returns.
- Assess brand-portfolio profitability.
Categories view
The system supports three category levels matching the store hierarchy. Select Level 1, 2 or 3. The report shows only that level’s categories, retaining filters and settings when switching levels.
| Field name | Description |
|---|---|
| Category | Category name at the selected level. |
| Product ID | Product variant ID within the category. |
| Product Group ID | Product group ID. |
| Pozostałe kolumny | Same as the product view. |
- Analyse category return trends.
- Identify categories requiring assortment optimisation.
- Plan logistics and forecast returns.
Column glossary
Product identifiers
| Field name | Description |
|---|---|
| Product ID | Unique product variant identifier. Example: KOS-001-BLU-XL. Analyse returns of specific variants. |
| Product Group ID | Product group identifier (all variants). Example: KOS-001. Analyse returns for the whole product. |
Return metrics
| Field name | Description |
|---|---|
| Returns Quantity | Number of returns. Unit: items. Number of returns containing this product. |
| Returned Product Quantity | Total number of returned items. Unit: items. Indicates the scale of returns. |
| Return Product Net Value | Return value excluding VAT. Unit: net PLN. Financial value of returns for the store. |
| Return Product Gross Value | Return value including VAT. Unit: gross PLN. Full value of customer returns. |
Category metrics (Categories only)
| Field name | Description |
|---|---|
| Category | Category name at the selected level. Category grouping and analysis. |
Limitations
- Daily refresh: previous-day data.
- Newest transactions may be delayed by up to 24 hours.
- Historical range: the store’s operating period.
Questions and answers
What insights does returns analysis provide?
It identifies high-return products, seasonality and trend changes, compares brands and categories, supports return-cost analysis and decisions about withdrawing problematic products.
Why distinguish Product ID from Product Group ID?
Product ID reveals variant-specific problems, while Product Group ID reveals whole-product problems. If mainly XL is returned, check sizing and size charts. If all sizes are returned, review quality or descriptions and consider changing suppliers or withdrawing the product.
How should net and gross values be interpreted?
Net values exclude VAT and support store financial/ROI analysis. Gross values include VAT and show the full return value relevant to cash flow and financial planning.
How often is data refreshed?
Daily. Today’s returns appear in the report tomorrow.
Why are the newest returns missing?
Returns from the last 24 hours may still be processing. The system may also be waiting for the return process to be finalised.
How do I save frequently used filter combinations?
Set date, product and brand filters; click Create View; give it a descriptive name (for example, “Premium brand returns Q4 2024”); save. It appears in the custom views menu.
Can I export report data?
No. Report downloads are currently unavailable. This feature is planned for the future.
What should I do after identifying a high-return product?
Review feedback and complaints, compare the scale with other products and industry norms, examine variants, improve descriptions or change suppliers or withdraw the product, then monitor results in later periods.