Data View Returns - Documentation
Introduction
Data View Returns is an analytical tool in Data Octopus that enables comprehensive analysis of product returns in online stores. The report provides key insights that allow for:
Identification of problematic products with high return rates
Analysis of return trends at brand and category levels
Assortment optimization through elimination of frequently returned products
Making informed decisions regarding inventory management
Navigation to the report
To access Data View Returns:
Go to the main panel of Data Octopus
Select your store from the list of available stores
Click the Data Views section in the navigation menu
Select IdoSell from available data sources
Click Data View Returns - the main report interface will open
Overview of three views
Data View Returns offers three complementary analysis perspectives:
๐ Product View
The most detailed perspective showing returns at the level of individual products and their variants. Ideal for identifying specific problematic items.
๐ท๏ธ Brands View
Return analysis broken down by brands with the ability to expand to product level. Allows evaluation of which brands generate the most returns.
๐ Categories View
Hierarchical return analysis at the product category level (level 1, 2, 3). Enables identification of problematic product categories.
Each view uses the same metrics but groups data differently, allowing for multi-faceted analysis of return issues.
General functionalities
Show Description
Function: Displays detailed information about the report, metric calculation methodology, and data interpretation
Use: Useful for new users or when needing to verify metric definitions
Create View
Purpose: Saving personalized views with applied filters
Process:
Set desired filters (e.g., specific products, brands, date ranges)
Click Create View
Name the view
Save for future use
Benefits: Eliminates the need to reconfigure filters during regular analyses
Date Picker
Functionality: Configuration of analysis time range
Available range: Full store operation period - if the store has been operating for 2 years, data for the entire period is available
Default setting: last 30 days
Filter system
Product Name: Filtering by product name (contains text)
Brand: Filtering by specific brands
Category: Filtering by product categories
Value ranges: Filtering by return values (net/gross)
Product View
Application
The product view is the most important perspective for identifying specific problematic products. It enables detailed analysis of returns at the level of individual items and their variants.
Column structure
Column | Usage Description |
Product ID | Unique variant ID (size, color, model) |
Product Name | Full product name with variant |
Product Group ID | Main product ID (variant group) |
Returned Product Quantity | Total quantity of returned pieces |
Return Product Net Value | Net value of returns (without VAT) |
Return Product Gross Value | Gross value of returns (with VAT) |
Key differences in identifiers
Product ID vs Product Group ID:
Product ID: Identifies a specific variant (e.g., "Blue shirt XL" has a different ID than "Blue shirt L")
Product Group ID: Identifies the main product (all variants of the blue shirt have the same Product Group ID)
Practical application:
Example: Men's shirt in 4 sizes Product Group ID: 12345 (for the entire group) Product ID: 12345-S, 12345-M, 12345-L, 12345-XL (for each size)
Best analysis practices
Sort by Returned Product Quantity to identify the most frequently returned products
Filter by Product Group ID to see all variants of a problematic product
Brands View
Drill-down mechanism
The brands view uses a hierarchical data structure with expansion capability:
Level 1: Return summary at brand level
Level 2: Detailed product data after clicking the expand arrow
Expansion functionality
Click the arrow next to the brand name to expand the product list
Automatic aggregation: Data at brand level is the sum of all products from that brand
Filter behavior: Filters applied at brand level propagate to product level
Data structure
Brand level:
Column | Description |
Brand | Brand name |
Aggregated metrics | Sum of all returns for the brand |
Product level (after expansion):
Identical columns as in product view
Contains only products belonging to the given brand
Analytical applications
Brand benchmarking: Comparison of return rates between brands
Identification of problematic brands: Brands with high returns
Portfolio analysis: Evaluation of individual brand profitability
Categories View
Category hierarchy
The system supports a 3-level category hierarchy according to categories set in the store.
Level navigation
Level switcher: Select Level 1, 2, or 3 from the interface
Automatic filtering: System shows only categories from the given level
Context preservation: Filters and settings are preserved when switching levels
Column structure
Column | Description |
Category | Category name from selected level |
Product ID | Variant ID of product in category |
Product Group ID | Product group ID |
Other columns | Identical to product view |
Strategic application
Categorical trend analysis: Which categories generate the most returns
Assortment optimization: Identification of categories requiring attention
Logistics planning: Forecasting returns at category level
Column dictionary
Product identifiers
Column | Definition | Example | Application |
| Unique product variant identifier | KOS-001-BLU-XL | Analysis of specific variant returns |
| Product group identifier (all variants) | KOS-001 | Analysis of entire product returns |
Return metrics
Column | Definition | Unit | Interpretation |
| Number of returns | Pieces | Quantity of returns in which the product appeared |
| Total number of returned pieces | Pieces | Scale of return problem |
| Return value without VAT | PLN net | Financial cost of returns for the store |
| Return value with VAT | PLN gross | Full customer return cost |
Category metrics (Categories view only)
Column | Definition | Application |
| Category name from selected level | Grouping and categorical analysis |
Limitations and restrictions
Time limitations
Data refresh: Daily (data from previous day)
Data delay: Up to 24 hours for newest transactions
Historical range: Limited to store operation period
Q&A Section
What insights does return analysis provide?
Data View Returns allows for:
Identification of products with highest return rates
Analysis of return trends over time (seasonality, increases/decreases)
Performance comparison of different brands and categories
Calculation of actual return handling costs
Making decisions about withdrawing problematic products
Why is the difference between Product ID and Product Group ID important?
Product ID allows identification of whether the problem concerns a specific variant (e.g., only size XL has high returns), while Product Group ID shows whether the entire product (all variants) is problematic.
Practical example:
If a shirt has high returns only in size XL: - Problem: sizing for large sizes - Solution: Improve size chart If all sizes have high returns: - Problem: product quality or description - Solution: Change supplier or withdraw product
How to interpret net vs gross data?
Net value: Actual cost for the store (basis for ROI analysis)
Gross value: Full impact on cash flow (important for financial planning)
How often is data refreshed?
Data is refreshed daily. Returns from today will appear in tomorrow's report. This ensures timeliness while maintaining system stability.
Why don't I see the latest returns?
Possible reasons:
Data delay: Returns from the last 24h may still be processing
Return status: System may be waiting for return process finalization
How to save frequently used filter combinations?
Set all needed filters (dates, products, brands)
Click Create View
Give a descriptive name (e.g., "Premium brands Q4 2024 returns")
Save the view
View will be available in custom views menu
Can I export data from the report?
No, currently there is no possibility to download data from reports. This feature is planned for the future.
What to do when I identify a product with high returns?
Action plan:
Verify the cause: Check customer reviews, complaints
Assess the scale: Compare with other products and industry norms
Analyze variants: Does the problem affect all or specific variants
Take action: Improve description, change supplier, withdraw product
Monitor effects: Check changes in subsequent periods
๐ก Need advanced analysis? Consider combining Data View Returns with other Data Octopus reports for a comprehensive picture of product performance.
๐ Want to automate monitoring? Use the Create View function to save key views and regularly monitor return indicators.