No data loaded. Upload your CSV files to populate the dashboard.
Returns Dashboard
Upload data to begin
All time
Return Rate (Units)?Returns ÷ demand units for orders placed in the selected period. A December order returned in February still counts in December — returns are attributed to the original order date, not when the return arrived back.
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vs prior
Return Rate ($)?Return value ÷ demand sales $ for orders placed in the selected period. Dollar rate can differ from unit rate due to price mix — high-value items returning at lower rates still move the dollar rate. Click the card to open Variance Analysis.
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vs prior
Total Returns?Count of returned units (Returns + Exchanges) for orders placed in the selected period. Cancellations are excluded — those are orders that never shipped. The delta shows % change vs the prior period.
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vs prior
Return Value?Total dollar value of returned merchandise (Returns + Exchanges) for orders placed in the selected period. Sourced from the Net Sales column in your Returns file. Cancellations excluded.
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vs prior
Return Rate Trend?Monthly return rate by order cohort — each bar represents orders placed that month and the % that were returned. Recent months may appear lower because some returns are still incoming. All returns are attributed to the original order month. Months shown in calendar order (Jan–Dec) per this client's fiscal calendar.
This year vs last year · monthly cohort
Order Outcomes
Per 100 orders
By Product Type
View all →| Category | Rate |
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| No data | |
By Channel
View all →| Channel | Rate |
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By Customer Type
View all →| Type | Rate |
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Top Returned Products
Ranked by return volume · vs category avg
| Product | Type | Returns | Unit Rate | vs Cat Avg |
|---|---|---|---|---|
| No data | ||||
Return Speed
Average days at each stage of the return journey · requires return portal data
Upload return portal files to see return speed reporting
Explorer
Select a dimension to explore
PERIOD
All time
GROUP BY
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Demand Units
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Return Units
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Unit Return Rate
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Return Value
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Results
| Select a dimension above |
No data loaded. Upload your CSV files to populate the heat map.
Returns Heat Map
Category & product return performance at a glance
All time
Synced from dashboard
Return Rate: loading…
Upload data to view heat map
Upload Narvar return portal files to populate Voice of Customer analysis. Upload data
Voice of Customer
Return reasons & customer comment themes from your returns data
The "Recent" window splits comments into recent vs historical for the theme tracker and shift chart. Days are counted back from the latest return date in your uploaded data — not from today. Upload return portal data to see the active date window.
Return Reason Codes
Structured return reasons · ranked by volume
No return reason data loaded
Comment Theme Tracker
Theme counts reflect the Recent window only (configurable top-right) — not the date pills above
Themes:
Stable
↑ Emerging
↓ Fading
No comment data
Recent vs Historical Theme Shift
Theme share of comments — recent period vs prior baseline
| Theme | Historical % | Recent % | Change | Signal | Count (recent) |
|---|---|---|---|---|---|
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Customer Comment Browser
All comments
No comments loaded
Products Needing Attention
High return rate + below-average review rating — click any product to open in Explorer
Upload a Product Reviews CSV to enable this analysis
No data loaded. Upload CSV files to detect outliers.
Outlier Alerts
Sorted by return value · products above category average
Upload data to detect outliers
Action Tracker
Assigned outliers, logged interventions, outcome tracking
No actions yet. Assign outliers from the Outliers tab.
Upload Data
Upload your Returns and Demand/Sales CSV files
File structure
- Returns file: One row per returned/exchanged item. Must have: Order Name, Product title, Return Type, Net sales
- Demand file: One row per sold item. Must have: Order Name, Product title, Demand U, Demand Sls $
- Narvar file (optional): Return portal data. Adds customer comments and return reasons. Joined on Order Number — Shopify data always takes precedence where both exist.
- Product Reviews file (optional): Customer review data. Adds star ratings and review text to Explorer product detail cards. Must have a rating column (e.g. Star Rating) and product title.
- Multiple years of each file can be uploaded at once.
Click to upload CSV files
Upload Returns and Demand files — multiple years OK
Processing...
The Returns Guy™ Insights
AI analysis trained and informed by The Returns Guy™ Reduce · Profit · Delight framework
Goal
Output
Best used when
Running analysis...
Data Specifications
Required fields and format requirements
Returns file
RequiredOrder name · Key/Product Key · Return Type (Returns/Exchanges) · Net sales · Product title · Product variant · Product type · Subbrand · Channel · Customer ID · Fiscal Month · New or returning customer · Original Order Year
Demand / Sales file
RequiredKey/Product Key · Order name · Customer email · Demand Sls $ · Demand U · Product title · Product variant · Product type · Fiscal Month · Channel · New or returning customer
Product reviews
Recommendedproduct_id · review_date · star_rating · review_text · size_feedback · fit_feedback · quality_feedback
Promotions data
Recommendedorder_id · promo_code · discount_type · discount_pct · promo_start · promo_end · urgency_flag
Format requirements
- Separate CSV files per year — Returns and Demand separately
- Filter Include? = X before exporting
- Return Type: include Returns + Exchanges only, exclude Cancellations
- Minimum 12 months — 24+ enables YOY analysis
- Row-level data only, not monthly summaries
- Remove PII — normalized Customer ID is sufficient
Client Settings
Configure this client's profile, return policy, and platform behavior. Changes take effect immediately for all AI analysis.
Client Identity
Basic profile — controls AI voice, vertical context, and platform labeling
Appears in all AI analysis outputs. Use the exact name the client uses publicly.
Drives vertical-specific language in all AI recommendations.
Return Policy
Used by the AI to avoid recommending what you already offer — keep this current as your policy evolves
days
How many days customers have to initiate a return after delivery.
Determines whether the AI recommends expanding exchange options as a Profit lever.
Paid exchange shipping is a friction point that suppresses exchange rates — the AI will flag this.
A bonus (e.g. 10% extra) offered to customers who choose store credit over a cash refund. If checked, the AI will NOT recommend adding this — it will instead evaluate how effectively it's being deployed.
Refunds issued before the return is received and inspected at the warehouse. If checked, the AI will not recommend adding fast refunds — it will evaluate segmentation and fraud risk management instead.
Comma-separated. Categories where returns are not accepted. Leave blank if no final sale items.
Comma-separated. All refund methods currently available to customers.
Used in data request letters and documentation.
Seasonal Periods
Controls seasonal coloring in the return rate trend chart. Holiday months appear in a distinct color.
Holiday / Peak Season
Select the months that represent your primary peak selling and return season. Default is Nov + Dec.
Secondary Peak (optional)
For clients with a second meaningful selling peak (e.g. Back to School, Valentine's Day).
Fiscal Calendar
Controls how data is attributed to fiscal years and quarters throughout the platform
Quarter Date Boundaries
Define the exact start and end date for each quarter. The platform uses these to attribute every order to the correct fiscal year and quarter. Add rows for each fiscal year you have data for.
| Fiscal Year | Quarter | Start Date | End Date |
|---|
How it works: Every order is attributed to the fiscal quarter whose date range contains its order date. If an order date falls outside all defined ranges, the platform falls back to calendar year attribution. Dates must not overlap between quarters.
Super Admin
Structural configuration — set once at onboarding, rarely changed. Controls identity, taxonomy, benchmarks, and column mapping for this client.
Client Identity
Core profile — controls AI voice, vertical context, and platform labeling
Appears in all AI analysis outputs. Use the exact public-facing brand name.
Drives vertical-specific language and benchmarks in all AI recommendations.
Set at deploy time. Visible in the sidebar as a deployment verification signal.
Product Taxonomy
Defines how products are organized in this client's data — drives outlier detection, heat map, Explorer, and AI analysis
The primary analysis level (outlier detection, benchmarking, heat map) is always set to the deepest level you define here that has meaningful data volume.
The level at which outlier alerts fire, benchmarks are calculated, and AI recommendations are scoped. Usually Department for apparel, Product Type for jewelry.
Column Name Mapping
Map each taxonomy level to the exact column name as it appears in this client's data exports. Leave blank if a level doesn't apply.
The main category column. Used in outlier detection and AI analysis.
Top-level grouping (e.g. Mens / Womens).
Sub-category level (e.g. Jeans, Dresses).
Return Rate Benchmarks
Thresholds used by outlier detection and AI analysis to flag elevated return rates. Set these based on this client's vertical and historical norms.
%
Return rates below this are considered low / healthy for this client.
%
Return rates above this trigger outlier alerts and AI recommendations.
Category-Level Benchmarks
Different product categories have different expected return rates. Set per-category thresholds so outlier detection fires against the right peer group.
| Category name | Low % | High % |
|---|
Category names must match exactly how they appear in your data. For apparel: Tops, Bottoms, Dresses, Footwear, Outerwear. For jewelry: Rings, Earrings, Bracelets, Necklaces.
Column Name Overrides
For non-Shopify clients — map this client's data column names to the platform's internal field names. Leave blank to use Shopify defaults.
Shopify clients: Leave all fields blank — the platform auto-detects Shopify column names. Only fill these in for BigCommerce, Magento, WooCommerce, or custom exports.
Column name matching is case-insensitive and supports partial matching. "Order ID" will match "Order Id", "order_id", and "OrderID". When in doubt, check your client's export headers on the Upload page after uploading a file.
Feature Flags
Enable or disable vertical-specific analysis modules for this client. Clients never see these controls — you configure them at onboarding based on the client's business.
When enabled: ring sizing gauge SKUs (those ending in
Disable for all non-jewelry clients. An apparel retailer has no ring sizer SKUs and this toggle should never be on for them.
-NA) are detected and excluded from all return rate calculations, preventing inflated rates. The Ring Sizer analysis module appears in the dashboard showing how many customers use the sizer before purchasing.
Disable for all non-jewelry clients. An apparel retailer has no ring sizer SKUs and this toggle should never be on for them.
Future feature flags
Additional vertical-specific modules will appear here as they are released — for example, Footwear Fit Analysis, Apparel Bracketing Heatmap, or Gifting Season Segmentation. Each flag will be off by default and enabled per-client at onboarding.
Client Request Letter
Generate a professional data request for your client's team
Drafting letter...