Overview
AfterShip Agent helps you understand your Returns Analytics faster and turn return data into clear next steps.
You can use AfterShip Agent to:
Summarize the dashboard you are viewing.
Compare periods.
Find the biggest contributors to a result.
Bring related metrics together in one analysis.
This makes it easier to move from a high-level result to the product, reason, customer, or operational detail behind it without manually collecting figures from different dashboard areas.
Start with AfterShip Agent
There are two ways to start using AfterShip Agent, depending on the context you need.
Start from a dashboard
Use this option when you are already viewing the Returns Analytics dashboard you want to analyze.
Go to Returns > Analytics and choose a dashboard.
Set the date range and filters you want included, such as store, status, source, country, product, or collection.
Click View summary in the top-right corner of the dashboard.
Review the summary generated from the dashboard context.
Continue with follow-up questions to compare periods, add a breakdown, or view supported details.
Tip: The summary uses the dashboard, date range, and filters currently in view. Set your filters before clicking View summary so the initial analysis starts with the context you want.
Start from the chat button
Use this option when you already know the question you want to answer.
Click the chat button in the bottom-right corner to open AfterShip Agent.
Ask a question about your Returns Analytics data.
Include the metric, time period, comparison, breakdown, or filters that matter to you.
Review the answer and use follow-up questions to refine it.
For example, you can ask:
“How many return requests did we receive in the last 30 days?”
“Compare return value this month with the previous month.”
“Which products had the highest item return rate in the last quarter?”
“What were the top return reasons for orders created in July 2026?”
“Show return label cost by carrier for returns requested last month.”
“How much upsell revenue did returns generate in the last 90 days?”
When you start a new chat, include enough context in your question for AfterShip Agent to identify the correct scope. You can add a store, date range, country, status, source, product, collection, or other supported filter.
What you can do with AfterShip Agent
AfterShip Agent can help you:
Get a quick summary of the key metrics and changes in an Analytics dashboard.
Compare periods using the same metric, scope, and filters.
View trends by day, week, or month.
Break down results by supported dimensions such as product, return reason, country, carrier, method, or status.
Rank categories to find the products, reasons, carriers, or countries contributing most to a result.
Connect related metrics such as return value, expected refund, retained revenue, return rate, processing time, and return label cost.
Review supported return-request details behind an aggregate result.
Continue the analysis with follow-up questions while keeping the confirmed context.
Connect data across dashboards
Some questions are easier to answer when related metrics are viewed together.
AfterShip Agent can connect supported data from different analytics areas so you can move from the overall result to the details behind it in one analysis.
When you want to understand | AfterShip Agent can connect |
What is driving a change in returns | Overall return performance with product-level results and return reasons |
How return activity and financial outcomes are connected | Return value and return volume with expected refunds, retained revenue, and exchange or replacement outcomes |
How return volume and operational metrics relate to return costs | Return volume and return value with processing time, status mix, automation activity, and return label cost |
How return activity is distributed across customers and products | Customer return value or returned-item volume with product-level return results and supported return reasons |
How returns translate into upsell and exchange outcomes | Return volume and return value with upsell revenue or rate and the products customers exchanged into |
For example, start with a summary of return performance, then ask which products and return reasons contributed most to the change.
You can also compare return volume with processing time and label cost, or review return value alongside expected refunds and retained revenue.
Common questions you can ask
Understand overall performance
Use AfterShip Agent to get a quick view of how your returns are performing:
“What changed in return value and return volume compared with the previous period?”
“Compare return requests, return value, and item return rate for the last 30 days and the previous 30 days.”
“Show the trend for return value by week over the last 12 weeks.”
“What percentage of return value was retained as revenue?”
Find products and reasons behind returns
Move from an overall metric to the products and reasons that explain it:
“Which products had the most returned items in the selected period?”
“Show item return rate by product collection.”
“What are the top return reasons for the products with the highest returned-item volume?”
“Which products had the highest item return rate, excluding products with fewer than 100 items sold?”
“Compare returned items by product for this month and last month.”
Tip: When ranking rates, include a minimum sold volume or another denominator so a small number of sales does not create a misleading result.
Review operational performance
Use the Operations data to understand workload, processing, and cost:
“Summarize Operations for returns requested last month.”
“Show return requests by status for the last 30 days.”
“What was the average processing time by return method?”
“Which carriers generated the highest return label cost?”
“Compare processing time and label cost with the previous month.”
“Show the volume handled by automation or each processor.”
Review upsell and exchange performance
Connect return activity with the outcomes that affect revenue and product movement:
“How much upsell revenue did returns generate last month?”
“Show the upsell rate trend over the last three months.”
“Which products did customers exchange into most often?”
“Compare exchange outcomes for this quarter with the previous quarter.”
Understand customer patterns
Use customer-level analysis to see where return value or returned-item volume is concentrated:
“Which customers returned the most value in the selected period?”
“Which customers had the highest number of returned items?”
“For the customers with the highest return value, what products or reasons appear in their returned items?”
“Compare customer return value between the current period and the previous period.”
Important: Customer-level results should be interpreted as return behavior, not as proof of fraud or abuse.
View supported return details
When supported, you can move from an aggregate result to the return requests included in it:
“Show the return requests included in this count.”
“Show the return requests for the top return reason.”
“Open the details behind the returned-item total for this product.”
The available details depend on the metric, dashboard, filters, and permissions used for the analysis.
Continue with follow-up questions
AfterShip Agent keeps the confirmed context of the conversation so you can refine an answer instead of starting over.
For example:
“Show returned items by product for the last 30 days.”
“Only include the US store.”
“Compare that with the previous 30 days.”
“Now break the top products down by return reason.”
“Show the return requests behind the top result.”
You can use follow-ups to:
Change the date range or comparison period.
Add or remove a store, country, status, source, product, or collection filter.
Add a product, reason, carrier, method, or status breakdown.
Switch between volume, value, rate, cost, and other supported metrics.
Request a chart, table, ranking, or supported detail view.
Check the updated context: After a follow-up changes the context, check that the response makes the updated period, filters, metric, comparison, and breakdown clear.
Returns Analytics areas
AfterShip Agent can use data from supported Returns Analytics dashboard areas, including:
Overview — return volume, return value, return rates, expected refund, retained revenue, return methods, reasons, and countries.
Products — returned items, return rates, refund or store-credit outcomes, replacement or exchange outcomes, products, variants, SKUs, collections, and reasons where available.
Operations — return requests, processing time, status, processor or automation activity, review results, and return label cost.
Upsell and Exchange — upsell revenue or rate and products customers exchanged into.
Customers — return value, returned orders or items, and customer return rates.
The metrics and breakdowns available in the answer depend on the dashboard, question, filters, and permissions used.
Ask a useful question
The more specific your question is, the easier it is to return a focused answer.
Include the details that determine how the data should be calculated:
Metric — what should be measured, such as return requests, returned items, return value, return rate, processing time, or label cost.
Time period — the date range or named period, such as last month, last 30 days, or July 2026.
Comparison — the previous period or another explicit date range.
Breakdown — the product, reason, country, carrier, method, status, customer, or other dimension you want to see.
Filters — the store, country, status, source, collection, SKU, or other supported scope.
Threshold — a minimum sold or returned volume when ranking rates.
For example:
“Compare item return rate for orders created in July 2026 with June 2026. Break it down by product collection, include the top return reasons.”
If you start from a dashboard, you can refer to the selected dashboard context instead of repeating every filter in your question.
If you open AfterShip Agent from the chat button, include the scope in your first question.
Understand your results
Keep these definitions in mind when reading an answer:
Order created means returns connected to orders created during the selected period. It does not mean the returns themselves were created during that period.
Returns created means returns created during the selected period.
Operational views may use the date a return was requested, while report views may also use the date it was processed. The answer indicates the date context used.
A return request, returned order, and returned item are different counting units. Check which unit a metric uses before comparing numbers.
Return value, expected refund, retained revenue, store credit, replacement, exchange, and actual refund are different measures.
Rate results should be read together with their numerator, denominator, and sample size.
Returns Analytics data is refreshed daily and generally includes data through the previous day.
The selectable Returns Analytics date range is limited to one year.
Monetary metrics use the applicable store currency.
Tips for better results
From a dashboard, set the date range and filters before clicking View summary.
When comparing periods, use the same metric, counting unit, filters, and date meaning for both periods.
Name the breakdown you want instead of asking for a general analysis.
Use a volume threshold when ranking rates or categories.
Ask one connected question at a time, then use follow-ups to add detail.
Check the answer's scope and date meaning before comparing it with another dashboard or report.
Key takeaways
Use View summary when you are already viewing a relevant Returns Analytics dashboard.
Use the chat button when you want to start with a specific question.
Set your date range and filters before generating a dashboard summary.
Include the metric, time period, comparison, breakdown, filters, and threshold when they matter to your question.
Use follow-up questions to refine the analysis without starting over.
AfterShip Agent can connect supported data across Overview, Products, Operations, Upsell and Exchange, and Customers.
Always check the date meaning, counting unit, scope, metric, and currency before comparing results.
Use volume thresholds when ranking rates so that small sample sizes do not create misleading results.
Returns Analytics data is refreshed daily and generally includes data through the previous day.
The selectable Returns Analytics date range is limited to one year.
FAQs
Q. What is the difference between View summary and starting a chat?
Use View summary when you are already looking at a relevant Returns Analytics dashboard and want AfterShip Agent to summarize the data in that view.
Start a chat from the chat button when the current page is not relevant or when you want to begin with a specific question and define the scope yourself.
Q. Do I need to type a prompt to use View summary?
No. Open a supported Returns Analytics dashboard, set the context you want, and click View summary in the top-right corner. After the summary opens, you can use follow-up questions to refine the analysis.
Q. Can AfterShip Agent analyze the Sales adjustments report?
No. The Sales adjustments report is not currently supported by AfterShip Agent.
Q. Can I analyze data from more than one dashboard area?
Yes. After the summary opens, you can ask for related supported metrics or breakdowns from Returns Analytics and review them together in one analysis.
Q. Can I change the filters after opening the summary?
Yes. Use a follow-up question to change the period, filter, comparison, or breakdown. The response should make the updated context clear.
Q. Why does the result differ from a dashboard or report?
Make sure you are comparing the same:
Store or organization scope
Date range
Date meaning
Status and other filters
Metric
Counting unit
Currency
Differences in any of these can change the result.
Q. What should I do if there is not enough data for an answer?
Try widening the date range, removing an overly narrow filter, or asking for a higher-level breakdown.
If you are ranking a rate, check whether the selected period has enough volume to make the result meaningful.