Return Analytics

Return analytics are most useful when they help you answer one plain question:

Where is return value concentrating, and what should we do about it?

That is the lens to keep. If the charts look impressive but do not tell you which product, supermarket, or delivery pattern deserves attention, they are not helping enough.

Where to look

There are two reporting layers that matter for returns:

• the main Reports page for headline totals and record-level review
• the analytics dashboard for filtered analysis across supermarkets, categories, and time periods

Use the first to spot a problem and the second to explain it.

Read return value before you read anything fancy

The most useful starting numbers are still the simplest:

• total returns
• total return value
• the period you are looking at

Those numbers tell you whether the return side of the business is stable or starting to distort the delivery work beneath it.

Find concentration by product and supermarket

The most actionable return analytics are usually concentration views.

Look for:

• products that keep appearing in return records
• supermarkets that produce repeated return value
• date ranges where return activity suddenly jumped

That gives you a short investigation list instead of a vague sense that "returns feel high."

Use filters to narrow the story

The analytics dashboard lets you narrow by fields such as:

• start date
• end date
• supermarket
• category

That makes it much easier to answer focused questions like:

• Are returns concentrated in one account?
• Is one category doing more damage than the rest?
• Did a recent period change actually improve the numbers?

Good analytics work is usually just careful narrowing.

Tabs that are worth your time

Depending on your data, the analytics dashboard can expose views such as:

• profit analysis
• cohort analysis
• seasonal trends
• inventory recommendations
• product correlation
• forecast views
• detailed data tables

For return work, the most valuable habit is to use these views selectively.

If one product is showing up repeatedly, open the views that help you understand its wider pattern. If one supermarket stands out, filter toward that account instead of reading the whole dashboard at once.

What strong return analysis looks like

Good return analysis usually connects three layers:

1. the headline period totals
2. the filtered pattern
3. the underlying delivery and return records

For example:

• the monthly return value is up
• most of that increase is concentrated in one category
• the same two supermarkets are responsible for most of it
• the linked delivery records show the same products repeating

That is actionable. A raw number by itself is not.

Exports for review and follow-up

When you need to share or rework the data outside wallmarkets, export it.

CSV is the most practical format for return analysis because it lets you:

• sort high-value returns quickly
• group by supermarket
• compare periods in a spreadsheet
• prepare a short review for finance or operations

Export after you understand the question, not before.

Common mistakes

Looking at returns without delivery context

Return value only becomes meaningful when you understand what delivery volume and delivery value sat beside it.

Chasing every tab equally

You do not need the whole analytics surface every time. Use the tab that helps answer the question you actually have.

Treating small repeated returns as noise

Repeated small losses often tell a clearer operational story than one dramatic outlier.

A practical review habit

Once a week, do this:

1. check headline return totals
2. filter to the supermarket or category that stands out
3. open the underlying return records
4. jump back to the original deliveries

That is enough to turn analytics into operational follow-up instead of passive observation.

Related guides

• Processing a return
• Tracking returns against deliveries
• Optimizing your return process