Using Analytics
Using Analytics and Reports
Analytics in wallmarkets are most useful when they stay close to the operational questions your team is already asking.
How much value moved this period?
How much came back as returns?
Which supermarkets matter most?
Which products are carrying the business, and which ones are creating drag?
This guide covers how to use the reporting and analytics views without turning them into a collection of charts nobody acts on.
Start with the date range
Before reading any number, make sure the time window is the one you actually mean.
Shorter ranges are better for operational monitoring. Longer ranges are better for pattern recognition.
As a practical rule:
- use a short range when checking how the last few days are behaving
- use a longer range when reviewing performance with the team
- compare one period to another only when the comparison is actually meaningful
If the date range is wrong, the rest of the page can still look polished while telling you the wrong story.
The core numbers to read first
The analytics and dashboard views revolve around a small set of numbers that matter more than the rest:
| Metric | What it helps you understand |
|---|---|
| Delivery value | How much product value moved |
| Return value | How much value came back |
| Net value | What remains after returns |
| Delivery count | Operational volume |
| Return count | Frequency of return events |
If you only have time for one check, read those together.
They tell you whether the business is simply busy or whether it is busy in a healthy way.
Use trend views to notice drift early
Trend views are most valuable when they help you notice something before it becomes a month-end surprise.
Look for:
- return value rising while delivery value stays flat
- a short dip in delivery volume that does not match what the team expected
- a recent improvement that might be worth repeating elsewhere
The right question is not "is this chart interesting?" The right question is "does this change what we should look at next?"
Read top products as concentration, not popularity
Top-product views are useful because they tell you where value and risk are concentrated.
If a small number of products drive a large share of delivery value, those products deserve more operational attention. That might mean tighter handling, clearer branch notes, or a closer look at return behavior. If a small number of products drive a large share of delivery value, those products deserve more operational attention. That might mean tighter handling, clearer branch data, or a closer look at return behavior.
Likewise, if a product appears regularly in returns, it should not be discussed as a vague quality problem. It should be reviewed as a specific operational pattern.
Read top supermarkets the same way
Supermarket-level views answer a similar question from the account side.
Which accounts are carrying most of the delivery volume?
Which ones generate the most friction?
Which ones create more returns than their size would suggest?
This is where the analytics become commercially useful. A supermarket account is not equally valuable just because it is equally busy.
Use returns as an analysis layer, not a separate topic
Teams often isolate returns into their own mental bucket. The analytics work better when returns are treated as part of the same business story as deliveries.
That means asking:
- which accounts are driving return value
- which products create the most repeated return work
- whether a recent process change improved or worsened return patterns
If analytics do not help you connect returns back to the original delivery operation, they are not yet doing enough.
Export when you need a fixed record
On-screen analytics are good for investigation. Exports are better when you need a stable version to share, review, or attach to a meeting.
Use exports when:
- leadership wants a clean snapshot
- finance needs a fixed record
- you want to compare periods outside the app
- an account conversation needs supporting numbers
As a rule of thumb:
- PDF is better for sharing and review
- CSV is better for follow-up analysis in spreadsheets
A simple review cadence that actually works
Most teams do not need a complicated analytics process. A simple cadence is enough.
Daily
Check delivery value, return value, net value, and recent activity.
Weekly
Review top products, top supermarkets, and any obvious change in return behavior.
Monthly
Look for patterns worth acting on: accounts that are harder to serve than they should be, products that keep causing friction, and periods where the operation performed better or worse than expected.
That is already enough to make analytics useful in a real business.
Common mistakes
Reading one metric in isolation
Revenue without returns is incomplete. Volume without net value can be misleading. One number is rarely the whole story.
Comparing the wrong periods
Not every period is worth comparing. If the contexts are different, the comparison may be tidy and still not be useful.
Treating exports as the first step
Usually it is better to understand the pattern on-screen first, then export what actually matters.
What to do when you see a problem
Analytics are only helpful if they change behavior.
If a supermarket stands out, review the deliveries tied to that account.
If a product stands out, review how it is being delivered, received, and returned.
If net value is underperforming, look at where returns are concentrated before assuming the issue is demand.
That is the habit that makes analytics operational instead of decorative.
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