Retail dashboards

Store performance dashboard examples for retail

Six retail dashboards: store performance, store margin, sell-through, retail stock, promotions, and fulfilment and returns. Open any of them to see it in full, then use it as a template for your own data.

Store-Level Margin dashboard: Gross Profit, Gross margin, Markdown rate and Basket size tiles, above Margin week by week.

Sell-Through by Category dashboard: Sell Through, Planned Sell Through, Weeks Of Cover and Net Sales tiles.

Store Comparison & Like-for-Like dashboard: Net Sales, Lfl Growth, Conversion rate and Basket size tiles.

Three of the six examples. All figures are sample data.

Browse the examples Try free

Sell on Shopify, or track sales in a spreadsheet?Pick where your data is. Each opens a free template that builds a dashboard like these.

The examples

Six retail dashboard examples and templates

Click a board to see all of it. Click Use template to start your own copy in Mammoth. All figures are sample data.

Sample data
Store margin dashboard
Which stores keep their margin, and which lose it to discounts?
- KPIGross profit
- KPIGross margin
- KPIMarkdown rate
- KPIBasket size
- Gross profit
- Gross margin
- Markdown rate
- Basket size
Use template
Sample data
Store performance dashboard
Which stores are improving, not counting new space?
- Net sales
- Lfl growth
- Conversion rate
- Basket size
Use template
Sample data
Sell-through dashboard
Is the season selling at the rate the buying plan expected?
- Sell through
- Planned sell through
- Weeks of cover
- Net sales
Use template
Sample data
Retail inventory dashboard
What is about to run out, and what has stopped selling?
- Closing stock units
- Weeks of cover
- Sell through
- Units sold
Use template
Sample data
Promotion dashboard
Did the promotion make money, or just move it?
- Uplift units
- Markdown
- Gross margin
- Net sales
Use template
Sample data
Fulfilment and returns dashboard
Are orders arriving on time, and how much is coming back?
- KPIOrders
- KPIOn-time rate
- KPIReturn rate
- KPIReturns value
- Orders
- On-time rate
- Return rate
- Returns value
Use template
Your data
Want this on your own numbers?
Pick where your data is. Mammoth turns it into a dashboard like these.
- Shopify
- A spreadsheet

Definition

What a retail dashboard is

A retail dashboard is one screen that shows how stores and products are selling: sales, margin, like-for-like growth and sell-through. It is also called a retail analytics, retail KPI or store performance dashboard.

Like-for-like growth
New stores left out, so growth is real.
Store by store
A chain total hides the stores that differ.
Margin after discounts
Markdowns charged to the product, not taken off at the end.

The metrics

The metrics a retail dashboard is built from

Eight numbers that show where sales and margin come from. Each can be made to look better by choosing the comparison badly. Open each one to see the data it needs.

Like-for-like growth
Is the chain selling better, or just bigger?
What the data needs
Only stores open in both periods, with new stores, closures and refit weeks left out. Leave them in and changes to the store estate look like growth.
Sell-through rate
How much of what we took in has sold?
What the data needs
Units sold divided by units received for the same season or delivery. Measured against current stock instead, it rises as stock runs out and looks best when there is nothing left to sell.
Gross margin by category
Which categories make money?
What the data needs
Sales and cost per product, with markdowns and staff discounts applied to the product rather than taken off at the end. Taken off at the end, discounted categories look as profitable as full-price ones.
Sales per square metre
Which stores earn their space?
What the data needs
Trading area for each store, kept up to date after refits and changes. Store files go out of date quietly, and this metric goes wrong with them.
Basket size and units per transaction
Are people buying more, or are more people buying?
What the data needs
Transaction data with returns handled separately rather than as negative baskets, which drag the average down without showing where.
Promotion uplift
Did the promotion sell more, or sell the same stock cheaper?
What the data needs
A normal baseline for each store and product, the promotion calendar, and the products next to the promoted ones. Comparing against a chain average hides cannibalisation: the promoted product rises and the one beside it falls.
Conversion rate
Of the people who came in, how many bought?
What the data needs
Transactions and footfall per store for the same hours. A footfall counter that misses an entrance makes that store look like it converts brilliantly.
Average basket size
Are customers spending more per visit, or just visiting more?
What the data needs
Sales value and number of transactions per store and week, with returns handled the same way everywhere. A store that books returns as negative sales has a basket that shrinks for no reason.

How to build one

How to build your own retail dashboard

From one of the examples above to a live dashboard on your own data.

1
Pick a template
Start from any of the six boards above.
2
Add your data
Upload a spreadsheet, or connect one of 100+ tested connectors.
3
Clean it once
Fix and combine your data once. Mammoth repeats those steps for you.
4
Set it to refresh
The dashboard updates on a schedule and flags data that fails a check.
5
Share the link
Public, password-protected or sign-in only, on every plan.

Start with a template

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Customers

Retail and consumer goods teams already using Mammoth

Published customer stories, not dashboards from this page. They are mostly about the hard part: combining retailer data, matching products and getting numbers that hold up.

FAQ

Retail dashboard questions

Last reviewed 1 October 2026 against the template catalogue.

What goes on a retail dashboard?

Sales and margin at a level someone can act on, stock cover and risk by category, and sell-through against the buying plan. The six examples above split those into separate dashboards instead of crowding one, so each answers one question.

What KPIs should a retail dashboard track?

Like-for-like sales, gross margin after markdowns, sell-through against plan and weeks of cover are the core four. Conversion and basket size explain why a store's sales moved. The list above shows each one with the data it needs.

How do I build a retail dashboard?

Start from the weekly trading meeting it has to serve. Then follow five steps.

Connect the till data, the stock system and, if you sell online, your e-commerce platform.
Agree the definitions once: which stores count as like-for-like, how markdowns are charged and the week the trading year starts.
Build one board per question (store performance, store margin, sell-through and stock cover) instead of one screen holding all four.
Put the problems below: stores behind last year, products selling slower than planned, stock about to run out.
Schedule it before the meeting and give every store manager the link to their own numbers.

Can it combine EPOS, warehouse and e-commerce data?

That is the usual reason to build one. Snowflake, SQL Server, Shopify and SFTP file drops are among the 100+ tested connectors, and Mammoth can also connect to anything with a REST API. The steps that combine them are saved and re-run, not redone each week.

How often should a retail dashboard refresh?

Store margin and sell-through are usually fine refreshed overnight. Stock cover and fulfilment are worth refreshing as often as your plan allows. See plans and refresh rates

Can every store manager see their own store?

Yes. Viewers are unlimited and free on every Mammoth plan, so giving every store manager access costs nothing. You decide what each of them sees when you publish the dashboard.

Are these real dashboards?

Yes. Each one is a Mammoth template, captured as it appears, running on the retail sample data it comes with. They are not screenshots of a customer's dashboard, and every figure is sample data, not a business result. The customer stories on this page are real, and they are about the data work behind dashboards, not any board shown here.