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Retail dashboards

Six retail dashboard examples

Six retail dashboards, each shown as it actually renders on a sample dataset rather than as a mock-up — so you can copy the thinking, not just the layout.
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The examples

Each dashboard, and the question it answers

Each of these is a Mammoth template, captured as it renders on the sample dataset it ships with, so every figure on them is illustrative rather than a business result. They run on two datasets — store-by-week trading and product-by-week performance — because a store question and a product-line question are not the same grain. What is worth copying is which question each one refuses to also answer.

  1. Example 01

    Store-Level Margin

    Two stores on the same margin rate — what is different about them?

    Margin rate store by store, with what the discounting costs shown next to it rather than buried in the total. The spread within each store format is the chart to look at twice: two Outlets on the same margin rate can be running two completely different businesses, and a chain-level average hides both.

    What is on it

    • Margin week by week
    • Margin rate by store
    • Sales against the margin they carry
    • Margin spread within each format
    • What discount depth costs
    • Margin by region and format

    Your data needsDashboard

    • a revenue or money column
    • a category column
    Store-Level Margin dashboard: Gross Profit, Gross Margin, Markdown Rate and Basket Size tiles, above Margin week by week.
    Sample data — illustrative figures, not business results. Cropped; the dashboard continues below.

  2. Example 02

    Store Comparison & Like-for-Like

    Which stores are genuinely improving, once new space is stripped out?

    This year against last, store by store, with comparable stores separated from new space so growth that is really just more floor is not counted as growth. Conversion is plotted against basket underneath, because a store can lift sales by serving more people or by selling them more, and the two lead to different decisions.

    What is on it

    • This year against last, store by store
    • Like-for-like movers, up and down
    • Comparable stores against new space
    • Basket spread against the cluster
    • Conversion against basket
    • Regional ranking, week by week

    Your data needsDashboard

    • a revenue or money column
    • a category column
    Store Comparison & Like-for-Like dashboard: Net Sales, Lfl Growth, Conversion Rate and Basket Size tiles.
    Sample data — illustrative figures, not business results. Cropped; the dashboard continues below.

  3. Example 03

    Sell-Through by Category

    Is the season selling through at the rate the buy was planned on?

    Actual sell-through against the planned curve, so a category that is behind shows up while there is still season left to act in. Full price is separated from marked down throughout — a category can hit its sell-through number entirely on discount, which is the same figure and a much worse result.

    What is on it

    • Actual against the planned curve
    • Sell-through by category
    • Full price against marked down
    • Cover remaining by category
    • Sold through against cover left
    • Category against week of season

    Your data needsDashboard

    • a rate or percentage
    • a category (~6 values)
    Sell-Through by Category dashboard: Sell Through, Planned Sell Through, Weeks Of Cover and Net Sales tiles.
    Sample data — illustrative figures, not business results. Cropped; the dashboard continues below.

  4. Example 04

    Inventory & Stockouts

    What is about to run out, and what has stopped moving?

    Stock running down through the season, with weeks of cover by category and the lines holding the most stock ranked underneath. At-risk and overstocked are plotted against each other on one chart: they look identical on a stock-value report and need opposite responses.

    What is on it

    • Stock running down through the season
    • Cover by category
    • Sell-through spread within each category
    • Which lines hold the stock
    • At risk against overstocked
    • Cover by category and channel

    Your data needsDashboard

    • a count column
    • a category (~6 values)
    Inventory & Stockouts dashboard: Closing Stock Units, Weeks Of Cover, Sell Through and Units Sold tiles.
    Sample data — illustrative figures, not business results. Cropped; the dashboard continues below.

  5. Example 05

    Promotion & Discount Effectiveness

    Did the promotion make money, or just move it?

    What actually sold against the baseline, with the markdown each mechanic gave away set beside the volume it gained. Promoted and full-price lines are shown separately by category, which is the cut that says whether a promotion grew the category or just pulled sales down the price ladder.

    What is on it

    • Baseline against what actually sold
    • What each mechanic gave away
    • Promoted against full price, by category
    • Margin spread by mechanic
    • Discount given against volume gained
    • Which lines the promotion actually moved

    Your data needsDashboard

    • a count column
    • a category (~6 values)
    Promotion & Discount Effectiveness dashboard: Uplift Units, Markdown, Gross Margin and Net Sales tiles.
    Sample data — illustrative figures, not business results. Cropped; the dashboard continues below.

  6. Example 06

    Fulfilment & Returns

    Are orders arriving on time, and how much is coming back?

    On-time rate by fulfilment method with return rate beside it, and the two plotted against each other — late delivery and returns tend to be the same problem seen twice. Return reasons are traced back to category, so a sizing problem in one range is not averaged into an overall returns percentage.

    What is on it

    • On-time rate by fulfilment method
    • Where orders are fulfilled from
    • Why things come back
    • Return rate spread by category
    • Arriving late against coming back
    • From category to reason

    Your data needsDashboard

    • a count column
    • a category (~4 values)
    Fulfilment & Returns dashboard: Orders, On Time Rate, Return Rate and Returns Value tiles.
    Sample data — illustrative figures, not business results. Cropped; the dashboard continues below.

Design notes

What makes a retail dashboard worth opening

Four things the examples above have in common. They matter more than which chart you pick.

Per store and per line, not just per chain

A chain-level number is a summary of two different businesses having two different weeks. Every one of these resolves to something a person owns — margin rate by store, sell-through by category, which lines hold the stock, which lines the promotion actually moved — because that is the level at which anyone can do something on Monday.

Discount is a cost, and is shown as one

Markdown is the easiest lever in retail and the one that quietly eats the year. Three of these six put it on the page as a cost rather than a tactic: Store-Level Margin carries a markdown rate and what discount depth costs, Sell-Through separates full price from marked down, and Promotion & Discount Effectiveness shows what each mechanic gave away against the volume it gained.

Inside the window you can still act in

Sell-through and cover only matter while there is season left to reorder or mark down in. These are read against the planned curve and the week of season rather than against the calendar month, which is the difference between noticing a category is behind and being able to do something about it.

Several systems, one clock

Tills, the warehouse system and the web store close their day at different times, in different formats, with different keys. These dashboards show channels side by side — where orders are fulfilled from, cover by category and channel — which only reads correctly because the reconciling happened once in the preparation rather than in each chart.

Proof

Retail and CPG teams already running on Mammoth

Not dashboards from this page — these are published customer stories, and they are mostly about the unglamorous half: reconciling retailer feeds, matching SKUs, and getting a number that survives being questioned.

From example to live dashboard

Pretty is easy. Current and true is the hard part.

Any of these can be drawn in a slide. What makes one worth keeping is the five steps underneath it, and they are the same five whichever example you start from.

  1. 01

    Connect the source

    The dashboard reads the system, not an export somebody saved to their desktop. 100+ tested connectors, plus anything with a REST API.

  2. 02

    Prepare it once

    Cleaning, joining and reshaping are recorded as steps you can read, change and re-run — not a formula buried three sheets deep.

  3. 03

    Check it before anyone sees it

    Data checks run with the refresh and flag the rows that fail, so a broken upstream file shows up as an alert rather than as a wrong number in a meeting.

  4. 04

    Put it on a schedule

    Refresh on a schedule and the dashboard stays current on its own. Starter schedules daily runs; Pro adds hourly refresh and event triggers.

  5. 05

    Publish it and send the link

    Public, password-protected or sign-in only. Every sharing mode is available on every plan, including the free one.

Sharing

Everyone can see it.
Nobody pays to look.

Most dashboard tools charge for every person who opens one, which is why so many dashboards end up as a screenshot pasted into a message. Mammoth charges for the people who build. Viewers are unlimited and free on every plan, including the free one.

  • Free

    $0

    Builders
    1 builder
    Dashboards
    3 published
    Viewers
    Unlimited, free

  • Starter

    $249/mo

    $199/mo billed annually

    Builders
    5 builders
    Dashboards
    10 published
    Viewers
    Unlimited, free

  • Pro

    $699/mo

    $559/mo billed annually

    Builders
    10 builders
    Dashboards
    50 published
    Viewers
    Unlimited, free

  • Enterprise

    From $5,000/mo

    Builders
    Unlimited builders
    Dashboards
    Unlimited published
    Viewers
    Unlimited, free

Published-dashboard counts are per workspace. Drafts, viewers and every sharing mode are unlimited and free on all four plans. Full pricing.

Questions people ask

What goes on a retail dashboard?
Sales and margin at the level someone can act on, cover and stock risk by category, and sell-through against the curve the buy was planned on. The six examples above split those across separate dashboards rather than crowding one — which is why each has a single question at the top of it.
Can it combine EPOS, warehouse and e-commerce data?
That is the usual reason for building one. Snowflake, SQL Server, Shopify and SFTP drops are among the 100+ tested connectors, plus anything with a REST API — and the joins between them are recorded as re-runnable steps rather than redone each week.
How often should a retail dashboard refresh?
Store margin and sell-through are usually fine on an overnight run before the morning. Stock cover and fulfilment are the cases for hourly refresh, which Pro adds along with event triggers; Starter schedules daily.
Can every store manager see their own store?
Viewers are unlimited and free on every Mammoth plan, so giving access to every manager in the estate costs nothing. How much each of them sees is an access decision to make when the dashboard is published.
Are these real dashboards?
Real. Each one is a Mammoth template captured as it renders, running on the retail sample data it ships with. What they are not is a screenshot of a customer's production dashboard — every figure on them is illustrative rather than a business result, and none of them carries a number we cannot show you the source for. The customer stories linked above are the real thing, and they are about the pipelines underneath rather than any dashboard on this page.

Build the retail dashboard you actually need.

Connect a source, prepare it once, and publish. Everyone who needs to see it can, at no per-viewer cost.

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