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Retail dashboards

Store performance dashboard examples for retail

Six retail dashboards running on sample data: store performance, store margin, sell-through, retail inventory, promotions, and fulfilment and returns.
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Store Comparison & Like-for-Like dashboard: Net Sales, Lfl Growth, Conversion rate and Basket size tiles.
Sample data: illustrative figures, not business results.

In short

What a retail dashboard is

A retail dashboard is a single screen showing how stores and products are trading: sales, margin, like-for-like growth and sell-through. Teams also call it a retail analytics, retail KPI or store performance dashboard. It answers where performance concentrates rather than what the total was: which store, which category, which promotion. A chain-level total hides the half-dozen locations doing something different. Each of the six below states the question it answers and the columns your data needs.

The examples

Six retail dashboard examples

Real Mammoth templates on sample data. Every figure is illustrative. Two datasets: store-by-week trading and product-by-week performance.

  1. Example 01· Template: Store-Level Margin

    Store margin dashboard

    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

    Start with this template

    Store-Level Margin dashboard: Gross Profit, Gross margin, Markdown rate and Basket size tiles, above Margin week by week, Margin rate by store, Sales against the margin they carry, Margin spread within each format and What discount depth costs.
    Sample data: illustrative figures, not business results.

  2. Example 02· Template: Store Comparison & Like-for-Like

    Store performance dashboard

    Which stores are 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

    Start with this template

    Store Comparison & Like-for-Like dashboard: Net Sales, Lfl Growth, Conversion rate and Basket size tiles, above This year against last, store by store, Like-for-like movers, up and down and Comparable stores against new space.
    Sample data: illustrative figures, not business results.

  3. Example 03· Template: Sell-Through by Category

    Sell-through dashboard

    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)

    Start with this template

    Sell-Through by Category dashboard: Sell Through, Planned Sell Through, Weeks Of Cover and Net Sales tiles, above Actual against the planned curve, Sell-through by category, Full price against marked down and Cover remaining by category.
    Sample data: illustrative figures, not business results.

  4. Example 04· Template: Inventory & Stockouts

    Retail inventory dashboard

    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)

    Start with this template

    Inventory & Stockouts dashboard: Closing Stock Units, Weeks Of Cover, Sell Through and Units Sold tiles, above Stock running down through the season, Cover by category, Sell-through spread within each category and Which lines hold the stock.
    Sample data: illustrative figures, not business results.

  5. Example 05· Template: Promotion & Discount Effectiveness

    Promotion dashboard

    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)

    Start with this template

    Promotion & Discount Effectiveness dashboard: Uplift Units, Markdown, Gross margin and Net Sales tiles, above Baseline against what actually sold, What each mechanic gave away, Promoted against full price, by category and Margin spread by mechanic.
    Sample data: illustrative figures, not business results.

  6. Example 06· Template: Fulfilment & Returns

    Fulfilment and returns dashboard

    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)

    Start with this template

    Fulfilment & Returns dashboard: Orders, On-time rate, Return rate and Returns Value tiles, above On-time rate by fulfilment method, Where orders are fulfilled from, Why things come back and Return rate spread by category.
    Sample data: illustrative figures, not business results.

The metrics

The metrics a retail dashboard is built from

Eight numbers that say where trade concentrates. Every one of them can be made to look better by choosing the comparison badly, which is what the last column is about.

Retail dashboard metrics: what each one answers and what the data must carry.
Like-for-like growthIs the estate trading better, or is it just larger?Only stores trading in both periods, with new openings, closures and refit weeks excluded. Leave them in and estate change reads as growth.
Sell-through rateHow much of what we took in has sold?Units sold over units received for the same season or delivery batch. Measured against current stock instead of intake, it rises as stock runs out and looks best when there is nothing left to sell.
Gross margin by categoryWhich categories actually make money?Sales and cost at line level, with markdowns and staff discounts applied to the line rather than netted off at the end. Netted at the end, the discounted categories look as profitable as the full-price ones.
Sales per square metreWhich stores earn their space?Trading area per store, maintained after refits and reallocations. Estate files go stale quietly and this is the metric that quietly goes with them.
Basket size and units per transactionAre people buying more, or are more people buying?Transaction-level data with returns handled explicitly rather than as negative baskets, which drag the average down without telling you where.
Promotion upliftDid the promotion sell more, or sell the same stock cheaper?A baseline period per store and product, the promotion calendar, and the neighbouring products. Uplift against a chain average hides cannibalisation: the promoted line rises and the one beside it falls.
Conversion rateOf the people who came in, how many bought?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 sizeAre customers spending more per visit, or just visiting more?Sales value and transaction count per store and week, with returns netted the same way everywhere. A store that books returns as negative sales has a basket that shrinks for no reason.

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 refreshes weekly, Team daily, and Pro every three hours.

  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.

  • You pay for

    The people who build

    Anyone connecting a source, preparing data or publishing a dashboard counts as a platform user.

  • You never pay for

    The people who look

    Viewers are unlimited and free on every plan, including the free one. A board, a client list or a whole company: the number does not change the bill.

Plans differ in how many builders and published dashboards a workspace gets. Drafts, viewers and every sharing mode are unlimited and free on all four. Full pricing.

Retail dashboard questions

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.
What KPIs should a retail dashboard track?
Like-for-like sales, gross margin after markdown, sell-through against plan and weeks of cover are the core four, with conversion and basket size explaining why a store's sales moved. The metrics table above lists them with what each one needs from your data.
How do I build a retail dashboard?
Start from the weekly trading meeting it has to serve, then work through five steps.
  1. Connect the till or EPOS data, the stock system and, if you sell online, the e-commerce platform.
  2. Agree the definitions once: which stores count as like-for-like, how markdown is charged, and the week the trading year starts.
  3. Build one board per question: store performance, store margin, sell-through and stock cover, rather than one screen holding all four.
  4. Put the exceptions underneath: stores below last year, lines selling slower than planned, stock about to run out.
  5. 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 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 the fastest cadence, which is Pro at every three hours; Starter refreshes weekly and Team 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.

Last reviewed against the template catalogue.

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