Store dashboard: which KPIs to track

A store dashboard should track a handful of KPIs grouped by goal: sales (revenue, number of transactions, average basket), profitability (gross margin, product mix), inventory (stockout risk, inventory turnover, dead stock), customers (purchase frequency, recency, amount spent) and day-to-day operations. A good store dashboard isn't about displaying as many figures as possible. It should quickly answer two questions: What's happening in my business? What needs my attention? Choosing the right KPIs matters more than how many you track.

Organize your KPIs by goal

A dashboard quickly becomes hard to read when every figure is given the same weight. Organizing by goal gives you five main areas:

• sales
• profitability
• inventory
• customers
• day-to-day operations

Each group answers a different question.

Sales KPIs

Revenue

Revenue is naturally one of the first KPIs people track. But displaying:

Revenue: €18,400

doesn't tell you whether that's good or bad. It needs context:

Revenue: €18,400 — +6% vs. the previous week

A comparison turns a number into information.

Number of transactions

The number of transactions helps explain changes in revenue. Two weeks can bring in exactly the same revenue under very different circumstances: the number of transactions may have gone up while the average basket went down, or vice versa.

Average basket (average transaction value)

The average basket is calculated like this:

Average basket = revenue ÷ number of transactions

It shows the average amount spent per transaction. Its trend is especially useful when read alongside the number of transactions: together, they tell you whether a change comes from foot traffic or from how much customers spend per purchase (see how to increase your average basket).

Sales by category and by item

Overall revenue doesn't show which products are driving it. Tracking sales by category or item lets you identify:

• the products that are growing
• the ones that are slowing down
• the categories that contribute the most to revenue
• the items whose sales are shifting sharply

This is often where you'll find the explanation for an overall change.

Profitability KPIs

Revenue measures sales, not how profitable they are. Two periods with the same revenue can produce different financial results depending on which products were sold and what discounts were given.

Gross margin

When the data is available, margin rounds out the revenue picture. A promotion, for example, can sharply boost sales while shrinking the margin it generates.

Product mix

How sales are split across products or categories can also affect profitability. More low-margin items and fewer high-margin ones can change your bottom line without any dramatic change in revenue. That's why the dashboard should show what's being sold, not just how much.

Inventory KPIs

An inventory dashboard isn't meant to list stock levels for thousands of catalog items. Its main job is to flag the situations that need checking (see inventory turnover and stockouts).

Stockout risk

An item whose stock is running low relative to its sales pace deserves special attention. Remaining stock alone doesn't tell you enough: five units can last several weeks for a slow seller or just a few hours for a high-demand item.

Inventory turnover

Inventory turnover measures how quickly stock sells through and is replaced. It helps you tell fast-moving items from those that tie up stock for a long time.

Dead stock and overstock

A product can be in stock in large quantities while barely selling. Flagging these items keeps problem stock from going unnoticed just because it never runs out (see dead stock). So your inventory dashboard should watch both extremes: too little stock and too much.

Customer KPIs

When sales are linked to known customers, several KPIs become useful.

Purchase frequency

This tells you how often customers come back.

Recency

The date of a customer's last purchase helps you spot those who are starting to drift away.

Amount spent

This lets you identify your best customers: the ones who contribute the most to revenue over a given period.

These metrics are best read together rather than in isolation. Your most valuable customer isn't necessarily the one with the biggest single purchase: consistency and frequency count too.

Compare periods

A dashboard should almost never show a KPI without a point of comparison. Depending on what you need, you can compare:

• today with yesterday
• this week with last week
• this month with last month
• a period with the same period last year
• actual results with a target

The right choice depends on the business. Comparing a Saturday with the previous Friday isn't necessarily meaningful if sales on those two days are very different. Likewise, a promotional week shouldn't be used as the baseline for a normal week without thinking it through. A good dashboard doesn't just show a gap: it helps you put it in the right context.

Compare locations

If you have several stores, you can compare the same KPIs across them (see multi-store management). But the goal isn't just to rank them. A location may have lower revenue because it's smaller, yet post a particularly strong average basket or inventory turnover. Gaps are mainly there to prompt questions:

• Why does this category perform better in this store?
• Why does this item run out of stock more often here?
• Why is the average basket trending differently at two locations?

The comparison then becomes an analysis tool rather than a leaderboard.

Operational alerts

Not everything useful on a dashboard is a KPI. Some information simply tells you what needs to be checked or handled. Depending on the business and the data available, examples include:

These alerts don't all have the same importance or urgency. The whole point of the dashboard is to surface the exceptions that matter, rather than forcing the store owner to check every tool separately.

How many KPIs should a dashboard show?

There's no magic number. A dashboard with 30 KPIs isn't necessarily better than one with 8. Each KPI should answer a useful question. If nobody knows what decision to make when a figure changes, it may not belong on the dashboard. A simple approach is to ask, for each piece of information:

• Why am I looking at it?
• How often can it change?
• What will I do if it changes sharply?

This helps you avoid dashboards full of data nobody really uses.

Not every KPI needs checking at the same frequency

Not every KPI needs to be checked daily. Some are useful every day:

• revenue
• transactions
• urgent incidents or alerts
• major stockouts

Others are better suited to a weekly review:

• average basket
• category trends
• inventory turnover
• customers who are starting to lapse

And some KPIs can be analyzed over longer periods. The right frequency mainly depends on how quickly a piece of information can lead to a useful decision.

From dashboard to analysis

A dashboard helps you spot a signal, but it doesn't necessarily explain the cause. Imagine you see:

Revenue: -8%

The dashboard shows there's a gap. The next step is to ask:

“Why did my revenue drop this week?”

The analysis can then look at the number of transactions, the average basket, the categories involved or the items that contribute most to the change. You move from see → understand → decide. That's the key difference between tracking KPIs and analyzing sales.

How does AI complement a dashboard?

AI doesn't necessarily replace the dashboard. The two complement each other. A dashboard is great for a quick look at the KPIs you track regularly. AI becomes especially useful when you need to dig into a question:

“Summarize this week's key points for me.”

Then:

“Why did my revenue drop?”

And next:

“Which items mainly explain this drop?”

The analysis can evolve question by question, without having to set up every filter or report in advance.

How can Gillia personalize your dashboard?

With Gillia, an analysis you use every day can be pinned to the dashboard: it becomes an always up-to-date indicator (a line graph, chart or table) that you can filter by location, sales associate and time period. For example:

“Show me yesterday's revenue, my average basket and the products about to run out of stock.”

Another store may prefer to track:

“My sales by location, products with no sales in 30 days and customers to follow up with.”

The idea isn't to force the same dashboard on every store: what you track can match your real priorities. You can also ask Gillia to dig deeper whenever a KPI needs a closer look.

When the goal is to act rather than just look (sending a report every Monday, preparing a reminder every morning), a routine takes over: it runs at the time you choose and reports back by email, in Gillia or both.

From signal to action

A KPI only matters if it can lead to a decision. An item close to running out may prompt you to prepare a restock order. A product whose sales have been slowing for a while may need a look at its stock levels, or it risks becoming dead stock. A customer who has stopped coming back can be flagged for follow-up. A drop in revenue can lead you to look for the categories or items behind the gap.

So the useful sequence is KPI → detection → analysis → decision → action. Not every action should be automatic. When it has a real impact on the business (an order, a promotion, a message to customers), it should stay under your control and require your approval.

To learn more, explore Gillia's use cases: reading dashboard alerts or triggering a quick action from an alert.

To go further, explore the Gillia POS: every sale updates your inventory, customer records and online store.