May 20, 2026
The Analytics page helps you understand the relationship between your revenue and advertising spend. It calculates Return on Ad Spend (ROAS) automatically and visualizes daily trends so you can see which campaigns are driving results.
Three metrics at the top of the page provide a quick overview for the selected period:
Filter data by:
The date filter applies to both orders (for revenue) and expenses (for ad spend).
Shows how many units customers bought each day in the selected period. Each bar is the total quantity sold (line item quantity minus refunds) for that day. A dashed orange line marks the period average per active day.
When the selected range supports it, the chart also compares against the previous comparable period so you can see whether sales are picking up or slowing down. The previous period is chosen automatically:
When a comparison is active, each day's bar is paired with a grey "Previous period" bar (drawn to the left), a thin dashed grey line marks the previous-period average, and a badge above the chart shows the change versus the previous period in both absolute units and percent.
Shows how customers tend to buy: how many orders contained 1 item, 2 items, 3 items, and so on. Each bar represents the number of orders with that exact item count. Item count is the line-item quantity minus refunds, summed across the order. Orders that net out to zero items (fully refunded) are excluded.
Badges above the chart summarise the period: the total number of orders counted, the average items per order, and – when a comparison is available – the change in average items per order versus the previous comparable period.
When the selected range supports it, each bucket also shows a grey "Previous period" bar to the left of the current one, with a thin dashed grey line marking the previous-period average. The previous period is picked using the same rules as the Daily items sold chart (today vs. yesterday, this month vs. last month, etc.); all-time has no comparison.
Shows how much customers spend per order, day by day: each point is the day's net payments divided by its orders. Days without orders leave a gap. Badges above the chart give the period's average order value, the change against the previous comparable period (picked with the same rules as the Daily items sold chart), and how many orders the average rests on; a dashed line marks the previous period day by day.
An interactive chart shows daily revenue and ad spend side by side. Each advertising source (e.g. Google Ads, Meta Ads) appears as a separate series so you can see exactly where your budget is going.
Revenue is the sum of net payments across all active orders for each day. Net payment is the amount actually received after refunds and adjustments. Ignored orders are excluded from the calculation.
Ad spend is pulled from your expenses that are categorized as "Ads". If an expense covers a date range (e.g. a monthly ad bill), the amount is spread evenly across each day in that range, clipped to the selected period.
For VAT-registered shops, the net (ex-VAT) amount is used, and linked VAT expenses are excluded to avoid double-counting.
Above the ad spend chart, the Marketing tab shows the period's new customers (first order in the period), returning customers, the cost per new customer – ad spend divided by new customers – and the share of revenue that came from returning customers. Read it next to ROAS: a channel can show a fine ROAS while bringing only one-off buyers.
Below the chart, a table breaks down ad spend by expense name. Each row shows:
This helps you compare spending across advertising channels at a glance.
Under the ads breakdown, this section counts scans of every QR code you've put out: the labels printed from the Products page and the page codes created under Settings > QR codes. It shows the total for the selected date range and the busiest codes. Product rows name the product and variant – click one to open its detail page, where every variant lists its own scan count. Page rows are marked Page and name the spot you put the code up in.
Only scans inside the selected date range are counted, so you can compare one period against another.
To see data on the Analytics page:
Open Analytics > Best sellers to see what's actually moving in your store. The page ranks items by net units sold – ordered quantity minus refunded or returned units, with cancelled and ignored orders excluded – within the selected date range (today, this month, last month, all time, or a custom range) and offers three views:
Every view also shows how many units came back ("Times returned") and what share of sales that is, so a strong seller with a weak return rate is easy to spot. Exchanges are not counted.
Each row links to the product or variant detail page so you can drill in.
Open Analytics > Option trends to see how buyers' choices shift over time – whether the mid sizes are gaining on the small ones, or a new colour is eating into the old favourite. Every line on the chart is one option value or one variant, drawn across the selected date range.
Two measures are available. Share – the default – shows each value's percentage of the units sold in the period, so a value that holds its share through a quiet month reads as steady rather than falling. Units shows the raw counts. The chart groups sales by day, week or month; Auto picks days for ranges up to a month, weeks up to about six months, and months beyond that.
The eight best-selling values keep their own lines and everything else is folded into Other. The table below the chart lists every value with its units, share, and how that share moved against the previous period of the same length.
Units are counted the same way as on Best sellers: ordered quantity minus refunded or returned units, dated by the order, with cancelled and ignored orders excluded. Option values are read from the variant as it is today, so a renamed value carries its history with it.
Customer value by first-order month groups customers by the month of their first order and shows what each group has spent since, after refunds: in total, per customer, and per customer within the first 3, 6 and 12 months (blank while that window is still running). Compare months to see whether newer customers are worth more or less than older ones.
New vs returning revenue by month splits each month's revenue between first orders (new customers, guest orders included) and orders from customers who had bought before, with the returning share.
The Customers tab shows orders by regular customers, how long customers take to buy again, and Retention by first-order month: for each month in which customers placed their first order, how many they were and what share of them ordered again one to six months later. A blank cell means that month hasn't started yet. Guest orders and ignored orders are left out.
The Fulfillment tab shows fulfillments and items shipped per day, the order-to-fulfillment time against the previous period, returns and exchanges, and how fulfillment speed relates to repeat purchases.
Fulfillment SLA shows how the shop keeps its promises: the share of shipped orders that went out within 24 and 48 hours, the share of ship-by dates met (an open order past its date counts as missed), a week-by-week table, and the current backlog – how many paid orders are still waiting and how old they are.
Delivery by carrier follows the period's parcels after they leave: per carrier, how many were delivered, are still moving, or came back, lost or cancelled, the average and median days from shipment to the carrier's delivered status, and the share delivered within 2 and 5 days. Carriers Panelque does not track stay "in transit".
Fulfillment by team member lists the period's orders by the team member they are assigned to: orders, how many shipped, items shipped, the average time from order to shipment, and Late – orders shipped after their ship-by date or still open past it. Orders nobody is assigned to appear as Unassigned. Assign orders on the Orders page.
The Returns tab shows how much of what you sell comes back:
Use it to spot problem products - a variant that dominates the returned list usually has a sizing, quality, or description issue worth fixing.
Open Analytics > Packaging waste to see the weight of packaging your shop ships to each country, broken down by material – the numbers EU Packaging and Packaging Waste Regulation (PPWR) producer registers ask for.
To feed the report, open each packaging component in Inventory (boxes, mailers, fillers), edit it, and set:
From then on, whenever tracked packaging is used on an order, its weight counts toward the order's destination country. The report shows:
Filter by date range like the other analytics pages. The destination country comes from the order's customer.
Open Analytics > Compare to see how a period is tracking against another on a day-by-day line chart. Pick a primary range with the date selector (today, this month, last month, or a custom range) and a comparison range with the second selector – it defaults to the same period one year ago, or you can choose the previous period or a custom range.
Switch between Orders, Revenue, and Items with the tabs at the top. The two ranges are overlaid by day-of-month so the same day lines up on both lines, and the totals show the difference between them.
When the primary range includes days that haven't happened yet – such as the rest of the current month – those days show a forecast (a dashed line) based on your recent daily trend and weekday pattern.
Open Analytics > P&L for the statement itself, one column per month plus a total, for the last 3, 6 or 12 months. It runs from merchandise sales through discounts, refunds, gift card sales and VAT to Net revenue, takes off the cost of goods sold for Gross profit, then packing and labour, postage, payment fees and Shopify fees for the Contribution margin, then operating expenses by category and shrinkage (write-offs) for Operating profit, and finally taxes for Net profit. Export CSV downloads the same table.
A few rules make the figures add up:
How complete these figures are lists, per month, the orders counted, orders hidden by your plan, orders shipped without a product cost or without packing and postage, and orders placed before Panelque was installed. Profit is overstated by whatever those orders are missing, so check them before trusting a month.
Connect Claude or ChatGPT to Panelque and ask about your costs, margins, stock and expenses in plain language. Read-only, and every connection is revocable on its own.
September 20, 2026A QR code pointing straight at your store tells you nothing. Panelque gives each code its own short link, named after the spot you put it up in, so the door and the counter are counted separately.
September 20, 2026All-time records for your shop: best revenue day, largest order, the closest two orders ever landed, top ROAS – and the one record you would rather not hold.
September 12, 2026Panelque ranks best sellers by what stayed sold – ordered units minus what came back – and shows the return rate next to every row. Sometimes the top of the list changes.
September 03, 2026The EU's Packaging and Packaging Waste Regulation is here. Panelque now tracks the weight and material of your packaging per destination country – the exact numbers PPWR reporting asks for.
August 25, 2026