Sales trends

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What this does

The Sales trends report shows daily sales and order counts over time. Use this to quickly see if sales are up or down, identify your busiest days, and compare performance across delivery channels, dine-in, and pickup.

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When to use this

Use this report to answer:

• Are today’s sales higher or lower than yesterday? (Daily comparison)
• Which days of the week are busiest? (Day-of-week patterns)
• Is dine-in or delivery driving more revenue? (Fulfillment type breakdown)
• How much do third-party platforms contribute to sales? (Source analysis)
• Are we trending up or down vs. last month or last year? (YoY and MTD comparisons)

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Filters

The report includes two sticky filters at the top and seven additional filters available by clicking ”+ 7”.

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Serving date/time (primary date filter)

Controls the date range displayed in the chart and metrics. Default: “in the past 7 complete days” (excludes today if it’s a partial day) How to use:

1. Click the date dropdown
2. Select an operator (e.g., “in the past”, “between”, “on the day”)
3. Enter a number and unit (e.g., “7 complete days” or “30 days”)
4. Click Update

Common operators:

• “in the past”: Last X days, weeks, or months (e.g., “in the past 30 days”)
• “between”: Specific date range (e.g., “between Dec 1 and Dec 31”)
• “on the day”: Single day only
• “in the month”: Entire calendar month

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Serving date granularity (group by)

Controls how data is grouped in the chart. Default: “Date” (one bar per calendar day) Options:

• “Date”: One bar per day (most detailed)
• “Week”: One bar per 7-day week
• “Day Of Week”: Aggregates all Mondays together, all Tuesdays together, etc.
• “Month”: One bar per calendar month

Use case: Set to “Day Of Week” to see which days are consistently busiest (e.g., Fridays vs. Tuesdays).

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Outlet(s)

Filter by specific outlet locations. Default: “is any value” (all outlets) How to use:

1. Click ”+ 7” to expand additional filters
2. Click the Outlet(s) dropdown
3. Select “is equal to” and check the outlets you want
4. Click Update

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Order created date/time

Filter by when orders were placed (not when they were served). Default: “anytime” (no restriction) Use case: Analyze ordering patterns separate from service times. Example: A delivery order placed at 5 PM but served at 6 PM has “Order created” = 5 PM and “Serving date” = 6 PM.

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Brand

Filter by brand if you operate multiple brands under one account. Default: “is any value” (all brands)

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Source

Filter by where the order originated. Default: “is any value” (all sources) Available sources:

• POS: In-restaurant point-of-sale (dine-in or walk-up)
• Scan to order: Table QR code scanning
• Kiosk: Self-order kiosk
• Web: Web ordering
• GrabFood: Third-party marketplace
• Deliveroo: Third-party marketplace
• Foodpanda: Third-party marketplace
• Admin: Manual or system-created orders

Use case: Compare how much revenue comes from POS vs. third-party platforms.

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Fulfillment type

Filter by how customers received their orders. Default: “is any value” (all types) Available types:

• Dine-in: Customer eats at restaurant
• Pickup: Customer picks up order
• Delivery: Third-party or restaurant delivery

Use case: See which fulfillment type has the highest average order value or order count.

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Serving hour of day

Filter to specific hours (e.g., breakfast, lunch, or dinner). Default: ”= any value” (all hours) Use case: Compare revenue from breakfast hours (7 AM–11 AM) vs. dinner hours (5 PM–9 PM).

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Serving day of week

Filter to specific days (e.g., weekdays vs. weekends). Default: “is any value” (all days) Available values: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday Use case: Compare weekday vs. weekend sales to plan staffing.

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Summary metrics

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Month-to-date net sales

Shows net sales (excluding tax) from the beginning of the current month through today. Comparisons displayed:

• vs. last month (same period): e.g., ”↓ $216.49 (−35%)” means Jan 1–7 this year vs. Dec 1–7 last month
• vs. last year (same period): e.g., ”↑ $395.50 (27%)” means Jan 1–7 this year vs. Jan 1–7 last year

Red arrow (↓) = sales decreased. Green arrow (↑) = sales increased.

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Week-to-date net sales

Shows net sales (excluding tax) from the beginning of the current week through today. Comparisons displayed:

• vs. last week: e.g., “$0.00 (0%)” means sales from 7 days ago
• vs. 4 weeks ago: e.g., ”↑ $54.11 (27%)” means sales from 28 days ago

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Metrics for period (left box)

These metrics apply to the entire selected date range:

• Gross sales: Total revenue before tax and discounts. Includes all order amounts.
• Net sales (excl. tax) Sum: Total revenue after subtracting discounts, service charges, and refunds, but before tax. This is the primary sales metric in Atlas.
• Order count: Number of completed orders in the selected period. Likely excludes cancelled orders and voids.
• Average order value: Net sales ÷ Order count. Shows average amount spent per order.
• Average cover: Average spend per customer or diner. Contact support for clarification on how this differs from Average order value.

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Chart: Daily sales and order count

The main chart shows two data series on dual axes:

• Blue bars (left Y-axis): Net sales (excl. tax) in dollars
• Orange line (right Y-axis): Order count (number of orders)

Interpretation:

• Tall bar + low line: Few high-value orders (e.g., expensive dine-in meals)
• Short bar + high line: Many low-value orders (e.g., cheap delivery items)

X-axis: Serving date (e.g., “Dec 31, 2025”, “Jan 1, 2026”)

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Actions

See Using reports - Downloads, alerts, and scheduled delivery for instructions on exporting data, setting up automated alerts, or scheduling report emails.

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Common workflows

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Check if yesterday’s sales were up or down

What should happen: You can quickly see if yesterday was a high-sales or low-sales day compared to recent trends.

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Identify busiest days to plan staffing

What should happen: You can see which days consistently have the highest sales and order counts (e.g., Fridays and Saturdays). What’s next: Use this data to schedule extra staff for high-sales days.

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Compare dine-in vs. delivery performance

What should happen: You’ll see which fulfillment type drives the most revenue and has the highest average order value. Example insight: “Delivery orders have lower average order value ($14) than dine-in ($22), but we get more of them. Overall, delivery brings in 40% of our weekly revenue.”

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Analyze third-party marketplace performance

What should happen: You’ll see which third-party platform brings in the most revenue and whether direct channels have higher average order values.

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Investigate a sudden drop in sales

What should happen: You’ll identify which channel or time period caused the drop. Example insight: “Sales were down 30% on Jan 5. All sources were down, but dine-in was hit hardest (−50%). Delivery was only −15%. Likely cause: it was a public holiday, so fewer people came in, but delivery customers were still ordering.”

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Compare this week/month to last year

What should happen: You’ll see how sales this week/month compare to the same period last year.

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What should happen

After applying filters and reviewing the report, you should see:

• Updated summary cards (Month-to-date and Week-to-date net sales with comparisons)
• A chart showing net sales (blue bars) and order count (orange line) based on your date range and granularity
• Summary metrics in the left box (Gross sales, Net sales, Order count, Average order value, Average cover)
• All data reflecting the most recent hourly refresh (check the banner at the top for last update time)

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If it doesn’t work

If metrics appear incorrect or the report doesn’t load:

1. Check your date range: Ensure “Serving date/time” includes the time period you want to analyze. If you want today’s sales, change from “in the past 7 complete days” to “in the past 7 days” (not “complete days”).
2. Verify filters: If “Outlet(s)”, “Source”, or “Fulfillment type” are set to specific values, only matching orders will be included. Change to “is any value” to see all data.
3. Check your permissions: You need Manager, Finance, or Admin role to access this report. Contact your account owner if you don’t have access.
4. Wait for data refresh: If you just made changes to orders or outlets, wait for the next hourly refresh (check the banner for last update time).
5. Remove all filters: Click Update after removing brand, outlet, source, and fulfillment type filters to see all data.

If the chart is blank or showing no data:

1. Expand the date range: If you filtered to a narrow range (e.g., “Dine-in” only), you might have no matching orders. Expand to “in the past 30 days” or remove the filter.
2. Check outlet filter: If you selected an outlet with no sales, the chart will be blank. Change to “is any value”.
3. Wait for the page to load: The chart may still be rendering. Wait 2–3 seconds and refresh the page if needed.

If your numbers don’t match your POS system:

1. Check if today is included: “Complete days” excludes today’s partial sales. Change to “in the past 7 days” (not “complete days”) to include today.
2. Check timezone: If orders are time-stamped in UTC but you’re in EST, dates may be off by a day. Verify your account timezone in the three-dot menu → Query info.
3. Check tax handling: This report shows “excl. tax”, meaning tax is not included. Your POS might show “incl. tax”. Subtract tax from your POS total to compare.
4. Check for voided or refunded orders: “Net sales” excludes refunds and may exclude voids. Your POS might show gross sales including voids. Check the “Gross sales” metric instead.
5. Wait for third-party platform sync: GrabFood, Deliveroo, and Foodpanda orders may be delayed. Allow 1–2 hours for third-party orders to appear.

If the issue persists, contact support at [email protected] with:

• The report URL
• The specific metric or chart that’s not working
• The filters you applied
• A screenshot if possible

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Next steps

After reviewing your sales trends report:

• Check the Daily outlet report to see detailed breakdowns by shift and payment method
• View the Sales order report to see line-item details for individual orders
• Review the Discount report to see how promotions affect sales
• Check the Product mix report to see which items are selling best