1. Lighthouse

Level 1 · Gross sales, discounts, net sales

The request

From Laura Bennett, Head of Finance

“Marketing says 2025 sales were $2.6M, my ledger says less. Before we argue, give me three numbers for 2025 on our definitions: gross sales, what discounts took off, and net sales.”

Your goal

Return one row with gross_sales, discounts and net_sales for 2025, each rounded to 2 decimals. Use booked orders only (status IN ('completed', 'refunded')), valid lines only (quantity > 0) and order_date >= '2025-01-01' AND order_date < '2026-01-01'. Gross = quantity * unit_price, discount = gross × discount_pct / 100, net = gross − discount.

  • Read from the orders tablenot done yet
  • Read from the order_items tablenot done yet
  • Show exactly gross_sales discounts net_salesnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of orders · press Run to see yours
    Preview of orders
    order_idcustomer_idorder_datestatuschannel
    10011252023-01-01 20:51:19completedweb
    10021562023-01-02 07:58:46completedweb
    1003222023-01-02 10:25:27completedmarketplace
    1004482023-01-02 11:21:51completedmarketplace

    Mote checks your result

    Press Run. Each row that matches the goal becomes a firefly in your lantern.

    Tables

    Harbor & Pine store data, ready to query.

    orders12,000 rows · 7 columns
    • order_idinteger
    • customer_idinteger
    • order_datetimestamp
    • statusvarchar
    • channelvarchar
    • shipping_feedecimal
    • coupon_codevarchar
    First 5 rows of orders
    order_idcustomer_idorder_datestatuschannelshipping_feecoupon_code
    10011252023-01-01 20:51:19completedweb0.00NULL
    10021562023-01-02 07:58:46completedweb0.00NULL
    1003222023-01-02 10:25:27completedmarketplace4.99WELCOME10
    1004482023-01-02 11:21:51completedmarketplace4.99WELCOME10
    1005822023-01-02 11:43:36completedweb0.00WELCOME10
    order_items30,338 rows · 6 columns
    • order_item_idinteger
    • order_idinteger
    • product_idinteger
    • quantityinteger
    • unit_pricedecimal
    • discount_pctdecimal
    First 5 rows of order_items
    order_item_idorder_idproduct_idquantityunit_pricediscount_pct
    11001121931.990.00
    21001154311.990.00
    31002154411.990.00
    410021941149.000.00
    51002193219.990.00
    customers2,018 rows · 9 columns
    • customer_idinteger
    • first_namevarchar
    • last_namevarchar
    • emailvarchar
    • cityvarchar
    • statevarchar
    • signup_datedate
    • segmentvarchar
    • referral_sourcevarchar
    First 5 rows of customers
    customer_idfirst_namelast_nameemailcitystatesignup_datesegmentreferral_source
    1OwenHudsonowen.hudson@icloud.comSan FranciscoCA2022-06-02consumersearch
    2DouglasMendozadmendoza69@gmail.comAtlantaGA2022-06-02consumerNULL
    3HenryBrookshbrooks@lakesidecafe.comLos AngelesCA2022-06-04businessNULL
    4BrianGrahambgraham@bluecedarhomes.comSeattleWA2022-06-06businesspinterest
    5KyleJimenezkjimenez27@yahoo.comIndianapolisIN2022-06-07consumerNULL
    products120 rows · 8 columns
    • product_idinteger
    • namevarchar
    • categoryvarchar
    • subcategoryvarchar
    • unit_pricedecimal
    • unit_costdecimal
    • launched_ondate
    • is_activeboolean
    First 5 rows of products
    product_idnamecategorysubcategoryunit_priceunit_costlaunched_onis_active
    101Vale Velvet LoveseatFurnitureSofas1659.00NULL2023-09-11true
    102Alder Oak Coffee TableFurnitureTables919.00503.502024-05-16true
    103Orchard Rattan Dining ChairFurnitureChairs789.00429.872023-05-27true
    104Upland Oak BookcaseFurnitureStorage429.00214.702022-12-20true
    105Avery Leather Bed FrameFurnitureBeds449.00232.702021-08-31true
    returns1,673 rows · 5 columns
    • return_idinteger
    • order_item_idinteger
    • returned_ondate
    • reasonvarchar
    • refund_amountdecimal
    First 5 rows of returns
    return_idorder_item_idreturned_onreasonrefund_amount
    1142023-01-10damaged83.69
    2422023-01-10wrong_item19.99
    31052023-01-14wrong_item10.99
    4242023-01-16changed_mind26.99
    51262023-01-16late20.99
    employees60 rows · 8 columns
    • employee_idinteger
    • first_namevarchar
    • last_namevarchar
    • departmentvarchar
    • titlevarchar
    • manager_idinteger
    • hire_datedate
    • salaryinteger
    First 5 rows of employees
    employee_idfirst_namelast_namedepartmenttitlemanager_idhire_datesalary
    1CatherineHaleExecutiveChief Executive OfficerNULL2016-03-01285000
    2RobertKimExecutiveChief Operating Officer12017-01-09235000
    3MeganCollinsSalesHead of Sales12018-04-16172000
    4ChloeGutierrezSalesAccount Executive32020-01-2172000
    5FrankBellSalesAccount Executive32023-05-2173000
    web_sessions40,000 rows · 7 columns
    • session_idinteger
    • customer_idinteger
    • started_attimestamp
    • devicevarchar
    • sourcevarchar
    • pages_viewedinteger
    • convertedboolean
    First 5 rows of web_sessions
    session_idcustomer_idstarted_atdevicesourcepages_viewedconverted
    1NULL2023-01-01 23:02:24mobilepaid1false
    2182023-01-01 15:07:49desktoppaid1false
    3NULL2023-01-01 16:55:46desktopdirect7false
    4NULL2023-01-01 12:43:28mobileorganic3false
    5NULL2023-01-01 13:06:21mobilesocial2false
    marketing_spend3,276 rows · 3 columns
    • spend_datedate
    • channelvarchar
    • amountdecimal
    First 5 rows of marketing_spend
    spend_datechannelamount
    2023-01-01paid492.99
    2023-01-01email37.38
    2023-01-01social200.53
    2023-01-02paid709.13
    2023-01-02email64.13

    The lesson

    Three numbers, one waterfall

    Revenue is not one number but a short waterfall. At the top is gross sales: every unit sold at the price on the order line. Coupons come off next, and what is left is net sales. Refunds come off after that (level 5), leaving net revenue. Finance reports the bottom of the waterfall; marketing likes the top. Both are fine as long as each number says which step it is.

    quantity * unit_price                          -- line gross
    quantity * unit_price * discount_pct / 100     -- line discount
    quantity * unit_price * (1 - discount_pct / 100)  -- line net

    discount_pct is stored in percentage points (0, 10, 15, 20 or 25), so divide by 100.

    Which rows belong

    Two filters decide most revenue disagreements.

    • Status. A cancelled order was never paid. A pending one may still fail. Harbor & Pine counts completed and refunded orders as booked: the customer paid. Refunded orders are booked and then taken back by the refund, which is the honest way to show a sale that came back.
    • Valid lines. 24 order lines have a quantity of zero or less. They are typing errors, not negative sales, so quantity > 0 keeps them out of every total.

    On top of that, the date window is half-open: order_date >= '2025-01-01' AND order_date < '2026-01-01'. order_date is a timestamp, so BETWEEN '2025-01-01' AND '2025-12-31' would drop every order placed after midnight on New Year’s Eve.

    Why the gap matters

    Gross and net are close at Harbor & Pine because only one order in eight uses a coupon. In a business that discounts heavily the gap can be 15 or 20 percent, and a “sales up 10%” headline can hide margin going backwards. Reporting both, with the discount in between, shows where the money went.

    Common mistakes

    • Counting every status. Revenue on cancelled orders is a wish, not a sale.
    • Summing products.unit_price. That is today’s list price. The line’s unit_price is what the customer paid; 30 products changed price in March 2025.
    • Rounding each line. Sum first, round once at the end, or cents add up into dollars.

    Interview note

    “What is the difference between gross and net revenue?” is asked in nearly every analyst screen. Name the steps in order (gross, discounts, net sales, returns, net revenue), say where shipping and tax sit in your company’s definition, and you have answered it better than most.

    Pick what may run on this site. You can change this any time from "Cookie settings" at the bottom of every page.