1. Lighthouse

Level 9 · Three-month trend per category

The request

From Thomas Whitaker, Head of Merchandising

“Monthly category revenue jumps around too much to plan buying from. For 2025, give me each category's monthly revenue with a three-month rolling average next to it, and make sure January and February include the end of 2024.”

Your goal

Using completed orders and lines with quantity > 0, compute net revenue (sum of quantity * unit_price * (1 - discount_pct / 100.0)) per category per month for all months. Add revenue_3m_avg: the average of that category's monthly revenue over the month and the 2 months before it. Return only the months of 2025 with: category, month (first day of the month, as a DATE), net_revenue and revenue_3m_avg (both rounded to 2). Sort by category, then month.

  • Read from the products tablenot done yet
  • Read from the orders tablenot done yet
  • Show exactly category month net_revenue revenue_3m_avgnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of products · press Run to see yours
    Preview of products
    product_idnamecategorysubcategoryunit_price
    101Vale Velvet LoveseatFurnitureSofas1659.00
    102Alder Oak Coffee TableFurnitureTables919.00
    103Orchard Rattan Dining ChairFurnitureChairs789.00
    104Upland Oak BookcaseFurnitureStorage429.00

    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.

    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
    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
    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

    A moving average replaces each value with the average of a small window around it, which smooths out noise while keeping the trend. The window frame says exactly which rows are included.

    Recap: the frame

    AVG(net_revenue) OVER (
      PARTITION BY category
      ORDER BY month
      ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
    )
    • PARTITION BY category: each category is averaged on its own.
    • ORDER BY month: “preceding” means earlier months.
    • ROWS BETWEEN 2 PRECEDING AND CURRENT ROW: this row and the two before it, three months in total. For a longer window, change the 2 (11 PRECEDING gives a 12-month average).

    ROWS counts rows, not months, so it is only a true three-month window when no month is missing. Every category here has all 36 months from 2023 to 2025; with gaps you would join a calendar spine first.

    Filter after the window

    Windows only see the rows left after WHERE. Here is Outdoor, computed both ways:

    month net_revenue 3m avg (correct) 3m avg (filtered first)
    2025-01 21,520.57 21,158.42 21,520.57
    2025-02 7,369.74 16,602.84 14,445.16
    2025-03 38,755.54 22,548.62 22,548.62

    From March on the two agree, because the window no longer reaches back into 2024. The first two months are quietly wrong when the filter runs first. So: compute the window in a CTE on full history, then keep 2025 in the outer query.

    The table also shows why merchandising wants smoothing: Outdoor revenue fell to $7,369.74 in February 2025 and then jumped to $38,755.54 in March as the season began.

    Common mistakes

    • No PARTITION BY. January 2025 of Furniture would average in the last months of Decor.
    • RANGE instead of ROWS with duplicate months gives different frames; ROWS is the predictable choice.
    • Rounding before averaging. Round only the final output.

    In SQL Server

    The same frame syntax works: AVG(net_revenue) OVER (PARTITION BY category ORDER BY month ROWS BETWEEN 2 PRECEDING AND CURRENT ROW). Note that AVG of an integer column returns an integer in SQL Server, so cast to decimal first when averaging counts.

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