1. Lighthouse

Level 6 · Costly returns that were our fault

The request

From Rachel Adams, Customer Support Lead

“I'm meeting the warehouse and our carrier next week. I need every 2025 return that was damaged or the wrong item, where we refunded more than $200. Those are the ones that hurt.”

Your goal

From returns, return return_id, order_item_id, returned_on, reason and refund_amount for returns whose reason is 'damaged' or 'wrong_item', whose refund_amount is greater than 200, and whose returned_on date is in 2025 (on or after 2025-01-01 and before 2026-01-01).

  • Read from the returns tablenot done yet
  • Show exactly return_id order_item_id returned_on reason refund_amountnot done yet
  • 69 rowsnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of returns · press Run to see yours
    Preview 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

    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.

    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
    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
    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
    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

    This ticket combines three filters you have already used: a list of allowed values, a number threshold and a date range. The table is new: returns, one row per returned order line, with the refund paid out.

    Three conditions, one WHERE

    Every condition Rachel listed must be true at once, so they are joined with AND. The two reasons go into an IN list:

    SELECT reason, refund_amount, returned_on
    FROM returns
    WHERE reason IN ('damaged', 'wrong_item')
      AND refund_amount > 200
      AND returned_on >= '2025-01-01'
      AND returned_on < '2026-01-01';

    That returns 69 rows. Across all three years there are 477 damaged and 255 wrong-item returns, so the threshold and the year cut the list down to the ones worth a meeting.

    returned_on is a DATE, so <= '2025-12-31' would also work here. The half-open range (>= the first day, < the first day of the next period) is still the habit to keep: it works unchanged when the column is a timestamp, where <= '2025-12-31' silently drops everything after midnight on December 31.

    Count as you go

    A quick way to trust a filter is to add one condition at a time and watch the row count. Start with the reasons, then add the threshold, then the year. Each step should shrink the result, and none should empty it. If adding a condition changes nothing, it is probably not doing what you think.

    Common mistakes

    • Mixing OR with AND without parentheses. reason = 'damaged' OR reason = 'wrong_item' AND refund_amount > 200 keeps every damaged return, cheap ones included. IN avoids the problem.
    • Using >= when the ticket says “more than”. More than $200 is > 200; a refund of exactly $200.00 does not qualify.
    • Typing reasons the way people say them. The stored values are lowercase with underscores, like 'wrong_item'. Check with SELECT DISTINCT reason FROM returns before you filter on text.

    In SQL Server

    The WHERE is identical, and date strings in the '2025-01-01' form compare the same way. To filter by year you will also see YEAR(returned_on) = 2025 there. It reads well, but wrapping the column in a function stops the database from using an index on it, so the half-open range is still the better habit on large tables:

    WHERE reason IN ('damaged', 'wrong_item')
      AND refund_amount > 200
      AND returned_on >= '2025-01-01' AND returned_on < '2026-01-01';

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