1. Lighthouse

Level 8 · Debug: the win-back list is too short

The request

From Lauren Hayes, Email Marketing Manager

“I asked for every customer who signed up in Q1 2025 with their completed orders in November, so I can email the ones who skipped Black Friday. This query only gives me 30 people and nobody has zero. The numbers look off. Can you fix it?”

Your goal

Fix the query. Return every customer with signup_date from 2025-01-01 to 2025-03-31 (126 customers), with: customer_id, email and nov_orders (the number of their orders with status 'completed' placed in November 2025). Customers with no such orders must appear with nov_orders = 0.

  • Read from the customers tablenot done yet
  • Read from the orders tablenot done yet
  • Show exactly customer_id email nov_ordersnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of customers · press Run to see yours
    Preview of customers
    customer_idfirst_namelast_nameemailcity
    1OwenHudsonowen.hudson@icloud.comSan Francisco
    2DouglasMendozadmendoza69@gmail.comAtlanta
    3HenryBrookshbrooks@lakesidecafe.comLos Angeles
    4BrianGrahambgraham@bluecedarhomes.comSeattle

    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.

    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
    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
    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
    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
    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

    The starter has two bugs that hide each other. Finding both, one at a time, is the skill this level practices.

    Spotting the dropped rows

    The quickest check for any LEFT JOIN report: compare the row count with the size of the left side. Q1 2025 has 126 sign-ups, but the starter returns 30 rows. Rows went missing, so a condition removed them.

    For a customer with no matching order, the LEFT JOIN fills every o. column with NULL. The WHERE condition o.status = 'completed' is then NULL = 'completed', which is not true, and the row is dropped. The LEFT JOIN has quietly become an inner join. Any condition on the right table that should only limit what counts as a match belongs in ON:

    LEFT JOIN orders AS o
      ON o.customer_id = c.customer_id
     AND o.status = 'completed'
     AND o.order_date >= '2025-11-01'
     AND o.order_date < '2025-12-01'

    Conditions on the left table, like the signup window, stay in WHERE, because they are meant to remove customers.

    The second bug

    After moving the filters, run it again. Now there are 126 rows, but the smallest nov_orders is 1. A customer with no match still has one row, padded with NULLs, and COUNT(*) counts rows. Count a column from the right table instead: COUNT(o.order_id) skips NULLs, so unmatched customers get 0. Of the 126, 96 placed no completed order in November.

    Common mistakes

    • Moving the signup filter into ON as well. On a LEFT JOIN, a condition on the left table in ON does not remove anyone, so every customer in the table would come back.
    • Writing order_date <= '2025-11-30'. order_date is a timestamp, so an order at 8 PM on November 30 is later than midnight and is lost. Use < '2025-12-01'.
    • Trusting a result because it “looks reasonable”. Thirty engaged customers sounded plausible; only the comparison with the 126 sign-ups showed the gap.

    In SQL Server

    Identical logic. If you want the zero shown explicitly in other contexts, SQL Server offers ISNULL(x, 0) next to the standard COALESCE(x, 0); COUNT already returns 0, so neither is needed here.

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