1. Lighthouse

Level 4 · Churn without subscriptions

The request

From Angela Rivera, Head of Customer Support

“We want to start a win-back email for customers we've lost. Nobody cancels with us, they just go quiet. How many have gone quiet, and is it worse for business accounts or for consumers?”

Your goal

Use booked orders (status IN ('completed', 'refunded')). A customer has churned if their last booked order is before 2025-07-04, i.e. they have no booked order in the 180 days up to 2025-12-31. Only customers whose first booked order is before 2025-07-04 can churn, so only they are counted. Return segment (from customers), customers (eligible customers), churned and churn_pct (churned × 100 / customers, rounded to 1), sorted by segment.

  • Read from the customers tablenot done yet
  • Read from the orders tablenot done yet
  • Show exactly segment customers churned churn_pctnot 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

    Nobody cancels a sofa

    A subscription business knows exactly when a customer leaves: they cancel. A store does not. Its customers simply stop ordering, and the only evidence is silence. So churn has to be defined: a customer has churned if they have not placed a booked order for some number of days.

    Here that window is 180 days, measured back from the as-of date, 2025-12-31. Subtract 180 days and you get 2025-07-04. A customer whose last booked order is before that date has churned:

    COUNT(*) FILTER (WHERE last_at < DATE '2025-07-04') AS churned

    Level 5 looks at how to choose the window. For now, take 180 days as given.

    Who can churn at all

    The denominator is the part people get wrong. A customer who first bought on 2025-11-20 has not had 180 days to go quiet. Counting them as “not churned” is like counting a newborn as “has never been to hospital”: true, but meaningless. Only customers whose first booked order is before the cut-off date are eligible:

    WHERE p.first_at < DATE '2025-07-04'

    Leave that filter out and the rate drops by about ten points, simply because many new customers arrived in the second half of 2025. A strong holiday season would then “reduce churn” on the dashboard without a single old customer coming back.

    Only paid orders count as activity

    Recency uses booked orders. A customer whose only recent order was cancelled, or is still pending, has not bought anything in the window. If you count those orders, a failed checkout keeps someone off the win-back list.

    Splitting by segment

    Business accounts at Harbor & Pine order often and in volume, so long silences are rarer for them. Consumers buy a sofa, then maybe a lamp, and then often nothing for a long time. A single churn rate blends both, and because consumers are most of the base, it mostly reports consumer behaviour. Split by the segment the action will target: the win-back email goes to people, not to an average.

    Common mistakes

    • Every customer in the denominator. New customers cannot have churned yet.
    • Counting cancelled or pending orders as activity.
    • Calling churned customers “lost”. Some come back after a long gap. Level 6 counts them.

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