1. Lighthouse

Level 8 · Customer value bands

The request

From Megan Collins, Head of Sales

“I want to know how concentrated our revenue is. Put every customer who has bought from us into a lifetime value band, and tell me how many customers and what share of revenue each band holds.”

Your goal

Using completed orders and lines with quantity > 0, compute each customer's lifetime revenue (sum of quantity * unit_price * (1 - discount_pct / 100.0)). Put customers in ltv_band: 'under_500' (below 500), '500_to_1999' (500 up to but not including 2000), '2000_to_9999' (2000 up to but not including 10000) or '10000_plus'. Return one row per band with: ltv_band, customers, avg_ltv (average lifetime revenue, rounded to 2), band_revenue (rounded to 2) and pct_of_revenue (band_revenue / total revenue of all customers * 100, rounded to 1).

  • Read from the customers tablenot done yet
  • Read from the orders tablenot done yet
  • Show exactly ltv_band customers avg_ltv band_revenue pct_of_revenuenot 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
    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
    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

    Some questions need several steps, each at a different grain. CTEs let you write them as a chain of named, readable steps instead of nested subqueries.

    Recap: one CTE per step

    WITH step_one AS (...),
         step_two AS (SELECT ... FROM step_one)
    SELECT ... FROM step_two;

    Only one WITH, commas between the steps, and each step can read the ones above it. For this ticket:

    1. customer_ltv: order lines summed per customer. One row per customer who completed an order.
    2. banded: the same rows with a CASE label.
    3. final SELECT: one row per band.

    Run each step on its own while you build it. The starter shows the first ten rows of step one; check that the numbers look like money before you band them.

    Reading the result

    ltv_band customers avg_ltv pct_of_revenue
    under_500 468 214.80 1.7
    500_to_1999 649 1,103.54 12.1
    2000_to_9999 486 4,277.26 35.3
    10000_plus 127 23,628.52 50.9

    127 customers, about 7% of the 1,730 who completed an order, bring in half of all revenue. That is the kind of concentration finance and sales want to see: losing a handful of accounts in the top band would hurt more than losing all 468 in the bottom one.

    The CASE conditions are checked from top to bottom and the first true one wins. That is why WHEN ltv < 2000 does not need ltv >= 500: anything below 500 was already labelled.

    Common mistakes

    • Banding at the wrong grain. A CASE on order lines bands lines, not customers.
    • Dividing by the wrong total. SUM(ltv) inside the grouped query is the band’s own total. The grand total needs its own subquery on the CTE (or a window function, next chapter).
    • Text bands that sort badly. ‘10000_plus’ sorts before ‘2000_to_9999’ alphabetically. If the order matters, add a numeric band_order column.

    In SQL Server

    CTEs are written the same way, but the statement before WITH must end with a semicolon, which is why you often see ;WITH in SQL Server code. CTEs cannot be followed by ORDER BY inside the CTE without TOP.

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