1. Lighthouse

Level 4 · A simple predictive LTV

The request

From Andrew Ramos, Marketing Manager

“I need one forward-looking LTV for the planning model, not history. The usual formula: what a customer spends per order, times orders per year, times how many years they stay. Can you build it from 2025 and show the margin version too?”

Your goal

Return one row with: aov (2025 net revenue / 2025 booked orders), orders_per_customer (2025 booked orders / customers with a booked order in 2025), annual_churn_pct (share of customers with a booked order in 2024 who have none in 2025, × 100), lifespan_years (100 / annual_churn_pct), revenue_ltv (aov × orders_per_customer × lifespan_years) and margin_ltv (revenue_ltv × 0.5384, the 2025 gross margin rate). Compute everything from unrounded values and round each output to 2. The CTE order_rev (one row per booked order with its net revenue) is in the starter.

  • Read from the orders tablenot done yet
  • Read from the order_items tablenot done yet
  • Show exactly aov orders_per_customer annual_churn_pct lifespan_years revenue_ltv margin_ltvnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of orders · press Run to see yours
    Preview of orders
    order_idcustomer_idorder_datestatuschannel
    10011252023-01-01 20:51:19completedweb
    10021562023-01-02 07:58:46completedweb
    1003222023-01-02 10:25:27completedmarketplace
    1004482023-01-02 11:21:51completedmarketplace

    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.

    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
    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
    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

    From history to a forecast

    Historic LTV only covers customers who have already had time to buy. Planning needs a number for a customer acquired today. The classic back-of-envelope formula multiplies three things:

    • AOV, net revenue per order;
    • purchase frequency, orders per active customer per year;
    • expected lifespan, how many years a customer keeps buying.

    AOV × frequency is what an active customer spends in a year. Times lifespan, it is what they spend in total.

    Lifespan from churn

    Nobody observes lifespan directly; it comes from churn. If a share c of this year’s customers does not come back next year, and that share stays constant, the average customer stays 1 / c years. Of the 1,003 customers with a booked order in 2024, 238 have none in 2025: annual churn is 23.73%, so the expected lifespan is 4.21 years.

    -- 2024 buyers with no 2025 order
    FROM c24 LEFT JOIN c25 USING (customer_id)
    WHERE c25.customer_id IS NULL

    Revenue LTV and margin LTV

    With 2025’s AOV of $503.82 and 3.01 orders per buyer, revenue LTV is $6,385.71. But the store does not keep revenue: it pays for the products. Multiplying by the gross margin rate (53.84% of 2025 net sales on kept lines with a known product cost) gives the margin LTV, $3,438.07. That is the number to set against acquisition cost, because CAC is paid out of margin.

    What the formula assumes

    Every input is an average, and the formula assumes they stay fixed for years: same basket size, same frequency, a constant churn rate. Reality breaks each one. New customers buy less often than the loyal base that dominates the 2025 frequency, churn is usually highest in a customer’s first year, and 4.21 years is longer than the data itself. Treat the result as an order of magnitude, and show how much it moves if churn is 30% instead of 24%.

    Common mistakes

    • Rounding the inputs first. Rounded AOV, frequency and lifespan multiplied together give $6,384.46, more than a dollar off. Round only the outputs.
    • Frequency over all customers ever. Orders per year must divide by customers active that year.
    • Calling revenue LTV “profit”. It is not; say which version you report.

    Interview note

    “How would you estimate LTV for a new e-commerce store?” Give this formula, say that lifespan comes from 1 / churn, switch to margin, and name the assumptions. That is a complete answer.

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