1. Lighthouse

Level 1 · Desktop versus mobile conversion

The request

From Daniel Foster, Mobile Product Manager

“Leadership keeps saying the mobile site 'converts terribly'. Before I defend it or fix it, I need the real numbers: how often does a desktop visit end in an order, how often does a mobile one, and how big is the gap?”

Your goal

Set result to a dict with "desktop" and "mobile", the conversion rate (share of sessions with converted True) for each device, and "lift", desktop's relative lift over mobile (desktop rate / mobile rate - 1). Compute the lift from the unrounded rates, then round all three to 4 decimals.

  • Use the web_sessions DataFramenot done yet
  • Store a dict in resultnot done yet

Your code

Python

Loading the editor…

    ResultPreview of web_sessions · press Run to see yours
    Preview of web_sessions
    session_idcustomer_idstarted_atdevicesource
    1NaN2023-01-01 23:02:24mobilepaid
    2182023-01-01 15:07:49desktoppaid
    3NaN2023-01-01 16:55:46desktopdirect
    4NaN2023-01-01 12:43:28mobileorganic

    Mote checks your result

    Press Run. Each row that matches the goal becomes a firefly in your lantern.

    DataFrames

    Loaded for you as pandas DataFrames, plus pd and np.

    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
    1NaN2023-01-01 23:02:24mobilepaid1false
    2182023-01-01 15:07:49desktoppaid1false
    3NaN2023-01-01 16:55:46desktopdirect7false
    4NaN2023-01-01 12:43:28mobileorganic3false
    5NaN2023-01-01 13:06:21mobilesocial2false
    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.comAtlantaGA2022-06-02consumerNaN
    3HenryBrookshbrooks@lakesidecafe.comLos AngelesCA2022-06-04businessNaN
    4BrianGrahambgraham@bluecedarhomes.comSeattleWA2022-06-06businesspinterest
    5KyleJimenezkjimenez27@yahoo.comIndianapolisIN2022-06-07consumerNaN
    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 LoveseatFurnitureSofas1659.00NaN2023-09-11true
    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 20:51:19completedweb0.00NaN
    10021562023-01-02 07:58:46completedweb0.00NaN
    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
    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 OfficerNaN2016-03-01285000
    2RobertKimExecutiveChief Operating Officer12017-01-09235000
    3MeganCollinsSalesHead of Sales12018-04-16172000
    4ChloeGutierrezSalesAccount Executive32020-01-2172000
    5FrankBellSalesAccount Executive32023-05-2173000
    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

    Conversion rate is a mean

    A conversion rate is the share of visits that end in an order: conversions divided by visits. In pandas you do not need to count anything. converted is a True/False column, True counts as 1 and False as 0, so its mean is the share of True values:

    web_sessions["converted"].mean()                       # 0.0298 overall
    web_sessions.groupby("device")["converted"].mean()
    device
    desktop    0.038718
    mobile     0.024131
    tablet     0.028133

    Desktop visits end in an order 3.87% of the time, mobile ones 2.41%. Always look at the counts too: 14,541 desktop sessions, 21,549 mobile, and only 3,910 tablet. Small groups have noisy rates.

    Two ways to say “bigger”

    The same gap can be written two ways, and mixing them up is one of the most common errors in reports.

    • Absolute difference, in percentage points: 3.87% - 2.41% = 1.46 points.
    • Relative lift, in percent: 3.87 / 2.41 - 1 = 0.60, so desktop converts 60% more often.

    “Conversion rose 2%” is ambiguous: from 3.0% to 5.0% (2 points) or from 3.0% to 3.06% (2% relative)? Write “points” for absolute differences and ”% higher” for relative ones, and give the base rate.

    What the gap does and doesn’t mean

    The numbers say mobile visits convert less. They do not say the mobile site is worse. Mobile visitors browse on the bus, compare prices, and come back on a laptop to buy. A session-level rate cannot see that journey. Comparing groups that chose themselves (people pick their device) shows a difference, not its cause. Only a controlled test, where a coin decides who sees what, can tell you whether a change to the mobile site would move the number. That is what the rest of this chapter builds toward.

    Common mistakes

    • Lift from rounded rates. 0.0387 / 0.0241 - 1 gives 0.6058, not 0.6045. Round at the end.
    • Points versus percent. A 1.46-point gap is a 60% lift. Say which one you mean.
    • Averaging rates across groups. The overall rate is not the mean of the three device rates: (3.87 + 2.41 + 2.81) / 3 = 3.03%, but the real overall rate is 2.98%, because mobile has more sessions. Recompute from the rows.
    • A rate without its count. A 6% rate on 50 sessions is three orders. Show n.

    Interview note

    “Our conversion went up 10%, is that good?” is a classic follow-up question. A strong answer asks whether that is 10% relative or 10 points, from what base, on how many sessions, and compared with what: the same period last year, or a randomly assigned control group.

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