Lighthouse

Level 3 · Desktop, mobile and the traffic mix

From Kevin Nguyen, Head of Engineering

“We're deciding whether the next quarter goes into the mobile checkout. For each year, how much of our traffic is on each device, and how does each device convert? I want the share and the rate side by side.”

Your goal

From web_sessions, return one row per year and device with: year (year of started_at), device, sessions, share_pct (that device's sessions * 100 / all sessions in that year, rounded to 2) and conversion_rate_pct (converted sessions * 100 / sessions, rounded to 2). Sort by year, then device, ascending.

Read from the web_sessions tablenot done yet
Show exactly year device sessions share_pct conversion_rate_pctnot done yet
9 rows, sorted as askednot done yet

Your query

SQL

Loading the editor…

Reference solution

ResultPreview of web_sessions · press Run to see yours

Preview of web_sessions
session_id	customer_id	started_at	device	source
1	NULL	2023-01-01 23:02:24	mobile	paid
2	18	2023-01-01 15:07:49	desktop	paid
3	NULL	2023-01-01 16:55:46	desktop	direct
4	NULL	2023-01-01 12:43:28	mobile	organic

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.

web_sessions40,000 rows · 7 columns

session_idinteger
customer_idinteger
started_attimestamp
devicevarchar
sourcevarchar
pages_viewedinteger
convertedboolean

First 5 rows of web_sessions
session_id	customer_id	started_at	device	source	pages_viewed	converted
1	NULL	2023-01-01 23:02:24	mobile	paid	1	false
2	18	2023-01-01 15:07:49	desktop	paid	1	false
3	NULL	2023-01-01 16:55:46	desktop	direct	7	false
4	NULL	2023-01-01 12:43:28	mobile	organic	3	false
5	NULL	2023-01-01 13:06:21	mobile	social	2	false

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_id	first_name	last_name	email	city	state	signup_date	segment	referral_source
1	Owen	Hudson	owen.hudson@icloud.com	San Francisco	CA	2022-06-02	consumer	search
2	Douglas	Mendoza	dmendoza69@gmail.com	Atlanta	GA	2022-06-02	consumer	NULL
3	Henry	Brooks	hbrooks@lakesidecafe.com	Los Angeles	CA	2022-06-04	business	NULL
4	Brian	Graham	bgraham@bluecedarhomes.com	Seattle	WA	2022-06-06	business	pinterest
5	Kyle	Jimenez	kjimenez27@yahoo.com	Indianapolis	IN	2022-06-07	consumer	NULL

products120 rows · 8 columns

product_idinteger
namevarchar
categoryvarchar
subcategoryvarchar
unit_pricedecimal
unit_costdecimal
launched_ondate
is_activeboolean

First 5 rows of products
product_id	name	category	subcategory	unit_price	unit_cost	launched_on	is_active
101	Vale Velvet Loveseat	Furniture	Sofas	1659.00	NULL	2023-09-11	true
102	Alder Oak Coffee Table	Furniture	Tables	919.00	503.50	2024-05-16	true
103	Orchard Rattan Dining Chair	Furniture	Chairs	789.00	429.87	2023-05-27	true
104	Upland Oak Bookcase	Furniture	Storage	429.00	214.70	2022-12-20	true
105	Avery Leather Bed Frame	Furniture	Beds	449.00	232.70	2021-08-31	true

orders12,000 rows · 7 columns

order_idinteger
customer_idinteger
order_datetimestamp
statusvarchar
channelvarchar
shipping_feedecimal
coupon_codevarchar

First 5 rows of orders
order_id	customer_id	order_date	status	channel	shipping_fee	coupon_code
1001	125	2023-01-01 20:51:19	completed	web	0.00	NULL
1002	156	2023-01-02 07:58:46	completed	web	0.00	NULL
1003	22	2023-01-02 10:25:27	completed	marketplace	4.99	WELCOME10
1004	48	2023-01-02 11:21:51	completed	marketplace	4.99	WELCOME10
1005	82	2023-01-02 11:43:36	completed	web	0.00	WELCOME10

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_id	order_id	product_id	quantity	unit_price
1	1001	121	9	31.99
2	1001	154	3	11.99
3	1002	154	4	11.99
4	1002	194	1	149.00
5	1002	193	2	19.99

returns1,673 rows · 5 columns

return_idinteger
order_item_idinteger
returned_ondate
reasonvarchar
refund_amountdecimal

First 5 rows of returns
return_id	order_item_id	returned_on	reason	refund_amount
1	14	2023-01-10	damaged	83.69
2	42	2023-01-10	wrong_item	19.99
3	105	2023-01-14	wrong_item	10.99
4	24	2023-01-16	changed_mind	26.99
5	126	2023-01-16	late	20.99

employees60 rows · 8 columns

employee_idinteger
first_namevarchar
last_namevarchar
departmentvarchar
titlevarchar
manager_idinteger
hire_datedate
salaryinteger

First 5 rows of employees
employee_id	first_name	last_name	department	title	manager_id	hire_date	salary
1	Catherine	Hale	Executive	Chief Executive Officer	NULL	2016-03-01	285000
2	Robert	Kim	Executive	Chief Operating Officer	1	2017-01-09	235000
3	Megan	Collins	Sales	Head of Sales	1	2018-04-16	172000
4	Chloe	Gutierrez	Sales	Account Executive	3	2020-01-21	72000
5	Frank	Bell	Sales	Account Executive	3	2023-05-21	73000

marketing_spend3,276 rows · 3 columns

spend_datedate
channelvarchar
amountdecimal

First 5 rows of marketing_spend
spend_date	channel	amount
2023-01-01	paid	492.99
2023-01-01	email	37.38
2023-01-01	social	200.53
2023-01-02	paid	709.13
2023-01-02	email	64.13

The lesson

Two numbers per segment

A conversion trend is driven by two things: how well each segment converts, and how much of the traffic each segment is. Engineering’s question needs both, per device and per year:

Rate: converted sessions / that device’s sessions.
Share: that device’s sessions / all sessions in the year.

They have different denominators, so they need different tools in one query.

Window over a GROUP BY

The rate is ordinary aggregation inside each (year, device) group. The share needs the total of the year, which is the sum of three groups. A window function on top of the aggregate gives it without a second query:

SUM(COUNT(*)) OVER (PARTITION BY year(started_at))

SQL first groups the rows (one COUNT(*) per year and device), then the window adds those counts within each year and puts the total on every row of that year. Divide and you have the share, and the three shares of a year add up to 100%.

What the table says

Desktop is the best converter every year, at 3.67% to 4.13%. Mobile is the worst, at 2.30% to 2.52%, and it is also the device that is growing: from 47.82% of sessions in 2023 to 57.98% in 2025. Tablet stays near 10% of traffic.

So the store is shifting its traffic toward the device that converts worst. Even if every device improves, the overall rate gets less of the improvement than any single device. Keep this table in mind for the mix-shift quiz later in the chapter.

For Kevin’s decision, the size of the prize matters more than the gap alone. Mobile is the largest and fastest-growing segment, so closing even part of its gap to desktop moves the most conversions. That argument needs both columns.

Common mistakes

Using the grand total as the share denominator. Partition the window by year.
Comparing raw conversion counts between devices. Mobile has the most conversions because it has the most sessions, not because it converts well.
Reporting rates without shares, which hides the mix change that moves the total.

Level 3 · Desktop, mobile and the traffic mix

Your goal

Your query

Reference solution

Mote checks your result

What Kevin gets, and why

Tables

The lesson

Two numbers per segment

Window over a GROUP BY

What the table says

Common mistakes

Lantern lit!

Level 3 · Desktop, mobile and the traffic mix

The request

Your goal

Your query

Reference solution

Mote checks your result

What Kevin gets, and why

Tables

The lesson

Two numbers per segment

Window over a GROUP BY

What the table says

What to recommend

Common mistakes

Cookie choices