Lighthouse

Level 1 · Sessions to orders, honestly

From Jordan Reyes, Head of Marketing

“Someone in the leadership meeting said our website converts 27% of visits, because we had about 4,400 orders and 16,000 sessions last year. That can't be right. What is our real conversion rate, and how has it moved since 2023?”

Your goal

From web_sessions, return one row per year with: year (the year of started_at), sessions (number of sessions), conversions (sessions with converted = true) and conversion_rate_pct (conversions * 100 / sessions, rounded to 2). Sort by year ascending.

Read from the web_sessions tablenot done yet
Show exactly year sessions conversions conversion_rate_pctnot done yet
3 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

What a conversion rate is

A conversion rate is the share of some unit that did the thing you wanted. In a web funnel the unit is usually the session: one visit, from arrival to leaving. The rate is

converted sessions * 100.0 / all sessions

Both counts come from web_sessions, in the same year, under the same filters. That is what makes the number mean something: every session in the denominator had the chance to be in the numerator.

Where the 27% came from

The number in the ticket divides 4,409 booked orders in 2025 by 16,225 web sessions. It looks like a conversion rate and is not one:

Orders come from three channels: web, app and marketplace. In 2025 only 1,805 booked orders were placed on the web. App and marketplace buyers never create a web session.
web_sessions has no order_id. It is a separate funnel dataset, and it does not cover every web order either: 2025 has 500 converted sessions against those 1,805 web orders.
One customer can visit five times and buy once. Orders and sessions count different things.

Dividing a count from one table by a count from another only works when the numerator is a subset of the denominator. Here it is not, so the result is meaningless, however precise it looks.

Session, visitor or order?

The same funnel gives different rates depending on the unit:

Per session: 500 / 16,225 = 3.08%. This is the standard web metric.
Per buyer: the 500 converted sessions belong to 431 different customers; a customer who buys twice counts once.
Per order: needs orders linked to sessions, which this data does not have.

None of them is wrong. Mixing them is. A report that says “conversion rate” without the unit invites the 27% mistake.

Reading the trend

2.99% in 2023, 2.86% in 2024, 3.08% in 2025. Sessions grew every year, so a flat rate still means more conversions: 317, 376, then 500. When someone asks whether conversion “improved”, show the rate and the counts together.

Common mistakes

Dividing orders by sessions. Different tables, different units, no link between them.
Integer division. COUNT(*) FILTER (...) * 100 / COUNT(*) on integers rounds down to whole percents. Use 100.0.
Dropping anonymous sessions. They are real visits. Removing them makes every rate look better.

Level 1 · Sessions to orders, honestly

Your goal

Your query

Reference solution

Mote checks your result

What Jordan gets, and why

Tables

The lesson

What a conversion rate is

Where the 27% came from

Session, visitor or order?

Reading the trend

Common mistakes

Lantern lit!

Level 1 · Sessions to orders, honestly

The request

Your goal

Your query

Reference solution

Mote checks your result

What Jordan gets, and why

Tables

The lesson

What a conversion rate is

Where the 27% came from

Session, visitor or order?

Reading the trend

Common mistakes

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