Lighthouse

Level 7 · Boss: the monthly KPI sheet

From Catherine Hale, Chief Executive Officer

“From January, leadership reviews one KPI sheet a month. Build 2025 as the first edition: the revenue block, the customer and traffic block, growth against last year, and a guardrail check that tells me which months broke the 7% return-rate limit, without counting months whose returns are not in yet.”

Your goal

Step 1 of 4. Revenue block. Set result to a DataFrame with one row per month of 2025 (sorted) and columns month, orders (distinct booked orders), net_sales, refunds, net_revenue (net_sales − refunds), aov (net_sales / orders) and return_rate_pct (refunds × 100 / net_sales), money and rates rounded to 2.

Use the order_items DataFramenot done yet
Use the returns DataFramenot done yet
Show exactly month orders net_sales refunds net_revenue aov return_rate_pctnot done yet

Step 1 · your code

Python

Loading the editor…

Reference solution

ResultPreview of order_items · press Run to see yours

Preview 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

Mote checks your result

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

Step 2 · your code

Python

Loading the editor…

Reference solution

ResultPreview of orders · press Run to see yours

Preview of orders
order_id	customer_id	order_date	status	channel
1001	125	2023-01-01 20:51:19	completed	web
1002	156	2023-01-02 07:58:46	completed	web
1003	22	2023-01-02 10:25:27	completed	marketplace
1004	48	2023-01-02 11:21:51	completed	marketplace

Mote checks your result

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

Step 3 · your code

Python

Loading the editor…

Reference solution

ResultPreview of orders · press Run to see yours

Preview of orders
order_id	customer_id	order_date	status	channel
1001	125	2023-01-01 20:51:19	completed	web
1002	156	2023-01-02 07:58:46	completed	web
1003	22	2023-01-02 10:25:27	completed	marketplace
1004	48	2023-01-02 11:21:51	completed	marketplace

Mote checks your result

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

Step 4 · your code

Python

Loading the editor…

Reference solution

ResultPreview of orders · press Run to see yours

Preview of orders
order_id	customer_id	order_date	status	channel
1001	125	2023-01-01 20:51:19	completed	web
1002	156	2023-01-02 07:58:46	completed	web
1003	22	2023-01-02 10:25:27	completed	marketplace
1004	48	2023-01-02 11:21:51	completed	marketplace

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.

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

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	NaN
1002	156	2023-01-02 07:58:46	completed	web	0.00	NaN
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

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	NaN	2023-01-01 23:02:24	mobile	paid	1	false
2	18	2023-01-01 15:07:49	desktop	paid	1	false
3	NaN	2023-01-01 16:55:46	desktop	direct	7	false
4	NaN	2023-01-01 12:43:28	mobile	organic	3	false
5	NaN	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	NaN
3	Henry	Brooks	hbrooks@lakesidecafe.com	Los Angeles	CA	2022-06-04	business	NaN
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	NaN

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	NaN	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

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	NaN	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

A KPI sheet is a page, not a dashboard

A KPI sheet is the one page a leadership team reads every month. It is short on purpose: the north star, the few numbers that drive it, growth against the same period last year, and the guardrails with a status. Everything on it has a written definition, and the definitions do not change between editions.

Harbor & Pine’s sheet has four blocks, and you build one per step.

Block 1: revenue

Orders, net sales, refunds, net revenue (the north star), AOV and the return rate, one row per month. All of it comes from one table of booked, valid lines with the refund attached, so the numbers add up across columns: net revenue is net sales minus refunds, and AOV × orders gives net sales back.

Block 2: customers and traffic

Sessions, conversions, conversion rate and new customers. These are the drivers: revenue grows because more people visit, more of them buy, or more of them are new. The conversion rate is conversions over sessions in the same month, computed from the totals.

Block 3: growth against last year

Harbor & Pine is seasonal, so the growth column is year over year, not month over month. Put orders growth next to revenue growth: when orders grow and revenue does not, orders got smaller, and that deserves a sentence on the sheet.

Block 4: guardrails with a maturity rule

The return-rate guardrail is 7% of net sales. A month can only breach it once its returns are in, which takes up to 40 days after the month ends. Months younger than that are listed as not final instead of being judged early, so the newest months cannot look better than they are.

Common mistakes

Averaging monthly rates for the year. Recompute annual rates from summed totals.
Month-over-month growth in a seasonal business. It measures the calendar.
Judging young months. Delayed metrics need a “final from” date.
Changing a definition between editions. If it must change, restate the history with it.

Level 7 · Boss: the monthly KPI sheet

Your goal

Step 1 · your code

Reference solution

Mote checks your result

Step 2 · your code

Reference solution

Mote checks your result

Step 3 · your code

Reference solution

Mote checks your result

Step 4 · your code

Reference solution

Mote checks your result

What Catherine gets, and why

DataFrames

The lesson

A KPI sheet is a page, not a dashboard

Block 1: revenue

Block 2: customers and traffic

Block 3: growth against last year

Block 4: guardrails with a maturity rule

Common mistakes

Lantern lit!

Level 7 · Boss: the monthly KPI sheet

The request

Your goal

Step 1 · your code

Reference solution

Mote checks your result

Step 2 · your code

Reference solution

Mote checks your result

Step 3 · your code

Reference solution

Mote checks your result

Step 4 · your code

Reference solution

Mote checks your result

What Catherine gets, and why

DataFrames

The lesson

A KPI sheet is a page, not a dashboard

Block 1: revenue

Block 2: customers and traffic

Block 3: growth against last year

Block 4: guardrails with a maturity rule

Common mistakes

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