The request

From Jordan Reyes, Head of Marketing
Your goal
For 2025, return one row per marketing channel and month with: channel, month (the first day of the month, as a DATE), spend (total marketing_spend.amount, rounded to 2), conversions (web_sessions with converted = true whose source is that channel) and cost_per_conversion (spend divided by conversions, rounded to 2, NULL when conversions is 0). Sort by channel, then month.
- Read from the
web_sessionstablenot done yet - Read from the
marketing_spendtablenot done yet - Show exactly
channelmonthspendconversionscost_per_conversionnot done yet
Your query
Loading the editor…
Reference solution
| 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.
What Jordan gets, and why
Tables
Harbor & Pine store data, ready to query.
web_sessions40,000 rows · 7 columns
session_idintegercustomer_idintegerstarted_attimestampdevicevarcharsourcevarcharpages_viewedintegerconvertedboolean
| 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 |
marketing_spend3,276 rows · 3 columns
spend_datedatechannelvarcharamountdecimal
| spend_date | channel | amount |
|---|---|---|
| 2023-01-01 | paid | 492.99 |
| 2023-01-01 | 37.38 | |
| 2023-01-01 | social | 200.53 |
| 2023-01-02 | paid | 709.13 |
| 2023-01-02 | 64.13 |
customers2,018 rows · 9 columns
customer_idintegerfirst_namevarcharlast_namevarcharemailvarcharcityvarcharstatevarcharsignup_datedatesegmentvarcharreferral_sourcevarchar
| customer_id | first_name | last_name | 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 | |
| 5 | Kyle | Jimenez | kjimenez27@yahoo.com | Indianapolis | IN | 2022-06-07 | consumer | NULL |
products120 rows · 8 columns
product_idintegernamevarcharcategoryvarcharsubcategoryvarcharunit_pricedecimalunit_costdecimallaunched_ondateis_activeboolean
| 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_idintegercustomer_idintegerorder_datetimestampstatusvarcharchannelvarcharshipping_feedecimalcoupon_codevarchar
| 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_idintegerorder_idintegerproduct_idintegerquantityintegerunit_pricedecimaldiscount_pctdecimal
| order_item_id | order_id | product_id | quantity | unit_price | discount_pct |
|---|---|---|---|---|---|
| 1 | 1001 | 121 | 9 | 31.99 | 0.00 |
| 2 | 1001 | 154 | 3 | 11.99 | 0.00 |
| 3 | 1002 | 154 | 4 | 11.99 | 0.00 |
| 4 | 1002 | 194 | 1 | 149.00 | 0.00 |
| 5 | 1002 | 193 | 2 | 19.99 | 0.00 |
returns1,673 rows · 5 columns
return_idintegerorder_item_idintegerreturned_ondatereasonvarcharrefund_amountdecimal
| 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_idintegerfirst_namevarcharlast_namevarchardepartmentvarchartitlevarcharmanager_idintegerhire_datedatesalaryinteger
| 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 |
The lesson
Rates are divisions, and a denominator can be zero: a channel with no conversions, a day with no
orders, a new product with no sales. What happens next depends on the database. Postgres, SQL Server
and Snowflake stop the whole query with a “division by zero” error. DuckDB returns Infinity for
decimal division, which flows straight into a chart.
NULLIF in one line
NULLIF(a, b) returns NULL when a = b, and a otherwise. Wrap the denominator:
spend / NULLIF(conversions, 0)
When conversions is 0 the division becomes spend / NULL, which is NULL: no error, no Infinity, and
a blank on the chart, which is the honest answer.
The real zeros in this data
Social is Harbor & Pine’s weakest converting source. In May 2025 the store spent $10,170.19 on social and those sessions brought 0 conversions out of 204 social sessions. September and October 2025 also had 0. Paid, with far more spend, never hit zero in 2025, but its lowest month (February) had a single conversion for $20,740.71. NULLIF handles the zeros; a cost of $20,000 per conversion is a business conversation, not a data problem.
NULLIF for sentinel values
The second use of NULLIF is cleaning. Older systems often store “unknown” as a sentinel such as 0,
-1 or an empty string. Turn the sentinel into a real NULL so aggregates skip it:
SELECT AVG(NULLIF(quantity, 0)) AS avg_quantity
FROM order_items;
Eight order lines have quantity 0 (data-entry errors). AVG(quantity) is 1.9954 and
AVG(NULLIF(quantity, 0)) is 1.9959. The difference is tiny here, but with a sentinel like -1 on
thousands of rows it decides the answer. For text: NULLIF(TRIM(coupon_code), '').
Common mistakes
- Wrapping the whole ratio.
NULLIF(spend / conversions, 0)still divides by zero first. - Inventing a value.
COALESCE(conversions, 1)or a ratio of 0 makes up a number. If the chart must show something, show NULL as “no conversions” in the label. - Filtering out the zero rows. It removes the error and also the evidence that money was spent with no result.
In SQL Server
NULLIF is identical and is the standard T-SQL guard against error 8134 (divide by zero). Beware of
integer division too: conversions / sessions between two INT columns returns 0, so multiply by
1.0 first.
SELECT channel, spend / NULLIF(conversions, 0) AS cost_per_conversion FROM monthly_marketing; Lantern lit!
Practised: nullifdivision by zeroratiosdata cleaning
- Solved
- No hints
- First run
