Did WELCOME50 bring customers back?

HardWindow functions and EXISTSFood delivery

The problem

Look at each customer's first delivered order, for first orders placed before 1 December 2025. Compare customers whose first order used the WELCOME50 coupon with everyone else: what share placed another delivered order within 30 days of that first order?

Return used_welcome50 (true or false, never NULL), customers and ordered_again_30d_pct (rounded to 1 decimal place).

Learn this in the course: Experiments and decisions (video and notes)

Tables · ClearBite food delivery

ClearBite is a fictional food-delivery app in 8 Indian cities. The data covers calendar year 2025 and is invented.

Click a table or column name to insert it into your query. Open a table to see its columns.

9,975 rows · 11 columns

INTEGER · primary key
INTEGER · → customers
INTEGER · → restaurants
TIMESTAMP · local time
TEXT · delivered | cancelled | refunded
TEXT · upi | card | cod | wallet
TEXT · → coupons, NULL if none
INTEGER · rupees, food only
INTEGER · rupees
INTEGER · rupees
INTEGER · subtotal − discount + delivery_fee

Sample rows (9,975 total)

Sample rows from orders
order_id	customer_id	restaurant_id	ordered_at	status	payment_method	coupon_code	subtotal	delivery_fee	total
1	2126	115	2025-01-01 12:05:36	delivered	wallet	NULL	180	39	219
2	797	216	2025-01-01 12:52:20	cancelled	upi	NULL	150	41	191
3	1724	2	2025-01-01 12:53:25	delivered	upi	NULL	45	29	74
4	2135	59	2025-01-01 12:57:02	cancelled	upi	NULL	195	34	229
5	2201	191	2025-01-01 13:38:18	delivered	cod	NULL	95	0	95

16,314 rows · 4 columns

INTEGER · → orders
INTEGER · → menu_items
INTEGER
INTEGER · rupees

Sample rows (16,314 total)

Sample rows from order_items
order_id	item_id	quantity	unit_price
1	957	1	180
2	1790	1	150
3	18	1	45
4	487	1	195
5	1575	1	95

3,000 rows · 6 columns

INTEGER · primary key
TEXT · not unique
INTEGER · → cities
DATE
TEXT · organic | referral | paid_social | search_ads | offline, can be NULL
BOOLEAN · free delivery membership

Sample rows (3,000 total)

Sample rows from customers
customer_id	name	city_id	signup_date	acquisition_channel	is_premium
1	Bhavna Khan	6	2024-04-28	search_ads	false
2	Ayesha Joshi	3	2025-05-29	search_ads	false
3	Isha Nair	2	2025-12-03	offline	false
4	Pranav Ghosh	5	2025-11-06	NULL	false
5	Farhan Gowda	6	2025-06-28	paid_social	false

300 rows · 7 columns

INTEGER · primary key
TEXT · not unique
INTEGER · → cities
TEXT
BOOLEAN
INTEGER · rupees
DATE

Sample rows (300 total)

Sample rows from restaurants
restaurant_id	name	city_id	cuisine	is_veg_only	cost_for_two	onboarded_date
1	Slice Kitchen	5	Pizza	false	850	2023-10-18
2	Napoli Pizza Co.	1	Pizza	false	750	2023-09-18
3	Hakka Garden	8	Chinese	false	600	2022-02-08
4	Sprout Kitchen	2	Healthy	false	850	2025-09-28
5	Coastal Tiffins	6	South Indian	true	400	2025-07-27

2,482 rows · 6 columns

INTEGER · primary key
INTEGER · → restaurants
TEXT
TEXT · main | starter | bread | rice | dessert | beverage
BOOLEAN
INTEGER · rupees

Sample rows (2,482 total)

Sample rows from menu_items
item_id	restaurant_id	item_name	category	is_veg	price
1	1	Pasta Arrabbiata	main	true	250
2	1	Choco Lava Cake	dessert	true	120
3	1	BBQ Chicken Pizza	main	false	365
4	1	Cold Coffee	beverage	true	135
5	1	Farmhouse Pizza	main	true	350

9,173 rows · 6 columns

INTEGER · primary key, → orders (delivered and refunded orders only)
INTEGER · → delivery_partners
TIMESTAMP
TIMESTAMP
NUMERIC(4,1)
INTEGER · time promised at checkout, from ordered_at

Sample rows (9,173 total)

Sample rows from deliveries
order_id	partner_id	picked_up_at	delivered_at	distance_km	promised_minutes
1	182	2025-01-01 12:31:48	2025-01-01 12:46:29	4.8	35
3	200	2025-01-01 13:09:13	2025-01-01 13:12:55	2.2	30
5	218	2025-01-01 13:51:37	2025-01-01 14:15:14	2.8	30
7	108	2025-01-01 16:44:53	2025-01-01 16:49:29	2.3	25
8	100	2025-01-01 18:32:46	2025-01-01 18:36:45	1.1	20

4,181 rows · 4 columns

INTEGER · primary key, → orders
INTEGER · 1–5
INTEGER · 1–5, can be NULL
TIMESTAMP

Sample rows (4,181 total)

Sample rows from ratings
order_id	food_rating	delivery_rating	rated_at
3	4	NULL	2025-01-02 08:00:55
5	5	NULL	2025-01-01 21:55:14
7	4	5	2025-01-02 06:12:29
9	4	4	2025-01-03 02:11:41
10	3	4	2025-01-02 15:35:00

400 rows · 5 columns

INTEGER · primary key
TEXT
INTEGER · → cities
TEXT · bike | scooter | ev_scooter | bicycle
DATE

Sample rows (400 total)

Sample rows from delivery_partners
partner_id	name	city_id	vehicle_type	joined_date
1	Ananya Gowda	5	scooter	2023-01-08
2	Omkar Ghosh	3	bike	2024-08-21
3	Ayesha Qureshi	8	bike	2025-03-02
4	Vikram Krishnan	7	bike	2023-02-12
5	Varun Reddy	5	bike	2023-08-15

8 rows · 7 columns

TEXT · primary key
TEXT
INTEGER
INTEGER · rupees
INTEGER · rupees, on subtotal
DATE
DATE

Sample rows (8 total)

Sample rows from coupons
coupon_code	description	discount_pct	max_discount	min_order_value	valid_from	valid_to
WELCOME50	50% off your first order	50	100	199	2025-01-01	2025-12-31
BITE20	20% off any order	20	80	299	2025-01-01	2025-12-31
WEEKEND15	15% off on Saturdays and Sundays	15	75	249	2025-01-01	2025-12-31
IPL25	25% off during the cricket season	25	125	399	2025-03-22	2025-06-03
MONSOON10	10% off in July and August	10	60	199	2025-07-01	2025-08-31

8 rows · 4 columns

INTEGER · primary key
TEXT
TEXT
INTEGER · 1 = metro, 2 = smaller city

Sample rows (8 total)

Sample rows from cities
city_id	city	state	tier
1	Bengaluru	Karnataka	1
2	Mumbai	Maharashtra	1
3	Delhi	Delhi	1
4	Hyderabad	Telangana	1
5	Pune	Maharashtra	1

Your SQL (PostgreSQL · runs in your browser)

SELECT 
FROM 
;

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Expected output (2 rows)

Expected output
used_welcome50	customers	ordered_again_30d_pct
false	1641	38.5
true	263	47.1

Row order doesn’t matter for this question. Numbers are compared with a tiny tolerance; text must match exactly.

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