Skip to content

Commit 0fd3bd5

Browse files
ikebudeikebude
committed
Commit
1 parent 425fbdb commit 0fd3bd5

File tree

4 files changed

+231
-0
lines changed

4 files changed

+231
-0
lines changed

‎QUERYING DATA WITH SQL.docx

21.4 KB
Binary file not shown.

‎QUERYING DATA WITH SQL.sql

Lines changed: 225 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,225 @@
1+
-- CASE STUDY: Danny’s Diner
2+
-- QUERYING DATA WITH SQL
3+
-- Mr Danny, a restaurant owner is interested in having information about his customers,
4+
-- especially about their visiting patterns, how much money they’ve spent and also, which menu items are their favorite.
5+
-- Having this deeper connection with his customers will help him deliver a better and more personalized experience for his loyal customers.
6+
-- He also needs these insights to help him decide whether he should expand the existing customer loyalty program or not.
7+
-- 3 key datasets were presented for analysis:
8+
-- sales
9+
-- menu
10+
-- members
11+
12+
QUESTIONS
13+
--1. What is the total amount each customer spent at the restaurant?
14+
SELECT s.customer_id, SUM(m.price) as total_spent
15+
FROM dannys_diner.sales s
16+
LEFT JOIN dannys_diner.menu m
17+
ON s.product_id = m.product_id
18+
GROUP BY 1
19+
ORDER BY 2
20+
21+
--2. How many days has each customer visited the restaurant?
22+
WITH sub1 AS
23+
(SELECT order_date,customer_id, COUNT(customer_id) as count
24+
FROM dannys_diner.sales
25+
GROUP BY 1,2
26+
ORDER BY 1)
27+
28+
SELECT customer_id, COUNT (sub1.order_date)
29+
FROM sub1
30+
GROUP BY 1
31+
ORDER BY 1
32+
33+
--3. What was the first item from the menu purchased by each customer?
34+
WITH sub1 AS (
35+
SELECT s.customer_id, s.order_date, m.product_name,
36+
RANK() OVER(PARTITION BY customer_id ORDER BY order_date) as rank
37+
FROM dannys_diner.sales s
38+
LEFT JOIN dannys_diner.menu m
39+
ON s.product_id = m.product_id
40+
ORDER BY 2)
41+
42+
SELECT DISTINCT customer_id, product_name, order_date
43+
FROM sub1
44+
WHERE rank = 1
45+
46+
--4. What is the most purchased item on the menu and how many times was it purchased by all customers?
47+
SELECT m.product_name,
48+
COUNT(s.product_id)
49+
FROM dannys_diner.sales s
50+
JOIN dannys_diner.menu m
51+
USING (product_id)
52+
GROUP BY 1
53+
ORDER BY 2 desc
54+
LIMIT 1
55+
56+
57+
58+
--5. Which item was the most popular for each customer?
59+
WITH sub1 AS (
60+
SELECT s.customer_id,
61+
m.product_name,
62+
COUNT(m.product_name) times_ordered,
63+
RANK() OVER(PARTITION BY customer_id ORDER BY COUNT(m.product_name)DESC) AS rank
64+
FROM dannys_diner.sales s
65+
LEFT JOIN dannys_diner.menu m
66+
ON s.product_id = m.product_id
67+
GROUP BY 1, 2)
68+
69+
SELECT customer_id, product_name, times_ordered
70+
FROM sub1
71+
WHERE rank=1
72+
73+
--6. Which item was purchased first by the customer after they became a member?
74+
WITH sub1 as (
75+
SELECT mb.customer_id, m.product_name, s.order_date, mb.join_date,
76+
RANK() OVER(PARTITION BY mb.customer_id ORDER BY s.order_date) AS rank
77+
FROM dannys_diner.sales s
78+
LEFT JOIN dannys_diner.menu m
79+
ON s.product_id = m.product_id
80+
LEFT JOIN dannys_diner.members mb
81+
ON s.customer_id = mb.customer_id
82+
WHERE s.order_date > mb.join_date)
83+
84+
SELECT *
85+
FROM sub1
86+
WHERE rank = 1
87+
88+
--7. Which item was purchased just before the customer became a member?
89+
WITH sub1 AS (
90+
SELECT mb.customer_id, m.product_name, s.order_date, mb.join_date,
91+
RANK() OVER(PARTITION BY mb.customer_id ORDER BY s.order_date desc) AS rank
92+
FROM dannys_diner.sales s
93+
LEFT JOIN dannys_diner.menu m
94+
ON s.product_id = m.product_id
95+
LEFT JOIN dannys_diner.members mb
96+
ON s.customer_id = mb.customer_id
97+
WHERE s.order_date < mb.join_date
98+
)
99+
100+
SELECT *
101+
FROM sub1
102+
WHERE rank = 1
103+
104+
--8. What is the total items and amount spent for each member before they became a member?
105+
SELECT DISTINCT s.customer_id, m.product_name, COUNT(s.product_id), SUM(m.price) as total_spent
106+
FROM dannys_diner.sales s
107+
LEFT JOIN dannys_diner.menu m
108+
ON s.product_id = m.product_id
109+
LEFT JOIN dannys_diner.members mb
110+
ON s.customer_id = mb.customer_id
111+
GROUP BY 1,2, s.order_date, mb.join_date
112+
HAVING s.order_date < mb.join_date
113+
ORDER BY 1
114+
115+
--9. If each $1 spent equates to 10 points and sushi has a 2x points multiplier - how many points would each customer have?
116+
WITH sub1 AS (
117+
SELECT s.customer_id, m.product_name, COUNT(s.product_id), SUM(m.price) as total_spent,
118+
CASE WHEN m.product_name= 'sushi' THEN 2 * 10 * SUM(m.price)
119+
ELSE 10 * SUM(m.price) end as points
120+
FROM dannys_diner.sales s
121+
LEFT JOIN dannys_diner.menu m
122+
ON s.product_id = m.product_id
123+
LEFT JOIN dannys_diner.members mb
124+
ON s.customer_id = mb.customer_id
125+
GROUP BY 1,2
126+
)
127+
SELECT customer_id, SUM(points)
128+
FROM sub1
129+
GROUP BY 1
130+
ORDER BY 1
131+
132+
--10. In the first week after a customer joins the program (including their join date), they earn 2x points on all items, not just sushi - how many points do customer A and B have at the end of January?
133+
With sub1 AS (
134+
SELECT s.customer_id, m.product_name,s.order_date, mb.join_date,
135+
COUNT(s.product_id), SUM(m.price) AS total_spent,
136+
CASE WHEN s.order_date BETWEEN mb.join_date - 1 AND mb.join_date + 7
137+
OR m.product_name= 'sushi' THEN 2 * 10 * SUM(m.price)
138+
ELSE 10 * SUM(m.price) END AS points
139+
FROM dannys_diner.sales s
140+
LEFT JOIN dannys_diner.menu m
141+
ON s.product_id = m.product_id
142+
LEFT JOIN dannys_diner.members mb
143+
ON s.customer_id = mb.customer_id
144+
GROUP BY 1,2,3,4
145+
)
146+
SELECT customer_id, SUM(points)
147+
FROM sub1
148+
GROUP BY 1
149+
ORDER BY 1
150+
151+
-- INSIGHTS
152+
-- • Customer A has spent the most money in the restaurant, and this is closely followed by customer B with a difference of just $2.
153+
-- • Customer B however, has visited the store more than any other customer has.
154+
-- • Curry was most ordered item on the menu for first time customers.
155+
-- • Overall, Ramen was the most purchased item on the menu.
156+
-- • Data trends amongst the individual purchases showed ramen as the most frequent purchase amongst all customers as well.
157+
-- • Customers spent more money when the became members, compared to when they were not members of the loyalty program
158+
-- • The point-based reward system can drive purchase of a particular product on the menu if its points are more rewarding.
159+
160+
-- RECOMMENDATIONS
161+
-- • The natural endearment towards ramen shows that it is a stellar product and this should be maximized. Ramen should be highlighted and prominently displayed on the menu with a compelling and mouth-watering description.
162+
-- • Special deals or promotions that focus on ramen, such as a discount for ordering it, more reward points, or a complimentary drink or dessert should be considered
163+
-- • More data that can encourage targeted serving of customers should be collected e.g. addresses, opinion on delivery services, customer feedback etc.
164+
-- • A positive experience in the restaurant will definitely increase sales and drive customer repeat purchases. This refers to how customers are treated by staff.
165+
-- • Members are observed to spend more money, so strategies must be put in place to convert customers to members
166+
-- • The point-based reward system works and should be encouraged. This system does not have to be fixed but can be flexible to ensure that different products can be promoted at different times.
167+
168+
169+
--These datasets were gotten from the Danny Ma’s 8 weeks SQL challenge.
170+
--The Schema SQL code for the creation of permanent tables used for this project is attached below
171+
172+
173+
CREATE SCHEMA dannys_diner;
174+
SET search_path = dannys_diner;
175+
176+
CREATE TABLE sales (
177+
"customer_id" VARCHAR(1),
178+
"order_date" DATE,
179+
"product_id" INTEGER
180+
);
181+
182+
INSERT INTO sales
183+
("customer_id", "order_date", "product_id")
184+
VALUES
185+
('A', '2021-01-01', '1'),
186+
('A', '2021-01-01', '2'),
187+
('A', '2021-01-07', '2'),
188+
('A', '2021-01-10', '3'),
189+
('A', '2021-01-11', '3'),
190+
('A', '2021-01-11', '3'),
191+
('B', '2021-01-01', '2'),
192+
('B', '2021-01-02', '2'),
193+
('B', '2021-01-04', '1'),
194+
('B', '2021-01-11', '1'),
195+
('B', '2021-01-16', '3'),
196+
('B', '2021-02-01', '3'),
197+
('C', '2021-01-01', '3'),
198+
('C', '2021-01-01', '3'),
199+
('C', '2021-01-07', '3');
200+
201+
202+
CREATE TABLE menu (
203+
"product_id" INTEGER,
204+
"product_name" VARCHAR(5),
205+
"price" INTEGER
206+
);
207+
208+
INSERT INTO menu
209+
("product_id", "product_name", "price")
210+
VALUES
211+
('1', 'sushi', '10'),
212+
('2', 'curry', '15'),
213+
('3', 'ramen', '12');
214+
215+
216+
CREATE TABLE members (
217+
"customer_id" VARCHAR(1),
218+
"join_date" DATE
219+
);
220+
221+
INSERT INTO members
222+
("customer_id", "join_date")
223+
VALUES
224+
('A', '2021-01-07'),
225+
('B', '2021-01-09');

‎README.md

Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,2 +1,5 @@
11
# Quering Data with SQL
22

3+
This project involves the analysis of customer behavior at a restaurant using SQL. The goal is to understand customer visiting patterns, spending habits, and menu preferences to enhance their dining experience and inform decisions about the customer loyalty program.
4+
5+
This project involves querying data with SQL to provide valuable insights for Mr. Danny, a restaurant owner. By analyzing three key datasets, we aim to help Mr. Danny understand his customers' visiting patterns, spending habits, and favorite menu items. The findings will assist in delivering a better and more personalized experience for loyal customers and inform decisions regarding the expansion of the existing customer loyalty program.

‎README.md.backup

Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,3 @@
1+
# Quering Data with SQL
2+
3+
This project involves the analysis of customer behavior at a restaurant using SQL. The goal is to understand customer visiting patterns, spending habits, and menu preferences to enhance their dining experience and inform decisions about the customer loyalty program.

0 commit comments

Comments
 (0)