Ecommerce Website Analysis by SQL, PostgreSQL

Ecommerce Website Analysis by SQL

-- E-commerce Website SQL Analysis Conducted by Ziying Yan

------------------------------------------------------------------------------------------

-- Database Script

CREATE TABLE Ecommerce_customers
    ("customerid" int, "country" varchar(11))
;
    
INSERT INTO Ecommerce_customers
    ("customerid", "country")
VALUES
    (1, 'Germany'),
    (2, 'Mexico'),
    (3, 'Mexico'),
    (4, 'UK'),
    (5, 'Sweden'),
    (6, 'Germany'),
    (7, 'France'),
    (8, 'Spain'),
    (9, 'France'),
    (10, 'Canada'),
    (11, 'UK'),
    (12, 'Argentina'),
    (13, 'Mexico'),
    (14, 'Switzerland'),
    (15, 'Brazil'),
    (16, 'UK'),
    (17, 'Germany'),
    (18, 'France'),
    (19, 'UK'),
    (20, 'Austria')
;

CREATE TABLE Ecommerce_categories
    ("categoryid" int, "categoryname" varchar(14))
;
    
INSERT INTO Ecommerce_categories
    ("categoryid", "categoryname")
VALUES
    (1, 'Beverages'),
    (2, 'Condiments'),
    (3, 'Confections'),
    (4, 'Dairy Products'),
    (5, 'Grains/Cereals'),
    (6, 'Meat/Poultry'),
    (7, 'Produce'),
    (8, 'Seafood')
;

CREATE TABLE Ecommerce_orderdetails
    ("orderid" int, "productid" int, "quantity" int)
;
    
INSERT INTO Ecommerce_orderdetails
    ("orderid", "productid", "quantity")
VALUES
    (10248, 11, 12),
    (10248, 42, 10),
    (10248, 72, 5),
    (10249, 14, 9),
    (10249, 51, 40),
    (10250, 41, 10),
    (10250, 51, 35),
    (10250, 65, 15),
    (10251, 22, 6),
    (10251, 57, 15),
    (10251, 65, 20),
    (10252, 20, 40),
    (10252, 33, 25),
    (10252, 60, 40),
    (10253, 31, 20),
    (10253, 39, 42),
    (10253, 49, 40),
    (10254, 24, 15),
    (10254, 55, 21),
    (10254, 74, 21),
    (10255, 2, 20),
    (10255, 16, 35),
    (10255, 36, 25),
    (10255, 59, 30),
    (10256, 53, 15),
    (10256, 77, 12),
    (10257, 27, 25),
    (10257, 39, 6),
    (10257, 77, 15),
    (10258, 2, 50),
    (10258, 5, 65),
    (10258, 32, 6),
    (10259, 21, 10),
    (10259, 37, 1),
    (10260, 41, 16),
    (10260, 57, 50),
    (10260, 62, 15),
    (10260, 70, 21),
    (10261, 21, 20),
    (10261, 35, 20),
    (10262, 5, 12),
    (10262, 7, 15),
    (10262, 56, 2),
    (10263, 16, 60),
    (10263, 24, 28),
    (10263, 30, 60),
    (10263, 74, 36),
    (10264, 2, 35),
    (10264, 41, 25),
    (10265, 17, 30),
    (10265, 70, 20),
    (10266, 12, 12),
    (10267, 40, 50),
    (10267, 59, 70),
    (10267, 76, 15),
    (10268, 29, 10),
    (10268, 72, 4),
    (10269, 33, 60),
    (10269, 72, 20),
    (10270, 36, 30),
    (10270, 43, 25),
    (10271, 33, 24),
    (10272, 20, 6),
    (10272, 31, 40),
    (10272, 72, 24),
    (10273, 10, 24),
    (10273, 31, 15),
    (10273, 33, 20),
    (10273, 40, 60),
    (10273, 76, 33),
    (10274, 71, 20),
    (10274, 72, 7),
    (10275, 24, 12),
    (10275, 59, 6),
    (10276, 10, 15),
    (10276, 13, 10),
    (10277, 28, 20),
    (10277, 62, 12),
    (10278, 44, 16),
    (10278, 59, 15),
    (10278, 63, 8),
    (10278, 73, 25),
    (10279, 17, 15),
    (10280, 24, 12),
    (10280, 55, 20),
    (10280, 75, 30),
    (10281, 19, 1),
    (10281, 24, 6),
    (10281, 35, 4),
    (10282, 30, 6),
    (10282, 57, 2),
    (10283, 15, 20),
    (10283, 19, 18),
    (10283, 60, 35),
    (10283, 72, 3),
    (10284, 27, 15),
    (10284, 44, 21),
    (10284, 60, 20),
    (10284, 67, 5)
;

CREATE TABLE Ecommerce_orders
    ("orderid" int, "customerid" int)
;
    
INSERT INTO Ecommerce_orders
    ("orderid", "customerid")
VALUES
    (10248, 90),
    (10249, 81),
    (10250, 34),
    (10251, 84),
    (10252, 76),
    (10253, 34),
    (10254, 14),
    (10255, 68),
    (10256, 88),
    (10257, 35),
    (10258, 20),
    (10259, 13),
    (10260, 55),
    (10261, 61),
    (10262, 65),
    (10263, 20),
    (10264, 24),
    (10265, 7),
    (10266, 87)
;

CREATE TABLE Ecommerce_products
    ("productid" int, "categoryid" int, "price" int)
;
    
INSERT INTO Ecommerce_products
    ("productid", "categoryid", "price")
VALUES
    (1, 1, 18),
    (2, 1, 19),
    (3, 2, 10),
    (4, 2, 22),
    (5, 2, 21.35),
    (6, 2, 25),
    (7, 7, 30),
    (8, 2, 40),
    (9, 6, 97),
    (10, 8, 31),
    (11, 4, 21),
    (12, 4, 38),
    (13, 8, 6),
    (14, 7, 23.25),
    (15, 2, 15.5),
    (16, 3, 17.45),
    (17, 6, 39),
    (18, 8, 62.5),
    (19, 3, 9.2),
    (20, 3, 81),
    (21, 3, 10),
    (22, 5, 21),
    (23, 5, 9),
    (24, 1, 4.5),
    (25, 3, 14),
    (26, 3, 31.23),
    (27, 3, 43.9),
    (28, 7, 45.6),
    (29, 6, 123.79),
    (30, 8, 25.89),
    (31, 4, 12.5),
    (32, 4, 32),
    (33, 4, 2.5),
    (34, 1, 14),
    (35, 1, 18),
    (36, 8, 19),
    (37, 8, 26),
    (38, 1, 263.5),
    (39, 1, 18),
    (40, 8, 18.4),
    (41, 8, 9.65),
    (42, 5, 14),
    (43, 1, 46),
    (44, 2, 19.45),
    (45, 8, 9.5),
    (46, 8, 12),
    (47, 3, 9.5),
    (48, 3, 12.75),
    (49, 3, 20)
;

------------------------------------------------------------------------------------------
-- Insight Finding Script

--#0. View Tables
select * from Ecommerce_customers limit 10;
select * from Ecommerce_categories limit 10;
select * from Ecommerce_orderdetails limit 10;
select * from Ecommerce_orders limit 10;
select * from Ecommerce_products limit 10;

--#1. union & union all difference: duplicate values
select customerid from Ecommerce_customers
union
select customerid from Ecommerce_orders;

select count(distinct customerid) from Ecommerce_customers;
select count(distinct customerid) from Ecommerce_orders;

select count(distinct customerid) from (
    select customerid from Ecommerce_customers
    union
    select customerid from Ecommerce_orders) as temp;
    
----most of customerid are not same between Ecommerce_customers and Ecommerce_orders tables
    
--#2. With Clause: create common expression table
with cte1 as (
    select customerid,country 
    from Ecommerce_customers
    )
    , cte2 as (
    select customerid,orderid
    from Ecommerce_orders
    )
select cte1.customerid,orderid,country
from cte1 join cte2 on 
cte1.customerid = cte2.customerid;

--#3. Window function: row_number, rank, dense_rank

--#3.1 row_number()
-- Top 2 product by sales per category
-- how to define sales? sales = price * quantity
-- how to define top 2, side by side? want only 2 products for each category -> row_number()
-- output -> product id, category id, sales, and rank for top 2
-- input -> product id, category id, price, quantity
-- tables -> Ecommerce_orderdetails, Ecommerce_products, join, foregin key: product id

select categoryid, productid, sales, row_num
from (
    select categoryid, 
           Ecommerce_orderdetails.productid as productid,
           price*quantity as sales,
           row_number() over (partition by categoryid order by price*quantity desc) as row_num
    from Ecommerce_orderdetails join Ecommerce_products 
    on Ecommerce_orderdetails.productid  = Ecommerce_products.productid) as temp
where row_num <= 2;

--#3.2 rank()
-- Top 2 product by sales per category
-- how to define sales? sales = price * quantity
-- how to define top 2, side by side? there are more than 2 products side by side, want the top 1 -> rank()
-- output -> product id, category id, sales, and rank for top 2
-- input -> product id, category id, price, quantity
-- tables -> Ecommerce_orderdetails, Ecommerce_products, join, foregin key: product id

select categoryid, productid, sales, rank_num
from (
    select categoryid, 
           Ecommerce_orderdetails.productid as productid,
           price*quantity as sales,
           rank() over (partition by categoryid order by price*quantity desc) as rank_num
    from Ecommerce_orderdetails join Ecommerce_products 
    on Ecommerce_orderdetails.productid  = Ecommerce_products.productid) as temp
where rank_num <= 2;

--#3.3 dense_rank()
-- Top 2 product by sales per category
-- how to define sales? sales = price * quantity
-- how to define top 2, side by side? want all the top 2 products for each category -> dense_rank()
-- output -> product id, category id, sales, and rank for top 2
-- input -> product id, category id, price, quantity
-- tables -> Ecommerce_orderdetails, Ecommerce_products, join, foregin key: product id

select categoryid, productid, sales, d_rank_num
from (
    select categoryid, 
           Ecommerce_orderdetails.productid as productid,
           price*quantity as sales,
           dense_rank() over (partition by categoryid order by price*quantity desc) as d_rank_num
    from Ecommerce_orderdetails join Ecommerce_products 
    on Ecommerce_orderdetails.productid  = Ecommerce_products.productid) as temp
where d_rank_num <= 2;

--#4. Window funtion: ntile()

-- set 2 buckets for each product order by quantity desc
select orderid, productid, quantity,
ntile (2) over (partition by productid order by quantity desc) as buckets 
from Ecommerce_orderdetails;

-- set 2 buckets for the whole table/ orderid order by quantity desc
select orderid, productid, quantity,
ntile (2) over (order by quantity desc) as buckets 
from Ecommerce_orderdetails;

--#5. Window + Aggregation funtion: sum() over(), avg() over, ...

-- calculate the running total sales quantity for all the orders, rank product quantity in each order 
-- denfine rank -> dense_rank
select orderid, productid, quantity, 
sum(quantity) over (order by orderid) as running_order_total, 
dense_rank() over (partition by orderid order by quantity desc) as d_rank_num
from Ecommerce_orderdetails;

-- calculate the average sales quantity for each order
select orderid, productid, quantity, 
avg(quantity) over (partition by orderid order by quantity) as running_order_avg
from Ecommerce_orderdetails;

--#6. set restriction to running total: using cte 
select orderid,	productid, quantity, running_order_total, running_order_total
from(select orderid, productid, quantity, sum(quantity) over (order by orderid) as running_order_total
from Ecommerce_orderdetails) as cte
where running_order_total >= 1000;

-- #7. Case when: 
-- basic: labeling
        
select country,
        case when country in ('UK') then 'main market'
             when country in ('Mexico') then 'emerging market'
        else 'other markets' end as country_flag
from Ecommerce_customers;

-- Case + aggregation: calculate several metrics at one time join
-- to find each order, count total products and how many beverages sales
-- ouput: orderid, total products, sales for beverages -- when sales is null, output is null
-- clarify: sales = price * quantity
-- input: orderid, productsid, categoryid, price, quantity -> Ecommerce_orderdetails, Ecommerce_products -> foreign key: productid

with cte as (
    select tb1.productid as productid, orderid, categoryid, price*quantity as sales
    from Ecommerce_orderdetails as tb1
    join Ecommerce_products as tb2
    on tb1.productid = tb2.productid)
    
select orderid,
       count(productid) as product_count,
       sum(case when categoryid = 1 then sales end) as beverages_sales
from cte
group by 1
order by 3 desc;

--#Q8. Total sales(quantity) by country: 
-- output: country, total quantity -- when quantity is null, output becomes 0 -> case when
-- input: country, quantity -> Ecommerce_customers, Ecommerce_orders, Ecommerce_orderdetails -> foreign key: customerid, orderid
-- left join, groupby country

select country, sum(case when quantity is not null then quantity else 0 end) as sales
from Ecommerce_customers as tb1
left join Ecommerce_orders as tb2
on tb1.customerid = tb2.customerid
left join Ecommerce_orderdetails as tb3
on tb2.orderid = tb3.orderid
group by 1
order by 2 desc;

--#9. Revenue by country
-- output: country, revenue -- define revenue: quantity * price
-- input: country, quantity, price -- Ecommerce_customers, Ecommerce_orderdetails, Ecommerce_orders, Ecommerce_products
-- left join: Ecommerce_customers - customerid - Ecommerce_orders - orderid - Ecommerce_orderdetails - productid - Ecommerce_products

select country, sum(quantity * price) as revenue
from Ecommerce_customers as tb1
left join Ecommerce_orders as tb2 
on tb1.customerid = tb2.customerid
left join Ecommerce_orderdetails as tb3
on tb2.orderid = tb3.orderid
left join Ecommerce_products as tb4
on tb3.productid = tb4.productid
group by 1
order by 2 desc;

-- the better way to replace null is to use case when: sum(case when a*b is not null then a*b else 0 end) 
select country, sum(case when quantity * price is not null then quantity * price else 0 end) as revenue
from Ecommerce_customers as tb1
left join Ecommerce_orders as tb2 
on tb1.customerid = tb2.customerid
left join Ecommerce_orderdetails as tb3
on tb2.orderid = tb3.orderid
left join Ecommerce_products as tb4
on tb3.productid = tb4.productid
group by 1
order by 2 desc;

-- #10. Revenue by category in Austria 
-- output: category, revenue -> quantity * price
-- input: category, country(=Austria), quantity, price -- Ecommerce_customers, Ecommerce_orderdetails, Ecommerce_orders, Ecommerce_products
-- with clause: select revenue in Austria -- group by category
-- in the with clause: Ecommerce_products - productid - Ecommerce_orderdetails - orderid - Ecommerce_orders - customerid - Ecommerce_customers

with cte as (
    select categoryid, case when quantity * price is not null then quantity * price else 0 end as revenue, country
    from Ecommerce_products as tb1
    left join Ecommerce_orderdetails as tb2 
    on tb1.productid = tb2.productid
    left join Ecommerce_orders as tb3
    on tb2.orderid = tb3.orderid
    left join Ecommerce_customers as tb4
    on tb3.customerid = tb4.customerid)

select categoryid, sum(revenue) as revenue
from cte
where country = 'Austria'
group by 1
order by 2 desc;

-- Method2:
-- country, category, revnue
-- c,p,ordetails
--c&o:customerid
--ord&orderdetails:orderid
--ordertail&products:productid

select country, categoryid, sum(price * quantity) as revenue
from Ecommerce_customers as tb1 
join Ecommerce_orders as tb2 
on tb1.customerid = tb2.customerid
join Ecommerce_orderdetails as tb3 
on tb2.orderid = tb3.orderid
join Ecommerce_products as tb4 
on tb3.productid = tb4.productid
where country = 'Austria'
group by 1,2
order by 1,2;

--#11. for each customer, what's the avg spend?
-- output: customerid, avg spend -- clarify: total spend/ total orders -> sum(quantity * price) / count(orders)
-- input: customerid, quanity, price, orderid -- Ecommerce_customers, Ecommerce_orderdetails, Ecommerce_orders, Ecommerce_products

select tb1.customerid as customerid, sum(quantity * price)/ count(distinct tb2.orderid) as avg_spend
from Ecommerce_customers as tb1
join Ecommerce_orders as tb2 
on tb1.customerid = tb2.customerid
join Ecommerce_orderdetails as tb3
on tb2.orderid = tb3.orderid
join Ecommerce_products as tb4
on tb3.productid = tb4.productid
group by 1
order by 2 desc;

-- safe_divide better version
select  tb1.customerid, 
        case when count(distinct tb2.orderid) > 0 then sum(price * quantity)/count(distinct tb2.orderid) else null end as avg_spend
from Ecommerce_customers as tb1
join Ecommerce_orders as tb2 
on tb1.customerid = tb2.customerid
join Ecommerce_orderdetails as tb3
on tb2.orderid = tb3.orderid
join Ecommerce_products as tb4
on tb3.productid = tb4.productid
group by 1
order by 2 desc;

-- #12. top 5 most popular product in each category
-- output: category, product -> top 5 clarify: only 5 records - row_number
-- input: category, product, sales (quantity) -> group by productid - row_number
-- tables: Ecommerce_orderdetails, Ecommerce_products

select *
from (
    select categoryid, productid, sales, row_number() over (partition by categoryid order by sales desc) as row_num
    from(
        select categoryid, tb1.productid as productid, sum(case when quantity is not null then quantity else 0 end) as sales
        from Ecommerce_products tb1
        left join Ecommerce_orderdetails tb2
        on tb1.productid = tb2.productid
        group by 1,2
        order by 3
        ) as temp1 
    ) as temp2
where row_num <= 5;

--#12.2 method2:

select * 
from (
    select categoryid, tb1.productid, sum(case when quantity is not null then quantity else 0 end) as sales,
    row_number() over (partition by categoryid order by sum(case when quantity is not null then quantity else 0 end) desc) as row_num
    from Ecommerce_products tb1
    left join Ecommerce_orderdetails tb2
    on tb1.productid = tb2.productid
    group by 1,2
    order by 1,4) as temp
where row_num <= 5;

--#Q13: reporting: each customer quantity on beverage, condiments and others
-- ouput: customerid, categoryname(beverages, condiments, others), quantity
-- input: customerid, categoryname, quantity -> case when(categoryname)
-- tables: Ecommerce_orders - orderid - Ecommerce_orderdetails - productid - Ecommerce_products - categoryid - Ecommerce_categories

select customerid, 
case when categoryname in ('Beverages','Condiments') then categoryname else 'Others' end as categoryname, sum(quantity) as quantity
from Ecommerce_orders tb1 
left join Ecommerce_orderdetails tb2
on tb1.orderid = tb2.orderid 
left join Ecommerce_products tb3
on tb2.productid = tb3.productid
left join Ecommerce_categories tb4
on tb3.categoryid = tb4.categoryid
group by 1,2
order by 2,3 desc;

-- not very clear
-- another method: let every category quantity become a column for each customer - sum(case when)

select customerid,
       sum(case when categoryname = 'Beverages' then quantity else 0 end) as Beverages_quantity,
       sum(case when categoryname = 'Condiments' then quantity else 0 end) as Condiments_quantity,
       sum(case when categoryname not in ('Beverages','Condiments') then quantity else 0 end) as Others_quantity
from Ecommerce_orders tb1 
left join Ecommerce_orderdetails tb2
on tb1.orderid = tb2.orderid 
left join Ecommerce_products tb3
on tb2.productid = tb3.productid
left join Ecommerce_categories tb4
on tb3.categoryid = tb4.categoryid
group by 1
order by 1 desc;

--#Q14: median quantity value of each category and product
-- output: categoryid, productid, quantity(median) 
-- how to calculate median? -- number of values is odds -> middle value -- number of values is even -> avg (number of values / 2, number of values /2 +1)

-- method1: 
-- use row_number, one is desc, one is asc, when abs(row_num_d-row_num_a) <= 1 -> avg
-- input: categoryid, productid, quantity
-- tables: Ecommerce_orderdetails - productid - Ecommerce_products
select categoryid, productid, round(avg(quantity)) as median
from (
    select tb1.categoryid as categoryid, tb2.productid as productid, quantity,
    row_number() over (partition by tb1.categoryid, tb2.productid order by quantity asc) as row_num_a,
    row_number() over (partition by tb1.categoryid, tb2.productid order by quantity desc) as row_num_d
    from Ecommerce_categories tb1
    join Ecommerce_products tb2
    on tb1.categoryid = tb2.categoryid
    join Ecommerce_orderdetails tb3
    on tb2.productid = tb3.productid) as temp
where abs(row_num_a - row_num_d) <= 1 
group by 1,2
order by 1,2;

-- method2: 
-- use row_number, and count -> row_number in avg(case when row_number between (count/2, count/2+1))
-- input: categoryid, productid, quantity
-- tables: Ecommerce_orderdetails - productid - Ecommerce_products
with cte1 as (
    select tb1.categoryid, tb2.productid as productid, count(quantity) as count_num
    from Ecommerce_categories tb1
    join Ecommerce_products tb2
    on tb1.categoryid = tb2.categoryid
    join Ecommerce_orderdetails tb3
    on tb2.productid = tb3.productid
    group by 1,2),
cte2 as (
    select tb1.categoryid, tb2.productid as productid, quantity, row_number() over(partition by tb1.categoryid, tb2.productid order by quantity asc) as row_num  
    from Ecommerce_categories tb1
    join Ecommerce_products tb2
    on tb1.categoryid = tb2.categoryid
    join Ecommerce_orderdetails tb3
    on tb2.productid = tb3.productid)
    
select cte1.categoryid, cte1.productid, round(avg(quantity)) as median
from cte1
join cte2
on cte1.categoryid = cte2.categoryid and cte1.productid = cte2.productid
where row_num between count_num/2 and (count_num/2+1)
group by 1,2
order by 1,2;


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