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) as R
select a.country,
      sum(case when d.price*c.quantity is not null then d.price*c.quantity else 0 end)as revenue
from Ecommerce_customers a
left join Ecommerce_orders b on a.customerid = b.customerid
left join Ecommerce_orderdetails c on b.orderid = c.orderid
left join Ecommerce_products d on c.productid = d.productid
group by 1
order by 2 DESC;

-- #11. Revenue by category in Austria 
-- country, category, revenue(p*q)
-- C & O : Cid
-- O & OD : Oid
-- OD & PROD : proid
-- group by

--Method1:
select a.country, 
    sum(case when d.price*c.quantity is not null then d.price*c.quantity else 0 end) as revenue 
from Ecommerce_customers a
left join Ecommerce_orders b on a.customerid = b.customerid
left join Ecommerce_orderdetails c on b.orderid = c.orderid
left join Ecommerce_products d on c.productid = d.productid
group by 1
order by 2 DESC;

-- Method2:
-- revenue by category in austria 
-- country, category, revnue(p*q)
-- c,p,ordetails
--c&o:customerid
--ord&orderdetails:orderid
--ordertail&products:productid

select a.country, d.categoryid, sum(d.price * c.quantity) as revenue
from Ecommerce_customers a 
join Ecommerce_orders b on a.customerid = b.customerid
join Ecommerce_orderdetails c on b.orderid = c.orderid
join Ecommerce_products d on c.productid = d.productid
group by 1,2
having a.country = 'Austria'
order by 1,2;

-- or

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

--#12. for each customer, what's the avg spend(total spend/ total order)?

-- customer id, total spend, total order, S/O

-- CUST, ORD, ORDETAIL, PROD

-- custo& or:customerid
-- orders & orderdtails: orderid
-- orderdetails&products:productid

select a.customerid, 
       sum(d.price * c.quantity) as total_spend,
        count(distinct c.orderid) as total_order,
       sum(d.price * c.quantity)/count(distinct c.orderid) as avg_spend
from Ecommerce_customers a
join Ecommerce_orders b on a.customerid = b.customerid
join Ecommerce_orderdetails c on b.orderid = c.orderid
join Ecommerce_products d on c.productid = d.productid
group by 1
order by 1,4;

-- safe_divide better version
select  a.customerid, 
        sum(d.price * c.quantity) as total_spend,
        count(distinct c.orderid) as total_order,
        case when count(distinct c.orderid) > 0 
        then sum(d.price*c.quantity)/count(distinct c.orderid) else null end as avg_spend
from Ecommerce_customers a
join Ecommerce_orders b on a.customerid = b.customerid
join Ecommerce_orderdetails c on b.orderid = c.orderid
join Ecommerce_products d on c.productid = d.productid
group by 1
order by 1,4;

-- #13. top 5 most pop product in each category

-- categoryid, categoryname, productid, quantity (5 for each category)

-- Ecommerce_orderdetails & Ecommerce_products:productid
-- Ecommerce_products & Ecommerce_categories:categoryid
-- limit 5, group by

--#13.1.rank_num
with cte as
(select b.categoryid,
       c.categoryname,
       b.productid,
       sum(a.quantity) as quantity,
       rank() over (partition by b.categoryid order by sum(a.quantity)DESC) as rank_num
from Ecommerce_orderdetails a
join Ecommerce_products b on a.productid = b.productid
join Ecommerce_categories c on b.categoryid = c.categoryid
group by 1,2,3
order by 1,5)
select * from cte
where rank_num <=5;

--#13.2.dense_rank
with cte as
(select b.categoryid,
       c.categoryname,
       b.productid,
       sum(a.quantity) as quantity,
       dense_rank() over (partition by b.categoryid order by sum(a.quantity)DESC) as dense_rank_num
from Ecommerce_orderdetails a
join Ecommerce_products b on a.productid = b.productid
join Ecommerce_categories c on b.categoryid = c.categoryid
group by 1,2,3
order by 1,5)
select * from cte
where dense_rank_num <=5;

--#13.3.row_number
select * from
(select b.categoryid,
       c.categoryname,
       b.productid,
       sum(a.quantity) as quantity,
       row_number() over (partition by b.categoryid order by sum(a.quantity)DESC) as row_num
from Ecommerce_orderdetails a
join Ecommerce_products b on a.productid = b.productid
join Ecommerce_categories c on b.categoryid = c.categoryid
group by 1,2,3
order by 1,5) as cte
where row_num <=5;

--#13.4.bucketile
select b.categoryid,
       c.categoryname,
       b.productid,
       sum(a.quantity) as quantity,
       NTILE(5) over (order by sum(a.quantity)DESC) as buckets
from Ecommerce_orderdetails a
join Ecommerce_products b on a.productid = b.productid
join Ecommerce_categories c on b.categoryid = c.categoryid
group by 1,2,3
order by 5;

--#13: one solution
select * from
(select b.categoryid,
       c.categoryname,
       b.productid,
       sum(a.quantity) as quantity,
       row_number() over (partition by b.categoryid order by sum(a.quantity)DESC) as row_num
from Ecommerce_orderdetails a
join Ecommerce_products b on a.productid = b.productid
join Ecommerce_categories c on b.categoryid = c.categoryid
group by 1,2,3
order by 1,5) as cte
where row_num <=5;

--#13:2nd solution: with cte table 
with cte as
(select a.categoryid, 
 a.productid, 
 sum(b.quantity) as sales,
 row_number() over(partition by categoryid order by sum(b.quantity) desc) as row_num 
from Ecommerce_products a
join Ecommerce_orderdetails b
on a.productid = b.productid
group by 1,2)
select *
from cte
where row_num<=5;

--#13. 3rd solution: select *: 
select * from(
select a.categoryid, 
       a.productid, 
       sum(b.quantity) as sales,
       row_number() over(partition by categoryid order by sum(b.quantity) desc) as row_num 
from Ecommerce_products a join Ecommerce_orderdetails b on a.productid = b.productid
group by 1,2) as cte
where row_num<=5;

--#13. 4th solution: step by step: sum, row_num, restriction 
select * 
from(
select categoryid,
       productid,
       sales,
       row_number() over (partition by categoryid order by sales DESC) as row_num
from(
select a.categoryid,
       a.productid,
       sum(b.quantity) as sales
from Ecommerce_products a join Ecommerce_orderdetails b on a.productid = b.productid
group by 1,2) as cte1
)as cte2
where row_num <=5;

--Q14: each customer quantity on beverage, condiments and others
-- customerid, categoryid, categoryname, quantity
-- cu&or:customerid
-- or&ord:orderid
-- ord&product: productid
-- product&categy:categoryid
--case when

--#14.1
--select customerid, categoryid, categoryname, sum(quantity) as sum_quant

-- building new columns: case when caculation: sum(case when categoryname not in ('A', 'B') then Q else 0 end) 
-- group by ID to give a general picture
-- use join to only exclude records with all 0s
select a.customerid,
       sum(case when e.categoryname =  'Beverages' then c.quantity else 0 end) as Bev_Sales,
       sum(case when e.categoryname =  'Condiments' then c.quantity else 0 end) as Condi_Sales,
       sum(case when e.categoryname not in ('Beverages','Condiments') then c.quantity else 0 end) as Other_Sales
from Ecommerce_customers a 
join Ecommerce_orders b on a.customerid = b.customerid
join Ecommerce_orderdetails c on b.orderid = c.orderid
join Ecommerce_products d on c.productid = d.productid
join Ecommerce_categories e on d.categoryid = e.categoryid
group by 1
order by 1;

--#14.2
-- group by id& name to give specific details 
select a.customerid,
       e.categoryname,
       sum(case when e.categoryname =  'Beverages' then c.quantity else 0 end) as Bev_Sales,
       sum(case when e.categoryname =  'Condiments' then c.quantity else 0 end) as Condi_Sale,
sum(case when e.categoryname not in ('Beverages','Condiments') then c.quantity else 0 end) as Other_Sales
from Ecommerce_customers a 
join Ecommerce_orders b on a.customerid = b.customerid
join Ecommerce_orderdetails c on b.orderid = c.orderid
join Ecommerce_products d on c.productid = d.productid
join Ecommerce_categories e on d.categoryid = e.categoryid
group by 1,2
order by 1;

--#14.3
-- another method: case when categoryname = '' or categoryname = '' but need to know all names in "other" filed

select a.customerid,
       e.categoryname,
       case when e.categoryname =  'Beverages' then sum(c.quantity) else 0 end as Bev_Sales,
       case when e.categoryname =  'Condiments' then sum(c.quantity) else 0 end as Condi_Sale,
       case when e.categoryname =  'Confections' 
              or e.categoryname = 'Dairy Products' 
              or e.categoryname = 'Grains/Cereals'
              or e.categoryname = 'Meat/Poultry'
              or e.categoryname = 'Seafood'
then sum(c.quantity) else 0 end as Other_Sales
from Ecommerce_customers a 
join Ecommerce_orders b on a.customerid = b.customerid
join Ecommerce_orderdetails c on b.orderid = c.orderid
join Ecommerce_products d on c.productid = d.productid
join Ecommerce_categories e on d.categoryid = e.categoryid
group by 1,2
order by 1;

--#15.median quantity value of each category and product

-- categoryid, productid, quantity
-- Ecommerce_orderdetails, Ecommerce_products
with cte as 
(select b.categoryid as categoryid, 
        b.productid as productid, 
        a.quantity as quantity,
        row_number() over (partition by b.categoryid order by a.quantity asc) as row_num1,
        row_number() over (partition by b.categoryid order by a.quantity desc) as row_num2
from Ecommerce_orderdetails a
join Ecommerce_products b
on a.productid = b.productid
group by 1,2,3),
cte2 as (
select categoryid, productid, quantity, row_num1, row_num2
from cte
where row_num1 in (row_num2, row_num2-1, row_num2+1))

select categoryid, productid, round(avg(quantity)) as med from cte2
group by 1,2
order by 1;


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