2. ABOUT ME
Hi, I am Noufal and I am a Data
Analyst. In this section, allow me to
give some of examples how to analyze
group of data by using Structured
Query Language (SQL). I am going to
show you simple exploratory data
analysis and some advanced analysis.
Stay tuned!!
3. 01 02
03 04
TABLE OF CONTENTS
Data Overview
Simple
Exploratory
Data Analysis
Inventory
Stock Analysis
Cohort
Analysis
5. DATA OVERVIEW
The Look eCommerce is a fictitious ecommerce clothing.
This dataset are provided to industry practitioners for the
purpose of testing, evaluation, and education. The dataset
contains information about customers, products, orders,
events and campaigns.
6. Entity Relationship Diagram
In this analysis, we would only use these fives
tables for our analysis:
● users
● orders
● products
● inventory_items
● orders_items
Important!!
It is strongly recommended to visualize our
dataset by using entity relationship diagram
before we dive into analysis. By doing so, we
would able to determine correlation between
tables in dataset.
7. Tools
Serverless infrastructure
with a cloud-based data
warehouse and powerful
analytical tools with SQL
program language.
Visualize the data into
informative, easy to read, easy
to share, fully customizable
and share our insights.
9. #1
Schema Table Result
## Monthly unique users, orders, and sale price per
status
SELECT
FORMAT_DATE("%B %Y", DATE (created_at)) AS Month_Year,
status AS Order_Status,
COUNT (DISTINCT user_id) AS Unique_Users,
COUNT (DISTINCT order_id) AS Total_Orders,
ROUND(SUM(sale_price),2) AS Total_Sale_Price
FROM `bigquery-public-data.thelook_ecommerce.order_items`
WHERE DATE (created_at) BETWEEN '2019-01-01' AND
'2022-08-31'
GROUP BY 1, 2
ORDER BY PARSE_DATE('%B %Y', Month_year);
Calculate the number of unique users, number of orders, and total sale
price per status and month in Jan 2019 until Aug 2022
Query
10. Insights:
Total sales significantly increase
every year. Unfortunately, total
percentage order cancelled up
to 60% compared with
complete ordered.
13.600 orders were cancelled in 2021 & 2022
Recommendation:
Strongly recommended to
minimize cancelled order to
increase major total sales.
11. #2
Schema Table Result
## Monthly frequencies, AOV, and unique users with
‘Complete’ status
SELECT
FORMAT_DATE("%B %Y", DATE (created_at)) AS Month_Year,
ROUND(COUNT(order_id)/COUNT(DISTINCT(user_id)),2) AS
Frequencies,
ROUND(SUM(sale_price) / COUNT(DISTINCT order_id),2) AS
AOV,
COUNT(DISTINCT user_id) AS Unique_Buyers
FROM `bigquery-public-data.thelook_ecommerce.order_items`
WHERE DATE(created_at) BETWEEN '2019-01-01' AND
'2022-08-31'
AND status = 'Complete'
GROUP BY 1
ORDER BY PARSE_DATE('%B %Y', Month_Year);
Calculate frequencies, average order value, and total number of unique
users where status is complete grouped by month
Query
12. Recommendation:
Keep the performance well. If
company desire to increase
their orders, program bundling
or marketed campaign would
likely increase AOV significantly.
Insights:
Average order values were
stable between 60 and 67.
Average order value (AOV) were STABLE
13. WITH product_profit_table AS(
SELECT
orders.product_id AS Product_ID,
product.name AS Product_Name,
product.category AS Category,
ROUND(product.cost,2) AS Cost_Price,
ROUND(orders.sale_price,2) AS Retail_Price,
COUNT(*) AS Items_Sold,
ROUND(SUM(orders.sale_price)-SUM(product.cost),2)
AS Total_Profit
FROM
`bigquery-public-data.thelook_ecommerce.order_item
s` AS orders
JOIN
`bigquery-public-data.thelook_ecommerce.products`
AS product
ON orders.product_id = product.id
WHERE status = 'Complete'
GROUP BY 1, 2, 3, 4, 5
)
#3 Top 5 Least and Most Profitable Products
Query
-- Main query
SELECT
Product_ID, Product_Name, Category,
Cost_Price, Retail_Price, Items_Sold,
Total_Profit
FROM
(
SELECT *,
RANK() OVER(ORDER BY Total_Profit DESC)
AS rank_profit_desc,
RANK() OVER(ORDER BY Total_Profit ASC)
AS rank_profit_asc
FROM product_profit_table
)
WHERE rank_profit_desc BETWEEN 1 AND 5
OR rank_profit_asc BETWEEN 1 AND 5
ORDER BY Total_Profit DESC;
15. Recommendation:
Focus sales in these 5 most
profitable products. Meanwhile,
take down these 5 least
profitable products.
Top 5 Least and Most Profitable Products
17. Before we continue with querying and analysis.
Let's equate our understanding of inventory.
In this case, inventory level is the total items at the end of the
month.
We will use this formula to calculate inventory level and growth rate.
Inventory Level = Initial qty + good receipt - expenditures
Growth Rate = (Inventory level - Prev. Inventory Level) / Prev. Inventory Level
Monthly Growth Rate of Inventory
18. ##Monthly growth rate of inventory
WITH sum_invent_in AS (
SELECT
DATE_TRUNC(DATE(created_at), MONTH) AS months,
product_category, COUNT(id) AS invent_in
FROM
`bigquery-public-data.thelook_ecommerce.inventory_items`
WHERE created_at BETWEEN '2019-01-01' AND '2022-04-30'
GROUP BY 1, 2
ORDER BY 2, 1),
sum_invent_out AS (
SELECT
DATE_TRUNC(DATE(sold_at), MONTH) AS months,
product_category, COUNT(id) AS invent_out
FROM
`bigquery-public-data.thelook_ecommerce.inventory_items`
WHERE created_at BETWEEN '2019-01-01' AND '2022-04-30'
AND sold_at IS NOT NULL
GROUP BY 1, 2
ORDER BY 2, 1),
Query
cumulative_in_out AS (
SELECT
a.months,
a.product_category,
SUM(a.invent_in) OVER(
PARTITION BY a.product_category
ORDER BY a.months) AS cum_invent_in,
SUM(IFNULL(b.invent_out,0)) OVER(
PARTITION BY a.product_category
ORDER BY a.months) AS cum_invent_out,
FROM sum_invent_in AS a
LEFT JOIN sum_invent_out AS b
ON a.months = b.months
AND a.product_category = b.product_category
GROUP BY 1, 2, a.invent_in, b.invent_out
ORDER BY 2, 1
),
inventory_level AS (
SELECT
months, product_category,
cum_invent_in,cum_invent_out,
cum_invent_in - cum_invent_out AS invent_level
FROM cumulative_in_out
ORDER BY 2, 1
),
lag_invent_level AS (
SELECT
months, product_category, invent_level,
LAG(invent_level) OVER(
PARTITION BY product_category
ORDER BY months ASC) AS level_prev_month,
FROM inventory_level
)
-- Main query
SELECT *,
CASE WHEN level_prev_month = 0 THEN NULL
ELSE ROUND((invent_level - level_prev_month) /
level_prev_month,2)
END AS growth_rate
FROM lag_invent_level
ORDER BY 2, 1;
Monthly Growth Rate of Inventory
20. Recommendation:
● Give discounts and prioritize to
sell old stocks products.
● Evaluate inventory control
system. Implement FIFO if
necessary.
● Do proper forecast for demands
and benchmark them in inventory
stock.
Inventory Level was PILLING UP in the warehouse
Insights:
There was massive growth
inventory in early 2020. However,
the inventory level kept
increasing. It means that
products weren’t selling and
pilling up in the warehouse
22. -- take all users' subsequent purchases after the
first month
SELECT
DATE_DIFF(EXTRACT(DATE FROM (a.created_at)),
b.cohort_month, MONTH) AS month_number,
a.user_id
FROM
`bigquery-public-data.thelook_ecommerce.orders` AS a
LEFT JOIN cohort_item AS b
ON a.user_id = b.user_id
WHERE EXTRACT(YEAR FROM created_at) = 2022
AND status = 'Complete'
GROUP BY 2, 1
),
retention_table AS (
## Monthly Retention Cohorts in 2022
WITH cohort_item AS (
-- take the first date of user made a complete purchase
SELECT
EXTRACT(DATE FROM MIN(created_at)) AS cohort_month,
user_id
FROM `bigquery-public-data.thelook_ecommerce.orders`
WHERE status = 'Complete'
GROUP BY user_id
ORDER BY cohort_month
),
cohort_size AS (
-- total users per starting month
SELECT
DATE_TRUNC(DATE(cohort_month),MONTH)AS cohort_month,
COUNT(user_id) AS num_users
FROM cohort_item
GROUP BY cohort_month
ORDER BY cohort_month
),
user_activities AS (
Monthly Retention Cohorts in 2022
Query
-- combine cohort month with subsequent months
SELECT
DATE_TRUNC(DATE(b.cohort_month), MONTH)
AS cohort_month,
CONCAT('M',c.month_number)
AS month_number,
COUNT(cohort_month) AS num_users
FROM user_activities AS c
LEFT JOIN cohort_item AS b
ON c.user_id = b.user_id
GROUP BY 1, 2
ORDER BY 1, 2
)
-- Main query
SELECT
d.cohort_month,
d.month_number,
e.num_users AS cohort_size,
d.num_users,
d.num_users/e.num_users AS ratio
FROM retention_table AS d
LEFT JOIN cohort_size AS e
ON d.cohort_month = e.cohort_month
GROUP BY 1, 2, 3, 4
ORDER BY 1, 2;
24. Average Retention Rate per Month 1.50%
SUM of ratio Month_Number
Year 2022 M0 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
January 100% 0.94% 1.29% 0.82% 1.29% 1.05% 0.47% 0.70% 1.64% 1.52% 0.70% 0.94%
February 100% 1.78% 1.78% 1.40% 1.27% 1.52% 1.27% 1.14% 1.27% 1.14% 1.91%
March 100% 1.35% 2.07% 1.35% 1.45% 1.24% 1.35% 1.76% 1.24% 1.56%
April 100% 1.33% 1.33% 1.73% 1.84% 1.84% 1.43% 1.22% 1.33%
May 100% 1.29% 0.99% 1.19% 1.69% 1.99% 1.89% 0.60%
June 100% 1.64% 1.64% 1.89% 1.72% 1.81% 2.24%
July 100% 2.12% 2.12% 1.78% 2.12% 2.46%
August 100% 1.97% 2.42% 1.66% 2.65%
September 100% 3.08% 2.57% 2.57%
October 100% 2.74% 3.14%
November 100% 3.68%
December 100%
Grand Total 100% 1.99% 1.93% 1.60% 1.75% 1.70% 1.44% 1.09% 1.37% 1.41% 1.30% 0.94%
Maximum retention rate 3.68%
Average retention rate per month 1.50%
25. Insights:
The number of customers had
decreased continuously from
January 2022 until end of the year.
It’s show by the decreasing of
purchase per customers month to
month. We can conclude that,
most of our customers do not
repeat order in our stores and
extremely low retention rate.
Monthly Retention Cohorts in 2022
Recommendation:
TheLook ecommerce needs to
improvise its customers
engagement and retention
immediately since this decrease in
activity has become a trend
(downtrend).
26. CREDITS: This presentation template was created by Slidesgo,
including icons by Flaticon and infographics & images by Freepik
Do you have any questions?
noufalzhafira@gmail.com
+62 821 7048 7070
https://linkedin.com/in/noufal-zhafira
THANKS
27. CONTACT DETAILS
ADDRESS Grogol, West Jakarta
MOBILE +62 821 7048 7070
EMAIL noufalzhafira@gmail.com
INSTAGRAM @noufalzhafira
LINKEDIN @noufalzhafira
