https://github.com/anhvu2201/e-commerce_website_performance_analysis

Ultilize SQL in Big Query to calculate the key metrics. Furthermore, identify current status of the business to assist in deciding the next business plan.
https://github.com/anhvu2201/e-commerce_website_performance_analysis

problem-solving sql sql-query

Last synced: about 2 months ago
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Ultilize SQL in Big Query to calculate the key metrics. Furthermore, identify current status of the business to assist in deciding the next business plan.

• Host: GitHub
• URL: https://github.com/anhvu2201/e-commerce_website_performance_analysis
• Owner: anhvu2201
• Created: 2024-11-17T02:48:18.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-25T15:40:35.000Z (about 2 months ago)
• Last Synced: 2024-12-06T00:12:08.893Z (about 2 months ago)
• Topics: problem-solving, sql, sql-query
• Homepage:
• Size: 24.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
#  E-commerce Website Performance Analysis

# I. Introduction

In this project, I will use SQL on Google [BigQuery](https://cloud.google.com/bigquery/) to explore an eCommerce business dataset, which is based on the Google Analytics public dataset.

# II. Dataset Exploration

There is 8 different queries in this project:

## Query 01: Calculate total visit, pageview, transaction and revenue of the business in January, February and March 2017, in order of month.

- SQL Code:

		SELECT

		    format_date('%Y%m',PARSE_DATE('%Y%m%d', date) ) month

		    ,SUM(totals.visits) visits

		    ,SUM(totals.pageviews) pageviews

		    ,SUM(totals.transactions) transactions

		FROM 

		    `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`

		WHERE 

		    date BETWEEN '20170101' AND '20170331'

		GROUP BY 

		    month

		ORDER BY 

		    month;

- Query Result:

			month	visits	pageviews	transactions

			201701	64694	257708		713

			201702	62192	233373		733

			201703	69931	259522		993

- Link To Result: [Link](https://drive.google.com/file/d/1TMId10oA9mxwMws7YoTywQspK7LW2il4/view?usp=sharing)

## Query 02: Bounce rate per traffic source in July 2017.

- SQL Code:

		SELECT 

		    trafficSource.source source

		    ,SUM(totals.visits) visits

		    ,SUM(totals.bounces) total_no_of_bounces

		    ,ROUND((SUM(totals.bounces) / SUM(totals.visits)) *100,3) bounce_rate

		FROM 

		    `bigquery-public-data.google_analytics_sample.ga_sessions_201707*`

		GROUP BY 

		    trafficSource.source

		ORDER BY 

		    visits DESC;

- Query Result:

		source		visits	total_no_of_bounces	bounce_rate

		google		38400	19798			51.557

		(direct)		19891	8606			43.266

		youtube.com		6351	4238			66.73

		analytics.google.com	1972	1064			53.955

		Partners		1788	936			52.349

		m.facebook.com	669	430			64.275

		google.com		368	183			49.728

		dfa			302	124			41.06

		sites.google.com	230	97			42.174

- Link To Result: [Link](https://drive.google.com/file/d/1a_w1-Brkxmsx2encFke0t704Yj9s6BBB/view?usp=sharing)

## Query 03: Revenue contributed by traffic source calculated by week and by month in June 2017.

- SQL Code:

		SELECT

		    'Month' time_type

		    , format_date('%Y%m',PARSE_DATE('%Y%m%d', date) ) time

		    , trafficSource.source source

		    , ROUND (SUM ((product.productRevenue) / 1000000),4) revenue

		FROM `bigquery-public-data.google_analytics_sample.ga_sessions_201706*`

		    ,UNNEST (hits) hits,

		    UNNEST (hits.product) product

		WHERE product.productRevenue is not null

		GROUP BY source, time

		

		UNION ALL

		

		SELECT

		    'Week' time_type

		    , format_date('%Y%W',PARSE_DATE('%Y%m%d', date) ) time

		    , trafficSource.source source

		    , ROUND (SUM ((product.productRevenue) / 1000000),4) revenue

		FROM `bigquery-public-data.google_analytics_sample.ga_sessions_201706*`

		    ,UNNEST (hits) hits,

		    UNNEST (hits.product) product

		WHERE product.productRevenue is not null

		GROUP BY source, time

		Order by revenue DESC;

- Query Result:

		time_type	time	source		revenue

		Month		201706	(direct)	97333.6197

		Week		201724	(direct)	30908.9099

		Week		201725	(direct)	27295.3199

		Month		201706	google		18757.1799

		Week		201723	(direct)	17325.6799

		Week		201726	(direct)	14914.81

		Week		201724	google		9217.17

		Month		201706	dfa		8862.23

		Week		201722	(direct)	6888.9

		Week		201726	google		5330.57

- Link To Result: [Link](https://drive.google.com/file/d/1bIS2-TLoupKlBFz62ECcB00XLRxeOs8h/view?usp=sharing)

## Query 04: Average number of product pageviews categorized by purchaser type (purchasers and non-purchasers) in June and July 2017.

- SQL Code:

		WITH 

		purchaser_data AS(

		  SELECT

		      FORMAT_DATE("%Y%m",PARSE_DATE("%Y%m%d",date)) AS month,

		      (SUM(totals.pageviews)/COUNT(DISTINCT fullvisitorid)) AS avg_pageviews_purchase,

		  FROM `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`

		    ,UNNEST(hits) hits

		    ,UNNEST(product) product

		  WHERE _table_suffix BETWEEN '0601' AND '0731'

		  AND totals.transactions>=1

		  AND product.productRevenue IS NOT NULL

		  GROUP BY month

		),

		

		non_purchaser_data AS(

		  SELECT

		      FORMAT_DATE("%Y%m",PARSE_DATE("%Y%m%d",date)) AS month,

		      SUM(totals.pageviews)/COUNT(DISTINCT fullvisitorid) AS avg_pageviews_non_purchase,

		  FROM `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`

		      ,UNNEST(hits) hits

		    ,UNNEST(product) product

		  WHERE _table_suffix BETWEEN '0601' AND '0731'

		  AND totals.transactions IS NULL

		  AND product.productRevenue IS NULL

		  GROUP BY month

		)

		

		SELECT

		    pd.*,

		    avg_pageviews_non_purchase

		FROM purchaser_data pd

		FULL JOIN non_purchaser_data USING(month)

		ORDER BY pd.month;

- Query Result:

		month		avg_pageviews_purchase	avg_pageviews_non_purchase

		201706	94.02050113895217	316.86558846341671

		201707	124.23755186721992	334.05655979568053

- Link To Result: [Link](https://drive.google.com/file/d/1XxcJESc57hGYPZOmuQ2H1o-DVRq1KB3x/view?usp=sharing)

## Query 05: Average number of transactions per user that made atleast a purchase in July 2017.

- SQL Code:

		SELECT

		    format_date('%Y%m',PARSE_DATE('%Y%m%d', date) ) Month

		    , sum(totals.transactions) / count (distinct (fullVisitorId)) Avg_total_transactions_per_user

		FROM `bigquery-public-data.google_analytics_sample.ga_sessions_201707*`

		    ,UNNEST (hits) hits,

		    UNNEST (hits.product) product

		WHERE totals.transactions >= 1 

		  and product.productRevenue is not null

		  and _table_suffix between '01' and '31'

		GROUP BY month;

		- Query Result:

- Query Result:

		Month		Avg_total_transactions_per_user

		201707	4.16390041493776

- Link To Result: [Link](https://drive.google.com/file/d/15zB2NTqVZiVx8lGYxuZdN7dbbjSryRx8/view?usp=sharing)

## Query 06: Average amount of money spent per session in July 2017.

- SQL Code:

		SELECT

		    format_date('%Y%m',PARSE_DATE('%Y%m%d', date) ) Month

		    , ROUND ((sum(product.productRevenue) / sum(totals.visits) / 1000000),2) avg_revenue_by_user_per_visit

		FROM `bigquery-public-data.google_analytics_sample.ga_sessions_201707*`

		    ,UNNEST (hits) hits,

		    UNNEST (hits.product) product

		WHERE totals.transactions is not null 

		    and product.productRevenue is not null

		    and _table_suffix between '01' and '31'

		GROUP BY month;

- Query Result:

		Month		avg_revenue_by_user_per_visit

		201707	43.86

- Link To Result: [Link](https://drive.google.com/file/d/1-YCR7yBo3gMGngwNUTfLfX58JoEOqeKQ/view?usp=sharing)

## Query 07: Other products purchased by customers who purchased product "YouTube Men's Vintage Henley" in July 2017.

- SQL Code:

		With customers_who_purchased_henley as (

		SELECT DISTINCT fullVisitorId

		FROM `bigquery-public-data.google_analytics_sample.ga_sessions_201707*`

		    ,UNNEST (hits) hits,

		    UNNEST (hits.product) product

		WHERE   

		     product.v2ProductName = "YouTube Men's Vintage Henley"

		    and _table_suffix between '01' and '31'

		    and totals.transactions is not null

		    and product.productRevenue is not null

		)

		SELECT 

		    product.v2ProductName other_purchased_products,

		    sum(product.productQuantity) quantity

		FROM `bigquery-public-data.google_analytics_sample.ga_sessions_201707*`

		    ,UNNEST (hits) hits,

		    UNNEST (hits.product) product

		INNER JOIN customers_who_purchased_henley

		USING (fullVisitorId)

		WHERE 

		    product.v2ProductName <> "YouTube Men's Vintage Henley"

		    and _table_suffix between '01' and '31'

		    and totals.transactions is not null

		    and product.productRevenue is not null

		GROUP BY other_purchased_products

		ORDER BY quantity DESC;

- Query Result:

		other_purchased_products				quantity

		Google Sunglasses					20

		Google Women's Vintage Hero Tee Black			7

		SPF-15 Slim & Slender Lip Balm			6

		Google Women's Short Sleeve Hero Tee Red Heather	4

		YouTube Men's Fleece Hoodie Black			3

		Google Men's Short Sleeve Badge Tee Charcoal		3

		Crunch Noise Dog Toy					2

		Android Wool Heather Cap Heather/Black		2

		YouTube Twill Cap					2

		Recycled Mouse Pad					2

- Link To Result: [Link](https://drive.google.com/file/d/1eWZoWVcLPy-2Uv4K1F_tjkrR7ZGE8hwi/view?usp=sharing)

## Query 08: Calculate cohort map from pageview to addtocart to purchase in the last 3 month.

- SQL Code:

		WITH product_data AS(

		SELECT

		    FORMAT_DATE('%Y%m', PARSE_DATE('%Y%m%d',date)) AS month,

		    COUNT(CASE WHEN eCommerceAction.action_type = '2' THEN product.v2ProductName END) AS num_product_view,

		    COUNT(CASE WHEN eCommerceAction.action_type = '3' THEN product.v2ProductName END) AS num_add_to_cart,

		    COUNT(CASE WHEN eCommerceAction.action_type = '6' AND product.productRevenue IS NOT NULL THEN product.v2ProductName END) AS num_purchase

		FROM `bigquery-public-data.google_analytics_sample.ga_sessions_*`

		,UNNEST(hits) AS hits

		,UNNEST (hits.product) AS product

		WHERE _table_suffix BETWEEN '20170101' AND '20170331'

		AND eCommerceAction.action_type IN ('2','3','6')

		GROUP BY month

		ORDER BY month

		)

		

		SELECT

		    *,

		    ROUND(num_add_to_cart/num_product_view * 100, 2) AS add_to_cart_rate,

		    ROUND(num_purchase/num_product_view * 100, 2) AS purchase_rate

		FROM product_data;

- Query Result:

		month		num_product_view	num_add_to_cart	  num_purchase	add_to_cart_rate	purchase_rate

		201701	25787			7342		  2143		28.47			8.31

		201702	21489			7360		  2060		34.25			9.59

		201703	23549			8782		  2977		37.29			12.64

- Link To Result: [Link](https://drive.google.com/file/d/1MiMC9QuzYWLw_or2QZt60T7YqvYhmM1y/view?usp=sharing)

# III. Conclusion

- In conclusion, analyzing the eCommerce dataset using SQL on Google BigQuery has uncovered key insights into total visits, pageviews, transactions, bounce rate, and revenue by traffic source, which can drive more informed business decisions.

- By exploring the dataset, a deeper understanding of critical metrics is achieved, setting the foundation for further analysis. The next step will involve using visualization tools like Power BI or Tableau to highlight key trends and patterns.

- Overall, this project showcases the effectiveness of combining SQL with big data tools like Google BigQuery to derive actionable insights from extensive datasets, emphasizing the value of data-driven decision-making.