📊 Project Title: AdventureWorks Dataset Exploring

🤵 Author: Tri Nguyen
📆 Date: Nov. 10, 2024
💻 Tools Used: SQL - BigQuery Platform

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📑 Table of Contents

1. 📌 Background & Overview
2. 📂 Dataset Description & Data Structure
3. 🧠 Problem Solving Process
4. 📊 Explore the Dataset & Generate Insights
5. 🔎 Final Conclusion & Recommendations

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📌 Background & Overview

Objective:

📖 What is this project about?

This project utilizes advanced SQL techniques to analyze the AdventureWorks dataset, revealing trends, enhancing data visualization, and supporting decision-making within a business environment.

👤 Who is this project for?

➡️ Sales manager who want to understand the Sales revenue components and Sales trend.

❓Business Questions:

• Analyze data on items, sales, order quantities, growth rates, top categories, top territories, and total discount costs by subcategories.
• Assess customer retention rates, stock level trends, month-over-month differences, and stock-to-sales ratios.
• Evaluate the number and value of pending orders in 2014.

🎯Project Outcome:

• Sales and Growth Trends: Bike Racks and Road Frames achieved the highest sales revenue and quantity, respectively. Mountain Frames and Socks showed notable year-over-year growth.
• Discounts and Customer Retention: Helmets had continuous discount promotions with increased discount costs from 2012 to 2013, while customer retention dropped significantly after the first purchase, indicating the need for better retention strategies.
• Stock and Order Management: Quarterly stock levels consistently declined, indicating robust sales. A substantial number of pending orders highlight the need to assess vendor performance for efficiency and suggest improvements to their order processes.

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📂 Dataset Description & Data Structure

📌 Data Source

• Source: The AdventureWorks2019 dataset by Microsoft is a comprehensive sample database that simulates a manufacturing company's operations.

• Size: Over 121000 rows

• Format:

  To access the dataset, follow these step

  • Log in to your Google Cloud Platform account and create a new project.
  • Navigate to the BigQuery console and select your newly created project.
  • In the navigation panel, select "Add Data" and then choose "Star a project by name".
  • Enter the project name "adventureworks2019" and click "Enter".
  • Click on the "adventureworks2019" table to open it.

📊 Data Structure & Relationships

1️⃣ Tables Used:

There're 6 tables were used in this project

2️⃣ Table Schema & Data Snapshot

Table 1: Sales.SalesOrderHeader

Name	Data type	Description / Attributes
SalesOrderID	int	Primary key. Identity / Auto increment column
RevisionNumber	tinyint	Incremental number to track changes to the sales order over time. Default: 0
OrderDate	datetime	Dates the sales order was created. Default: getdate()
DueDate	datetime	Date the order is due to the customer.
ShipDate	datetime	Date the order was shipped to the customer.
Status	tinyint	Order current status. 1 = In process; 2 = Approved; 3 = Backordered; 4 = Rejected; 5 = Shipped; 6 = Cancelled Default: 1
OnlineOrderFlag	bit	0 = Order placed by sales person. 1 = Order placed online by customer. Default: 1
SalesOrderNumber	nvarchar(25)	Unique sales order identification number. Computed: isnul(N'SO'+CONVERT(nvarchar(23),[SalesOrderID]),N'*** ERROR ***')
PurchaseOrderNumber	nvarchar(25)	Customer purchase order number reference.
AccountNumber	nvarchar(15)	Financial accounting number reference.
CustomerID	int	Customer identification number. Foreign key to Customer.BusinessEntityID.
SalesPersonID	int	Sales person who created the sales order. Foreign key to SalesPerson.BusinessEntityID.
TerritoryID	int	Territory in which the sale was made. Foreign key to SalesTerritory.SalesTerritoryID.
BillToAddressID	int	Customer billing address. Foreign key to Address.AddressID.
ShipToAddressID	int	Customer shipping address. Foreign key to Address.AddressID.
ShipMethodID	int	Shipping method. Foreign key to ShipMethod.ShipMethodID.
CreditCardID	int	Credit card identification number. Foreign key to CreditCard.CreditCardID.
CreditCardApprovalCode	varchar(15)	Approval code provided by the credit card company.
CurrencyRateID	int	Currency exchange rate used. Foreign key to CurrencyRate.CurrencyRateID.
SubTotal	money	Sales subtotal. Computed as SUM(SalesOrderDetail.LineTotal) for the appropriate SalesOrderID. Default: 0.00
TaxAmt	money	Tax amount. Default: 0.00
Freight	money	Shipping cost. Default: 0.00
TotalDue	money	Total due from customer. Computed as Subtotal + TaxAmt + Freight. Computed: isnul(((SubTotal)+(TaxAmt))+(Freight),(0))
Comment	nvarchar(128)	Sales representative comments.
rowguid	uniqueidentifier	ROWGUIDCOL number uniquely identifying the record. Used to support a merge replication sample. Default: newid()
ModifiedDate	datetime	Date and time the record was last updated. Default: getdate()

Table 2: Sales.SalesOrderDetail

Column Name	Data Type	Description/Attributes
SalesOrderID	int	Primary key. Foreign key to SalesOrderHeader.SalesOrderID.
SalesOrderDetailID	int	Primary key. One incremental unique number per product sold. Identity / Auto increment.
CarrierTrackingNumber	nvarchar(25)	Shipment tracking number supplied by the shipper.
OrderQty	smallint	Quantity ordered per product.
ProductID	int	Product sold to customer. Foreign key to Product.ProductID.
SpecialOfferID	int	Promotional code. Foreign key to SpecialOffer.SpecialOfferID.
UnitPrice	money	Selling price of a single product.
UnitPriceDiscount	money	Discount amount. Default: 0.0.
LineTotal	numeric(38, 6)	Per product subtotal. Computed as UnitPrice * (1 - UnitPriceDiscount) * OrderQty. Computed: isnull((([UnitPrice]((1.0-[UnitPriceDiscount])[OrderQty]),(0.0))
rowguid	uniqueidentifier	ROWGUIDCOL number uniquely identifying the record. Used to support a merge replication sample. Default: newid()
ModifiedDate	datetime	Date and time the record was last updated. Default: getdate()

Table 3: Production.Product

Name	Data type	Description / Attributes
ProductID	int	Primary key for Product records. Identity / Auto increment column
Name	nvarchar(50)	Name of the product.
ProductNumber	nvarchar(25)	Unique product identification number.
MakeFlag	bit	0 = Product is purchased, 1 = Product is manufactured in-house. Default: 1
FinishedGoodsFlag	bit	0 = Product is not a salable item. 1 = Product is salable. Default: 1
Color	nvarchar(15)	Product color.
SafetyStockLevel	smallint	Minimum inventory quantity.
ReorderPoint	smallint	Inventory level that triggers a purchase order or work order.
StandardCost	money	Standard cost of the product.
ListPrice	money	Selling price.
Size	nvarchar(5)	Product size.
SizeUnitMeasureCode	nchar(3)	Unit of measure for Size column.
WeightUnitMeasureCode	nchar(3)	Unit of measure for Weight column.
Weight	decimal(8, 2)	Product weight.
DaysToManufacture	int	Number of days required to manufacture the product.
ProductLine	nchar(2)	R = Road, M = Mountain, T = Touring, S = Standard
Class	nchar(2)	H = High, M = Medium, L = Low
Style	nchar(2)	W = Womens, M = Mens, U = Universal
ProductSubcategoryID	int	Product is a member of this product subcategory. Foreign key to ProductSubCategory.ProductSubCategoryID.
ProductModelID	int	Product is a member of this product model. Foreign key to ProductModel.ProductModelID.
SellStartDate	datetime	Date the product was available for sale.
SellEndDate	datetime	Date the product was no longer available for sale.
DiscontinuedDate	datetime	Date the product was discontinued.
rowguid	uniqueidentifier	ROWGUIDCOL number uniquely identifying the record. Used to support a merge replication sample. Default: newid()
ModifiedDate	datetime	Date and time the record was last updated. Default: getdate()

Table 4: Production.ProductSubcategory

Name	Data type	Description / Attributes
ProductSubcategoryID	int	Primary key for ProductSubcategory records. Identity / Auto increment column.
ProductCategoryID	int	Product category identification number. Foreign key to ProductCategory.ProductCategoryID.
Name	nvarchar(50)	Subcategory description.
rowguid	uniqueidentifier	ROWGUIDCOL number uniquely identifying the record. Used to support a merge replication sample. Default: newid()
ModifiedDate	datetime	Date and time the record was last updated. Default: getdate()

Table 5: Production.WorkOrder

Name	Data type	Description / Attributes
WorkOrderID	int	Primary key for WorkOrder records. Identity / Auto increment column
ProductID	int	Product identification number. Foreign key to Product.ProductID
OrderQty	int	Product quantity to build
StockedQty	int	Quantity built and put in inventory. Computed: isnull([OrderQty] - [ScrappedQty], 0)
ScrappedQty	smallint	Quantity that failed inspection
StartDate	datetime	Work order start date
EndDate	datetime	Work order end date
DueDate	datetime	Work order due date
ScrapReasonID	smallint	Reason for inspection failure
ModifiedDate	datetime	Date and time the record was last updated. Default: getdate()

Table 6: Purchasing.PurchaseOrderHeader

Name	Data type	Description / Attributes
PurchaseOrderID	int	Primary key. Identity / Auto increment column.
RevisionNumber	tinyint	Incremental number to track changes to the purchase order over time. Default: 0
Status	tinyint	Order current status. 1 = Pending; 2 = Approved; 3 = Rejected; 4 = Complete. Default: 1
EmployeeID	int	Employee who created the purchase order. Foreign key to Employee.BusinessEntityID.
VendorID	int	Vendor with whom the purchase order is placed. Foreign key to Vendor.BusinessEntityID.
ShipMethodID	int	Shipping method. Foreign key to ShipMethod.ShipMethodID.
OrderDate	datetime	Purchase order creation date. Default: getdate()
ShipDate	datetime	Estimated shipment date from the vendor.
SubTotal	money	Purchase order subtotal. Computed as SUM(PurchaseOrderDetail.LineTotal) for the appropriate PurchaseOrderID. Default: 0.00
TaxAmt	money	Tax amount. Default: 0.00
Freight	money	Shipping cost. Default: 0.00
TotalDue	money	Total due to vendor. Computed as Subtotal + TaxAmt + Freight. Computed: isnull((([SubTotal] + [TaxAmt]) + [Freight]), (0))
ModifiedDate	datetime	Date and time the record was last updated. Default: getdate()

3️⃣ Data Relationships:

• Field SalesOrderDetai.SalesOrderID is foreign key to SalesOrderHeader.SalesOrderID
• Field SalesOrderDetai.ProductID is foreign key to Product.ProductID
• Field Product.ProductID is foreign key to WorkOrder.ProductID
• Field Product.ProductSubcategoryID is foreign key to ProductSubCategory.ProductSubcategoryID

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🧠 Problem Solving Process

1️⃣ Understand Problem	2️⃣ Break it down into smaller pieces	3️⃣ Ideate	4️⃣ Implement and Review
Which to be calculated (sum, count, ratio, etc.) and grouped? Does it needs any filter (time, conditions, etc.)?	Which tables have data that I want to get? Which columns have data corresponded to the problem? Can I get the data I by one step, if not, break down even smaller?	How many step did I need to get the final result? Is it optimized?	We'll go through this step in the order of each query listed below ⬇️

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📊 Explore the Dataset & Generate Insights

Query 1️⃣: Calc Quantity of items, Sales value & Order quantity by each Subcategory in Last 12 Mth

To gain a deeper understanding of our sales performance, we need to calculate and analyze the following metrics for each subcategory over the past 12 months

SELECT 
  format_datetime('%b %Y', sod.ModifiedDate) as period
  ,pps.Name as name
  ,sum(sod.OrderQty) as qty_item
  ,round(sum(sod.LineTotal),2) as total_sales
  ,count(distinct sod.SalesOrderID) as order_cnt
FROM `adventureworks2019.Sales.SalesOrderDetail` as sod
left join `adventureworks2019.Production.Product` as pp 
  on sod.ProductID = pp.ProductID
left join `adventureworks2019.Production.ProductSubcategory` as pps 
  on cast(pp.ProductSubcategoryID as int) = pps.ProductSubcategoryID
where date(sod.ModifiedDate) >= (select date_add(max(date(ModifiedDate)), interval -12 month)
                             from `adventureworks2019.Sales.SalesOrderDetail`)
group by 1,2
order by 2,1 desc;

Row	period	name	qty_item	total_sales	order_cnt
1	Jun 2013	Bib-Shorts	2	116.99	1
2	Jul 2013	Bib-Shorts	2	116.99	1
3	Feb 2014	Bib-Shorts	4	233.97	2
4	Apr 2014	Bib-Shorts	4	233.97	1
5	Sep 2013	Bike Racks	312	22828.51	71
6	Oct 2013	Bike Racks	284	21181.2	70
7	Nov 2013	Bike Racks	142	11472.0	50
8	...

💡 Bike Racks have had the highest sales revenue in the past 12 months, indicating strong market demand.

Query 2️⃣: Calc % YoY growth rate by SubCategory & release top 3 cat with highest grow rate. Can use metric: quantity_item. Round results to 2 decimal

To calculate the year-over-year (YoY) growth rate by subcategory and determine which subcategories are on the top 3 by their growth rates using the metric quantity_item

with qty_data as (
    select 
      pps.Name as name
      ,format_date("%Y", sod.ModifiedDate) as year 
      ,sum(sod.OrderQty) as qty_item
    from `adventureworks2019.Sales.SalesOrderDetail` as sod
    left join `adventureworks2019.Production.Product` as pp on sod.ProductID = pp.ProductID
    left join `adventureworks2019.Production.ProductSubcategory` as pps on cast(pp.ProductSubcategoryID as int) = pps.ProductSubcategoryID
    group by 1,2
  ),
  sale_diff as (
    select *
      ,lag(qty_item) over (partition by name order by year) as prv_qty
      ,round((qty_item - lag(qty_item) over (partition by name order by year))/(lag(qty_item) over (partition by name order by year)),2) as qty_diff
    from qty_data 
  ),
  sale_rk as (
    select *
      ,dense_rank() over (order by qty_diff desc) as dkr 
    from sale_diff 
  )
select distinct 
  name
  ,qty_item 
  ,prv_qty
  ,qty_diff
  ,dkr 
from sale_rk 
where dkr <= 3 
order by dkr;

Row	name	qty_item	prv_qty	qty_diff	dkr
1	Mountain Frames	3168	510	5.21	1
2	Socks	2724	523	4.21	2
3	Road Frames	5564	1137	3.89	3

💡 Road Frames had the highest quantity sales, while Mountain Frames and Socks showed the highest year-over-year growth rates.

Query 3️⃣: Query 3: Ranking Top 3 TeritoryID with biggest Order quantity of every year. If there's TerritoryID with same quantity in a year, do not skip the rank number

To evaluate the sales performance across different territories by analyze the total number of orders for each territory.

with 
  data as (
    select 
      format_date("%Y", sod.ModifiedDate) as year 
      ,soh.TerritoryID
      ,sum(sod.OrderQty) as order_cnt
    from `adventureworks2019.Sales.SalesOrderDetail` as sod
    left join `adventureworks2019.Sales.SalesOrderHeader` as soh on sod.SalesOrderID = soh.SalesOrderID
    group by 1,2 
    order by 1 desc 
  ),
  ranking as (
    select *
      ,dense_rank() over (partition by year order by order_cnt desc) as rk
    from data 
    order by 1 desc 
  )
select * 
from ranking 
where rk <=3;

Row	year	TerritoryID	order_cnt	rk
1	2014	4	11632	1
2	2014	6	9711	2
3	2014	1	8823	3
4	2013	4	26682	1
5	2013	6	22553	2
6	2013	1	17452	3
7	2012	4	17553	1
8	2012	6	14412	2
9	2012	1	8537	3
10	2011	4	3238	1
11	2011	6	2705	2
12	2011	1	1964	3

💡 Territory No. 4 consistently had the highest order rates each year, with Territory No. 6 following closely behind.

Query 4️⃣: Query 4: Calc Total Discount Cost belongs to Seasonal Discount for each SubCategory

To compare and evaluate the total cost of seasonal discounts for each subcategory by year

with discount_data as (
  select distinct 
    sod.ModifiedDate 
    ,ps.Name 
    ,so.Type
    ,so.DiscountPct*sod.UnitPrice*sod.OrderQty as dis_cost 
  from `adventureworks2019.Sales.SalesOrderDetail` as sod
  left join `adventureworks2019.Production.Product` as p  on sod.ProductID = p.ProductID
  left join `adventureworks2019.Production.ProductSubcategory` as ps on cast(p.ProductSubcategoryID as int) = ps.ProductSubcategoryID
  left join `adventureworks2019.Sales.SpecialOffer` as so on so.SpecialOfferID = sod.SpecialOfferID
  where lower(so.Type) like '%seasonal discount%'
  )
select 
  format_date("%Y", ModifiedDate) as year
  ,Name
  ,sum(dis_cost) as total_cost
from discount_data
group by 1,2
order by 2,1; 

Row	year	Name	total_cost
1	2012	Helmets	149.71669
2	2013	Helmets	543.21975

💡 The "Helmets" sub-category was the only one with discount promotions in both 2012 and 2013. There was a significant increase in discount costs from 2012 to 2013, suggesting a more aggressive discount strategy or higher sales volumes benefiting from seasonal discounts.

Query 5️⃣: Query 5: Retention rate of Customer in 2014 with status of Successfully Shipped (Cohort Analysis)

To understand why customers left despite positive order statuses

with 
  info as (
    select 
      extract(year from ModifiedDate) as yr
      ,extract(month from ModifiedDate) as mth 
      ,CustomerID 
      ,count(distinct SalesOrderID) sale_cnt 
    from `adventureworks2019.Sales.SalesOrderHeader`
    where extract(year from ModifiedDate) = 2014 and Status = 5
    group by 1,2,3
  )
  ,row_num as (
    select 
      *
      ,row_number() over (partition by CustomerID order by mth) as row_nb
    from info 
    order by CustomerID, mth 
  )
  , first_order as (
    select distinct
      mth
      ,yr
      ,CustomerID
    from row_num
    where row_nb = 1
  )
  , all_join as (
    select distinct 
      a.mth as mth_order
      ,a.yr
      ,a.CustomerID
      ,b.mth as mth_join
      ,concat('M - ',a.mth - b.mth) as mth_diff
    from info as a 
    left join first_order as b on a.CustomerID = b.CustomerID
    order by 3
  )
select
  mth_order, mth_diff, count(CustomerID) as customer_cnt
from all_join 
group by 1,2 
order by 1,2;

Row	mth_order	mth_diff	customer_cnt
1	1	M - 0	2076
2	2	M - 0	1805
3	2	M - 1	78
4	3	M - 0	1918
5	3	M - 1	51
6	3	M - 2	89
7	4	M - 0	1906
8	4	M - 1	43
9	4	M - 2	61
10	4	M - 3	252
11	5	M - 0	1947
12	5	M - 1	34
13	5	M - 2	58
14	5	M - 3	234
15	5	M - 4	96
16	6	M - 0	909
17	6	M - 1	40
18	6	M - 2	44
19	6	M - 3	44
20	6	M - 4	58
21	6	M - 5	61
22	7	M - 0	148
23	7	M - 1	10
24	7	M - 2	7
25	7	M - 3	7
26	7	M - 4	11
27	7	M - 5	8
28	7	M - 6	18

💡 Retention drops significantly from the first month to subsequent months, with few customers returning after their initial purchase. This trend is consistent across all months analyzed, highlighting the need for improved customer retention strategies.

Query 6️⃣: Trend of Stock level & MoM diff % by all product in 2011. If %gr rate is null then 0. Round to 1 decimal

Analyzing stock trends can provide insights into the efficiency of the purchasing department's and manufacturer's performance

with data_2011 as (
  select 
    p.Name
    ,extract(month from wo.ModifiedDate) as mth
    ,extract(year from wo.ModifiedDate) as yr 
    ,sum(StockedQty) as stock_crt
  from `adventureworks2019.Production.WorkOrder` as wo
  left join `adventureworks2019.Production.Product` as p on wo.ProductID = p.ProductID
  where FORMAT_TIMESTAMP("%Y", wo.ModifiedDate) = '2011'
  group by 1,2,3 
  order by 1,2 desc
)
select 
  Name
  ,mth ,yr
  ,stock_crt, stock_prv 
  ,round(coalesce((stock_crt/stock_prv - 1)*100,0),2) as diff_p
from (select * 
        ,lag(stock_crt,1) over (partition by name order by mth) as stock_prv
      from data_2011 ) 
order by 1,2 desc;

Row	Name	mth	yr	stock_crt	stock_prv	diff_p
1	BB Ball Bearing	12	2011	8475	14544	-41.73
2	BB Ball Bearing	11	2011	14544	19175	-24.15
3	BB Ball Bearing	10	2011	19175	8845	116.79
4	BB Ball Bearing	9	2011	8845	9666	-8.49
5	BB Ball Bearing	8	2011	9666	12837	-24.7
6	BB Ball Bearing	7	2011	12837	5259	144.1
7	BB Ball Bearing	6	2011	5259	null	0.0
8	...

💡 Over the last 6 months of the year, the data shows fluctuations and variations in stock quantity for each product. There is a consistent decrease each quarter, suggesting that the products were selling well and steadily.

Query 7️⃣: Calc Ratio of Stock / Sales in 2011 by product name, by month Order results by month desc, ratio desc. Round Ratio to 1 decimal mom yoy

Analyzing stock trends can provide insights into the efficiency of the sales department's performance

with sale_data as (
  select 
    extract(month from sod.ModifiedDate) as mth 
    ,extract(year from sod.ModifiedDate) as yr 
    ,sod.ProductID
    ,p.Name
    ,sum(sod.OrderQty) as sales
  from `adventureworks2019.Sales.SalesOrderDetail` as sod
  left join `adventureworks2019.Production.Product` as p 
    on sod.ProductID = p.ProductID
  where format_date("%Y", sod.ModifiedDate) = '2011'
  group by 1,2,3,4
),
stock_data as ( 
  select
    extract(month from ModifiedDate) as mth 
    ,extract(year from ModifiedDate) as yr 
    ,ProductID
    ,sum(StockedQty) as stocks
  from `adventureworks2019.Production.WorkOrder` 
  where format_date("%Y", ModifiedDate) = '2011'
  group by 1,2,3 
)
select 
  a.*
  ,b.stocks 
  ,round(coalesce(b.stocks,0)/a.sales,2) as ratio 
from sale_data as a 
left join stock_data as b 
  on a.ProductID = b.ProductID 
  and a.mth = b.mth 
  and a.yr = b.yr 
order by 1 desc, 7 desc ;

Row	mth	yr	ProductID	Name	sales	stocks	ratio
1	12	2011	758	Road-450 Red, 52	37	518	14.0
2	12	2011	754	Road-450 Red, 58	29	348	12.0
3	12	2011	755	Road-450 Red, 60	18	162	9.0
4	12	2011	774	Mountain-100 Silver, 48	22	189	8.59
5	12	2011	762	Road-650 Red, 44	82	680	8.29
6	12	2011	756	Road-450 Red, 44	23	184	8.0
7	12	2011	761	Road-650 Red, 62	62	465	7.5
8	...

💡 Higher sales and a lower stock-to-sales ratio over the months indicate that the products were performing well in the market. This suggests strong demand and efficient inventory management.

Query 8️⃣: No of order and value at Pending status in 2014

Pending orders can reflect the efficiency and performance of our vendors

select 
  extract(year from ModifiedDate) as yr
  ,Status
  ,count(PurchaseOrderID) as order_cnt
  ,sum(TotalDue) as value 
from `adventureworks2019.Purchasing.PurchaseOrderHeader` 
where format_timestamp('%Y', ModifiedDate) = '2014'
  and Status = 1
group by 1,2;

Row	yr	Status	order_cnt	value
1	2014	1	224	3873579.0123000029

💡 The significant number of pending orders underscores the necessity of evaluating vendor performance to gauge their efficiency and recommending a revamp of their order processes.

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🔎 Final Conclusion & Recommendations

This SQL project, through its analysis of the AdventureWorks dataset, provided several valuable business insights:

• Revenue and Quantity Sales: Identifying Bike Racks with the highest sales revenue and Road Frames with the highest quantity sales informed inventory and marketing strategies to meet strong market demand. Notable year-over-year growth in Mountain Frames and Socks highlighted growth opportunities.
• Monthly and Yearly Trends: Tracking sales trends month-over-month and year-over-year revealed seasonal patterns and growth rates. This enabled businesses to optimize promotional activities, align inventory management, and allocate resources more efficiently.
• Customer and Order Management: Insights into customer retention emphasized the need for improved retention strategies. Observing stock quantity trends and the high number of pending orders underscored the importance of effective inventory and order process management to boost sales revenue.

Overall, this project demonstrated the power of data-driven strategies in optimizing business operations and making informed decisions, enhancing SQL skills, and showcasing the practical applications of data analysis in a business context.