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SQL/Luxtoys/Transaction Analysis - Luxtoys.sql

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+-- Transaction Analysis
+USE LUXTOYS_DB
+
+--1 What are the monthly sales trends in terms of gross and nett transactions, in 2024?
+SELECT
+[Month] = MONTH(tr.trDate),
+[Gross_Sales] = FORMAT(SUM(tr.gross_tr), 'N0'),
+[Nett_Sales] = FORMAT(SUM(tr.nett_tr), 'N0')
+FROM Transact tr
+WHERE YEAR(tr.trDate) like '2024'
+GROUP BY MONTH(tr.trDate)
+
+--2 Which payment types are most commonly used in transactions?
+SELECT
+[Pay_Type] = tr.payType,
+[Count] = COUNT(tr.payType)
+FROM Transact tr
+GROUP BY tr.payType
+ORDER BY COUNT(tr.payType) DESC
+
+--3 Which days of the week have the highest transaction volumes and Nett revenue?
+SELECT
+[Day_of_Week] = DATENAME(WEEKDAY, tr.trDate),
+[Volume] = FORMAT(COUNT(tr.trID), 'N0'),
+[Nett_Revenue] = FORMAT(SUM(tr.nett_tr), 'N0')
+FROM Transact tr JOIN transactDetail td ON tr.trID = td.trID
+GROUP BY DATENAME(WEEKDAY, tr.trDate), DATEPART(WEEKDAY, tr.trDate)
+ORDER BY DATEPART(WEEKDAY, tr.trDate) ASC
+
+--4 How many transactions involve promotional campaigns?
+SELECT
+[Is_Campaign] = td.is_campaign,
+[Count] = COUNT(tr.trID)
+FROM Transact tr JOIN transactDetail td ON tr.trID = td.trID
+GROUP BY td.is_campaign
+
+--5 What is the average transaction size by store?
+SELECT
+[Store] = s.storeName,
+[total_sold] = SUM(td.qty),
+[avg_sold] = AVG(td.qty)
+FROM Store s JOIN Transact tr ON s.storeID = tr.storeID JOIN transactDetail td ON tr.trID = td.trID 
+GROUP BY s.storeName
+
+--6 Compare Q1 performance of 2023 and 2024
+WITH Q123 AS (
+	SELECT
+		[Period] = 'Q1 2023',
+		[Gross] = FORMAT(SUM(tr.gross_tr), 'N0'),
+		[Qty] = FORMAT(SUM(td.qty), 'N0'),
+		[Nett] = FORMAT(SUM(tr.nett_tr), 'N0')
+	FROM Transact tr 
+	JOIN transactDetail td ON tr.trID = td.trID
+	WHERE DATEPART(YEAR, tr.trDate) = 2023
+),
+Q124 AS (
+	SELECT
+		[Period] = 'Q1 2024',
+		[Gross] = FORMAT(SUM(tr.gross_tr), 'N0'),
+		[Qty] = FORMAT(SUM(td.qty), 'N0'),
+		[Nett] = FORMAT(SUM(tr.nett_tr), 'N0')
+	FROM Transact tr 
+	JOIN transactDetail td ON tr.trID = td.trID
+	WHERE DATEPART(YEAR, tr.trDate) = 2024
+)
+SELECT 
+	[Period],
+	[Qty],
+	[Gross],
+	[Nett]
+FROM Q123
+UNION ALL
+SELECT 
+	[Period],
+	[Qty],
+	[Gross],
+	[Nett]
+FROM Q124
+
+--7 Determine the frequency of transactions per customer 
+-- in May 2024 and identify customers who have 
+-- an unusually high number of transactions compared to the average in that month.
+WITH unhigh AS(
+	SELECT
+	[cuID] = c.cusID,
+	[cuName] = c.uName,
+	[count_tr] = COUNT(DISTINCT tr.trID),
+	[Rnk] = ROW_NUMBER() OVER (ORDER BY COUNT(DISTINCT tr.trID) DESC)
+	FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+	WHERE YEAR(tr.trDate) = 2024 AND DATEPART(MONTH,tr.trDate) = 5
+	GROUP BY c.cusID, c.uName
+), meanTr AS(
+	SELECT
+	[average_tr] = AVG(count_tr)
+	FROM (
+		SELECT
+		[cuID] = c.cusID,
+		[count_tr] = COUNT(DISTINCT tr.trID)
+		FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+		WHERE YEAR(tr.trDate) = 2024 AND DATEPART(MONTH,tr.trDate) = 5
+		GROUP BY c.cusID
+	) AS trCtz
+)
+SELECT
+unhigh.[cuID], 
+unhigh.[cuName], 
+unhigh.[count_tr], 
+meanTr.average_tr, 
+[Difference] = meanTr.[average_tr] - unhigh.[count_tr] 
+FROM unhigh CROSS JOIN meanTr 
+WHERE [Rnk] <=5
+
+
+--8 Calculate the percentage change in monthly transaction counts in Jan-24 for each store compared to Dec-23
+WITH on24 AS(
+	SELECT
+	[store] = s.storeName,
+	[Jan-24] = COUNT(DISTINCT tr.trID)
+	FROM Store s JOIN Transact tr ON s.storeID = tr.storeID
+	WHERE YEAR(tr.trDate) = 2024 AND DATEPART(MONTH, tr.trDate) = 1
+	GROUP BY s.storeName
+), on23 AS(
+	SELECT
+	[store] = s.storeName,
+	[Dec-23] = COUNT(DISTINCT tr.trID)
+	FROM Store s JOIN Transact tr ON s.storeID = tr.storeID
+	WHERE YEAR(tr.trDate) = 2023 AND DATEPART(MONTH, tr.trDate) = 12
+	GROUP BY s.storeName
+)
+SELECT
+on24.[store],
+on24.[Jan-24],
+on23.[Dec-23],
+[Change] = FORMAT(((on24.[Jan-24] - on23.[Dec-23]) * 1.0 / on23.[Dec-23]), 'P0')
+FROM 
+on23 JOIN on24 ON on23.[store] = on24.[store]
+
+--9 Find the top-performing products 
+--based on the revenue generated from transactions in the Q1 2024, 
+--and compare their performance to Q4 2023.
+WITH RankQ124 AS(
+	SELECT
+	[Period] = 'Q1 2024',
+	[Prod_Nm] = p.prodName,
+	[Nett_Revenue] = SUM(td.nett_detail),
+	[Rnk] = ROW_NUMBER() OVER (ORDER BY SUM(td.nett_detail) DESC)
+	FROM Product p JOIN transactDetail td ON p.prodID = td.prodID JOIN Transact tr ON tr.trID = td.trID
+	WHERE DATEPART(QUARTER,tr.trDate) = 1 AND DATEPART(YEAR,tr.trDate) = 2024
+	GROUP BY p.prodName
+), RankQ423 AS(
+	SELECT
+	[Period] = 'Q4 2023',
+	[Prod_Nm] = p.prodName,
+	[Nett_Revenue] = SUM(td.nett_detail),
+	[Rnk] = ROW_NUMBER() OVER (ORDER BY SUM(td.nett_detail) DESC)
+	FROM Product p JOIN transactDetail td ON p.prodID = td.prodID JOIN Transact tr ON tr.trID = td.trID
+	WHERE DATEPART(QUARTER,tr.trDate) = 4 AND DATEPART(YEAR,tr.trDate) = 2023
+	GROUP BY p.prodName
+)
+SELECT
+[Period], [Prod_Nm], FORMAT([Nett_Revenue],'N0') AS Nett, [Rnk]
+FROM RankQ124
+WHERE Rnk <6
+UNION ALL
+SELECT
+[Period], [Prod_Nm], FORMAT([Nett_Revenue],'N0') AS Nett, [Rnk]
+FROM RankQ423
+WHERE Rnk <6
+
+-- 10 Analyze the impact of promotional campaigns on transaction volumes and revenue, 
+-- comparing Q1 2023 and Q1 2024 with and without campaigns.
+WITH YCamp AS(
+	SELECT
+	[Year] = YEAR(tr.trDate),
+	[Campaign_Type] = td.is_campaign,
+	[tr_Volume] = COUNT(DISTINCT tr.trID),
+	[revenue] = SUM(tr.nett_tr)
+	FROM Transact tr JOIN transactDetail td ON tr.trID = td.trID
+	WHERE YEAR(tr.trDate) = 2023
+	GROUP BY YEAR(tr.trDate), td.is_campaign
+), NCamp AS(
+	SELECT
+	[Year] = YEAR(tr.trDate),
+	[Campaign_Type] = td.is_campaign,
+	[tr_Volume] = COUNT(DISTINCT tr.trID),
+	[revenue] = SUM(tr.nett_tr)
+	FROM Transact tr JOIN transactDetail td ON tr.trID = td.trID
+	WHERE YEAR(tr.trDate) = 2024
+	GROUP BY YEAR(tr.trDate), td.is_campaign
+)
+SELECT
+[Year], [Campaign_Type], [tr_Volume], FORMAT([revenue],'N0') AS Revenue FROM YCamp 
+UNION ALL
+SELECT
+[Year], [Campaign_Type], [tr_Volume], FORMAT([revenue],'N0') AS Revenue FROM NCamp