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SQL/Luxtoys/Customer Analysis - Luxtoys.sql

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+-- Customer Analysis
+USE LUXTOYS_DB
+
+--1 What is the distribution of customers based on their regions and levels?
+SELECT
+[Region] = c.uRegion,
+[Level] = c.uLevel,
+[Count] = COUNT(DISTINCT c.cusID)
+FROM Customers c
+GROUP BY c.uLevel, c.uRegion
+
+--2 What is the distribution of customers that haven't filled their address info based on their Regions?
+SELECT
+[Region] = c.uRegion,
+[Count] = FORMAT(COUNT(c.uAddress),'N0')
+FROM Customers c
+WHERE c.uAddress like ''
+GROUP BY c.uRegion
+ORDER BY COUNT(c.uAddress) DESC
+
+--3 What is the average purchase frequency of customers, from each level, in 2024?
+SELECT
+[Level] = c.uLevel,
+[Purchase_Freq] = FORMAT(COUNT(tr.trID), 'N0')
+FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+WHERE YEAR(tr.trDate) like '2024'
+GROUP BY c.uLevel
+ORDER BY COUNT(tr.trID) DESC
+
+--4 What are the top regions contributing to sales revenue in 2024?
+SELECT
+[Region] = c.uRegion,
+[Gross_Sales] = FORMAT(SUM(tr.gross_tr), 'N0'),
+[Nett_Sales] = FORMAT(SUM(tr.nett_tr), 'N0')
+FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+WHERE YEAR(tr.trDate) like '2024'
+GROUP BY c.uRegion
+ORDER BY SUM(tr.gross_tr) DESC
+
+--5 How does the average transaction value differ across customer levels, in 2024?
+SELECT
+[Level] = c.uLevel,
+[Avg_Gross_Sales] = FORMAT(AVG(tr.gross_tr), 'N0'),
+[Avg_Nett_Sales] = FORMAT(AVG(tr.nett_tr), 'N0')
+FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+WHERE YEAR(tr.trDate) like '2024'
+GROUP BY c.uLevel
+ORDER BY AVG(tr.gross_tr) DESC
+
+--6 Identify the top 10 customers based on the total gross revenue generated from their transactions in Q1 2024.
+WITH RankCust AS(
+	SELECT
+	[Cust_Nm] = c.uName,
+	[Gross] = FORMAT(SUM(tr.gross_tr), 'N0'),
+	[Rank] = ROW_NUMBER() OVER (ORDER BY SUM(tr.gross_tr) DESC)
+	FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+	WHERE DATEPART(Q, tr.trDate) = 1 AND DATEPART(YEAR, tr.trDate) = 2024
+	GROUP BY c.uName
+)
+SELECT
+[Rank], [Cust_Nm], [Gross]
+FROM
+RankCust
+WHERE [Rank] <= 10
+
+--7 Identify customers who made less than 3 in 2024 
+-- but had at least one transaction in Q4 2023.
+WITH lasix AS(
+	SELECT
+	[ID] = c.cusID,
+	[Name] = c.uName,
+	[2024] = COUNT(tr.trID)
+	FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+	WHERE DATEPART(YEAR,tr.trDate) = 2024
+	GROUP BY c.cusID, c.uName
+), prsix AS(
+	SELECT
+	[ID] = c.cusID,
+	[Name] = c.uName,
+	[Q4_2023] = COUNT(tr.trID)
+	FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+	WHERE DATEPART(YEAR,tr.trDate) = 2023 AND DATEPART(QUARTER,tr.trDate) = 4
+	GROUP BY c.cusID, c.uName
+)
+SELECT
+lasix.[ID],
+lasix.[Name],
+prsix.[Q4_2023],
+lasix.[2024]
+FROM lasix join prsix ON lasix.[ID] = prsix.[ID]
+WHERE lasix.[2024] <3
+
+--8 Identify customers who have increased their purchase frequency 
+-- by more than 50% in the last quarter compared to the previous quarter.
+WITH Q423 AS(
+	SELECT
+	[ID] = c.cusID,
+	[Name] = c.uName,
+	[Q4_23] = COUNT(tr.trID)
+	FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+	WHERE DATEPART(YEAR,tr.trDate) = 2023 AND DATEPART(QUARTER,tr.trDate) = 4
+	GROUP BY c.cusID, c.uName
+), Q124 AS(
+	SELECT
+	[ID] = c.cusID,
+	[Name] = c.uName,
+	[Q1_24] = COUNT(tr.trID)
+	FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+	WHERE DATEPART(YEAR,tr.trDate) = 2024 AND DATEPART(QUARTER,tr.trDate) = 1
+	GROUP BY c.cusID, c.uName
+)
+SELECT
+Q423.[ID],
+Q423.[Name],
+Q423.[Q4_23],
+Q124.[Q1_24],
+[Pct%] = FORMAT(((Q124.[Q1_24] - Q423.[Q4_23]) * 1.0 / Q423.[Q4_23]), 'P0')
+FROM Q423 JOIN Q124 ON Q423.[ID] = Q124.ID
+WHERE ((Q124.[Q1_24] - Q423.[Q4_23]) * 1.0 / Q423.[Q4_23]) > 0.5
+
+--9 Analyze the total spending of high-value customers (e.g., top 10% by total spending) 
+-- compared to other customers in the past year.
+WITH custlist AS(
+	SELECT
+	[ID] = c.cusID,
+	[Name] = c.uName,
+	[gros] = SUM(tr.gross_tr)
+	FROM Customers c JOIN Transact tr ON c.cusID = tr.cusID
+	WHERE DATEPART(YEAR,tr.trDate) = 2023
+	GROUP BY c.cusID, c.uName
+), allcust AS(
+	SELECT
+	[ID], 
+	[gros],
+	NTILE(10) OVER (ORDER BY custlist.[gros]) AS spendRank
+	From custlist
+), top10 AS(
+	SELECT
+	[Cust_Group] = 'Top 10%',
+	[Count] = COUNT(*),
+	[total_gross] = SUM(allcust.[gros])
+	FROM allcust 
+	WHERE spendRank = 1
+), oth90 AS(
+	SELECT
+	[Cust_Group] = 'Other 90%',
+	[Count] = COUNT(*),
+	[total_gross] = SUM(allcust.[gros])
+	FROM allcust 
+	WHERE spendRank > 1
+)
+SELECT
+[Cust_Group],
+[Count],
+[Gross] = FORMAT([total_gross], 'N0'), 
+[Contrib%] = FORMAT([total_gross] / (SELECT SUM([gros]) FROM custlist), 'P0')
+FROM top10
+UNION ALL
+SELECT
+[Cust_Group],
+[Count],
+[Gross] = FORMAT([total_gross], 'N0'), 
+[Contrib%] = FORMAT([total_gross] / (SELECT SUM([gros]) FROM custlist), 'P0')
+FROM oth90
+
+
+--10 Identify the top 3 regions with the highest grossing value for campaigns in 2023, then compare it to its 2024 performance.
+WITH TopRegions AS (
+    SELECT
+        c.uRegion,
+        SUM(tr.gross_tr) AS total_Gross,
+        ROW_NUMBER() OVER (ORDER BY SUM(tr.gross_tr) DESC) AS Rank
+    FROM Customers c 
+    JOIN Transact tr ON c.cusID = tr.cusID
+    WHERE DATEPART(YEAR, tr.trDate) = 2023
+    GROUP BY c.uRegion
+)
+SELECT
+    t.uRegion,
+    [Gross 2023] = FORMAT(t.total_Gross, 'N0'),
+    [Gross 2024] = FORMAT(t24.total_Gross, 'N0')
+FROM TopRegions t
+LEFT JOIN (
+    SELECT
+        c.uRegion,
+        SUM(tr.gross_tr) AS total_Gross
+    FROM Customers c 
+    JOIN Transact tr ON c.cusID = tr.cusID
+    WHERE DATEPART(YEAR, tr.trDate) = 2024
+    GROUP BY c.uRegion
+) t24 ON t.uRegion = t24.uRegion
+WHERE t.Rank <= 3