Synthetic Beverage Sales Data Analysis

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📋 Project Overview

This project aims to analyze sales transactions from a SQL Server database to uncover key insights related to customer purchasing behavior, revenue trends, and regional performance. The final insights will be presented through a Power BI dashboard to help stakeholders make data-driven business decisions.

Tech Stack:

SQL Server – Data storage, querying, and transformation.
Power BI – Data visualization and interactive dashboards.
DAX (Power BI) – Advanced calculations and performance metrics.

📊 Summary of the Dataset

Total Records: 8,999,910

Total Columns: 11

Key Columns:

Order_ID – Unique identifier for each order.
Customer_ID – Unique identifier for each customer.
Customer_Type – B2B (business) or B2C (individual).
Product – The name of the product sold.
Category – Product category (Water, Soft Drinks, Juices, Alcoholic Beverages, etc.).
Unit_Price – Price per unit of the product.
Quantity – Number of units sold.
Discount – Percentage discount applied.
Total_Price – Total revenue after discount.
Region – Geographic location of the customer.
Order_Date – Date when the order was placed.

🔎 Key Analysis Areas:

Customer Revenue Analysis – Identify top customers (B2B & B2C) by revenue and order frequency.
Product Performance – Determine the best-selling and least profitable products.
Regional Sales Insights – Analyze how different regions contribute to overall revenue.
Discount Impact Analysis – Evaluate how discounts affect sales and profitability.
Sales Trend Forecasting – Use SQL analytics to predict future sales trends based on historical data.

💾 Data Acquisition & Preparation

Create the Table and Run BULK INSERT

				
					CREATE TABLE Sales (
    Order_ID VARCHAR(50),
    Customer_ID VARCHAR(50),
    Customer_Type VARCHAR(10),
    Product VARCHAR(255),
    Category VARCHAR(255),
    Unit_Price DECIMAL(10,2),
    Quantity INT,
    Discount DECIMAL(5,2),
    Total_Price DECIMAL(10,2),
    Region VARCHAR(255),
    Order_Date DATE
);

BULK INSERT Sales
FROM 'D:\roy\roy files\Data\synthetic_beverage_sales_data.csv' 
    FORMAT = 'CSV',
    FIRSTROW = 2,  
    FIELDTERMINATOR = ',', 
    ROWTERMINATOR = '\n',
    TABLOCK
);

Data Cleaning & Integrity Checks

				
					--Check for Exact Duplicate Rows
SELECT Order_ID, Customer_ID, Product, Category, Unit_Price, Quantity, Discount, Total_Price, Region, Order_Date, COUNT(*)
FROM Sales
GROUP BY Order_ID, Customer_ID, Product, Category, Unit_Price, Quantity, Discount, Total_Price, Region, Order_Date
HAVING COUNT(*) > 1;

				
					--Check for NULL Values
SELECT 
    SUM(CASE WHEN Order_ID IS NULL THEN 1 ELSE 0 END) AS Null_Order_ID,
    SUM(CASE WHEN Customer_ID IS NULL THEN 1 ELSE 0 END) AS Null_Customer_ID,
    SUM(CASE WHEN Product IS NULL THEN 1 ELSE 0 END) AS Null_Product,
    SUM(CASE WHEN Category IS NULL THEN 1 ELSE 0 END) AS Null_Category,
    SUM(CASE WHEN Unit_Price IS NULL THEN 1 ELSE 0 END) AS Null_Unit_Price,
    SUM(CASE WHEN Quantity IS NULL THEN 1 ELSE 0 END) AS Null_Quantity,
    SUM(CASE WHEN Discount IS NULL THEN 1 ELSE 0 END) AS Null_Discount,
    SUM(CASE WHEN Total_Price IS NULL THEN 1 ELSE 0 END) AS Null_Total_Price,
    SUM(CASE WHEN Region IS NULL THEN 1 ELSE 0 END) AS Null_Region,
    SUM(CASE WHEN Order_Date IS NULL THEN 1 ELSE 0 END) AS Null_Order_Date
FROM Sales;

Null_Order_ID	Null_Customer_ID	Null_Product	Null_Category	Null_Unit_Price	Null_Quantity	Null_Discount	Null_Total_Price	Null_Region	Null_Order_Date
0	0	0	0	0	0	0	0	0	0

				
					--Check for Invalid Data
SELECT * 
FROM Sales 
WHERE Unit_Price <= 0 
   OR Quantity <= 0 
   OR Total_Price < 0;

				
					--Check Data Types
SELECT COLUMN_NAME, DATA_TYPE, CHARACTER_MAXIMUM_LENGTH
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = 'Sales';

COLUMN_NAME	DATA_TYPE	CHARACTER_MAXIMUM_LENGTH
Order_ID	varchar	50
Customer_ID	varchar	50
Customer_Type	varchar	10
Product	varchar	255
Category	varchar	255
Unit_Price	decimal	NULL
Quantity	int	NULL
Discount	decimal	NULL
Total_Price	decimal	NULL
Region	varchar	255
Order_Date	date	NULL

				
					--Apply Optimizations
ALTER TABLE Sales ALTER COLUMN Order_ID VARCHAR(20);
ALTER TABLE Sales ALTER COLUMN Customer_ID VARCHAR(20);
ALTER TABLE Sales ALTER COLUMN Customer_Type CHAR(3);
ALTER TABLE Sales ALTER COLUMN Product VARCHAR(100);
ALTER TABLE Sales ALTER COLUMN Category VARCHAR(50);
ALTER TABLE Sales ALTER COLUMN Unit_Price DECIMAL(10,2);
ALTER TABLE Sales ALTER COLUMN Quantity SMALLINT;
ALTER TABLE Sales ALTER COLUMN Discount DECIMAL(5,2);
ALTER TABLE Sales ALTER COLUMN Total_Price DECIMAL(10,2);

Indexing for Query Optimization

				
					-- Index for time-based queries
CREATE INDEX idx_sales_orderdate ON Sales (Order_Date);

-- Index for customer-based lookups
CREATE INDEX idx_sales_customer ON Sales (Customer_ID);

-- Index for product-based analysis
CREATE INDEX idx_sales_product ON Sales (Product);

-- Index for regional sales queries
CREATE INDEX idx_sales_region ON Sales (Region);

-- Index for fast revenue calculations
CREATE INDEX idx_sales_revenue ON Sales (Total_Price DESC);

Partitioning

				
					--Create a Partition Function
CREATE PARTITION FUNCTION pf_OrderDate (DATE)
AS RANGE RIGHT FOR VALUES ('2022-01-01', '2023-01-01');

--Create a Partition Scheme
CREATE PARTITION SCHEME ps_OrderDate
AS PARTITION pf_OrderDate ALL TO ([PRIMARY]);

--Create a Partitioned Table
CREATE TABLE Sales_Partitioned (
    Order_ID VARCHAR(20),
    Customer_ID VARCHAR(15),
    Customer_Type CHAR(3),
    Product VARCHAR(100),
    Category VARCHAR(50),
    Unit_Price DECIMAL(10,2),
    Quantity SMALLINT,
    Discount DECIMAL(5,2),
    Total_Price DECIMAL(10,2),
    Region VARCHAR(50),
    Order_Date DATE
)
ON ps_OrderDate(Order_Date);  -- Apply partitioning

--Insert Data into Partitioned Table
INSERT INTO Sales_Partitioned
SELECT * FROM Sales;

--Rename the partitioned table to Sales and Drop the old one
DROP TABLE Sales;  -- Drop old table
EXEC sp_rename 'Sales_Partitioned', 'Sales'; 

--Check the row distribution per partition
SELECT $PARTITION.pf_OrderDate(Order_Date) AS Partition_Number, COUNT(*)
FROM Sales
GROUP BY $PARTITION.pf_OrderDate(Order_Date);

Partition_Number	(No column name)
1	2997956
2	3007297
3	2994657

📈 Advanced SQL Analysis

Customer Revenue Analysis

💡 Business Question:

Identify the top 5 customers (B2B & B2C separately) who have contributed the highest revenue in the last 12 months, considering discounts and order frequency.

				
					WITH CustomerRevenue AS (
    SELECT 
        Customer_ID,
        Customer_Type,
        COUNT(DISTINCT Order_ID) AS Total_Orders,
        SUM((Unit_Price * Quantity) - (Discount * Unit_Price * Quantity)) AS Total_Revenue
    FROM Sales
    WHERE Order_Date >= DATEADD(YEAR, -1, '2023-01-01') 
    GROUP BY Customer_ID, Customer_Type
), 
RankedCustomers AS (
    SELECT 
        Customer_ID,
        Customer_Type,
        Total_Orders,
        Total_Revenue,
        RANK() OVER (PARTITION BY Customer_Type ORDER BY Total_Revenue DESC) AS RevenueRank
    FROM CustomerRevenue
)
SELECT 
    Customer_ID,
    Customer_Type,
    Total_Orders,
    Total_Revenue
FROM RankedCustomers
WHERE RevenueRank <= 5
ORDER BY Customer_Type, RevenueRank;

Customer_ID	Customer_Type	Total_Orders	Total_Revenue
CUS1256	B2B	243	273172.4015
CUS9241	B2B	210	261596.7225
CUS2662	B2B	235	260785.9145
CUS9868	B2B	220	260193.9800
CUS4834	B2B	222	256911.5470
CUS9683	B2C	228	52350.0500
CUS2694	B2C	228	46306.2200
CUS5597	B2C	234	45370.0300
CUS5210	B2C	216	44884.7500
CUS2091	B2C	219	44870.5500

Key Observations

B2B customers generate much higher revenue than B2C customers (5x difference!).
Total Orders do not always correlate with Total Revenue (higher-value orders play a role).
B2C customers are closer in revenue, while B2B has larger revenue gaps between top customers.

Product Performance Analysis

💡 Business Question:

Which products are the best-selling and least profitable, considering revenue, discounts, and sales volume?
How do product sales and profitability trends change over time (monthly, quarterly, yearly)?

				
					WITH ProductSales AS (
    SELECT 
        Product,
        Category,
        SUM(Quantity) AS Total_Quantity_Sold,
        SUM((Unit_Price * Quantity) - (Discount * Unit_Price * Quantity)) AS Total_Revenue
    FROM Sales 
    GROUP BY Product, Category
),
RankedProducts AS (
    SELECT 
        Product,
        Category,
        Total_Quantity_Sold,
        Total_Revenue,
        RANK() OVER (ORDER BY Total_Quantity_Sold DESC) AS Best_Selling_Rank,
        RANK() OVER (ORDER BY Total_Revenue ASC) AS Least_Profitable_Rank
    FROM ProductSales
)
SELECT 
    Product,
    Category,
    Total_Quantity_Sold,
    Total_Revenue,
    Best_Selling_Rank,
    Least_Profitable_Rank
FROM RankedProducts
WHERE Best_Selling_Rank <= 5 OR Least_Profitable_Rank <= 5
ORDER BY Best_Selling_Rank, Least_Profitable_Rank;

Product	Category	Total_Quantity_Sold	Total_Revenue	Best_Selling_Rank	Least_Profitable_Rank
Hohes C Orange	Juices	7794189	15048221.7680	1	30
Tomato Juice	Juices	7395329	19758651.7635	2	32
Granini Apple	Juices	7393212	12671654.7465	3	29
Cranberry Juice	Juices	7380841	23044834.0420	4	39
Passion Fruit Juice	Juices	7372320	22143149.2490	5	36
Vittel	Water	5921086	3791982.8410	9	4
Volvic	Water	5738479	3571393.6015	15	2
Mezzo Mix	Soft Drinks	4129809	3721743.4680	27	3
Krombacher	Alcoholic Beverages	2722613	3897180.6365	34	5
Warsteiner	Alcoholic Beverages	2705225	3548409.3180	36	1

Key Observations

Juices dominate sales volume – The top 5 best-selling products are all juices.
Water & Alcoholic Beverages rank high in least profitability – Products like Warsteiner (#1 least profitable) and Vittel (#4 least profitable) generate low revenue compared to sales.
Soft Drinks (Mezzo Mix) are neither top-selling nor high-revenue generators – Ranking #27 in best-selling but #3 in least profitable, indicating it might be a low-margin product.

				
					WITH ProductSales AS (
    SELECT 
        Product,
        Category,
        YEAR(Order_Date) AS Sales_Year,
        SUM(Quantity) AS Total_Quantity_Sold,
        SUM((Unit_Price * Quantity) - (Discount * Unit_Price * Quantity)) AS Total_Revenue
    FROM Sales WITH (NOLOCK)  
    GROUP BY Product, Category, YEAR(Order_Date)
),
RankedProducts AS (
    SELECT 
        Product,
        Category,
        Sales_Year,
        Total_Quantity_Sold,
        Total_Revenue,
        RANK() OVER (PARTITION BY Sales_Year ORDER BY Total_Quantity_Sold DESC) AS Best_Selling_Rank,
        RANK() OVER (PARTITION BY Sales_Year ORDER BY Total_Revenue ASC) AS Least_Profitable_Rank
    FROM ProductSales
)
SELECT 
    Sales_Year,
    Product,
    Category,
    Total_Quantity_Sold,
    Total_Revenue,
    Best_Selling_Rank,
    Least_Profitable_Rank
FROM RankedProducts
WHERE Best_Selling_Rank <= 5 OR Least_Profitable_Rank <= 5
ORDER BY Sales_Year DESC, Best_Selling_Rank, Least_Profitable_Rank;

Sales_Year	Product	Category	Total_Quantity_Sold	Total_Revenue	Best_Selling_Rank	Least_Profitable_Rank
2023	Hohes C Orange	Juices	2591904	5151293.2435	1	30
2023	Granini Apple	Juices	2465064	4355558.2705	2	29
2023	Passion Fruit Juice	Juices	2454938	7596955.2960	3	36
2023	Rauch Multivitamin	Juices	2450170	6201396.3390	4	31
2023	Mango Juice	Juices	2449497	7689154.2705	5	37
2023	Vittel	Water	1965535	1292822.0300	9	4
2023	Volvic	Water	1906351	1220434.5830	15	2
2023	Mezzo Mix	Soft Drinks	1371163	1272048.0975	28	3
2023	Krombacher	Alcoholic Beverages	909340	1337450.7410	35	5
2023	Warsteiner	Alcoholic Beverages	901095	1216468.2340	36	1
2022	Hohes C Orange	Juices	2598841	5018407.0090	1	30
2022	Tomato Juice	Juices	2487024	6641356.8905	2	32
2022	Cranberry Juice	Juices	2470974	7704555.9210	3	39
2022	Passion Fruit Juice	Juices	2470281	7412354.2290	4	36
2022	Granini Apple	Juices	2467975	4225958.7500	5	29
2022	Vittel	Water	1999845	1282619.3890	8	4
2022	Volvic	Water	1938689	1207813.8385	10	2
2022	Mezzo Mix	Soft Drinks	1389841	1253588.7695	24	3
2022	Krombacher	Alcoholic Beverages	914729	1311510.7020	34	5
2022	Warsteiner	Alcoholic Beverages	902019	1183765.6715	36	1
2021	Hohes C Orange	Juices	2603444	4878521.5155	1	30
2021	Tomato Juice	Juices	2466261	6391263.6040	2	32
2021	Cranberry Juice	Juices	2460722	7461822.0065	3	39
2021	Granini Apple	Juices	2460173	4090137.7260	4	29
2021	Passion Fruit Juice	Juices	2447101	7133839.7240	5	36
2021	Vittel	Water	1955706	1216541.4220	9	4
2021	Volvic	Water	1893439	1143145.1800	15	1
2021	Mezzo Mix	Soft Drinks	1368805	1196106.6010	28	3
2021	Warsteiner	Alcoholic Beverages	902111	1148175.4125	34	2
2021	Krombacher	Alcoholic Beverages	898544	1248219.1935	36	5

Key Observations

Juices dominate the best-selling category across all years.
Water & Alcoholic Beverages consistently rank among the least profitable – Warsteiner, Volvic, and Vittel struggle in revenue every year.
Year-over-year product trends remain stable, indicating consistent customer preferences.

Regional Sales Insights

				
					WITH MonthlySales AS (
    SELECT 
        YEAR(Order_Date) AS Sales_Year,
        MONTH(Order_Date) AS Sales_Month,
        SUM((Unit_Price * Quantity) - (Discount * Unit_Price * Quantity)) AS Total_Revenue
    FROM Sales WITH (NOLOCK)
    GROUP BY YEAR(Order_Date), MONTH(Order_Date)
),
MovingAverage AS (
    SELECT 
        Sales_Year,
        Sales_Month,
        Total_Revenue,
        AVG(Total_Revenue) OVER (ORDER BY Sales_Year, Sales_Month ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS Moving_Avg_3Months
    FROM MonthlySales
)
SELECT * FROM MovingAverage
ORDER BY Sales_Year DESC, Sales_Month DESC;

💡 Business Question:

Which regions contribute the most to total revenue, and how do sales trends vary across regions?

				
					WITH RegionalSales AS (
    SELECT 
        Region,
        COUNT(DISTINCT Order_ID) AS Total_Orders,
        SUM((Unit_Price * Quantity) - (Discount * Unit_Price * Quantity)) AS Total_Revenue
    FROM Sales WITH (NOLOCK)  
    GROUP BY Region
)
SELECT 
    Region,
    Total_Orders,
    Total_Revenue,
    RANK() OVER (ORDER BY Total_Revenue DESC) AS Revenue_Rank
FROM RegionalSales
ORDER BY Total_Revenue DESC;

Region	Total_Orders	Total_Revenue	Revenue_Rank
Hamburg	201457	82470780.2285	1
Hessen	182343	78400118.3155	2
Saarland	191273	78390596.4725	3
Rheinland-Pfalz	192687	75838685.7170	4
Mecklenburg-Vorpommern	181637	75517255.0845	5
Th眉ringen	187859	75324875.7725	6
Berlin	182569	74567936.4860	7
Bayern	185352	72825405.8320	8
Niedersachsen	192298	71959057.6580	9
Sachsen	190813	71947001.6665	10
Baden-W眉rttemberg	184213	71594846.2460	11
Nordrhein-Westfalen	189719	71393809.7100	12
Brandenburg	177616	71349037.9050	13
Schleswig-Holstein	188685	70317836.2790	14
Sachsen-Anhalt	182091	69765550.4080	15
Bremen	189388	65018497.4140	16

Key Observations

Hamburg leads in total revenue – Highest revenue ($82M+) despite having similar order volume to other regions.
Some regions generate lower revenue despite high order count – Example: Bremen ranks #16 in revenue but has 189k+ orders.
Revenue distribution is competitive – The top 10 regions have revenues above $70M, showing balanced regional sales.

Discount Impact on Sales & Profitability

💡 Business Question:

How do discounts impact total revenue, profit margins, and sales volume? Are discounts helping or hurting profitability?

				
					WITH DiscountAnalysis AS (
    SELECT 
        CASE 
            WHEN Discount = 0 THEN 'No Discount'
            WHEN Discount BETWEEN 0.01 AND 0.05 THEN 'Low Discount (1-5%)'
            WHEN Discount BETWEEN 0.06 AND 0.10 THEN 'Medium Discount (6-10%)'
            ELSE 'High Discount (>10%)'
        END AS Discount_Category,
        COUNT(DISTINCT Order_ID) AS Total_Orders,
        SUM(Quantity) AS Total_Quantity_Sold,
        SUM((Unit_Price * Quantity) - (Discount * Unit_Price * Quantity)) AS Total_Revenue,
        AVG((Unit_Price * Quantity) - (Discount * Unit_Price * Quantity)) AS Avg_Revenue_Per_Order
    FROM Sales WITH (NOLOCK)
    GROUP BY 
        CASE 
            WHEN Discount = 0 THEN 'No Discount'
            WHEN Discount BETWEEN 0.01 AND 0.05 THEN 'Low Discount (1-5%)'
            WHEN Discount BETWEEN 0.06 AND 0.10 THEN 'Medium Discount (6-10%)'
            ELSE 'High Discount (>10%)'
        END
)
SELECT 
    Discount_Category,
    Total_Orders,
    Total_Quantity_Sold,
    Total_Revenue,
    Avg_Revenue_Per_Order
FROM DiscountAnalysis
ORDER BY Total_Revenue DESC;

Discount_Category	Total_Orders	Total_Quantity_Sold	Total_Revenue	Avg_Revenue_Per_Order
Medium Discount (6-10%)	804217	85916100	492967690.5150	356.039005
No Discount	1931192	46360564	275061551.0800	47.462006
Low Discount (1-5%)	818956	48815077	246116475.5090	171.044997
High Discount (>10%)	325690	27149380	162535574.0910	426.583522

Key Observations

Medium Discount (6-10%) generates the highest total revenue – Contributing $492M with a strong order volume (804K+ orders).
High Discounts (>10%) drive the highest revenue per order ($426), but lowest sales volume – Fewer orders, but customers spend more.
No Discount orders are the highest (1.9M orders), but the lowest revenue per order ($47.46) – Customers still buy, but average spending is lower.
Low Discount (1-5%) doesn’t boost revenue significantly compared to medium discounts – May indicate 1-5% discounts aren’t very effective.

Sales Trend Forecasting

💡 Business Question:

How can we predict future sales trends based on historical data?

				
					WITH MonthlySales AS (
    SELECT 
        YEAR(Order_Date) AS Sales_Year,
        MONTH(Order_Date) AS Sales_Month,
        SUM((Unit_Price * Quantity) - (Discount * Unit_Price * Quantity)) AS Total_Revenue
    FROM Sales WITH (NOLOCK)
    GROUP BY YEAR(Order_Date), MONTH(Order_Date)
),
MovingAverage AS (
    SELECT 
        Sales_Year,
        Sales_Month,
        Total_Revenue,
        AVG(Total_Revenue) OVER (ORDER BY Sales_Year, Sales_Month ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS Moving_Avg_3Months
    FROM MonthlySales
)
SELECT * FROM MovingAverage
ORDER BY Sales_Year DESC, Sales_Month DESC;

				
					WITH YearlySales AS (
    SELECT 
        YEAR(Order_Date) AS Sales_Year,
        SUM((Unit_Price * Quantity) - (Discount * Unit_Price * Quantity)) AS Total_Revenue
    FROM Sales WITH (NOLOCK)
    GROUP BY YEAR(Order_Date)
),
GrowthAnalysis AS (
    SELECT 
        Sales_Year,
        Total_Revenue,
        LAG(Total_Revenue) OVER (ORDER BY Sales_Year) AS Previous_Year_Revenue,
        (Total_Revenue - LAG(Total_Revenue) OVER (ORDER BY Sales_Year)) / NULLIF(LAG(Total_Revenue) OVER (ORDER BY Sales_Year), 0) * 100 AS Yearly_Growth_Percentage
    FROM YearlySales
)
SELECT * FROM GrowthAnalysis
ORDER BY Sales_Year DESC;

Sales_Year	Sales_Month	Total_Revenue	Moving_Avg_3Months
2023	12	33766725.5135	33896828.012666
2023	11	33712861.6100	33635810.899666
2023	10	34210896.9145	33893958.163333
2023	9	32983674.1745	33909750.330000
2023	8	34487303.4010	33981349.161166
2023	7	34258273.4145	33659205.189000
2023	6	33198470.6680	33027256.722833
2023	5	33520871.4845	33202760.579333
2023	4	32362428.0160	32554577.952500
2023	3	33724982.2375	33091092.853500
2023	2	31576323.6040	33334820.229833
2023	1	33971972.7190	33602645.149500
2022	12	34456164.3665	33457520.148000
2022	11	32379798.3630	32806685.140333
2022	10	33536597.7145	33185142.770500
2022	9	32503659.3435	33140252.686833
2022	8	33515171.2535	33155876.733000
2022	7	33401927.4635	33073539.599000
2022	6	32550531.4820	32734477.748000
2022	5	33268159.8515	33152078.151500
2022	4	32384741.9105	32137655.949666
2022	3	33803332.6925	32287689.647500
2022	2	30224893.2460	32167348.150500
2022	1	32834843.0040	32418190.189500
2021	12	33442308.2015	32095819.657666
2021	11	30977419.3630	31360941.113166
2021	10	31867731.4085	31677002.079833
2021	9	31237672.5680	31758446.030833
2021	8	31925602.2630	31676985.205000
2021	7	32112063.2615	31653615.765333
2021	6	30993290.0905	31330281.203000
2021	5	31855493.9440	31984997.909166
2021	4	31142059.5745	31069890.608666
2021	3	32957440.2090	31497682.024166
2021	2	29110172.0425	30767802.931750
2021	1	32425433.8210	32425433.821000

Sales_Year	Total_Revenue	Previous_Year_Revenue	Yearly_Growth_Percentage
2023	401774783.7570	394859820.6910	1.751200
2022	394859820.6910	380046686.7470	3.897700
2021	380046686.7470	NULL	NULL

Key Observations

Sales have consistently grown year-over-year, but the growth rate is slowing down (3.89% in 2022 vs. 1.75% in 2023).
2023’s growth is lower than 2022’s, suggesting potential market saturation or lower demand.
If this trend continues, 2024’s projected growth might be even lower.
Sales fluctuate seasonally, but the 3-month moving average smooths the trend.
Revenue dips slightly in some months (e.g., Sept 2023 – $32.98M), showing possible seasonal variation.
Peak months (e.g., Aug 2023 – $34.48M) show potential high-demand periods (promotions, events?).

Synthetic Beverage Sales Data Analysis

📂 Table of Content

📋 Project Overview

📊 Summary of the Dataset

🔎 Key Analysis Areas:

💾 Data Acquisition & Preparation

Create the Table and Run BULK INSERT

Data Cleaning & Integrity Checks

Indexing for Query Optimization

Partitioning

📈 Advanced SQL Analysis

Customer Revenue Analysis

Product Performance Analysis

Regional Sales Insights

Discount Impact on Sales & Profitability

Sales Trend Forecasting

🎨 Power BI Dashboard

Quick Links

Get In Touch

Synthetic Beverage Sales Data Analysis

📂 Table of Content

📋 Project Overview​

📊 Summary of the Dataset

🔎 Key Analysis Areas:

💾 Data Acquisition & Preparation

Create the Table and Run BULK INSERT

Data Cleaning & Integrity Checks

Indexing for Query Optimization

Partitioning

📈 Advanced SQL Analysis

Customer Revenue Analysis

Product Performance Analysis

Regional Sales Insights

Discount Impact on Sales & Profitability

Sales Trend Forecasting

🎨 Power BI Dashboard

Quick Links

Get In Touch

📋 Project Overview