This dataset contains a list of sales and movement data by item and department appended monthly.

It is rich in information that can be leveraged for various data science applications. For instance, analyzing this dataset can offer insights into consumer behavior, such as preferences for specific types of beverages (e.g., wine, beer) during different times of the year. Furthermore, the dataset can be used to identify trends in sales and transfers, highlighting seasonal effects or the impact of certain suppliers on the market.

One could start with exploratory data analysis (EDA) to understand the basic distribution of sales and transfers across different item types and suppliers. Time series analysis can provide insights into seasonal trends and sales forecasts. Cluster analysis might reveal groups of suppliers or items with similar sales patterns, which can be useful for targeted marketing and inventory management.

This dataset contains detailed sales transactions, including order details, revenue, profit, and customer information. It can be used for sales analysis, trend forecasting, and business intelligence insights. The data covers multiple product categories and is structured to facilitate easy analysis of sales performance across different locations and time periods.

1) Data Introduction • The Sample Sales Data is a retail sales dataset of 2,823 orders and 25 columns that includes a variety of sales-related data, including order numbers, product information, quantity, unit price, sales, order date, order status, customer and delivery information.

2) Data Utilization (1) Sample Sales Data has characteristics that: • This dataset consists of numerical (sales, quantity, unit price, etc.), categorical (product, country, city, customer name, transaction size, etc.), and date (order date) variables, with missing values in some columns (STATE, ADDRESSLINE2, POSTALCODE, etc.). (2) Sample Sales Data can be used to: • Analysis of sales trends and performance by product: Key variables such as order date, product line, and country can be used to visualize and analyze monthly and yearly sales trends, the proportion of sales by product line, and top sales by country and region. • Segmentation and marketing strategies: Segmentation of customer groups based on customer information, transaction size, and regional data, and use them to design targeted marketing and customized promotion strategies.

Product Sales and Marketing Analytics Dataset This dataset provides a comprehensive view of product performance across various categories, focusing on sales metrics, marketing efforts, and consumer feedback. With 500 rows and 15 columns, it is an ideal resource for analyzing trends, optimizing marketing strategies, and predicting product success.

Key Features:

Product Details: Product_Name: Name of the product. Category: General category (e.g., Home & Kitchen, Sports & Outdoors). Sub_category: Specific sub-category (e.g., Cookware, Outdoor Gear). Pricing and Discounts: Price: Product price in local currency. Discount: Discount percentage offered on the product. Customer Feedback: Rating: Average customer rating (scale of 1 to 5). No_rating: Total number of customer reviews. Sales Metrics: Sales_y: Total yearly sales. Sales_m: Monthly sales, providing a more granular sales trend. Marketing and Operational Data: M_Spend: Marketing expenditure for the product. Supply_Chain_E: Efficiency rating of the supply chain. Market and Seasonal Trends: Market_T: Market trend index (indicates current market conditions). Seasonality_T: Seasonality trend index (impact of seasonal factors). Performance Metric: Success_Percentage: Success rate of the product, combining multiple performance indicators. Potential Use Cases:

Sales Forecasting: Use historical sales data and trends to predict future sales. Marketing Optimization: Identify products that yield the highest returns for marketing investment. Customer Insights: Analyze ratings and reviews to understand customer preferences. Trend Analysis: Study the impact of market and seasonality trends on sales. Product Success Prediction: Assess key factors contributing to a product’s success.

Target Audience: This dataset is designed for data analysts, business strategists, and machine learning enthusiasts looking to explore:

1. Sales forecasting models.
2. Marketing spend optimization
3. Consumer behavior analysis.

Additional Notes: Data is pre-cleaned and ready for analysis. Suitable for regression, classification, and clustering tasks.

The Online Sales Dataset provides a detailed overview of global online sales transactions across various product categories. It includes transaction details such as order ID, date, product category, product name, quantity, unit price, total price, region, and payment method.

Company Datasets for valuable business insights!

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This dataset contains a synthetic but realistic sample of e-commerce sales for an online store, covering the period from 2024 to 2025. It includes details about orders, customers, products, regions, pricing, discounts, sales, profit, and payment modes.

It is designed for data analysis, visualization, and machine learning projects. Beginners and advanced users can use this dataset to practice:

Exploratory Data Analysis (EDA)

Sales trend analysis

Profit margin and discount analysis

Customer segmentation

Predictive modeling (e.g., sales or profit prediction)

Sales Analytics Software Market Outlook

The global sales analytics software market size was valued at approximately USD 3.4 billion in 2023 and is projected to reach around USD 10.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.8% during the forecast period. The growth of this market is being driven by several factors, including the increasing adoption of advanced analytics platforms by organizations to gain competitive advantages and the rising demand for data-driven decision-making processes.

One of the primary growth factors for the sales analytics software market is the increasing emphasis on data-driven decision-making within enterprises. Modern businesses are increasingly leveraging big data and analytics to gain insights into customer behavior, sales trends, and market dynamics. This data-centric approach allows companies to make informed decisions, optimize sales strategies, and improve overall business performance. As a result, the demand for sophisticated sales analytics tools is on the rise, propelling the market forward.

Additionally, the rapid digitization across various industries is significantly contributing to the market growth. With the proliferation of digital channels and e-commerce platforms, companies now have access to vast amounts of data generated from online transactions, customer interactions, and social media activities. Sales analytics software helps organizations to sift through this data, identify trends, and derive actionable insights. This capability is particularly crucial in the retail and e-commerce sectors, where understanding consumer preferences and buying patterns can directly impact sales and profitability.

Another crucial growth factor is the increasing integration of artificial intelligence (AI) and machine learning (ML) technologies within sales analytics solutions. These advanced technologies enhance the predictive and prescriptive capabilities of sales analytics software, enabling businesses to anticipate market trends, forecast sales performance, and personalize customer experiences. As AI and ML continue to evolve, their integration into sales analytics tools is expected to further drive market growth by providing more accurate and actionable insights.

Data Analytics Software plays a pivotal role in the evolution of sales analytics solutions. As organizations strive to harness the power of data, the integration of comprehensive data analytics software becomes essential. These tools not only facilitate the analysis of vast datasets but also enable businesses to derive meaningful insights that drive strategic decision-making. By leveraging data analytics software, companies can enhance their understanding of market trends, customer behaviors, and sales performance, leading to more informed business strategies. This integration is particularly beneficial in industries with complex sales processes, where the ability to quickly analyze and interpret data can provide a competitive edge.

From a regional perspective, North America is expected to hold a significant share of the sales analytics software market. The region's dominance can be attributed to the presence of major technology companies, high adoption rates of advanced analytics solutions, and a robust digital infrastructure. Furthermore, the increasing focus on improving customer experiences and the willingness of enterprises to invest in innovative technologies are likely to sustain the market's growth in this region.

Component Analysis

The sales analytics software market can be segmented by component into software and services. The software segment encompasses various types of analytics software, including descriptive, diagnostic, predictive, and prescriptive analytics tools. These tools help organizations to analyze historical sales data, identify patterns, and predict future sales trends. The growing need for real-time analytics and the ability to integrate these tools with existing CRM systems are driving the demand for sales analytics software.

Within the software segment, cloud-based solutions are gaining significant traction due to their scalability, flexibility, and cost-effectiveness. Cloud-based sales analytics software allows businesses to access data and insights from anywhere, facilitating remote work and collaboration. Additionally, the continuous advancements in cloud technology, such as enhanced security features and increased storage capabilitie

🛒 E-Commerce Customer Behavior and Sales Dataset 📊 Dataset Overview This comprehensive dataset contains 5,000 e-commerce transactions from a Turkish online retail platform, spanning from January 2023 to March 2024. The dataset provides detailed insights into customer demographics, purchasing behavior, product preferences, and engagement metrics.

🎯 Use Cases This dataset is perfect for:

Customer Segmentation Analysis: Identify distinct customer groups based on behavior Sales Forecasting: Predict future sales trends and patterns Recommendation Systems: Build product recommendation engines Customer Lifetime Value (CLV) Prediction: Estimate customer value Churn Analysis: Identify customers at risk of leaving Marketing Campaign Optimization: Target customers effectively Price Optimization: Analyze price sensitivity across categories Delivery Performance Analysis: Optimize logistics and shipping 📁 Dataset Structure The dataset contains 18 columns with the following features:

Order Information Order_ID: Unique identifier for each order (ORD_XXXXXX format) Date: Transaction date (2023-01-01 to 2024-03-26) Customer Demographics Customer_ID: Unique customer identifier (CUST_XXXXX format) Age: Customer age (18-75 years) Gender: Customer gender (Male, Female, Other) City: Customer city (10 major Turkish cities) Product Information Product_Category: 8 categories (Electronics, Fashion, Home & Garden, Sports, Books, Beauty, Toys, Food) Unit_Price: Price per unit (in TRY/Turkish Lira) Quantity: Number of units purchased (1-5) Transaction Details Discount_Amount: Discount applied (if any) Total_Amount: Final transaction amount after discount Payment_Method: Payment method used (5 types) Customer Behavior Metrics Device_Type: Device used for purchase (Mobile, Desktop, Tablet) Session_Duration_Minutes: Time spent on website (1-120 minutes) Pages_Viewed: Number of pages viewed during session (1-50) Is_Returning_Customer: Whether customer has purchased before (True/False) Post-Purchase Metrics Delivery_Time_Days: Delivery duration (1-30 days) Customer_Rating: Customer satisfaction rating (1-5 stars) 📈 Key Statistics Total Records: 5,000 transactions Date Range: January 2023 - March 2024 (15 months) Average Transaction Value: ~450 TRY Customer Satisfaction: 3.9/5.0 average rating Returning Customer Rate: 60% Mobile Usage: 55% of transactions 🔍 Data Quality ✅ No missing values ✅ Consistent formatting across all fields ✅ Realistic data distributions ✅ Proper data types for all columns ✅ Logical relationships between features 💡 Sample Analysis Ideas Customer Segmentation with K-Means Clustering

Segment customers based on spending, frequency, and recency Sales Trend Analysis

Identify seasonal patterns and peak shopping periods Product Category Performance

Compare revenue, ratings, and return rates across categories Device-Based Behavior Analysis

Understand how device choice affects purchasing patterns Predictive Modeling

Build models to predict customer ratings or purchase amounts City-Level Market Analysis

Compare market performance across different cities 🛠️ Technical Details File Format: CSV (Comma-Separated Values) Encoding: UTF-8 File Size: ~500 KB Delimiter: Comma (,) 📚 Column Descriptions Column Name Data Type Description Example Order_ID String Unique order identifier ORD_001337 Customer_ID String Unique customer identifier CUST_01337 Date DateTime Transaction date 2023-06-15 Age Integer Customer age 35 Gender String Customer gender Female City String Customer city Istanbul Product_Category String Product category Electronics Unit_Price Float Price per unit 1299.99 Quantity Integer Units purchased 2 Discount_Amount Float Discount applied 129.99 Total_Amount Float Final amount paid 2469.99 Payment_Method String Payment method Credit Card Device_Type String Device used Mobile Session_Duration_Minutes Integer Session time 15 Pages_Viewed Integer Pages viewed 8 Is_Returning_Customer Boolean Returning customer True Delivery_Time_Days Integer Delivery duration 3 Customer_Rating Integer Satisfaction rating 5 🎓 Learning Outcomes By working with this dataset, you can learn:

Data cleaning and preprocessing techniques Exploratory Data Analysis (EDA) with Python/R Statistical analysis and hypothesis testing Machine learning model development Data visualization best practices Business intelligence and reporting 📝 Citation If you use this dataset in your research or project, please cite:

E-Commerce Customer Behavior and Sales Dataset (2024) Turkish Online Retail Platform Data (2023-2024) Available on Kaggle ⚖️ License This dataset is released under the CC0: Public Domain license. You are free to use it for any purpose.

🤝 Contribution Found any issues or have suggestions? Feel free to provide feedback!

📞 Contact For questions or collaborations, please reach out through Kaggle.

Happy Analyzing! 🚀

Keywords: e-c...

🛍️ Product Sales Dataset (2023–2024)

📌 Overview

This dataset contains 200,000 synthetic sales records simulating real-world product transactions across different U.S. regions. It is designed for data analysis, business intelligence, and machine learning projects, especially in the areas of sales forecasting, customer segmentation, profitability analysis, and regional trend evaluation.

The dataset provides detailed transactional data including customer names, product categories, pricing, and revenue details, making it highly versatile for both beginners and advanced analysts.

📂 Dataset Structure

• Rows: 200,000
• Columns: 14

Features

1. Order_ID – Unique identifier for each order
2. Order_Date – Date of transaction
3. Customer_Name – Name of the customer
4. City – City of the customer
5. State – State of the customer
6. Region – Region (East, West, South, Centre)
7. Country – Country (United States)
8. Category – Broad product category (e.g., Accessories, Clothing & Apparel)
9. Sub_Category – Subdivision of category (e.g., Sportswear, Bags)
10. Product_Name – Product description
11. Quantity – Units purchased
12. Unit_Price – Price per unit (USD)
13. Revenue – Total sales amount (Quantity × Unit Price)
14. Profit – Net profit earned from the transaction

🎯 Potential Use Cases

• Sales Analysis: Track revenue, profit, and performance by product, category, or region.
• Customer Analytics: Identify top customers, purchasing frequency, and loyalty patterns.
• Profitability Insights: Compare profit margins across categories and sub-categories.
• Time-Series Analysis: Study seasonal demand and forecast future sales.
• Visualization Projects: Build dashboards in Power BI, Tableau, or Excel.
• Machine Learning: Train models for demand prediction, price optimization, or segmentation.

📊 Example Insights

• Which region generates the highest revenue?
• What are the top 10 most profitable products?
• Are some product categories more popular in certain regions?
• Which customers contribute the most to total revenue?

🏷️ Tags

business · sales · profitability · forecasting · customer analysis · retail

📜 License

This dataset is synthetic and created for educational and analytical purposes. You are free to use, modify, and share it under the CC BY 4.0 License.

🙌 Acknowledgments

This dataset was generated to provide a realistic foundation for learning and practicing Data Analytics, Power BI, Tableau, Python, and Excel projects.

Explore the world of data visualization with this Power BI dataset containing HR Analytics and Sales Analytics datasets. Gain insights, create impactful reports, and craft engaging dashboards using real-world data from HR and sales domains. Sharpen your Power BI skills and uncover valuable data-driven insights with this powerful dataset. Happy analyzing!

This dataset contains comprehensive sales data that can be used for analysis, visualization, and modeling. It includes key attributes such as:

order_id: Unique identifier for each order. product: Name of the product sold. quantity_ordered: Quantity of the product purchased in each transaction. price_each: Price of a single unit of the product. order_date: Date and time when the order was placed. purchase_address: Full address of the purchase, including street, city, and state.

Potential Use Cases Sales Analysis: Identify trends in product performance and seasonal demand. Revenue Insights: Analyze total and per-unit revenue across products or cities. Geographical Analysis: Discover top-performing cities and regions. Time-Based Trends: Analyze monthly sales trends and patterns. Machine Learning Applications: Build predictive models for sales forecasting or customer segmentation.

Looking painstakingly at the dataset, it's noticeable that some inconsistencies are messing up our data. In fact, the columns Product and line should count for a sigle attribut. Then, the actual observation should be Camping Equipment. Similarily, columns such as Retailer and country, are undergoing the same issue. In addition, the values of the rows regarding the attributs order and method do not convey any relevant information. Consequently, some supplemental work need to be done in the analysis.

Sales Data Description This dataset represents synthetic sales data generated for practice purposes only. It is not real-time or based on actual business operations, and should be used solely for educational or testing purposes. The dataset contains information that simulates sales transactions across different products, regions, and customers. Each row represents an individual sale event with various details associated with it.

Columns in the Dataset

1. Product_ID: Unique identifier for each product sold. Randomly generated for practice purposes.
2. Sale_Date: The date when the sale occurred. Randomly selected from the year 2023.
3. Sales_Rep: The sales representative responsible for the transaction. The dataset includes five random sales representatives (Alice, Bob, Charlie, David, Eve).
4. Region: The region where the sale took place. The possible regions are North, South, East, and West.
5. Sales_Amount: The total sales amount for the transaction, including discounts if any. Values range from 100 to 10,000 (in currency units).
6. Quantity_Sold: The number of units sold in that transaction, randomly generated between 1 and 50.
7. Product_Category: The category of the product sold. Categories include Electronics, Furniture, Clothing, and Food.
8. Unit_Cost: The cost per unit of the product sold, randomly generated between 50 and 5000 currency units.
9. Unit_Price: The selling price per unit of the product, calculated to be higher than the unit cost.
10. Customer_Type: Indicates whether the customer is a New or Returning customer.
11. Discount: The discount applied to the sale, randomly chosen between 0% and 30%.
12. Payment_Method: The method of payment used by the customer (e.g., Credit Card, Cash, Bank Transfer).
13. Sales_Channel: The channel through which the sale occurred. Either Online or Retail.
14. Region_and_Sales_Rep: A combined column that pairs the region and sales representative for easier tracking.

Disclaimer

Please note: This data was randomly generated and is intended solely for practice, learning, or testing. It does not reflect real-world sales, customers, or businesses, and should not be considered reliable for any real-time analysis or decision-making.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19501062%2Fecf38457fd8d1cc58196670eb79cfb1e%2FScreenshot%202024-06-10%20232658.png?generation=1718042573089666&alt=media" alt=""> This dataset provides a detailed analysis of sales and profit data for corporate customers across various regions and provinces. It has been meticulously cleaned, formatted, and enriched with visualizations to uncover valuable insights for business decision-making.

Key Features: Customer Segmentation: The data is specifically focused on corporate customers, allowing for targeted analysis of this segment's purchasing behavior.

Regional Breakdown: Sales and profits are analyzed across different regions and provinces, enabling the identification of regional trends and opportunities.

Profitability Analysis: The dataset highlights the most profitable and least profitable product sub-categories within each region, aiding in strategic inventory and pricing decisions.

Conditional Formatting: Visual cues highlight top-performing orders, profit margins, and regional demarcations, making the data easier to interpret.

Pivot Tables: Included pivot tables summarize key findings, such as the top 3 profitable sub-categories per region.

Potential Use Cases: Sales Strategy: Identify high-performing regions and product categories to focus marketing and sales efforts.

Inventory Management: Optimize inventory levels by understanding which products are most profitable in each region.

Pricing Optimization: Adjust pricing strategies based on regional profit margins and product performance.

Customer Insights: Gain a deeper understanding of corporate customer preferences and buying patterns.

Business Reporting: Utilize the formatted data and visualizations to create compelling reports for stakeholders.

🔍 Total Sales: Achieved $456,000 in revenue across 1,000 transactions, with an average transaction value of $456.00.

👥 Customer Demographics:

Average Age: 41.39 years Gender Distribution: 51% male, 49% female Most active age groups: 31-40 & 41-50 years 🏷️ Product Performance:

Top Categories: Electronics and Clothing led the sales, each contributing $160,000, followed by Beauty products with $140,000. Quantity Sold: Clothing topped the charts with 894 units sold. 📈 Sales Trends: Identified key sales peaks, especially in May 2023, indicating the success of targeted promotional strategies.

Why This Matters:

Understanding these metrics allows for better-targeted marketing, efficient inventory management, and strategic planning to capitalize on peak sales periods. This project demonstrates the power of data-driven decision-making in retail!

💡 Takeaway: Power BI continues to be a game-changer in visualizing and interpreting complex data, helping businesses to not just see numbers but to translate them into actionable insights.

I’m always looking forward to new challenges and projects that push my skills further. If you're interested in diving into the details or discussing data insights, feel free to reach out!

PowerBI #DataAnalysis #RetailSales #DataVisualization #BusinessIntelligence #DataDriven

About Datasets:

Domain : Sales Project: Coca Cola Sales Analysis Datasets: Power BI Dataset vF Dataset Type: Excel Data Dataset Size: 52k+ records

KPI's: 1. Analyze Profit Margins per Brand 2. Sales by Region 3. Price per unit 4. Operating Profit 5. Additional Analysis

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains Power Query, Q&A visual, Key influencers visual, map chart, matrix, dynamic timeline, dashboard, formatting, text box.

Advertisement Sales Dataset

The Advertisement Sales dataset is a collection of data points used to analyze the impact of advertising on sales. This dataset consists of 200 entries, each representing a unique observation with data on various types of media advertising and corresponding sales figures.

Key Features: ID: A unique identifier for each observation. TV: The amount of money spent on TV advertising (in thousands of dollars). Radio: The amount of money spent on Radio advertising (in thousands of dollars). Newspaper: The amount of money spent on Newspaper advertising (in thousands of dollars). Sales: The sales figures for the product (in thousands of units).

Summary Statistics: TV advertising: Ranges from $0.7k to $296.4k, with an average spend of $147.03k. Radio advertising: Ranges from $0k to $49.6k, with an average spend of $23.29k. Newspaper advertising: Ranges from $0.3k to $114k, with an average spend of $30.55k. Sales: Ranges from 1.6k to 27k units, with an average of 14.04k units.

Use Cases: Advertising Strategy: Businesses can use this dataset to understand the effectiveness of different advertising channels (TV, Radio, Newspaper) on sales performance. Predictive Modeling: Analysts can build predictive models to forecast sales based on advertising spend across different media.

ROI Analysis: Marketers can calculate the return on investment (ROI) for each advertising channel to optimize their budgets. Correlation Studies: Researchers can study the correlation between advertising spend and sales to derive insights on consumer behavior.

Potential Analyses: Regression Analysis: Determine how changes in advertising budgets influence sales. Comparative Analysis: Compare the effectiveness of different advertising mediums. Trend Analysis: Identify trends in advertising spending and sales performance over time.

This dataset provides a robust foundation for exploring the relationships between advertising expenditures and sales outcomes, enabling data-driven decision-making for marketing strategies. ​

Blinkit Sales Dataset: Analyzing Online Grocery Trends

This dataset contains sales transaction data from Blinkit, an online grocery delivery platform. It provides valuable insights into customer purchasing behavior, product demand, revenue trends, and sales performance over time.

Dataset Overview:

• Contains sales data from Blinkit, including product details, order quantities, revenue, and timestamps.
• Useful for demand forecasting, price optimization, trend analysis, and business insights.
• Helps in understanding customer behavior and seasonal variations in online grocery shopping.

Potential Use Cases:

• Time Series Analysis: Analyze sales trends over different periods.
• Demand Forecasting: Predict future product demand based on historical data.
• Price Optimization: Identify the impact of pricing on sales and revenue.
• Customer Behavior Analysis: Understand buying patterns and preferences.
• Market Trends: Explore how different factors affect grocery sales performance.

This dataset can be beneficial for data scientists, business analysts, and researchers looking to explore e-commerce and retail trends. Feel free to use it for analysis, machine learning models, and business intelligence projects.

This dataset contains retail sales records from a superstore, including detailed information on orders, products, categories, sales, discounts, profits, customers, and regions.

It is widely used for business intelligence, data visualization, and machine learning projects. With features such as order date, ship mode, customer segment, and geographic region, the dataset is excellent for:

Sales forecasting

Profitability analysis

Market basket analysis

Customer segmentation

Data visualization practice (Tableau, Power BI, Excel, Python, R)

Inspiration:

Great dataset for learning how to build dashboards.

Commonly used in case studies for predictive analytics and decision-making.

Source: Originally inspired by a sample dataset frequently used in Tableau training and BI case studies.