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.

Data Set Description This dataset simulates a retail environment with a million rows and 100+ columns, covering customer information, transactional data, product details, promotional information, and customer behavior metrics. It includes data for predicting total sales (regression) and customer churn (classification).

Detailed Column Descriptions Customer Information:

customer_id: Unique identifier for each customer. age: Age of the customer. gender: Gender of the customer (e.g., Male, Female, Other). income_bracket: Income bracket of the customer (e.g., Low, Medium, High). loyalty_program: Whether the customer is part of a loyalty program (Yes/No). membership_years: Number of years the customer has been a member. churned: Whether the customer has churned (Yes/No) - Target for classification. marital_status: Marital status of the customer. number_of_children: Number of children the customer has. education_level: Education level of the customer (e.g., High School, Bachelor's, Master's). occupation: Occupation of the customer. Transactional Data:

transaction_id: Unique identifier for each transaction. transaction_date: Date of the transaction. product_id: Unique identifier for each product. product_category: Category of the product (e.g., Electronics, Clothing, Groceries). quantity: Quantity of the product purchased. unit_price: Price per unit of the product. discount_applied: Discount applied on the transaction. payment_method: Payment method used (e.g., Credit Card, Debit Card, Cash). store_location: Location of the store where the purchase was made. Customer Behavior Metrics:

avg_purchase_value: Average value of purchases made by the customer. purchase_frequency: Frequency of purchases (e.g., Daily, Weekly, Monthly, Yearly). last_purchase_date: Date of the last purchase made by the customer. avg_discount_used: Average discount percentage used by the customer. preferred_store: Store location most frequently visited by the customer. online_purchases: Number of online purchases made by the customer. in_store_purchases: Number of in-store purchases made by the customer. avg_items_per_transaction: Average number of items per transaction. avg_transaction_value: Average value per transaction. total_returned_items: Total number of items returned by the customer. total_returned_value: Total value of returned items. Sales Data:

total_sales: Total sales amount for each customer over the last year - Target for regression. total_transactions: Total number of transactions made by each customer. total_items_purchased: Total number of items purchased by each customer. total_discounts_received: Total discounts received by each customer. avg_spent_per_category: Average amount spent per product category. max_single_purchase_value: Maximum value of a single purchase. min_single_purchase_value: Minimum value of a single purchase. Product Information:

product_name: Name of the product. product_brand: Brand of the product. product_rating: Customer rating of the product. product_review_count: Number of reviews for the product. product_stock: Stock availability of the product. product_return_rate: Rate at which the product is returned. product_size: Size of the product (if applicable). product_weight: Weight of the product (if applicable). product_color: Color of the product (if applicable). product_material: Material of the product (if applicable). product_manufacture_date: Manufacture date of the product. product_expiry_date: Expiry date of the product (if applicable). product_shelf_life: Shelf life of the product (if applicable). Promotional Data:

promotion_id: Unique identifier for each promotion. promotion_type: Type of promotion (e.g., Buy One Get One Free, 20% Off). promotion_start_date: Start date of the promotion. promotion_end_date: End date of the promotion. promotion_effectiveness: Effectiveness of the promotion (e.g., High, Medium, Low). promotion_channel: Channel through which the promotion was advertised (e.g., Online, In-store, Social Media). promotion_target_audience: Target audience for the promotion (e.g., New Customers, Returning Customers). Geographical Data:

customer_zip_code: Zip code of the customer's residence. customer_city: City of the customer's residence. customer_state: State of the customer's residence. store_zip_code: Zip code of the store. store_city: City where the store is located. store_state: State where the store is located. distance_to_store: Distance from the customer's residence to the store. Seasonal and Temporal Data:

holiday_season: Whether the transaction occurred during a holiday season (Yes/No). season: Season of the year (e.g., Winter, Spring, Summer, Fall). weekend: Whether the transaction occurred on a weekend (Yes/No). Customer Interaction Data:

customer_support_calls: Number of calls made to customer support. email_subscription...

The extent to which individual businesses in Great Britain experienced actual changes in their sales.

🔍 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

📜 Dataset Overview

This dataset contains 3,400 records of fashion retail sales, capturing various details about customer purchases, including item details, purchase amounts, ratings, and payment methods. It is useful for analyzing customer buying behavior, product popularity, and payment preferences.

📂 Dataset Details

🔍 Key Insights

• The dataset contains 3,400 transactions.
• Missing values are present in:
  • Purchase Amount (USD): 650 missing values
  • Review Rating: 324 missing values
• Payment Method includes multiple categories, allowing analysis of payment trends.
• Date Purchase is in DD-MM-YYYY format, which can be useful for time-series analysis.
• The dataset can help analyze sales trends, customer preferences, and payment behaviors in the fashion retail industry.

📊 Potential Use Cases

• Sales Analysis: Understanding which fashion items are selling the most.
• Customer Insights: Analyzing purchase behaviors and spending patterns.
• Trend Forecasting: Identifying seasonal trends in fashion retail.
• Payment Method Preferences: Understanding how customers prefer to pay.

Retail Sales in the United States increased 0.20 percent in September of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Global Retail Sales Data provided here is a self-generated synthetic dataset created using Random Sampling techniques provided by the Numpy Package. The dataset emulates information regarding merchandise sales through a retail website set up by a popular fictional influencer based in the US between the '23-'24 period. The influencer would sell clothing, ornaments and other products at variable rates through the retail website to all of their followers across the world. Imagine that the influencer executes high levels of promotions for the materials they sell, prompting more ratings and reviews from their followers, pushing more user engagement.

This dataset is placed to help with practicing Sentiment Analysis or/and Time Series Analysis of sales, etc. as they are very important topics for Data Analyst prospects. The column description is given as follows:

Order ID: Serves as an identifier for each order made.

Order Date: The date when the order was made.

Product ID: Serves as an identifier for the product that was ordered.

Product Category: Category of Product sold(Clothing, Ornaments, Other).

Buyer Gender: Genders of people that have ordered from the website (Male, Female).

Buyer Age: Ages of the buyers.

Order Location: The city where the order was made from.

International Shipping: Whether the product was shipped internationally or not. (Yes/No)

Sales Price: Price tag for the product.

Shipping Charges: Extra charges for international shipments.

Sales per Unit: Sales cost while including international shipping charges.

Quantity: Quantity of the product bought.

Total Sales: Total sales made through the purchase.

Rating: User rating given for the order.

Review: User review given for the order.

Explore how a detailed Retail Sales Performance Analysis uncovers key consumer trends, top-selling products, and seasonal sales peaks.

View monthly updates and historical trends for US Retail Sales. from United States. Source: Census Bureau. Track economic data with YCharts analytics.

Retail Sales - Report on Monthly Survey of Retail Sales [Report]

This dataset, identified by the series ID RSXFS, is sourced from the U.S. Census Bureau and is available through the Federal Reserve Economic Data (FRED) system of the St. Louis Fed. It provides a monthly measure of retail sales across the United States. The data represents the total value of sales at retail and food services stores, measured in millions of dollars and adjusted for seasonal variations. It is important to note that the most recent month's value is an advance estimate, which is subject to revision in subsequent months as more comprehensive data becomes available. As a key economic indicator, this series is widely used by economists and analysts to gauge consumer spending and assess the overall health of the U.S. economy.

Suggested Use Cases: - This dataset is highly valuable for economic analysis and can be used to: - Conduct time series analysis and modeling. - Track consumer spending patterns. - Forecast future retail sales. - Analyze the impact of economic events on the retail sector.

License The RSXFS dataset is sourced from the U.S. Census Bureau and is considered Public Domain: Citation Requested. This means the data is freely available for use, but you must cite the source and acknowledge that the data was obtained from FRED. If you plan on using any copyrighted series from other data providers on FRED for commercial purposes, you would need to contact the original data owner for permission.

Data Fields: The dataset primarily contains two columns: - observation_date: The date of the monthly data point, recorded as the first day of each month from January 1992 to July 2025. - RSXFS: The value of advance retail sales in millions of dollars.

Citation and Provenance:
Source: U.S. Census Bureau
Release: Advance Monthly Sales for Retail and Food Services
FRED Link: https://fred.stlouisfed.org/series/RSXFS
Citation: U.S. Census Bureau, Advance Retail Sales: Retail Trade [RSXFS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/RSXFS, September 8, 2025.

This statistic shows a trend in total retail sales, including food services, in the United States from January 2017 to July 2025. In July 2025, U.S. retail sales had amounted to an estimated *********** U.S. dollars (not adjusted), which is an increase of approximately ** ******* U.S. dollars compared to the same month one year earlier.

Dataset Description:

• The dataset represents retail transactional data. It contains information about customers, their purchases, products, and transaction details. The data includes various attributes such as customer ID, name, email, phone, address, city, state, zipcode, country, age, gender, income, customer segment, last purchase date, total purchases, amount spent, product category, product brand, product type, feedback, shipping method, payment method, and order status.

Key Points:

Customer Information:

• Includes customer details like ID, name, email, phone, address, city, state, zipcode, country, age, and gender. Customer segments are categorized into Premium, Regular, and New. ##Transaction Details:
• Transaction-specific data such as transaction ID, last purchase date, total purchases, amount spent, total purchase amount, feedback, shipping method, payment method, and order status. ##Product Information:
• Contains product-related details such as product category, brand, and type. Products are categorized into electronics, clothing, grocery, books, and home decor. ##Geographic Information:
• Contains location details including city, state, and country. Available for various countries including USA, UK, Canada, Australia, and Germany. ##Temporal Information:
• Last purchase date is provided along with separate columns for year, month, date, and time. Allows analysis based on temporal patterns and trends. ##Data Quality:
• Some rows contain null values, and others are duplicates, which may need to be handled during data preprocessing. Null values are randomly distributed across rows. Duplicate rows are available at different parts of the dataset. ##Potential Analysis:
• Customer segmentation analysis based on demographics, purchase behavior, and feedback. Sales trend analysis over time to identify peak seasons or trends. Product performance analysis to determine popular categories, brands, or types. Geographic analysis to understand regional preferences and trends. Payment and shipping method analysis to optimize services. Customer satisfaction analysis based on feedback and order status. ##Data Preprocessing:
• Handling null values and duplicates. Parsing and formatting temporal data. Encoding categorical variables. Scaling numerical variables if required. Splitting data into training and testing sets for modeling.

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Retail Analytics Market Size 2025-2029

The retail analytics market size is forecast to increase by USD 28.47 billion, at a CAGR of 29.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing volume and complexity of data generated by retail businesses. This data deluge offers valuable insights for retailers, enabling them to optimize operations, enhance customer experience, and make data-driven decisions. However, this trend also presents challenges. One of the most pressing issues is the increasing adoption of Artificial Intelligence (AI) in the retail sector. While AI brings numerous benefits, such as personalized marketing and improved supply chain management, it also raises privacy and security concerns among customers.
Retailers must address these concerns through transparent data handling practices and robust security measures to maintain customer trust and loyalty. Navigating these challenges requires a strategic approach, with a focus on data security, customer privacy, and effective implementation of AI technologies. Companies that successfully harness the power of retail analytics while addressing these challenges will gain a competitive edge in the market.

What will be the Size of the Retail Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the constant need for businesses to gain insights from their data and adapt to shifting consumer behaviors. Entities such as text analytics, data quality, price optimization, customer journey mapping, mobile analytics, time series analysis, regression analysis, social media analytics, data mining, historical data analysis, and data cleansing are integral components of this dynamic landscape. Text analytics uncovers hidden patterns and trends in unstructured data, while data quality ensures the accuracy and consistency of information. Price optimization leverages historical data to determine optimal pricing strategies, and customer journey mapping provides insights into the customer experience.

Mobile analytics caters to the growing number of mobile shoppers, and time series analysis identifies trends and patterns over time. Regression analysis uncovers relationships between variables, social media analytics monitors brand sentiment, and data mining uncovers hidden patterns and correlations. Historical data analysis informs strategic decision-making, and data cleansing prepares data for analysis. Customer feedback analysis provides valuable insights into customer satisfaction, and association rule mining uncovers relationships between customer behaviors and purchases. Predictive analytics anticipates future trends, real-time analytics delivers insights in real-time, and market basket analysis uncovers relationships between products. Data security safeguards sensitive information, machine learning (ML) and artificial intelligence (AI) enhance data analysis capabilities, and cloud-based analytics offers flexibility and scalability.

Business intelligence (BI) and open-source analytics provide comprehensive data analysis solutions, while inventory management and supply chain optimization streamline operations. Data governance ensures data is used ethically and effectively, and loyalty programs and A/B testing optimize customer engagement and retention. Seasonality analysis accounts for seasonal trends, and trend analysis identifies emerging trends. Data integration connects disparate data sources, and clickstream analysis tracks user behavior on websites. In the ever-changing retail landscape, these entities are seamlessly integrated into retail analytics solutions, enabling businesses to stay competitive and adapt to evolving market dynamics.

How is this Retail Analytics Industry segmented?

The retail analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  In-store operation
  Customer management
  Supply chain management
  Marketing and merchandizing
  Others


Component

  Software
  Services


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The in-store operation segment is estimated to witness significant growth during the forecast period. In the realm of retail, the in-store operation segment of the market plays a pivotal role in optimizing brick-and-mortar retail operations. This segment encompasses various data analytics applications within phys

Snapshot img

E-Commerce Retail Market Size 2025-2029

The e-commerce retail market size is forecast to increase by USD 4,833.5 billion at a CAGR of 12% between 2024 and 2029.

The market is experiencing significant growth, driven by the advent of personalized shopping experiences. Consumers increasingly expect tailored recommendations and seamless interactions, leading retailers to integrate advanced technologies such as Artificial Intelligence (AI) to enhance the shopping journey. However, this market is not without challenges. Strict regulatory policies related to compliance and customer protection pose obstacles for retailers, requiring continuous investment in technology and resources to ensure adherence.
Retailers must navigate these challenges to effectively capitalize on the market's potential and deliver value to customers. By focusing on personalization and regulatory compliance, e-commerce retailers can differentiate themselves, build customer loyalty, and ultimately thrive in this dynamic market. Balancing the need for innovation with regulatory requirements is a delicate task, necessitating strategic planning and operational agility. Fraud prevention and customer retention are crucial aspects of e-commerce, with payment gateways ensuring secure transactions.

What will be the Size of the E-Commerce Retail Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, shopping carts and checkout processes streamline transactions, while sales forecasting and marketing automation help businesses anticipate consumer demand and optimize promotions. SMS marketing and targeted advertising reach customers effectively, driving sales growth. Warranty claims and customer support chatbots ensure post-purchase satisfaction, bolstering customer loyalty. Retail technology advances, including sustainable packaging, green logistics, and mobile optimization, cater to environmentally-conscious consumers. Legal compliance, data encryption, and fraud detection safeguard businesses and consumer trust. Product reviews, search functionality, and personalized recommendations enhance the shopping experience, fostering customer engagement.
Dynamic pricing and delivery networks adapt to market fluctuations and consumer preferences, respectively. E-commerce software integrates various functionalities, from circular economy initiatives and website accessibility to email automation and real-time order tracking. Overall, the e-commerce landscape continues to evolve, with businesses adopting innovative strategies to meet the needs of diverse customer segments and stay competitive.

How is this E-Commerce Retail Industry segmented?

The e-commerce retail industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Apparel and accessories
  Groceries
  Footwear
  Personal and beauty care
  Others


Modality

  Business to business (B2B)
  Business to consumer (B2C)
  Consumer to consumer (C2C)


Device

  Mobile
  Desktop


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Product Insights

The apparel and accessories segment is estimated to witness significant growth during the forecast period. The market for apparel and accessories is experiencing significant growth, fueled by several key trends. Increasing consumer affluence and a shift toward premiumization are driving this expansion, with the organized retail sector seeing particular growth. Influenced by social media trends, the Gen Z demographic is a major contributor to this rise in online shopping. This demographic is known for their preference for the latest fashion trends and their willingness to invest in premium products, making them a valuable market segment. Machine learning and artificial intelligence are increasingly being used for returns management and personalized recommendations, enhancing the customer experience.

Ethical sourcing and supply chain optimization are also essential, as consumers demand transparency and sustainability. Cybersecurity threats continue to pose challenges, requiring robust strategies and technologies. B2C and C2C e-commerce are thriving, with influencer marketing and e-commerce analytics playing significant roles. Customer reviews are essential for building trust and brand loyalty, while reputation management and affiliate marketing help expand reach. Sustainable e-commerce and b2b e-commerce are also gaining traction, with third-party logistics and social commerce offering new opportunities. Augment

Sales of Convenience Confectionery products in the United States are estimated at USD 17.1 billion in 2025, with projections indicating a rise to USD 21.9 billion by 2035, reflecting a CAGR of approximately 2.5% over the forecast period. This growth reflects both a stabilizing consumer base and steady per capita consumption across diverse demographic segments.

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

The Retail Market is Segments by Product Type (Food, Beverage, and Grocery, Personal and Household Care, Apparel, Footwear and Accessories, and More), by Distribution Channel (Supermarkets/Hypermarkets, Convenience and Discount Stores, Specialty Stores, and More), and by Geography (North America, Europe, Asia-Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

In 2025, the global in-store or brick-and-mortar retail channel generated an estimated **** trillion U.S. dollars in sales. Total retail sales (online and offline retail) worldwide amounted to around **** trillion U.S. dollars that year. By 2029, it is estimated that the value of e-commerce retail sales will reach **** trillion U.S. dollars on a global scale.

The global Retail Analytics Services market is booming, projected to reach [estimated 2033 value] by 2033, with a CAGR of 5.8%. This in-depth analysis explores market drivers, trends, and key players like IBM, Oracle, and Microsoft, covering segments like merchandising, pricing, and performance analysis. Discover the future of retail analytics.