Retail Fashion Boutique Data Sales Analytics 2025

Overview

This comprehensive fashion retail synthetic dataset contains 2,176 real-world style records spanning seasonal collections, customer purchasing behavior, pricing strategies, and return analytics. Perfect for data science projects, machine learning models, and business intelligence dashboards focused on retail analytics and e-commerce insights.

Dataset Highlights

• 📊 Complete Sales Cycle: Purchase patterns, pricing strategies, and customer feedback
• 🔄 Return Analytics: Detailed return tracking with specific reasons and patterns
• 🛍️ Multi-Brand Coverage: 8 major fashion brands across diverse product categories
• 📈 Seasonal Intelligence: Four-season data with realistic markdown strategies
• ⭐ Customer Insights: Rating systems and purchasing behavior analysis
• 💰 Pricing Analytics: Original pricing, markdown percentages, and final pricing data

Key Applications

• Retail Analytics: Sales performance analysis and trend identification
• Customer Segmentation: Behavior analysis and purchasing pattern recognition
• Inventory Management: Stock optimization and seasonal demand forecasting
• Return Prediction: Machine learning models for return likelihood prediction
• Pricing Strategy: Dynamic pricing and markdown optimization analysis
• Business Intelligence: Comprehensive retail KPI dashboards and reporting

Column Details

Data Quality Features

• ✅ Realistic Business Logic: 15% return rate matching industry standards
• ✅ Seasonal Pricing: Authentic markdown patterns aligned with retail cycles
• ✅ Missing Data Handling: Strategic nulls for data cleaning practice (15% in ratings, size nulls for accessories)
• ✅ Balanced Distribution: Even representation across brands, categories, and seasons
• ✅ Price Consistency: Mathematically accurate pricing with discount calculations

Perfect For

• Data Analytics Projects: Retail KPI analysis, sales forecasting, customer behavior studies
• Machine Learning Models: Return prediction, demand forecasting, recommendation systems
• Business Intelligence: Executive dashboards, performance tracking, trend analysis
• Academic Research: Retail analytics case studies, pricing strategy research
• Portfolio Development: Comprehensive data science project demonstrations

File Formats Available

• CSV: Universal compatibility for data analysis tools
• Excel: Business reporting and stakeholder presentations
• JSON: API integration and web applications
• SQL: Database integration and advanced querying

Sample Use Cases

1. Return Prediction Model: Build ML models to predict return likelihood based on product attributes
2. Seasonal Demand Forecasting: Analyze purchasing patterns across different seasons and categories
3. Pricing Optimization: Study markdown effectiveness and optimal pricing strategies
4. Customer Satisfaction Analysis: Correlate ratings with return patterns and product characteristi...

This comprehensive retail point-of-interest (POI) dataset provides a detailed map of retail establishments across the United States and Canada. Retail strategists, market researchers, and business developers can leverage precise store location data to analyze market distribution, identify emerging trends, and develop targeted expansion strategies.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive retail landscape of location intelligence.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive retail store data database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail store locations -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping centers and malls, and more

Why Choose LocationsXYZ for Your Retail POI Data Needs? At LocationsXYZ, we: -Deliver POI data with 95% accuracy for reliable store location data -Refresh POIs every 30, 60, or 90 days to ensure the most recent retail location information -Create on-demand POI datasets tailored to your specific retail data requirements -Handcraft boundaries (geofences) for shopping center locations to enhance accuracy -Provide retail POI data and polygon data in multiple file formats

Unlock the Power of Retail Location Intelligence With our point-of-interest data for retail stores, you can: -Perform thorough market analyses using comprehensive store location data -Identify the best locations for new retail stores -Gain insights into consumer behavior and shopping patterns -Achieve an edge with competitive intelligence in retail markets

LocationsXYZ has empowered businesses with geospatial insights and retail location data, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge retail POI data and shopping center location intelligence.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Comprehensive dataset containing 1 verified Fancy Boutique locations in United States with complete contact information, ratings, reviews, and location data.

1) Data Introduction • The Grocery Store Dataset is a tabulated retail dataset of detailed information, including detailed classifications, prices, discounts, ratings, product names, currencies, key features, and detailed descriptions of groceries collected from the Costco online market.

2) Data Utilization (1) Grocery Store Dataset has characteristics that: • Each row contains a variety of attributes needed for grocery analysis, including detailed categories of products, prices, applied discounts, customer ratings, product names, currencies, key features, and detailed descriptions. • The data encompasses a wide range of products and is organized to enable multi-faceted analysis of price policies, promotions, customer evaluations, and product characteristics. (2) Grocery Store Dataset can be used to: • Analysis of pricing and discount strategies: Use price, discount, and rating data to create effective pricing policies and promotion strategies. • Product recommendations and popularity analysis by category: Based on product characteristics, ratings, and detailed descriptions, it can be applied to recommend customized products and derive popular products by category.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Explore the Meijer Grocery Store Dataset, a comprehensive collection of data on products available at Meijer, a leading American grocery store chain. This dataset includes detailed information on a wide variety of grocery items such as fresh produce, dairy, meat, beverages, household essentials, and more. Each product entry provides essential details, including product names, categories, prices, brands, descriptions, and availability, offering valuable insights for researchers, data analysts, and retail professionals.

Key Features:

• Extensive Product Range: Contains a wide array of grocery items from Meijer, covering multiple categories like fresh produce, dairy, meat, beverages, household essentials, and more.
• Detailed Product Information: Each entry includes key details such as product name, category, price, brand, description, and availability, allowing for in-depth analysis of retail trends and consumer preferences.
• Ideal for Market Analysis: Perfect for researchers, data scientists, and retail professionals interested in analyzing consumer behavior, studying grocery market trends, or optimizing inventory strategies in the retail sector.
• Rich Source of Retail Data: Provides a comprehensive overview of the grocery market at Meijer, helping professionals stay updated on the latest trends, popular products, and pricing strategies.

Whether you're analyzing market trends in the grocery sector, researching consumer behavior, or developing new retail strategies, the Meijer Grocery Store Dataset is an invaluable resource that provides detailed insights and extensive coverage of products available at Meijer.

This dataset provides a curated subset of the anonymized Google Analytics event data for three months of the Google Merchandise Store. The full dataset is available as a BigQuery Public Dataset.

The data includes information on items sold in the store and how much money was spent by users over time. It is both comprehensive enough to invite real analysis yet simple enough to facilitate teaching.

Original Data

Acknowledgement

Foto von Arthur Osipyan auf Unsplash

This core point of interest dataset consists of 1M location information of retail stores in the US and Canada. The POI database includes electronic stores, supermarkets and groceries, specialty retailers, home improvement and convenience stores, and apparel and accessories shops.

SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 52M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

This dataset represents simulated sales data for an electronics shop operating in the United States from 2024 (January to November). It is designed for individuals who want to practice data analysis, visualization, and machine learning techniques. The dataset reflects real-world sales scenarios, including various products, customer information, order statuses, and sales channels. It is ideal for learning and experimenting with data analytics, business insights, and visualization tools like Power BI, Tableau, or Python libraries.

Dataset Features ProductID: Unique identifier for each product. ProductName: Name of the electronic product (e.g., Phone, Laptop, Drone). ProductPrice: The price of the product is in USD. OrderedQuantity: Number of units ordered by the customer. OrderStatus: Status of the order (e.g., Delivered, In Process, On Hold, Canceled). CustomerName: Name of the customer who placed the order. State: State of the customer in the United States (e.g., California, Texas). City: City of the customer within the state. Latitude & Longitude: Geographic coordinates of the customer's location for mapping purposes. OrderChannel: Channel through which the order was placed (e.g., Website, Phone, Physical Store, Social Media). OrderDate: Date of the order (range: January 1, 2024, to November 30, 2024).

Potential Use Cases

Exploratory Data Analysis (EDA): Analyze sales trends across months, states, or product categories. Identify the most popular sales channels or products. Examine the distribution of order statuses.

Data Visualization: Create dashboards to visualize sales performance, customer demographics, and geographic distribution. Plot order locations on a map using latitude and longitude.

Machine Learning: Predict future sales trends using historical data. Classify order statuses based on product and order details. Cluster customers based on purchase behavior or location.

Business Insights: Analyze revenue contributions from different states or cities. Understand customer preferences across product categories.

Technical Details File Format: Excel (with a .xlsx extension) Number of Rows: 11000 Period: January 1, 2024, to November 30, 2024 Simulated Data: The data is entirely synthetic and does not represent real customers or transactions.

Why Use This Dataset? This dataset is tailored for individuals and students interested in: Building their data analysis and visualization skills. Learning how to work with real-world-like business datasets. Practicing machine learning with structured data. Acknowledgment This dataset was generated to mimic real-world sales data scenarios for educational and research purposes. Feel free to use it for learning and projects, and share your insights with the community!

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Comprehensive dataset containing 884 verified Boutique businesses in Louisiana, United States with complete contact information, ratings, reviews, and location data.

This entry provides access to the data elements available in the Operational Data Store (ODS) for my.Harvard Student Information System. These data are available through a request process. What are the goals of the Operational Data Store? Provide data in a more real-time environment than the Warehouse (refresh 1x a day) while not putting additional load on the transactional my.harvard system. Provide a single (university-wide) standard set of exports and then web-services for retrieving key Student data. Provide the ability to incrementally load the SIS Data warehouse star schemas, making it possible to refresh certain stars more than once a day. Provide Institutional Research and Registrar power-users the ability to investigate the Student data via direct SQL access. What is the SIS Operational Data Store (SIS ODS)? A database schema on the SIS Datawarehouse that will contain replicated core tables of the my.harvard transactional system along with standardized, simplified and performant views for extracting that data. We intend to make most data available through web services before the end of academic year 2015-2016. However, our first iteration will to be make data available via db views. The refresh schedule for the SIS ODS tables for this first release will be: Academic Class Data - 1x a day between 5:30am and 6:00am. What data will be available in the SIS ODS? ODS - Academic Class v SISODS_1.0.6.xlsx follow link to get to older versions ODS - Bio Demo v SISODS_1.0.5.xlsx follow link to get to older versions ODS - Class Enrollment.xlsx ODS - Student Career Program Plan v SISODS_1.0.6.xlsx ODS - Admissions v. SISODS_1.0.7 Document coming Snapshots - non-FAS. For FAS Snapshots, please contact Harvard College Institutional Research. How can I request access to the SIS ODS? Send an email to myharvard_support@harvard.edu to request access Please indicate what data you want to access through the ODS: School & Component Available components: Academic Class (course descriptors). Biographic - Demographic Class Enrollment Student Career Program Plan Please indicate whether the request is for a personal account or for an application integration account. For personal accounts, please provide the HUIDs of the individuals to be set up. How do I connect to the SIS ODS? SIS ODS connections are currently limited to ODBC/JDBC connections to a database. The attached instructions explain how to install SQL Developer and configure a connection.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

• Data Analysis Project of a Clothing Store- done by using Microsoft Excel.
• Analysis is done regarding Sales trend, according to Gender, Month, Age Group etc.
• The Analysis mainly is done using Pivot Table and Charts , with exception of Data Cleaning.
• The Data is shown collectively in in a Dashboard esque sheet named 'Vrinda Store Report'.

Success.ai’s Ecommerce Store Data for the APAC E-commerce Sector provides a reliable and accurate dataset tailored for businesses aiming to connect with e-commerce professionals and organizations across the Asia-Pacific region. Covering roles and businesses involved in online retail, marketplace management, logistics, and digital commerce, this dataset includes verified business profiles, decision-maker contact details, and actionable insights.

With access to continuously updated, AI-validated data and over 700 million global profiles, Success.ai ensures your outreach, market analysis, and partnership strategies are effective and data-driven. Backed by our Best Price Guarantee, this solution helps you excel in one of the world’s fastest-growing e-commerce markets.

Why Choose Success.ai’s Ecommerce Store Data?

1. Verified Profiles for Precision Engagement

   • Access verified profiles, business locations, employee counts, and decision-maker details for e-commerce businesses across APAC.
   • AI-driven validation ensures 99% accuracy, improving engagement rates and reducing outreach inefficiencies.

2. Comprehensive Coverage of the APAC E-commerce Sector

   • Includes businesses from major e-commerce hubs such as China, India, Japan, South Korea, Australia, and Southeast Asia.
   • Gain insights into regional e-commerce trends, digital transformation efforts, and logistics innovations.

3. Continuously Updated Datasets

   • Real-time updates ensure that business profiles, employee roles, and operational insights remain accurate and relevant.
   • Stay aligned with dynamic market conditions and emerging opportunities in the APAC region.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access business profiles for e-commerce professionals and organizations across APAC.
• Firmographic Insights: Gain detailed information, including business locations, employee counts, and operational details.
• Decision-maker Profiles: Connect with key e-commerce leaders, managers, and strategists driving online retail innovation.
• Industry Trends: Understand emerging e-commerce trends, consumer behavior, and market dynamics in the APAC region.

Key Features of the Dataset:

1. Comprehensive E-commerce Business Profiles

   • Identify and connect with businesses specializing in online retail, marketplace management, and digital commerce logistics.
   • Target decision-makers involved in supply chain optimization, digital marketing, and platform development.

2. Advanced Filters for Precision Campaigns

   • Filter businesses and professionals by industry focus (fashion, electronics, grocery), geographic location, or employee size.
   • Tailor campaigns to address specific goals, such as promoting technology adoption, enhancing customer engagement, or expanding supply chains.

3. Regional and Sector-specific Insights

   • Leverage data on APAC’s fast-growing e-commerce markets, consumer purchasing trends, and regional challenges.
   • Refine your marketing strategies and outreach efforts to align with market priorities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Outreach

   • Promote e-commerce solutions, logistics services, or digital commerce tools to businesses and professionals in the APAC region.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media campaigns.

2. Partnership Development and Vendor Collaboration

   • Build relationships with e-commerce marketplaces, logistics providers, and payment solution companies seeking strategic partnerships.
   • Foster collaborations that drive operational efficiency, enhance customer experiences, or expand market reach.

3. Market Research and Competitive Analysis

   • Analyze regional e-commerce trends, consumer preferences, and logistics challenges to refine product offerings and business strategies.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in the e-commerce industry recruiting for roles in operations, logistics, and digital marketing.
   • Provide workforce optimization platforms or training solutions tailored to the digital commerce sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality e-commerce store data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified e-commerce data into CRM systems, analytics platforms, or market...

Success.ai delivers unparalleled access to Retail Store Data for Asia’s retail and e-commerce sectors, encompassing subcategories such as ecommerce data, ecommerce merchant data, ecommerce market data, and company data. Whether you’re targeting emerging markets or established players, our solutions provide the tools to connect with decision-makers, analyze market trends, and drive strategic growth. With continuously updated datasets and AI-validated accuracy, Success.ai ensures your data is always relevant and reliable.

Key Features of Success.ai's Retail Store Data for Retail & E-commerce in Asia:

Extensive Business Profiles: Access detailed profiles for 70M+ companies across Asia’s retail and e-commerce sectors. Profiles include firmographic data, revenue insights, employee counts, and operational scope.

Ecommerce Data: Gain insights into online marketplaces, customer demographics, and digital transaction patterns to refine your strategies.

Ecommerce Merchant Data: Understand vendor performance, supply chain metrics, and operational details to optimize partnerships.

Ecommerce Market Data: Analyze purchasing trends, regional preferences, and market demands to identify growth opportunities.

Contact Data for Decision-Makers: Reach key stakeholders, such as CEOs, marketing executives, and procurement managers. Verified contact details include work emails, phone numbers, and business addresses.

Real-Time Accuracy: AI-powered validation ensures a 99% accuracy rate, keeping your outreach efforts efficient and impactful.

Compliance and Ethics: All data is ethically sourced and fully compliant with GDPR and other regional data protection regulations.

Why Choose Success.ai for Retail Store Data?

Best Price Guarantee: We deliver industry-leading value with the most competitive pricing for comprehensive retail store data.

Customizable Solutions: Tailor your data to meet specific needs, such as targeting particular regions, industries, or company sizes.

Scalable Access: Our data solutions are built to grow with your business, supporting small startups to large-scale enterprises.

Seamless Integration: Effortlessly incorporate our data into your existing CRM, marketing, or analytics platforms.

Comprehensive Use Cases for Retail Store Data:

1. Market Entry and Expansion:

Identify potential partners, distributors, and clients to expand your footprint in Asia’s dynamic retail and e-commerce markets. Use detailed profiles to assess market opportunities and risks.

1. Personalized Marketing Campaigns:

Leverage ecommerce data and consumer insights to craft highly targeted campaigns. Connect directly with decision-makers for precise and effective communication.

1. Competitive Benchmarking:

Analyze competitors’ operations, market positioning, and consumer strategies to refine your business plans and gain a competitive edge.

1. Supplier and Vendor Selection:

Evaluate potential suppliers or vendors using ecommerce merchant data, including financial health, operational details, and contact data.

1. Customer Engagement and Retention:

Enhance customer loyalty programs and retention strategies by leveraging ecommerce market data and purchasing trends.

APIs to Amplify Your Results:

Enrichment API: Keep your CRM and analytics platforms up-to-date with real-time data enrichment, ensuring accurate and actionable company profiles.

Lead Generation API: Maximize your outreach with verified contact data for retail and e-commerce decision-makers. Ideal for driving targeted marketing and sales efforts.

Tailored Solutions for Industry Professionals:

Retailers: Expand your supply chain, identify new markets, and connect with key partners in the e-commerce ecosystem.

E-commerce Platforms: Optimize your vendor and partner selection with verified profiles and operational insights.

Marketing Agencies: Deliver highly personalized campaigns by leveraging detailed consumer data and decision-maker contacts.

Consultants: Provide data-driven recommendations to clients with access to comprehensive company data and market trends.

What Sets Success.ai Apart?

70M+ Business Profiles: Access an extensive and detailed database of companies across Asia’s retail and e-commerce sectors.

Global Compliance: All data is sourced ethically and adheres to international data privacy standards, including GDPR.

Real-Time Updates: Ensure your data remains accurate and relevant with our continuously updated datasets.

Dedicated Support: Our team of experts is available to help you maximize the value of our data solutions.

Empower Your Business with Success.ai:

Success.ai’s Retail Store Data for the retail and e-commerce sectors in Asia provides the insights and connections needed to thrive in this competitive market. Whether you’re entering a new region, launching a targeted campaign, or analyzing market trends, our data solutions ensure measurable success.

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