****Attribute information:****

Row ID: A unique identifier for each row in the table Order ID: The identifier for each sales order Order Date: The date the order was placed Ship Date: The date the order was shipped Delivery Duration: The amount of time it took to deliver the order Ship Mode: The shipping method used for the order Customer ID: The identifier for the customer who placed the order Customer Name: The name of the customer who placed the order Country: The customer's country City: The customer's city State: The customer's state Postal Code: The customer's postal code Region: The customer's region Product ID: The identifier for the product that was ordered Category: The category of the product that was ordered (e.g., furniture, office supplies, technology) Sub-Category - This attribute likely refers to a subcategory within a larger product category (e.g., Tables within Furniture). (Bookcases - Chairs - Labels - Tables - Storage - Furnishings - Art - Phones - Binders - Appliances - Paper - Others). Product Name - This attribute specifies the name of the product sold. (Bush Somerset Collection Bookcase - Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back - Self-Adhesive Address Labels for Typewriters by Universal - Bretford CP4500 Series Slim Rectangular Table - Others).

Sales - This attribute shows the total sales amount for each product. Values are listed in currency format Quantity - This attribute specifies the number of units sold for each product. Integer values. Discount - This attribute indicates the discount offered on the product. Discount Value - This attribute shows the total discount amount applied to the product. Profit - This attribute shows the profit earned on the sale of each product. COGS - This attribute likely refers to each product's Cost of Goods Sold. COGS = Sales - Profit

Welcome to the Retail Sales and Customer Demographics Dataset! This synthetic dataset has been meticulously crafted to simulate a dynamic retail environment, providing an ideal playground for those eager to sharpen their data analysis skills through exploratory data analysis (EDA). With a focus on retail sales and customer characteristics, this dataset invites you to unravel intricate patterns, draw insights, and gain a deeper understanding of customer behavior.

****Dataset Overview:**

This dataset is a snapshot of a fictional retail landscape, capturing essential attributes that drive retail operations and customer interactions. It includes key details such as Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, and Total Amount. These attributes enable a multifaceted exploration of sales trends, demographic influences, and purchasing behaviors.

Why Explore This Dataset?

• Realistic Representation: Though synthetic, the dataset mirrors real-world retail scenarios, allowing you to practice analysis within a familiar context.
• Diverse Insights: From demographic insights to product preferences, the dataset offers a broad spectrum of factors to investigate.
• Hypothesis Generation: As you perform EDA, you'll have the chance to formulate hypotheses that can guide further analysis and experimentation.
• Applied Learning: Uncover actionable insights that retailers could use to enhance their strategies and customer experiences.

Questions to Explore:

• How does customer age and gender influence their purchasing behavior?
• Are there discernible patterns in sales across different time periods?
• Which product categories hold the highest appeal among customers?
• What are the relationships between age, spending, and product preferences?
• How do customers adapt their shopping habits during seasonal trends?
• Are there distinct purchasing behaviors based on the number of items bought per transaction?
• What insights can be gleaned from the distribution of product prices within each category?

Your EDA Journey:

Prepare to immerse yourself in a world of data-driven exploration. Through data visualization, statistical analysis, and correlation examination, you'll uncover the nuances that define retail operations and customer dynamics. EDA isn't just about numbers—it's about storytelling with data and extracting meaningful insights that can influence strategic decisions.

Embrace the Retail Sales and Customer Demographics Dataset as your canvas for discovery. As you traverse the landscape of this synthetic retail environment, you'll refine your analytical skills, pose intriguing questions, and contribute to the ever-evolving narrative of the retail industry. Happy exploring!

Autos include all passenger cars, including station wagons. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

Update Frequency: Yearly

Access to Residential, Condominium, Commercial, Apartment properties and vacant land sales history data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

This synthetic dataset simulates daily-level FMCG sales transactions for three consecutive years (2022, 2023, 2024), designed for practicing time series forecasting, demand planning, and machine learning in realistic business conditions.

Inspired by real-world scenarios (e.g. Nestlé, Unilever, P&G), it includes: - Product hierarchy: SKU → Brand → Segment → Category - Sales channels: Retail / Discount / E-commerce - Regions: Central, North, and South (Poland) - Daily sales quantities, prices, promotions, stock, delivery lag (lead time) - Pack types: Single / Multipack / Carton - Seasonality and product introductions: - New SKUs are introduced in 2024 only - Prices gradually increase over the years

Possible Use Cases - Weekly sales forecasting - Promotion effect analysis - Seasonality and trend modeling - New product forecasting (cold start) - Feature engineering for ML models

Created by: Beata Faron
LinkedIn profile
Data Scientist working on demand forecasting, NLP, and business-oriented ML.

Image by iuriimotov on Freepik

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4MM+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Retail Sales - Table 620-67001 : Total Retail Sales

Large Scale Retail Stores: Sales: Hamamatsu: Others data was reported at 1.859 JPY bn in Jan 2020. This records a decrease from the previous number of 2.420 JPY bn for Dec 2019. Large Scale Retail Stores: Sales: Hamamatsu: Others data is updated monthly, averaging 1.948 JPY bn from Jul 2015 (Median) to Jan 2020, with 55 observations. The data reached an all-time high of 2.823 JPY bn in Dec 2015 and a record low of 1.525 JPY bn in Oct 2019. Large Scale Retail Stores: Sales: Hamamatsu: Others data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.H005: Large Scale Retail Stores: Sales & Commodity Stock Value.

CN: Industrial Enterprise: YoY: Cost of Sales: ytd: Tianjin data was reported at 1.600 % in Mar 2025. This records an increase from the previous number of 1.100 % for Feb 2025. CN: Industrial Enterprise: YoY: Cost of Sales: ytd: Tianjin data is updated monthly, averaging 2.500 % from Jan 2019 (Median) to Mar 2025, with 75 observations. The data reached an all-time high of 42.200 % in Feb 2021 and a record low of -18.500 % in Mar 2020. CN: Industrial Enterprise: YoY: Cost of Sales: ytd: Tianjin data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data: Cost of Sales: By Province.

This table contains property sales information including sale date, price, and amounts for properties within Fairfax County. There is a one to many relationship to the parcel data. Refer to this document for descriptions of the data in the table.

In 2023 and the first half of 2024, the largest property sale in the data center real estate market in Europe was DATA4 Paris-Saclay in Paris. In April 2023, Brookfield bought the ****** square meter property from AXA for an undisclosed price. The most expensive sale was Digital Frankfurt I. The valuation of the site was *** million U.S. dollars and Digital Core REIT obtained **** percent from Digital Realty.

The purpose of this fictional sales dataset is to provide data for Data Analysis practice. The 3 tables must be joined before one can analyze the data.

This fictional data set consists of 3 tables: 1. Customer dimension (history preserving) 2. Product dimension (history preserving) 3. Sales Transactions

The Customer Dimension dataset includes unique customer IDs, addresses, ages, and indicators of current records, with effective start and end dates for each customer.

The Product Dimension dataset details unique product IDs, names, prices, and their validity periods, along with indicators of current price records.

The Sales Transactions dataset captures sales activities with unique order IDs, product IDs, customer IDs, quantities sold, and order dates. Together, these datasets offer a comprehensive view of customer demographics, product pricing history, and sales transactions.

Maximize your business potential with Success.ai's LinkedIn Company and Contact Data, a comprehensive solution designed to empower your business with strategic insights drawn from one of the largest professional networks in the world. This extensive dataset includes in-depth profiles from over 700 million professionals and 70 million companies globally, making it a goldmine for businesses aiming to enhance their marketing strategies, refine competitive intelligence, and drive robust B2B lead generation.

Transform Your Email Marketing Efforts With Success.ai, tap into highly detailed and direct contact data to personalize your communications effectively. By accessing a vast array of email addresses, personalize your outreach efforts to dramatically improve engagement rates and conversion possibilities.

Data Enrichment for Comprehensive Insights Integrate enriched LinkedIn data seamlessly into your CRM or any analytical system to gain a comprehensive understanding of your market landscape. This enriched view helps you navigate through complex business environments, enhancing decision-making and strategic planning.

Elevate Your Online Marketing Deploy targeted and precision-based online marketing campaigns leveraging detailed professional data from LinkedIn. Tailor your messages and offers based on specific professional demographics, industry segments, and more, to optimize engagement and maximize online marketing ROI.

Digital Advertising Optimized Utilize LinkedIn’s precise company and professional data to create highly targeted digital advertising campaigns. By understanding the profiles of key decision-makers, tailor your advertising strategies to resonate well with your target audience, ensuring high impact and better expenditure returns.

Accelerate B2B Lead Generation Identify and connect directly with key stakeholders and decision-makers to shorten your sales cycles and close deals quicker. With access to high-level contacts in your industry, streamline your lead generation process and enhance the efficiency of your sales funnel.

Why Partner with Success.ai for LinkedIn Data? - Competitive Pricing Assurance: Success.ai guarantees the most aggressive pricing, ensuring you receive unbeatable value for your investment in high-quality professional data. - Global Data Access: With coverage extending across 195 countries, tap into a rich reservoir of professional information, covering diverse industries and market segments. - High Data Accuracy: Backed by advanced AI technology and manual validation processes, our data accuracy rate stands at 99%, providing you with reliable and actionable insights. - Custom Data Integration: Receive tailored data solutions that fit seamlessly into your existing business processes, delivered in formats such as CSV and Parquet for easy integration. - Ethical Data Compliance: Our data sourcing and processing practices are fully compliant with global standards, ensuring ethical and responsible use of data. - Industry-wide Applications: Whether you’re in technology, finance, healthcare, or any other sector, our data solutions are designed to meet your specific industry needs.

Strategic Use Cases for Enhanced Business Performance - Email Marketing: Leverage accurate contact details for personalized and effective email marketing campaigns. - Online Marketing and Digital Advertising: Use detailed demographic and professional data to refine your online presence and digital ad targeting. - Data Enrichment and B2B Lead Generation: Enhance your databases and accelerate your lead generation with enriched, up-to-date data. - Competitive Intelligence and Market Research: Stay ahead of the curve by using our data for deep market analysis and competitive research.

With Success.ai, you’re not just accessing data; you’re unlocking a gateway to strategic business growth and enhanced market positioning. Start with Success.ai today to leverage our LinkedIn Company Data and transform your business operations with precision and efficiency.

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Home sales data aggregated by boundaries (neighborhood, zip code, city, etc) in increments of month, quarter, or year

The global Sales Data Fusion market is experiencing robust growth, driven by the increasing need for businesses to leverage disparate data sources for improved sales performance and strategic decision-making. The market's expansion is fueled by the rising adoption of cloud-based solutions, advancements in artificial intelligence (AI) and machine learning (ML) for data integration and analysis, and the growing demand for real-time sales insights. Key players like Thomson Reuters, AGT International, and LexisNexis are leading the charge, offering comprehensive platforms that consolidate data from CRM systems, marketing automation tools, and other relevant sources. This consolidation provides a holistic view of customer interactions, sales performance, and market trends, enabling businesses to optimize sales strategies, improve forecasting accuracy, and ultimately enhance revenue generation. The market is segmented by deployment (cloud, on-premise), by industry (BFSI, retail, healthcare, manufacturing), and by component (software, services). While data security and privacy concerns represent a potential restraint, the overall market outlook remains positive, indicating continued growth driven by technological advancements and the ever-increasing value placed on data-driven decision-making within organizations. The forecast period of 2025-2033 is expected to witness significant expansion, building upon a strong historical period (2019-2024). Assuming a conservative CAGR of 15% (a reasonable estimate given the growth drivers mentioned), we can expect substantial market expansion. This growth will be particularly evident in regions with high technological adoption rates and robust digital infrastructures. The competitive landscape is characterized by both established players and emerging technology companies, creating a dynamic and innovative ecosystem. Future growth will likely be shaped by advancements in big data analytics, improved data integration capabilities, and the increasing availability of sophisticated sales intelligence tools. The market will continue to attract investments as businesses recognize the critical role of effective sales data fusion in achieving a competitive advantage.

United States Avg Sale to List: Single-Family: Mount Vernon, WA data was reported at 99.994 % in Jul 2020. This records an increase from the previous number of 99.946 % for Jun 2020. United States Avg Sale to List: Single-Family: Mount Vernon, WA data is updated monthly, averaging 98.679 % from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 100.583 % in Mar 2017 and a record low of 95.302 % in Feb 2012. United States Avg Sale to List: Single-Family: Mount Vernon, WA data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB050: Average Sales to List: by Metropolitan Areas.

A searchable electronic database of all real property upon which a deed restriction was imposed by the Department of Citywide Administrative Services, pursuant to Local Law 176 of 2016. Current data: 2006 - present.

Disclaimer: Data, descriptions and other information posted within this dataset, published and/or distributed by DCAS, or statements made by officials, agents and employees of the City concerning information contained within this dataset are for informational purposes only and should be independently verified by anyone accessing this data. The City does not warranty the completeness, accuracy, content or fitness for any particular purpose or use of the information provided herein nor are any such warranties to be implied or inferred with respect to the data furnished herein. The existence of this dataset shall not be construed to create a private right of action to enforce its provisions. The existence of any inaccuracies or deficiencies in the dataset shall not result in liability to the City. No such data, description or other information, or omissions thereof shall be deemed to be a representation or warranty and the viewer acknowledges not having relied on any representation or warranty or omissions thereof, concerning this data.

🌍 Global B2B Leads Data | 293M Emails + 100M Mobile Numbers | 95% Accuracy | API & Bi-Weekly Updates Fuel your sales pipeline with the world’s largest, most accurate B2B contact database—verified, actionable, and refreshed every two weeks.

The Forager.ai Global B2B Leads Dataset delivers 293M+ verified emails and 100M+ mobile numbers, all validated for 95%+ accuracy and updated bi-weekly. Ideal for cold outreach, CRM enrichment, and hyper-targeted campaigns, this dataset covers decision-makers across industries, company sizes, and geographies.

📊 Key Features ✅ 270M+ Total Contacts – One of the largest B2B leads database available. ✅ 95% Accuracy Guarantee – AI-validated emails & mobile numbers. ✅ Bi-Weekly Updates – Fresh data to reduce bounce rates. ✅ Global Coverage – North America, Europe, APAC & emerging markets.

📋 Core Data Fields: ✔ Professional/personal Emails (293M+) ✔ Mobile Numbers (100M+) – Direct lines for higher response rates ✔ Full Name, Job Title, Seniority Level ✔ Company Name, Industry, Revenue, Employee Size ✔ Location (Country, City, LinkedIn URL)

🎯 Top Use Cases 🔹 High-Volume Cold Outreach

Launch email/SMS campaigns with verified contacts.

Reduce bounce rates with 95% accurate data.

🔹 CRM & Prospecting Tools

Enrich Salesforce, HubSpot, or Outreach.io instantly.

Build targeted lead lists using firmographics.

🔹 ABM & Intent Data

Layer contacts with technographics for precision targeting.

Track account movements and job changes.

🔹 Recruitment & Partnerships

Source executive/candidates contacts profiles.

Map organizational hierarchies.

⚡ Delivery & Integration REST API – Real-time access for sales tools.

CSV/JSON Files – Bulk delivery via S3, Wasabi, or Snowflake.

Custom Feeds – Managed database solutions.

🔒 Data Quality & Compliance GDPR-Compliant – Ethically sourced, legally compliant.

Suppression Lists – Auto-remove opt-outs and hard bounces.

🚀 Why Forager.ai? ✔ Highest Accuracy (95%) – Industry-leading verification. ✔ Built for Sales Teams – Optimized for cold email/SMS performance. ✔ Enterprise-Grade Freshness – Bi-weekly updates = fewer dead leads. ✔ Dedicated Support – SLA-backed onboarding & troubleshooting.

Tags: B2B Leads | Personal / Work Email Database | Mobile Numbers | Sales Prospecting | CRM Enrichment | Cold Outreach | 95% Accuracy | API Integration

This dataset provides values for EXISTING HOME SALES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.