1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

2. Citation

Please cite the following papers when using this dataset:

1. I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

3. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File	Period	Number of Samples (days)
product 1 2020.xlsx	01/01/2020–31/12/2020	363
product 1 2021.xlsx	01/01/2021–31/12/2021	364
product 1 2022.xlsx	01/01/2022–31/12/2022	365
product 2 2020.xlsx	01/01/2020–31/12/2020	363
product 2 2021.xlsx	01/01/2021–31/12/2021	364
product 2 2022.xlsx	01/01/2022–31/12/2022	365
product 3 2020.xlsx	01/01/2020–31/12/2020	363
product 3 2021.xlsx	01/01/2021–31/12/2021	364
product 3 2022.xlsx	01/01/2022–31/12/2022	365
product 4 2020.xlsx	01/01/2020–31/12/2020	363
product 4 2021.xlsx	01/01/2021–31/12/2021	364
product 4 2022.xlsx	01/01/2022–31/12/2022	364
product 5 2020.xlsx	01/01/2020–31/12/2020	363
product 5 2021.xlsx	01/01/2021–31/12/2021	364
product 5 2022.xlsx	01/01/2022–31/12/2022	365
product 6 2020.xlsx	01/01/2020–31/12/2020	362
product 6 2021.xlsx	01/01/2021–31/12/2021	364
product 6 2022.xlsx	01/01/2022–31/12/2022	365
product 7 2020.xlsx	01/01/2020–31/12/2020	362
product 7 2021.xlsx	01/01/2021–31/12/2021	364
product 7 2022.xlsx	01/01/2022–31/12/2022	365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature	Description	Unit
Day	day of the month	-
Month	Month	-
Year	Year	-
daily_unit_sales	Daily sales - the amount of products, measured in units, that during that specific day were sold	units
previous_year_daily_unit_sales	Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year	units
percentage_difference_daily_unit_sales	The percentage difference between the two above values	%
daily_unit_sales_kg	The amount of products, measured in kilograms, that during that specific day were sold	kg
previous_year_daily_unit_sales_kg	Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year	kg
percentage_difference_daily_unit_sales_kg	The percentage difference between the two above values	kg
daily_unit_returns_kg	The percentage of the products that were shipped to selling points and were returned	%
previous_year_daily_unit_returns_kg	The percentage of the products that were shipped to

The growth of supermarkets in most populated cities are increasing and market competitions are also high. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset.

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this graph was created in R and Canva :

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The dataset offers a comprehensive view of grocery inventory, covering 990 products across multiple categories such as Grains & Pulses, Beverages, Fruits & Vegetables, and more. It includes crucial details about each product, such as its unique identifier (Product_ID), name, category, and supplier information, including Supplier_ID and Supplier_Name. This dataset is particularly valuable for businesses aiming to optimize inventory management, sales tracking, and supply chain efficiency.

Key inventory-related fields include Stock_Quantity, which indicates the current stock level, and Reorder_Level, which determines when a product should be reordered. The Reorder_Quantity specifies how much stock to order when inventory falls below the reorder threshold. Additionally, Unit_Price provides insight into pricing, helping businesses analyze cost trends and profitability.

To manage product flow, the dataset includes dates such as Date_Received, which tracks when the product was added to the warehouse, and Last_Order_Date, marking the most recent procurement. For perishable goods, the Expiration_Date column is critical, allowing businesses to minimize waste by monitoring shelf life. The Warehouse_Location specifies where each product is stored, facilitating efficient inventory handling.

Sales and performance metrics are also included. The Sales_Volume column records the total number of units sold, providing insights into consumer demand. Inventory_Turnover_Rate helps businesses assess how quickly a product sells and is replenished, ensuring better stock management. The dataset also tracks the Status of each product, indicating whether it is Active, Discontinued, or Backordered.

The dataset serves multiple purposes in inventory management, sales performance evaluation, supplier analysis, and product lifecycle tracking. Businesses can leverage this data to refine reorder strategies, ensuring optimal stock levels and avoiding stockouts or excessive inventory. Sales analysis can help identify high-demand products and slow-moving items, enabling better decision-making in pricing and promotions. Evaluating suppliers based on their performance, pricing, and delivery efficiency helps streamline procurement and improve overall supply chain operations.

Furthermore, the dataset can support predictive analytics by employing machine learning techniques to estimate reorder quantities, forecast demand, and optimize stock replenishment. Inventory turnover insights can aid in maintaining a balanced supply, preventing unnecessary overstocking or shortages. By tracking trends in sales, businesses can refine their marketing and distribution strategies, ensuring sustained profitability.

This dataset is designed for educational and demonstration purposes, offering fictional data under the Creative Commons Attribution 4.0 International License. Users are free to analyze, modify, and apply the data while providing proper attribution. Additionally, certain products are marked as discontinued or backordered, reflecting real-world inventory dynamics. Businesses dealing with perishable goods should closely monitor expiration and last order dates to avoid losses due to spoilage.

Overall, this dataset provides a versatile resource for those interested in inventory management, sales analysis, and supply chain optimization. By leveraging the structured data, businesses can make data-driven decisions to enhance operational efficiency and maximize profitability.

Tabulating and Visualizing Supermarket Data

In this portfolio, I present an analysis of supermarket data, focusing on total sales, product categories, highest-spending customers, states with the highest and lowest sales, top-selling regions, and the most profitable city. This analysis provides valuable insights into supermarket performance and customer behavior.

Total Sales:

This chart illustrates the total sales over a specific time period. It serves as a key indicator of the supermarket's financial performance, showing revenue trends.

Product Categories:

A pie chart displays the distribution of sales across various product categories. It helps identify which product categories are the most popular and which may require additional marketing efforts.

Highest-Spending Customer:

The bar chart reveals the highest-spending customer, allowing the supermarket to recognize and reward loyal customers, while also gaining insights into their preferences.

States with the Highest Sales:

A map or bar chart showcases the states with the highest sales. This data can inform inventory management and marketing strategies.

Top-Selling Regions:

A bar chart displays the regions that generate the most sales, enabling the supermarket to concentrate resources where they are most effective.

Most Profitable City:

The pie chart reveals the city with the highest sales, providing insights into localized market dynamics.

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Power BI:

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1) Data Introduction • The Amazon Sales Dataset includes e-commerce product and consumer feedback data, including details on more than 1,000 products collected from Amazon's official website, discount prices, ratings, reviews, and categories.

2) Data Utilization (1) Amazon Sales Dataset has characteristics that: • The dataset includes a variety of product and review-related attributes, including product ID, product name, category, real and discounted prices, discount rates, ratings, rating numbers, product descriptions, user reviews, images, and product links. (2) Amazon Sales Dataset can be used to: • Product Rating and Review Analysis: Use rating and review data to analyze consumer satisfaction, popular products, review trends, and develop marketing strategies for each product. • Development of Price Policy and Recommendation System: Based on price information such as actual price, discount price, and discount rate, it can be used for price policy analysis, product recommendation system, consumer purchasing behavior prediction, etc.

Exploring Online Sales Data with Power BI !!

Another productive day diving into online sales dataset! Here’s a roundup of the insights I uncovered today:

Revenue by Category: Analyzed revenue distribution across different product categories to identify high-performing sectors.

Revenue by Sub-Category: Drilled down into sub-categories for a more granular view of revenue streams.

Revenue by Payment Mode: Examined revenue patterns based on payment methods to understand customer preferences.

Revenue by State: Mapped out revenue by state to pinpoint geographical strengths and opportunities.

Profit by Category: Evaluated profitability across product categories to assess which categories yield the highest profit margins.

Profit by Sub-Category: Explored profit levels at a sub-category level to identify the most profitable segments.

Profit by Payment Mode: Analyzed profit distribution across different payment methods.

Top 5 States by Revenue and Profit: Highlighted the top 5 states driving the most revenue and profit, offering insights into regional performance.

Sales Map by State: Visualized sales data on a map to provide a geographical perspective on sales distribution.

Total Quantity, Revenue, and Profit: Aggregated data to give an overview of total quantities sold, overall revenue, and total profit.

Filter by Category: Added a filter functionality to focus on specific categories and refine data analysis.

Revolutionize Customer Engagement with Our Comprehensive Ecommerce Data

Our Ecommerce Data is designed to elevate your customer engagement strategies, providing you with unparalleled insights and precision targeting capabilities. With over 61 million global contacts, this dataset goes beyond conventional data, offering a unique blend of shopping cart links, business emails, phone numbers, and LinkedIn profiles. This comprehensive approach ensures that your marketing strategies are not just effective but also highly personalized, enabling you to connect with your audience on a deeper level.

What Makes Our Ecommerce Data Stand Out?

• Unique Features for Enhanced Targeting
  Our Ecommerce Data is distinguished by its depth and precision. Unlike many other datasets, it includes shopping cart links—a rare and valuable feature that provides you with direct insights into consumer behavior and purchasing intent. This information allows you to tailor your marketing efforts with unprecedented accuracy. Additionally, the integration of business emails, phone numbers, and LinkedIn profiles adds multiple layers to traditional contact data, enriching your understanding of clients and enabling more personalized engagement.

• Robust and Reliable Data Sourcing
  We pride ourselves on our dual-sourcing strategy that ensures the highest levels of data accuracy and relevance:

  • Real-Time Information from 10 Active Publication Sites: Our databases are continuously updated with the latest information, sourced from ten active publication sites that provide real-time data.
  • Dedicated Contact Discovery Team: Complementing our automated sources, our dedicated Contact Discovery Team conducts thorough research and investigations, ensuring that every piece of data is accurate and reliable. This two-pronged approach guarantees that our Ecommerce Data is both up-to-date and relevant, providing you with a solid foundation for your business strategies.

  Primary Use Cases Across Industries

Our Ecommerce Data is versatile and can be leveraged across various industries for multiple applications: - Precision Targeting in Marketing: Create personalized marketing campaigns based on detailed shopping cart activities, ensuring that your outreach resonates with individual customer preferences. - Sales Enrichment: Sales teams can benefit from enriched client profiles that include comprehensive contact information, enabling them to connect with key decision-makers more effectively. - Market Research and Analytics: Research and analytics departments can use this data for in-depth market studies and trend analyses, gaining valuable insights into consumer behavior and market dynamics.

Global Coverage for Comprehensive Engagement

Our Ecommerce Data spans across the globe, providing you with extensive reach and the ability to engage with customers in diverse regions: - North America: United States, Canada, Mexico - Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Sweden, and more - Asia: China, Japan, India, South Korea, Singapore, Malaysia, and more - South America: Brazil, Argentina, Chile, Colombia, and more - Africa: South Africa, Nigeria, Kenya, Egypt, and more - Australia and Oceania: Australia, New Zealand - Middle East: United Arab Emirates, Saudi Arabia, Israel, Qatar, and more

Comprehensive Employee and Revenue Size Information

Our dataset also includes detailed information on: - Employee Size: Whether you’re targeting small businesses or large corporations, our data covers all employee sizes, from startups to global enterprises. - Revenue Size: Gain insights into companies across various revenue brackets, enabling you to segment the market more effectively and target your efforts where they will have the most impact.

Seamless Integration into Broader Data Offerings

Our Ecommerce Data is not just a standalone product; it is a critical piece of our broader data ecosystem. It seamlessly integrates with our comprehensive suite of business and consumer datasets, offering you a holistic approach to data-driven decision-making: - Tailored Packages: Choose customized data packages that meet your specific business needs, combining Ecommerce Data with other relevant datasets for a complete view of your market. - Holistic Insights: Whether you are looking for industry-specific details or a broader market overview, our integrated data solutions provide you with the insights necessary to stay ahead of the competition and make informed business decisions.

Elevate Your Business Decisions with Our Ecommerce Data

In essence, our Ecommerce Data is more than just a collection of contacts—it’s a strategic tool designed to give you a competitive edge in understanding and engaging your target audience. By leveraging the power of this comprehensive dataset, you can elevate your business decisions, enhance customer interactions, and navigate the digital landscape with confi...

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.

1) Data Introduction • The Grocery Sales Database is a retail dataset of relational tables of grocery store sales transactions, customer information, product details, employee records, geographic information, and more across cities and countries.

2) Data Utilization (1) Grocery Sales Database has characteristics that: • The data consists of seven tables, including product categories, city/country information, customer/employee/product details, and sales details, each of which is interconnected by a unique ID. • Sales data are linked to products, customers, employees, and regions, enabling a variety of business analyses, including monthly sales, popular products, customer behavior, and regional performance. (2) Grocery Sales Database can be used to: • Analysis of sales trends and popular products: It can be used to identify trends and derive best-selling products by analyzing sales by monthly and category and sales by product. • Customer Segmentation and Marketing Strategy: Define customer groups based on customer frequency of purchases, total expenditure, and regional information and apply them to developing customized marketing and promotion strategies.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Global Fashion Retail Analytics Dataset

📊 Dataset Overview

This synthetic dataset simulates two years of transactional data for a multinational fashion retailer, featuring:
- 📈 4+ million sales records
- 🏪 35 stores across 7 countries:
🇺🇸 United States | 🇨🇳 China | 🇩🇪 Germany | 🇬🇧 United Kingdom | 🇫🇷 France | 🇪🇸 Spain | 🇵🇹 Portugal

Currencies Covered: Each transaction includes detailed currency information, covering multiple currencies:
💵 USD (United States) | 💶 EUR (Eurozone) | 💴 CNY (China) | 💷 GBP (United Kingdom)

Designed for Detailed and Multifaceted Analysis

🌐 Geographic Sales Comparison
Gain insights into how sales performance varies between regions and countries, and identify trends that drive success in different markets.

👥 Analyze Staffing and Performance
Evaluate store staffing ratios and analyze the impact of employee performance on store success.

🛍️ Customer Behavior and Segmentation
Understand regional customer preferences, analyze demographic factors such as age and occupation, and segment customers based on their purchasing habits.

💱 Multi-Currency Analysis
Explore how transactions in different currencies (USD, EUR, CNY, GBP) are handled, analyze currency exchange effects, and compare sales across regions using multiple currencies.

👗 Product Trends
Assess how product categories (e.g., Feminine, Masculine, Children) and specific product attributes (size, color) perform across different regions.

🎯 Pricing and Discount Analysis
Study how different pricing models and discounts affect sales and customer decisions across diverse geographies.

📊 Advanced Cross-Country & Currency Analysis
Conduct complex, multi-dimensional analytics that interconnect countries, currencies, and sales data, identifying hidden correlations between economic factors, regional demand, and financial performance.

Synthetic Data Advantages

Generated using algorithms, it simulates real-world retail dynamics while ensuring privacy.

• Privacy-Safe: All customer and employee data is artificially generated to ensure privacy and compliance with data protection regulations. Personal details, such as emails and phone numbers, are anonymized.
• Scalable Patterns: The data replicates real-world retail dynamics, ensuring scalability of patterns for testing algorithms and analytics models.
• Controlled Complexity: The dataset introduces intentional complexities (e.g., missing job titles, inconsistent phone number formats) to offer a more realistic and challenging exploration experience for exploratory data analysis.
• Customizable for Various Use Cases: Whether you're performing sales forecasting, employee performance analysis, or customer segmentation, this dataset offers a flexible foundation for diverse analytical tasks.

This dataset is an ideal resource for retail analysts, data scientists, and business intelligence professionals aiming to explore multinational retail data, optimize operations, and uncover new insights into customer behavior, sales trends, and employee efficiency.

This dataset is about books. It has 1 row and is filtered where the book is Jump start your business brain : win more, lose less and make more money, with your sales, marketing and business development. It features 7 columns including author, publication date, language, and book publisher.

Success.ai’s LinkedIn Data Solutions offer unparalleled access to a vast dataset of 700 million public LinkedIn profiles and 70 million LinkedIn company records, making it one of the most comprehensive and reliable LinkedIn datasets available on the market today. Our employee data and LinkedIn data are ideal for businesses looking to streamline recruitment efforts, build highly targeted lead lists, or develop personalized B2B marketing campaigns.

Whether you’re looking for recruiting data, conducting investment research, or seeking to enrich your CRM systems with accurate and up-to-date LinkedIn profile data, Success.ai provides everything you need with pinpoint precision. By tapping into LinkedIn company data, you’ll have access to over 40 critical data points per profile, including education, professional history, and skills.

Key Benefits of Success.ai’s LinkedIn Data: Our LinkedIn data solution offers more than just a dataset. With GDPR-compliant data, AI-enhanced accuracy, and a price match guarantee, Success.ai ensures you receive the highest-quality data at the best price in the market. Our datasets are delivered in Parquet format for easy integration into your systems, and with millions of profiles updated daily, you can trust that you’re always working with fresh, relevant data.

API Integration: Our datasets are easily accessible via API, allowing for seamless integration into your existing systems. This ensures that you can automate data retrieval and update processes, maintaining the flow of fresh, accurate information directly into your applications.

Global Reach and Industry Coverage: Our LinkedIn data covers professionals across all industries and sectors, providing you with detailed insights into businesses around the world. Our geographic coverage spans 259M profiles in the United States, 22M in the United Kingdom, 27M in India, and thousands of profiles in regions such as Europe, Latin America, and Asia Pacific. With LinkedIn company data, you can access profiles of top companies from the United States (6M+), United Kingdom (2M+), and beyond, helping you scale your outreach globally.

Why Choose Success.ai’s LinkedIn Data: Success.ai stands out for its tailored approach and white-glove service, making it easy for businesses to receive exactly the data they need without managing complex data platforms. Our dedicated Success Managers will curate and deliver your dataset based on your specific requirements, so you can focus on what matters most—reaching the right audience. Whether you’re sourcing employee data, LinkedIn profile data, or recruiting data, our service ensures a seamless experience with 99% data accuracy.

• Best Price Guarantee: We offer unbeatable pricing on LinkedIn data, and we’ll match any competitor.
• Global Scale: Access 700 million LinkedIn profiles and 70 million company records globally.
• AI-Verified Accuracy: Enjoy 99% data accuracy through our advanced AI and manual validation processes.
• Real-Time Data: Profiles are updated daily, ensuring you always have the most relevant insights.
• Tailored Solutions: Get custom-curated LinkedIn data delivered directly, without managing platforms.
• Ethically Sourced Data: Compliant with global privacy laws, ensuring responsible data usage.
• Comprehensive Profiles: Over 40 data points per profile, including job titles, skills, and company details.
• Wide Industry Coverage: Covering sectors from tech to finance across regions like the US, UK, Europe, and Asia.

Key Use Cases:

• Sales Prospecting and Lead Generation: Build targeted lead lists using LinkedIn company data and professional profiles, helping sales teams engage decision-makers at high-value accounts.
• Recruitment and Talent Sourcing: Use LinkedIn profile data to identify and reach top candidates globally. Our employee data includes work history, skills, and education, providing all the details you need for successful recruitment.
• Account-Based Marketing (ABM): Use our LinkedIn company data to tailor marketing campaigns to key accounts, making your outreach efforts more personalized and effective.
• Investment Research & Due Diligence: Identify companies with strong growth potential using LinkedIn company data. Access key data points such as funding history, employee count, and company trends to fuel investment decisions.
• Competitor Analysis: Stay ahead of your competition by tracking hiring trends, employee movement, and company growth through LinkedIn data. Use these insights to adjust your market strategy and improve your competitive positioning.
• CRM Data Enrichment: Enhance your CRM systems with real-time updates from Success.ai’s LinkedIn data, ensuring that your sales and marketing teams are always working with accurate and up-to-date information.
• Comprehensive Data Points for LinkedIn Profiles: Our LinkedIn profile data includes over 40 key data points for every individual and company, ensuring a complete understandin...

Survey of innovation and business strategy, percentage of sales from highest selling good or service, by North American Industry Classification System (NAICS) and enterprise size for Canada and regions from 2009 to today.

Use our Owler companies dataset, a sales intelligence and business information research company, to map your ecosystem, and find market trends and investment opportunities. Access a database of competitors, revenue, employees, and funding for any company. Depending on your needs, you may purchase the entire dataset or a customized subset. The Owler companies information dataset offers public information on all companies listed in Owler. The dataset includes all major data points: Company size Revenue News Key executives Location Website and more. Freshness configuration: monthly refreshes refresh rate of up to 8 million records a month

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Retail Sales in the United States increased 0.60 percent in June 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.

Analysis of ‘Top 100 Biggest Restaurant Chains 2021’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/johnharshith/top-100-biggest-restaurant-chains-2021 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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Context

This Dataset contains the data compiled by Technomic and reported by Restaurant Business magazine, the top 100 most popular restaurant chains in the United States in terms of the latest 2020 sales which were responsible for three-fourths of the total industry sales growth last year.

Content

The data was obtained from the Restaurant Business magazine website. The columns contain stats such as position of restaurant chains, 2020 U.S. sales, YOY sales change, 2020 U.S. units, YOY unit change, segment and menu types. This data can be found from the website https://www.restaurantbusinessonline.com/top-500-chains with detailed analysis.

Inspiration

While 2016 was a rough year for chain restaurants, more than half of the industry wealth of $521.9 billion still comes from the Top 500 chains and nearly 94% of those dollars and 93% of those units are represented in the Top 250. These stats have made me curious to find out interesting profit patterns from this dataset.

Dataset Usage

This Dataset can be used to study interesting patterns using various classification techniques and arrive at some exciting conclusions. One can create amazing visualisations using the different columns of the dataset. We can also find out and design an effective business model from the given dataset and take one step closer to your most successful restaurant chain startup ever!

--- Original source retains full ownership of the source dataset ---

1) Data Introduction • The Amazon Products Sales Dataset 2023 is a large e-commerce dataset that summarizes various product information in a tabular format, including product name, price, rating, discount information, images, and links by 142 major categories collected from Amazon's website.

2) Data Utilization (1) Amazon Products Sales Dataset 2023 has characteristics that: • Each row contains 10 key attributes, including product name, main/subcategory, image, Amazon link, rating, number of ratings, discount price, and actual price. • The data encompasses a wide range of products and is structured to enable multi-faceted analysis such as price policy, customer evaluation, and trend by category. (2) Amazon Products Sales Dataset 2023 can be used to: • Product Recommendation and Marketing Strategy: Use rating, price, and category data to develop a customized recommendation system, analyze popular products, and establish a category-specific marketing strategy. • Price and Discount Policy Analysis—Based on discounted prices and actual prices, ratings, reviews, etc., it can be applied to effective pricing policies, promotion strategies, market competitiveness analyses, and more.