Dataset Card for [best-selling-video-games]

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[More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/arjunpatel/best-selling-video-games.

Amazon Best Seller data contains information about the best-selling products on Amazon; this information is very useful for monitoring the best-selling products in various categories and sub-categories.

A. Usecase/Applications possible with the data:

1. Competition Monitoring: Amazon's Best Sellers Data contains the data of best-selling goods on Amazon, which features a lot about the top e-commerce trends. Direct competition with these items might be challenging, but the Best Sellers list can be a source of inspiration for new products and help e-commerce merchants keep ahead of the game. Getting your item onto the Best Sellers list and keeping it there is one of the most reliable strategies to ensure sales for your company. Once a product makes the Best Sellers list, e-commerce businesses increasingly use web scraping to keep track of new items and change their own to compete.

2. New Product Launch: Amazon Best Sellers Data is critical when it comes to launching a new product or repositioning existing products. Indeed, Amazon's best seller rank data can be used as a guide, indicating when you and your products are on the right track.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

Small, clean dataset for learning purposes.

Data sourced from QSR Magazine, a business-to-business magazine in the quick service restaurant industry. This dataset includes the top 50 fast food chains in the U.S. in 2020. Contains information on the total sales, sales per unit, franchise units, company owned units, and unit change from 2018.

Columns include: - Company Name - Category (pizza, burger, etc) - Sales in Millions (2019) - Sales Per Unit in Thousands (2019) - # of Franchised Units (2019) - # of Company Owned Units (2019) - # of Total Units (2019) - Unit # Change from 2018

Success.ai’s B2B Marketing Data and Contact Data for Global Marketing Leaders empowers businesses to connect with chief marketing officers (CMOs), marketing strategists, and industry decision-makers worldwide. With access to over 170M verified profiles, including work emails and direct phone numbers, this dataset ensures your outreach efforts reach the right audience effectively.

Our AI-powered platform continuously updates and validates contact data to maintain 99% accuracy, providing actionable insights for marketing campaigns, sales strategies, and recruitment initiatives. Whether you’re targeting CMOs in Fortune 500 companies or strategists in innovative startups, Success.ai delivers reliable data tailored to meet your business goals.

Key Features of Success.ai’s Marketing Leader Contact Data - Comprehensive Coverage Across the Marketing Industry Access profiles for marketing leaders across diverse industries and regions:

Chief Marketing Officers (CMOs): Decision-makers shaping global marketing strategies. Marketing Strategists: Experts driving innovative campaigns and business growth. Digital Marketing Heads: Leaders overseeing digital transformation initiatives. Brand Managers: Professionals managing brand identity and outreach efforts. Content and SEO Specialists: Key contributors to content strategy and visibility.

• Verified Accuracy with Continuous Updates

AI-Validated Accuracy: Industry-leading AI technology ensures every contact detail is verified. Real-Time Profile Updates: Data is continuously refreshed to reflect the most current information. Reliable Engagement: Minimized bounce rates for seamless communication with decision-makers.

• Tailored Data Delivery Options Choose the delivery method that aligns with your operational requirements:

API Integration: Seamlessly integrate contact data into your CRM or marketing platforms. Custom Flat Files: Receive datasets customized to your specifications, ready for immediate use.

Why Choose Success.ai for Marketing Data?

• Best Price Guarantee We provide the most competitive pricing in the industry, ensuring the best value for global, verified contact data.

• Global Compliance and Ethical Practices Our data collection and processing adhere to strict compliance standards, including GDPR, CCPA, and other regional data regulations, ensuring ethical and secure usage.

• Strategic Advantages for Your Business

  Precise Marketing Campaigns: Create highly targeted campaigns that resonate with marketing leaders. Effective Sales Outreach: Accelerate sales processes with direct access to CMOs and strategists. Recruitment Efficiency: Source top-tier marketing talent with verified contact data. Market Intelligence: Leverage enriched data insights to understand industry trends and optimize strategies. Partnership Development: Build and nurture relationships with influential marketing professionals.

• Data Highlights 170M+ Verified Professional Profiles 50M Work Emails 700M Global Professional Profiles 70M Verified Company Profiles

Key APIs for Enhanced Functionality

• Enrichment API Keep your contact database updated with real-time enrichment capabilities, ensuring relevance for dynamic outreach efforts.

• Lead Generation API Maximize your lead generation campaigns with accurate, verified data, including contact information for global marketing leaders. Our API supports up to 860,000 API calls per day, enabling robust scalability for your business.

• Use Cases

1. Targeted Marketing Campaigns Reach CMOs and marketing strategists with personalized campaigns designed to deliver measurable ROI.

2. Sales Pipeline Acceleration Engage directly with decision-makers to shorten sales cycles and boost deal closure rates.

3. Talent Recruitment Identify and recruit top-tier marketing talent to strengthen your team.

4. Partnership Building Establish meaningful connections with global marketing leaders to foster collaboration.

5. Strategic Planning Utilize detailed firmographic and demographic insights for data-driven decision-making.

What Makes Success.ai Stand Out?

• Unmatched Data Quality: AI-driven verification ensures 99% accuracy for all profiles. Comprehensive Reach: Covering marketing professionals across various industries and regions worldwide.
• Flexible Integration Options: Customizable delivery formats to suit your business needs.
• Ethical and Compliant Data: Fully aligned with global data protection regulations.

Success.ai’s B2B Contact Data for Global Marketing Leaders is your ultimate solution for connecting with top-tier marketing professionals. From CMOs driving global strategies to strategists shaping impactful campaigns, our database ensures you reach the right audience to grow your business.

No one beats us on price. Period.

Introduction

PlayStation Statistics: PlayStation, developed by Sony Interactive Entertainment, has significantly influenced the gaming industry since the launch of its first console on December 3, 1994. As of December 31, 2024, cumulative sales of PlayStation consoles have surpassed 450 million units worldwide. The PlayStation 2 remains the best-selling console with over 160 million units sold, followed by the PlayStation 4 at 117 million units, the original PlayStation at 102.4 million units, and the PlayStation 3 at 87.4 million units. The PlayStation Portable (PSP) has sold more than 76.4 million units, while the PlayStation 5 has reached over 74.9 million units in sales.

In terms of software, the PlayStation 2 leads with over 1.5 billion units sold, and the original PlayStation has sold over 962 million software units. Financially, PlayStation reported a revenue of USD 27.5 billion for the fiscal year ending March 31, 2024. These figures underscore PlayStation's enduring impact and success in the gaming industry.

PlayStation's impact on the gaming industry is profound. It has shaped the landscape of interactive entertainment and maintained a loyal global fan base.

Introduction

Walmart Statistics: Walmart Inc., established by Sam Walton in 1962, has grown to become the world's largest retailer, renowned for its "Everyday Low Prices" strategy. Headquartered in Bentonville, Arkansas, Walmart operates over 10,500 stores and numerous eCommerce websites across 19 countries, serving approximately 240 million customers each week.

The company's vast product range includes groceries, apparel, electronics, and more, catering to diverse consumer needs. Beyond retail, Walmart is committed to sustainability, aiming for 100% renewable energy by 2030 and zero emissions by 2040. With over 2.1 million associates globally, Walmart remains a pivotal force in retail and employment, continually evolving to meet modern challenges and opportunities.

This article will guide you accordingly, as it includes more information about the global market based on recent data and analyses from different sources.

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.

Introduction

Bayer Statistics: One of the top players within the German-based multinational pharmaceutical and life sciences company, Bayer AG, has constantly developed its market position due to innovation, diversification, and global expansion. Like many organizations oriented towards health and agriculture, Bayer has several pharmaceutical and consumer health products, as well as crop science solutions that enable health and food sustainability around the world.

In 2024, Bayer's statistics indicators are attributed to the strategy, with the focus placed on revenue growth, market share, and investments in research and development.

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.

Introduction

Smartphone Sales Statistics: It's hard to imagine life without smartphones. They have become an essential part of our daily lives. Smartphones are everywhere, with over 80% of people in the United States using them for browsing the web, conducting business, messaging, and communication.

Despite being a relatively recent invention, smartphones can do a lot, from recognizing faces and enabling video chats to take high-quality photos and effortless internet browsing. Let's take a closer look at smartphone sales statistics

Introduction

Dropshipping Statistics: Dropshipping remains one of the most accessible eCommerce models worldwide, combining minimal upfront investment with global market reach. In 2024, the global dropshipping market is estimated at approximately $351.8 billion, marking a +23.6% increase from 2023’s $284.6 billion. Forecasts predict growth to $435.0 billion in 2025 and $537.8 billion in 2026. Another source estimates the market at $243.42 billion in 2024, rising to $372.47 billion in 2025 and $476.1 billion by 2026. Over 27% of online retailers now use dropshipping as their main fulfillment method, while dropshipping accounts for 23% of all online sales.

Profit margins typically range between 15% and 30%. Market penetration varies regionally: electronics comprise 30% of the North American dropshipping market, Asia-Pacific holds over 35% market share, and top niches include fashion ($802 billion), beauty & personal care ($672.2 billion), and home & garden ($130 billion) by 2025.

Here are the latest and greatest dropshipping statistics that may help entrepreneurs, investors, and enthusiasts.

Find details of Up Top Auto Parts Sales And Trucking Limited Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

Taiwan Business Sales: Computer and Information Services (CI) data was reported at 84,526.706 NTD mn in Mar 2019. This records a decrease from the previous number of 99,834.471 NTD mn for Dec 2018. Taiwan Business Sales: Computer and Information Services (CI) data is updated quarterly, averaging 69,917.291 NTD mn from Jun 2007 (Median) to Mar 2019, with 48 observations. The data reached an all-time high of 99,834.471 NTD mn in Dec 2018 and a record low of 51,546.104 NTD mn in Mar 2009. Taiwan Business Sales: Computer and Information Services (CI) data remains active status in CEIC and is reported by Ministry of Economic Affairs. The data is categorized under Global Database’s Taiwan – Table TW.H012: Business Sales: Ministry of Economics Affairs.

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.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

As the fashion e-commerce markets rapidly develop, tens of thousands of products are registered daily on e-commerce platforms. Individual sellers register products after setting up a product category directly on a fashion e-commerce platform. However, many sellers fail to find a suitable category and mistakenly register their products under incorrect ones. Precise category matching is important for increasing sales through search optimization and accurate product exposure. However, manually correcting registered categories is time-consuming and costly for platform managers. To resolve this problem, this study proposes a methodology for fashion e-commerce product classification based on multi-modal deep learning and transfer learning. Through the proposed methodology, three challenges in classifying fashion e-commerce products are addressed. First, the issue of extremely biased e-commerce data is addressed through under-sampling. Second, multi-modal deep learning enables the model to simultaneously use input data in different formats, which helps mitigate the impact of noisy and low-quality e-commerce data by providing richer information.Finally, the high computational cost and long training times involved in training deep learning models with both image and text data are mitigated by leveraging transfer learning. In this study, three strategies for transfer learning to fine-tune the image and text modules are presented. In addition, five methods for fusing feature vectors extracted from a single modal into one and six strategies for fine-tuning multi-modal models are presented, featuring a total of 14 strategies. The study shows that multi-modal models outperform unimodal models based solely on text or image. It also suggests the optimal conditions for classifying e-commerce products, helping fashion e-commerce practitioners construct models tailored to their respective business environments more efficiently.

United States PPI: Svcs: Info: PI: NB: PP: PS: Advertising Sales (PA) data was reported at 104.152 Dec2014=100 in Dec 2022. This stayed constant from the previous number of 104.152 Dec2014=100 for Nov 2022. United States PPI: Svcs: Info: PI: NB: PP: PS: Advertising Sales (PA) data is updated monthly, averaging 104.000 Dec2014=100 from Dec 2014 (Median) to Dec 2022, with 97 observations. The data reached an all-time high of 108.100 Dec2014=100 in Sep 2018 and a record low of 100.000 Dec2014=100 in Dec 2014. United States PPI: Svcs: Info: PI: NB: PP: PS: Advertising Sales (PA) data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Services: Information.

Korea CI Sales: Knowledge Information (Info) data was reported at 12,342,103.000 KRW mn in 2015. This records an increase from the previous number of 11,343,642.000 KRW mn for 2014. Korea CI Sales: Knowledge Information (Info) data is updated yearly, averaging 7,242,686.000 KRW mn from Dec 2005 (Median) to 2015, with 11 observations. The data reached an all-time high of 12,342,103.000 KRW mn in 2015 and a record low of 3,040,869.000 KRW mn in 2005. Korea CI Sales: Knowledge Information (Info) data remains active status in CEIC and is reported by Korea Creative Content Agency. The data is categorized under Global Database’s Korea – Table KR.H087: Contents Industry Sales: by Industry.

United States PPI: Svcs: Info: PI: NB: PP: PS: Subs & Single Copy Sales (SS) data was reported at 103.868 Dec2014=100 in Dec 2022. This stayed constant from the previous number of 103.868 Dec2014=100 for Nov 2022. United States PPI: Svcs: Info: PI: NB: PP: PS: Subs & Single Copy Sales (SS) data is updated monthly, averaging 102.900 Dec2014=100 from Dec 2014 (Median) to Dec 2022, with 97 observations. The data reached an all-time high of 112.427 Dec2014=100 in Oct 2022 and a record low of 100.000 Dec2014=100 in Jun 2015. United States PPI: Svcs: Info: PI: NB: PP: PS: Subs & Single Copy Sales (SS) data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Services: Information.

Japan Information Service Sales: SDP: Software Products data was reported at 136,582.000 JPY mn in Sep 2018. This records an increase from the previous number of 100,984.000 JPY mn for Aug 2018. Japan Information Service Sales: SDP: Software Products data is updated monthly, averaging 92,208.500 JPY mn from Feb 2007 (Median) to Sep 2018, with 140 observations. The data reached an all-time high of 197,192.000 JPY mn in Mar 2008 and a record low of 54,232.000 JPY mn in May 2011. Japan Information Service Sales: SDP: Software Products 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.H016: Information Services Sales.