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!

ZARA is one of the world's largest apparel and fashion retailers. The CrawlFeeds team has successfully extracted over 10,000 product records from ZARA USA, including titles, prices, images, availability, and more.

You can customize the dataset to match your specific needs, such as format adjustments, re-extraction, or additional data points.

If you're looking for retail data solutions, you can customize the current dataset or extract ZARA product data from other countries like Spain, the UK, and India.

Find here latest zara us products listings (https://crawlfeeds.com/datasets/download-the-complete-zara-product-dataset)

Retail Sales - Table 620-67001 : Total Retail Sales

A listing of all retail food stores which are licensed by the Department of Agriculture and Markets.

This statistic shows the value of the retail analytics market worldwide in 2016, with a forecast from 2017 to 2022. The global retail analytics market was valued at **** billion U.S. dollars in 2016, and was forecast to reach about *** billion dollars by 2022.

This table contains 3 series, with data for years 2016 - 2017 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Sales (3 items: Retail trade; Electronic shopping and mail-order houses; Retail E-commerce sales).

A detailed dataset exploring the retail industry in 2025, including market size, store counts, revenue trends, AI integration, and consumer behavior across the US and globally.

Grocery Retail

## Overview

Grocery Retail is a dataset for object detection tasks - it contains Grocery annotations for 685 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Success.ai’s Retail Data for the Retail Sector in North America offers a comprehensive dataset designed to connect businesses with key players across the diverse retail industry. Covering everything from department stores and supermarkets to specialty shops and e-commerce platforms, this dataset provides verified contact details, business locations, and leadership profiles for retail companies in the United States, Canada, and Mexico.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, marketing, and business development efforts are powered by accurate, continuously updated, and AI-validated data.

Backed by our Best Price Guarantee, this solution empowers businesses to thrive in North America’s competitive retail landscape.

Why Choose Success.ai’s Retail Data for North America?

1. Verified Contact Data for Precision Outreach

   • Access verified phone numbers, work emails, and LinkedIn profiles of retail executives, store managers, and decision-makers.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and efficient campaign execution.

2. Comprehensive Coverage Across Retail Segments

   • Includes profiles of retail businesses across major markets, from large department stores and grocery chains to boutique retailers and online platforms.
   • Gain insights into the operational dynamics of retail hubs in cities such as New York, Los Angeles, Toronto, and Mexico City.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, new store openings, market expansions, and shifts in consumer preferences.
   • Stay aligned with evolving industry trends and emerging opportunities in the North American retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other privacy regulations, ensuring responsible and lawful use of data in your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, marketing directors, and operations managers across the North American retail sector.
• 30M Company Profiles: Access firmographic data, including revenue ranges, store counts, and geographic footprints.
• Store Location Data: Pinpoint retail outlets, regional offices, and distribution centers to refine supply chain and marketing strategies.
• Leadership Contact Details: Connect with CEOs, CMOs, and procurement officers influencing retail operations and vendor selections.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles

   • Identify and engage with store owners, category managers, and marketing directors shaping customer experiences and product strategies.
   • Target professionals responsible for inventory planning, vendor contracts, and store performance.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (luxury, grocery, e-commerce), geographic location, company size, or revenue range.
   • Tailor outreach to align with regional market trends, customer demographics, and operational priorities.

3. Market Trends and Operational Insights

   • Analyze trends such as online shopping growth, sustainability practices, and supply chain optimization.
   • Leverage insights to refine product offerings, identify partnership opportunities, and design effective campaigns.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technology solutions to retail procurement teams, marketing departments, and operations managers.
   • Build relationships with retailers seeking innovative tools, efficient supply chain solutions, or unique product offerings.

2. Market Research and Consumer Insights

   • Analyze retail trends, customer behaviors, and seasonal demands to inform marketing strategies and product launches.
   • Benchmark against competitors to identify gaps, emerging niches, and growth opportunities.

3. E-Commerce and Digital Strategy Development

   • Target e-commerce managers and digital transformation teams driving online retail initiatives and omnichannel integration.
   • Offer solutions to enhance online shopping experiences, logistics, and customer loyalty programs.

4. Recruitment and Workforce Solutions

   • Engage HR professionals and hiring managers in recruiting talent for store operations, customer service, or marketing roles.
   • Provide workforce optimization tools, training platforms, or staffing services tailored to retail environments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality retail data at competitive prices, ensuring strong ROI for your marketing and outreach efforts in North America.

2. Seamless Integration
   ...

Graph and download economic data for Retail Sales: Nonstore Retailers (MRTSSM454USS) from Jan 1992 to Jun 2025 about retail trade, sales, retail, and USA.

A global survey from Capgemini showed that retail companies were lagging behind consumer products enterprises in the use of data. The gap was significant in the automation of processes and in data collecting: only ** percent of retailers automated data collection, against ** percent of consumer goods companies. However, one in **** organizations in both categories reported to have implemented practices involving data engineering, machine learning, and DevOps.

The rapid ascent of e-commerce and omnichannel strategies is reshaping consumer engagement and purchasing patterns, driving a wave of transformation across the retail trade sector. As of 2025, the sector is expected to log $7.4 trillion in revenue, although its growth is anticipated to decelerate slightly to 0.4% in the current year. Gen Z and millennials have championed the digital shopping revolution, pushing retailers to prioritize online sales and customer engagement platforms. However, brick-and-mortar stores retain a pivotal role in supporting ongoing customer engagement alongside the online momentum as retailers blend physical and digital experiences. As automation has augmented efficiency across operations, retailers have also strategically diversified product lines and incorporated sustainability into their brands to meet changing consumer expectations. Over the past five years, the retail sector has seen a compound annual growth rate of 2.2%, which underscores the impact of diversified strategies in maintaining momentum. The adoption of automation has produced mixed results. Self-checkout systems, for example, have reduced payroll expenses for businesses while streamlining the customer experience, though several studies have reported that some customer segments dislike self-checkout due to technological glitches and some retailers have struggled with implementation and reported a rise in theft. Major chains like Target have honed their product diversification strategies, transforming their stores into one-stop shops that blend essential goods with discretionary items and healthcare, driving up revenue in multiple categories. Sustainability is another theme of the current period, with the sector’s commitment marked by increased budgets for eco-friendly practices and a growing market for pre-owned goods. Despite high inflation during the period giving way to high interest rates that stayed stagnant for a year before beginning to fall again in September 2024, retailers managed to navigate the challenges of economic fluctuations and keep consumer interest high through diversification. A projected compound annual growth rate of 0.9% for the next five years would set revenue on a steady path toward an expected $7.7 trillion through the end of 2030. Artificial intelligence is set to further revolutionize retail operations, enhancing stock management, logistics and consumer personalization. Augmented and virtual reality technologies will prove integral to engaging the tech-savvy younger generations by offering novel ways to interact with products before purchase. However, global trade tensions and tariffs could challenge profitability as retailers manage higher import costs. Reverse logistics will thrive as consumers’ eco-consciousness continues to grow, turning returns into revenue opportunities and aligning with trends toward sustainable consumption. The sector’s profit is expected to remain steady over the next five years, bolstered by consumers’ willingness to trade up to items that mix luxury and affordability.

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

Retail Sales: Other Services data was reported at 60,205.069 VND bn in Mar 2025. This records an increase from the previous number of 57,704.476 VND bn for Feb 2025. Retail Sales: Other Services data is updated monthly, averaging 37,805.584 VND bn from Jan 2010 (Median) to Mar 2025, with 181 observations. The data reached an all-time high of 63,480.068 VND bn in Dec 2024 and a record low of 11,273.432 VND bn in Jul 2010. Retail Sales: Other Services data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.H001: Retail Sales.

Retail Market Outlook

According to our latest research, the global retail market size reached USD 29.4 trillion in 2024, with a compound annual growth rate (CAGR) of 5.1% recorded over recent years. This robust expansion is primarily driven by evolving consumer preferences, digital transformation, and the rapid adoption of omnichannel retail strategies. Based on current growth trends and our comprehensive analysis, the global retail market is forecasted to achieve a value of USD 46.1 trillion by 2033, underscoring the sector's pivotal role in the global economy and its consistent appeal across diverse demographics and geographies.

A significant growth factor for the retail market is the accelerated shift towards digitalization and e-commerce. The proliferation of internet connectivity, smartphone adoption, and advanced payment solutions has fundamentally transformed how consumers interact with retail brands. Retailers are leveraging artificial intelligence, big data analytics, and personalized marketing to enhance the customer experience and drive sales. The integration of online and offline channels, commonly known as omnichannel retailing, allows businesses to offer seamless shopping experiences, enabling consumers to research, purchase, and return products across multiple platforms. This digital evolution is not only attracting tech-savvy younger generations but also expanding the reach of retail businesses to previously underserved markets, thereby fueling overall industry growth.

Another crucial driver is the increasing focus on sustainability and ethical consumption. Modern consumers are becoming more environmentally conscious, demanding transparency in sourcing, production, and distribution processes. Retailers are responding by adopting sustainable supply chains, eco-friendly packaging, and responsible sourcing practices. This trend is particularly prominent in the apparel, food and beverage, and health and personal care segments, where ethical considerations significantly influence purchasing decisions. Retailers who prioritize sustainability are gaining a competitive edge, building brand loyalty, and attracting a growing segment of consumers willing to pay a premium for ethically produced goods. This shift towards responsible retailing is expected to further accelerate market growth in the coming years.

Additionally, the expansion of organized retail formats and the modernization of traditional retail infrastructure are propelling the market forward. Emerging economies are witnessing a transformation from unorganized, fragmented retail landscapes to more structured, organized formats such as supermarkets, hypermarkets, and specialty stores. This transition is driven by urbanization, rising disposable incomes, and shifting lifestyles, particularly in Asia Pacific and Latin America. The entry of international retail giants and the rise of homegrown organized retail chains are enhancing product accessibility, variety, and quality. As organized retail continues to penetrate deeper into rural and semi-urban areas, it is expected to unlock new growth avenues and contribute significantly to the overall expansion of the global retail market.

From a regional perspective, Asia Pacific remains the dominant force in the global retail market, accounting for the largest share in 2024. The region's growth is underpinned by rapid urbanization, a burgeoning middle class, and high consumer spending, particularly in China and India. North America and Europe continue to exhibit steady growth, driven by technological innovation and mature retail infrastructures. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by improving economic conditions and increasing investments in retail development. This diverse regional outlook highlights the global nature of the retail industry and the multitude of opportunities available for market participants across different geographies.

Product Type Analysis

The retail market is segmented by product type into food & bev

According to Cognitive Market Research, the global Artificial Intelligence in Retail market size is USD 4951.2 million in 2023and will expand at a compound annual growth rate (CAGR) of 39.50% from 2023 to 2030.

Enhanced customer personalization to provide viable market output
Demand for online remains higher in Artificial Intelligence in the Retail market.
The machine learning and deep learning category held the highest Artificial Intelligence in Retail market revenue share in 2023.
North American Artificial Intelligence In Retail will continue to lead, whereas the Asia-Pacific Artificial Intelligence In Retail market will experience the most substantial growth until 2030.

Market Dynamics of the Artificial Intelligence in the Retail Market

Key Drivers for Artificial Intelligence in Retail Market

Enhanced Customer Personalization to Provide Viable Market Output

A primary driver of Artificial Intelligence in the Retail market is the pursuit of enhanced customer personalization. A.I. algorithms analyze vast datasets of customer behaviors, preferences, and purchase history to deliver highly personalized shopping experiences. Retailers leverage this insight to offer tailored product recommendations, targeted marketing campaigns, and personalized promotions. The drive for superior customer personalization not only enhances customer satisfaction but also increases engagement and boosts sales. This focus on individualized interactions through A.I. applications is a key driver shaping the dynamic landscape of A.I. in the retail market.

January 2023 - Microsoft and digital start-up AiFi worked together to offer Smart Store Analytics. It is a cloud-based tracking solution that helps merchants with operational and shopper insights for intelligent, cashierless stores.

Source-techcrunch.com/2023/01/10/aifi-microsoft-smart-store-analytics/

Improved Operational Efficiency to Propel Market Growth

Another pivotal driver is the quest for improved operational efficiency within the retail sector. A.I. technologies streamline various aspects of retail operations, from inventory management and demand forecasting to supply chain optimization and cashier-less checkout systems. By automating routine tasks and leveraging predictive analytics, retailers can enhance efficiency, reduce costs, and minimize errors. The pursuit of improved operational efficiency is a key motivator for retailers to invest in AI solutions, enabling them to stay competitive, adapt to dynamic market conditions, and meet the evolving demands of modern consumers in the highly competitive artificial intelligence (AI) retail market.

January 2023 - The EY Retail Intelligence solution, which is based on Microsoft Cloud, was introduced by the Fintech business EY to give customers a safe and efficient shopping experience. In order to deliver insightful information, this solution makes use of Microsoft Cloud for Retail and its technologies, which include image recognition, analytics, and artificial intelligence (A.I.).

Source-www.ey.com/en_gl/news/2023/01/ey-announces-launch-of-retail-solution-that-builds-on-the-microsoft-cloud-to-help-achieve-seamless-consumer-shopping-experiences

Key Restraints for Artificial Intelligence in Retail Market

Data Security Concerns to Restrict Market Growth

A prominent restraint in Artificial Intelligence in the Retail market is the pervasive concern over data security. As retailers increasingly rely on A.I. to process vast amounts of customer data for personalized experiences, there is a growing apprehension regarding the protection of sensitive information. The potential for data breaches and cyberattacks poses a significant challenge, as retailers must navigate the delicate balance between utilizing customer data for AI-driven initiatives and safeguarding it against potential security threats. Addressing these concerns is crucial to building and maintaining consumer trust in A.I. applications within the retail sector.

Key Trends for Artificial Intelligence in Retail Market

Surge in Voice-Enabled Shopping Interfaces Reshaping Retail Experiences

Voice-enabled A.I. assistants such as Amazon Alexa and Google Assistant are revolutionizing the way consumers engage with retail platforms. Shoppers can now utilize voice commands to search, compare, and purchase products, thereby streamlining and accelerating the buying process. Retailers...

United States Retail Sales: sa: Department stores ex Leased Departments (DS) data was reported at 12.360 USD bn in Sep 2018. This records a decrease from the previous number of 12.454 USD bn for Aug 2018. United States Retail Sales: sa: Department stores ex Leased Departments (DS) data is updated monthly, averaging 16.813 USD bn from Jan 1992 (Median) to Sep 2018, with 321 observations. The data reached an all-time high of 19.904 USD bn in Jan 2001 and a record low of 12.325 USD bn in Nov 2016. United States Retail Sales: sa: Department stores ex Leased Departments (DS) data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System. All estimates for department stores exclude leased departments.

Retail Sales m/m reflect a change in the US retail sails in the reported month compared to the previous one. The indicator is calculated based on statistics received from 5,000 retail stores of

Context

A retail bank would like to hire you to build a credit default model for their credit card portfolio. The bank expects the model to identify the consumers who are likely to default on their credit card payments over the next 12 months. This model will be used to reduce the bank’s future losses. The bank is willing to provide you with some sample datathat they can currently extract from their systems. This data set (credit_data.csv) consists of 13,444 observations with 14 variables.

Content

Based on the bank’s experience, the number of derogatory reports is a strong indicator of default. This is all that the information you are able to get from the bank at the moment. Currently, they do not have the expertise to provide any clarification on this data and are also unsure about other variables captured by their systems

United States Retail Sales: Miscellaneous Stores Retail data was reported at 11.376 USD bn in Jun 2018. This records a decrease from the previous number of 12.174 USD bn for May 2018. United States Retail Sales: Miscellaneous Stores Retail data is updated monthly, averaging 8.662 USD bn from Jan 1992 (Median) to Jun 2018, with 318 observations. The data reached an all-time high of 12.350 USD bn in Dec 1999 and a record low of 3.642 USD bn in Jan 1992. United States Retail Sales: Miscellaneous Stores Retail data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.