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

Context:

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

Inspiration:

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

Dataset Information:

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

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

Use Cases:

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

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

According to a survey conducted in May 2021, more than half of consumers in the older age groups (** and over) in the United States preferred big box/department stores and pharmacy/convenience stores for their retail purchases compared to consumers in the younger age groups. Online marketplaces were popular across both younger and older consumers. Over ********* of respondents in the age groups 18-34 and 35-54 stated to have used online marketplaces such as Amazon and Etsy in the past three months. This rate was even higher with those aged over ** (at ** percent).

Clothing Retail Market Outlook

The global clothing retail market size is projected to grow from $1.5 trillion in 2023 to reach approximately $2.3 trillion by 2032, exhibiting a compound annual growth rate (CAGR) of 4.8%. This growth is driven by several factors, including the rising disposable income, increasing fashion consciousness among consumers, and the rapid expansion of e-commerce platforms. The market size growth is a testament to the robust demand for apparel across various demographics and regions, with the market adapting to ever-changing consumer preferences and technological advancements.

One of the significant growth factors for the clothing retail market is the increasing disposable income among consumers, especially in emerging economies. As disposable income rises, consumers are more likely to spend on non-essential items, including fashionable clothing. This trend is further augmented by urbanization, where city dwellers have better access to retail outlets and are more exposed to fashion trends. Moreover, the growing middle class in countries like China and India has significantly boosted the demand for clothing, thereby contributing to the market's overall growth.

Another critical factor contributing to the market's growth is the increasing awareness and adoption of sustainable and ethical fashion. Consumers today are more conscientious about the environmental impact of their purchases and prefer brands that prioritize sustainability. This shift has prompted many retailers to adopt eco-friendly practices, such as using organic materials and ensuring fair labor practices. These initiatives not only attract environmentally conscious consumers but also help in building a brand's reputation, thereby driving sales and market growth.

The rapid expansion of e-commerce has also played a pivotal role in the growth of the clothing retail market. Online shopping offers convenience, a wider variety of choices, and competitive pricing, making it an attractive option for consumers. The integration of advanced technologies like artificial intelligence and augmented reality in online platforms has enhanced the shopping experience, allowing consumers to virtually try on clothes before making a purchase. This has significantly increased online sales, contributing to the overall growth of the clothing retail market.

The concept of Genderless Clothing is gaining traction in the clothing retail market, reflecting a shift in consumer attitudes towards more inclusive and diverse fashion choices. This trend is driven by a growing awareness and acceptance of gender fluidity, with consumers increasingly seeking clothing that transcends traditional gender norms. Retailers are responding by offering collections that are not confined to specific gender categories, allowing for greater freedom of expression. This movement towards gender-neutral fashion is not only appealing to younger, progressive consumers but also aligns with the broader trend of personalization and individuality in fashion. As a result, genderless clothing is becoming an integral part of the market's evolution, contributing to its growth and diversification.

Regionally, the Asia Pacific is expected to dominate the clothing retail market, driven by the growing middle-class population, increasing urbanization, and rising disposable incomes. North America and Europe are also significant players, with a well-established retail infrastructure and high consumer spending on fashion. However, regions like Latin America and the Middle East & Africa are also showing potential for growth, driven by improving economic conditions and a growing young population interested in fashion trends.

Product Type Analysis

The clothing retail market is segmented by product type into men's wear, women's wear, children's wear, sportswear, and others. Men's wear continues to be a substantial segment owing to the steady demand for formal and casual clothing. The rising trend of corporate culture and the increasing number of working professionals drive the demand for formal attire. Additionally, the casual wear segment for men is witnessing growth due to changing lifestyle trends and increased spending on leisure and sports activities.

Women's wear is another significant segment within the clothing retail market. This segment has traditionally dominated the market due to the wide variety of options and frequently changing fashi

Dataset Description

This dataset is a collection of customer, product, sales, and location data extracted from a CRM and ERP system for a retail company. It has been cleaned and transformed through various ETL (Extract, Transform, Load) processes to ensure data consistency, accuracy, and completeness. Below is a breakdown of the dataset components: 1. Customer Information (s_crm_cust_info)

This table contains information about customers, including their unique identifiers and demographic details.

Columns:

  cst_id: Customer ID (Primary Key)

  cst_gndr: Gender

  cst_marital_status: Marital status

  cst_create_date: Customer account creation date

Cleaning Steps:

  Removed duplicates and handled missing or null cst_id values.

  Trimmed leading and trailing spaces in cst_gndr and cst_marital_status.

  Standardized gender values and identified inconsistencies in marital status.

1. Product Information (s_crm_prd_info / b_crm_prd_info)

This table contains information about products, including product identifiers, names, costs, and lifecycle dates.

Columns:

  prd_id: Product ID

  prd_key: Product key

  prd_nm: Product name

  prd_cost: Product cost

  prd_start_dt: Product start date

  prd_end_dt: Product end date

Cleaning Steps:

  Checked for duplicates and null values in the prd_key column.

  Validated product dates to ensure prd_start_dt is earlier than prd_end_dt.

  Corrected product costs to remove invalid entries (e.g., negative values).

1. Sales Details (s_crm_sales_details / b_crm_sales_details)

This table contains information about sales transactions, including order dates, quantities, prices, and sales amounts.

Columns:

  sls_order_dt: Sales order date

  sls_due_dt: Sales due date

  sls_sales: Total sales amount

  sls_quantity: Number of products sold

  sls_price: Product unit price

Cleaning Steps:

  Validated sales order dates and corrected invalid entries.

  Checked for discrepancies where sls_sales did not match sls_price * sls_quantity and corrected them.

  Removed null and negative values from sls_sales, sls_quantity, and sls_price.

1. ERP Customer Data (b_erp_cust_az12, s_erp_cust_az12)

This table contains additional customer demographic data, including gender and birthdate.

Columns:

  cid: Customer ID

  gen: Gender

  bdate: Birthdate

Cleaning Steps:

  Checked for missing or null gender values and standardized inconsistent entries.

  Removed leading/trailing spaces from gen and bdate.

  Validated birthdates to ensure they were within a realistic range.

1. Location Information (b_erp_loc_a101)

This table contains country information related to the customers' locations.

Columns:

  cntry: Country

Cleaning Steps:

  Standardized country names (e.g., "US" and "USA" were mapped to "United States").

  Removed special characters (e.g., carriage returns) and trimmed whitespace.

1. Product Category (b_erp_px_cat_g1v2)

This table contains product category information.

Columns:

  Product category data (no significant cleaning required).

Key Features:

Customer demographics, including gender and marital status

Product details such as cost, start date, and end date

Sales data with order dates, quantities, and sales amounts

ERP-specific customer and location data

Data Cleaning Process:

This dataset underwent extensive cleaning and validation, including:

Null and Duplicate Removal: Ensuring no duplicate or missing critical data (e.g., customer IDs, product keys).

Date Validations: Ensuring correct date ranges and chronological consistency.

Data Standardization: Standardizing categorical fields (e.g., gender, country names) and fixing inconsistent values.

Sales Integrity Checks: Ensuring sales amounts match the expected product of price and quantity.

This dataset is now ready for analysis and modeling, with clean, consistent, and validated data for retail analytics, customer segmentation, product analysis, and sales forecasting.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

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.

A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date census data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

Use cases for the Global Census Database (Consumer Demographic Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our demographic databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

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.

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.

This is a dataset containing information of customers such as buying behavior, id, purchased items etc. You can use this dataset for customer segmentation, analytics etc.

According to a survey conducted by CSP Magazine in 2019, ** percent of urban consumers stated that they are visiting convenience stores more often than they were two years ago, versus only ** percent of rural consumers and ** percent of suburban customers.

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

Convenience Store Retailing Market Outlook

The global convenience store retailing market size was valued at approximately $2.3 trillion in 2023 and is projected to reach around $3.7 trillion by 2032, growing at a robust CAGR of 5.5% over the forecast period. This growth is driven by evolving consumer lifestyles favoring quick and accessible shopping experiences, as well as the strategic expansion of store networks by key players across emerging markets. The demand for convenience stores is being fueled by the increasingly busy schedules of consumers who seek efficient and immediate access to a wide range of products, from essential groceries to personal care items, without the hassle of navigating larger retail formats.

One of the primary growth factors of the convenience store retailing market is the rapid urbanization and the rising number of working professionals, which have led to an increased demand for convenient shopping solutions. As more individuals and families opt for time-saving shopping options, convenience stores are ideally positioned to meet these needs with their extended operating hours and strategic locations that are often situated in high-traffic areas. Additionally, the growth in disposable incomes in urban areas has led consumers to prioritize convenience over cost, further pushing the demand for these retail outlets. The ability of convenience stores to quickly adapt to consumer preferences through localized product assortments and promotions also plays a crucial role in driving market growth.

The technological advancement in retail, such as the adoption of contactless payment methods, mobile apps, and automated checkout systems, is also a significant growth catalyst for the convenience store retailing market. These innovations enhance the shopping experience by reducing wait times and improving customer satisfaction, thus attracting a larger customer base. Furthermore, the integration of online and offline shopping experiences through omni-channel strategies allows convenience stores to maintain competitiveness in an increasingly digital world. By offering services such as online ordering and home delivery, convenience stores are able to reach a broader audience and cater to the growing preference for online shopping.

Furthermore, the increasing focus on health and wellness has encouraged convenience stores to diversify their product offerings, particularly in the food and beverage segment, with healthier options and organic products. This shift is driven by consumer awareness about nutrition and lifestyle diseases, prompting convenience stores to stock a wider range of health-focused products. The trend towards health-conscious consumption is expected to continue, providing further impetus to the market. In parallel, the demand for ready-to-eat and on-the-go meals is also rising, offering convenience stores an opportunity to expand their fresh food offerings and attract customers seeking quick meal solutions.

On a regional scale, the Asia Pacific region is experiencing significant growth in the convenience store retailing market due to its large population base and rapid urbanization. Countries such as China, Japan, and South Korea are at the forefront of this expansion, driven by a strong preference for convenience and an increasing number of middle-class consumers with higher spending power. North America, with its mature market, continues to see steady growth, emphasized by innovations in product offerings and store formats. Europe, on the other hand, exhibits moderate growth due to market saturation but is witnessing a shift in consumer preferences towards healthier and organic products. The Middle East & Africa, although a smaller market, presents untapped opportunities due to favorable demographic trends and economic growth.

Store Type Analysis

In the context of the convenience store retailing market, the segmentation by store type plays a crucial role in understanding market dynamics and tailoring strategies accordingly. Traditional convenience stores continue to hold a significant share of the market due to their established presence and trusted brand image among consumers. These stores typically offer a wide range of products, from groceries to personal care items, and are strategically located in neighborhoods and high-traffic areas, making them accessible to a broad customer base. The format of traditional convenience stores has evolved over time to include modern amenities and services, such as ATMs and bill payment counters, to enhance customer convenience.

Gas station convenience stores, also known as forecourt retailers

Envestnet®| Yodlee®'s Online Purchase Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Sourcing accurate and up-to-date geodemographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium geodemographics data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Geodemographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

Analysis of ‘Retail Case Study Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/darpan25bajaj/retail-case-study-data on 28 January 2022.

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

Analytics in Retail:

With the retail market getting more and more competitive by the day, there has never been anything more important than the ability for optimizing service business processes when trying to satisfy the expectations of customers. Channelizing and managing data with the aim of working in favor of the customer as well as generating profits is very significant for survival.

Ideally, a retailer’s customer data reflects the company’s success in reaching and nurturing its customers. Retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions. These measurements provided general insight into the behavioral tendencies of customers.

Customer intelligence is the practice of determining and delivering data-driven insights into past and predicted future customer behavior.To be effective, customer intelligence must combine raw transactional and behavioral data to generate derived measures. In a nutshell, for big retail players all over the world, data analytics is applied more these days at all stages of the retail process – taking track of popular products that are emerging, doing forecasts of sales and future demand via predictive simulation, optimizing placements of products and offers through heat-mapping of customers and many others.

About the Data

A Retail store is required to analyze the day-to-day transactions and keep a track of its customers spread across various locations along with their purchases/returns across various categories.

What can be done with the data?

Create a report and display the calculated metrics, reports and inferences.

Data Schema

This book has three sheets (Customer, Transaction, Product Hierarchy):

• Customer: Customer information including demographics
• Transaction: Transaction of customers
• Product Hierarchy: Product information

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

In 2023, Hobby Lobby was the leading hobby and home specialty retailer in terms of customer satisfaction in the United States. The company scored ** on a 100-point scale, overtaking TJX (HomeGoods) by one point that year.

Market Size statistics on the Retail Trade industry in the US