Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

Retail Analytics Software Market Outlook

As of 2023, the global retail analytics software market size is valued at approximately $5 billion, and it is projected to reach around $13 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 11.2% over the forecast period. The substantial growth is driven primarily by the increasing reliance on data-driven decision-making within the retail industry. As retailers aim to enhance customer experiences, optimize inventory management, and streamline operational efficiencies, the adoption of retail analytics software is poised to expand significantly.

The growth of the retail analytics software market is fueled by the rapid digital transformation across the retail sector. As more retailers embrace e-commerce and omnichannel strategies, the need for effective analytics tools becomes critical to gain insights into consumer preferences and behavior. Retailers are leveraging these software solutions to analyze large volumes of data, enabling them to make more informed decisions about merchandising, marketing, and customer engagement. Additionally, the evolution of artificial intelligence and machine learning technologies is enhancing the capabilities of retail analytics platforms, allowing for more accurate predictions and personalized consumer experiences.

Another significant growth factor is the increasing focus on customer-centric strategies. Today’s consumers demand personalized experiences and expect retailers to anticipate their needs. Retail analytics software allows businesses to analyze customer data and segment them based on buying behavior, preferences, and demographics. This enables retailers to tailor their offerings and marketing efforts to individual customer segments, thereby enhancing customer satisfaction and loyalty. As competition in the retail space intensifies, the ability to deliver personalized experiences becomes a crucial differentiator, further propelling the demand for advanced analytics solutions.

Moreover, the need for operational efficiency and cost optimization is driving the adoption of retail analytics software. In a highly competitive market, retailers are under constant pressure to reduce costs while maintaining quality service. Analytics tools help retailers optimize inventory levels, reduce stockouts and overstock situations, and improve supply chain efficiencies. By leveraging predictive analytics, retailers can forecast demand more accurately, plan inventory purchases, and minimize waste, ultimately leading to improved profitability. The capability to streamline operations and enhance efficiency positions retail analytics software as an indispensable tool for modern retailers.

From a regional perspective, North America currently dominates the retail analytics software market, attributed to the presence of major retail players and the early adoption of advanced technologies. The region’s mature retail market and the increasing consumer shift towards online shopping are contributing to the demand for sophisticated analytics solutions. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by the rapid expansion of the retail sector in emerging economies such as China and India. Rising smartphone penetration and internet usage in these countries are paving the way for the growth of e-commerce, thereby increasing the demand for retail analytics software.

Component Analysis

The retail analytics software market is segmented by component into software and services. The software segment holds the lion’s share of the market, driven by the increasing need for comprehensive analytics tools that can process large amounts of data and provide actionable insights. Retailers are increasingly investing in advanced software solutions that offer features like predictive analytics, customer segmentation, and real-time reporting. These capabilities enable them to make informed decisions about inventory management, marketing strategies, and customer engagement. As the retail landscape becomes more complex, the demand for sophisticated software solutions is expected to grow significantly.

The services segment, although smaller than the software segment, is also experiencing notable growth. As retailers implement new analytics tools, there is a growing need for professional services such as consulting, implementation, and support. These services help retailers tailor analytics solutions to their specific needs and ensure a seamless integration with existing systems. Additionally, as retailers continue to innovate and adopt new techn

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level 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 AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

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

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

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

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

Sourcing accurate and up-to-date demographics GIS 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 demographics GIS 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 Demographics GIS 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

Envestnet®| Yodlee®'s Consumer Behavior 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

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

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.

Snapshot img

Mexico Retail Market Size 2024-2028

The mexico retail market size is forecast to increase by USD 78.49 billion, at a CAGR of 4.5% between 2023 and 2028.

The market is witnessing significant growth, driven by the influx of numerous retail stores and innovative packaging and marketing initiatives by prominent companies. This dynamic market environment presents both opportunities and challenges for retailers. On the one hand, the increasing competition necessitates continuous innovation and differentiation to capture consumer attention. Retailers are investing in unique product offerings, enhanced shopping experiences, and creative marketing strategies to stand out from the crowd. Additionally, the adoption of technology, such as mobile payments and e-commerce platforms, is becoming increasingly common, providing new avenues for growth. On the other hand, issues related to logistics and supply chain operations pose significant challenges. Mexico's complex geography and infrastructure can make distribution and delivery difficult and costly, particularly for perishable goods. Retailers must navigate these obstacles to ensure timely and cost-effective delivery, while also maintaining the quality and freshness of their products. In conclusion, the market is characterized by a competitive landscape and a growing consumer base. Retailers seeking to succeed in this market must focus on innovation, differentiation, and effective logistics management to capitalize on opportunities and overcome challenges. By staying agile and responsive to changing market conditions, retailers can thrive in this dynamic and exciting market.

What will be the size of the Mexico Retail Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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In Mexico's retail market, payment systems continue to evolve, with contactless and digital payments gaining traction. Retail infrastructure development remains a priority, shaping store locations and shopping habits. Consumer preferences shift towards convenience and personalized experiences, driving retail innovation and technological disruption. Risk management and retail metrics are crucial for competitive analysis, as market penetration and price elasticity impact sales growth. Emerging technologies, such as augmented reality and artificial intelligence, reshape retail partnerships and product differentiation strategies. Lease agreements and import duties pose challenges for retailers, requiring careful consideration in business decisions. Labor costs, consumer confidence, and the retail workforce are essential retail metrics, impacting brand loyalty and store expansion plans. E-commerce security and data privacy concerns persist, necessitating robust risk management strategies. Supply chain resilience and disaster recovery plans are essential for business continuity in the face of economic factors and population demographics. Crisis management and crisis communication are vital skills for retailers in a volatile market. Private label brands and income distribution patterns influence consumer behavior and economic trends. Retail real estate and population demographics shape store expansion plans, while crisis management and business continuity plans ensure operational resilience.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ProductPFD and AB and TPPersonal and household careAF and AElectrical and electronicsOthersDistribution ChannelOfflineOnlineGeographyNorth AmericaMexico

By Product Insights

The pfd and ab and tp segment is estimated to witness significant growth during the forecast period.

The Mexican retail market is witnessing significant developments in various sectors, including packaged food and drinks, alcoholic beverages, and tobacco products. The upward trend in commodity prices is driving growth in these categories. Consumers' increasing preference for imported goods, particularly processed foods, is expected to result in the highest growth rate during the forecast period. Mini marts are gaining popularity in both big cities and small towns, primarily selling instant food and beverage products. Ready-to-eat food products have seen a surge in sales, leading manufacturers to launch and promote healthier options. In the realm of technology, energy efficiency, fraud prevention, and point-of-sale systems are essential for retailers. Supply chain sustainability and ethical sourcing are becoming crucial factors in consumer decision-making. Social media marketing and digital marketing are essential tools for retailers to engage with customers and build loya

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.

Demografy is a privacy by design customer demographics prediction AI platform.

Core features: - Demographic segmentation - Demographic analytics - API integration - Data export

Key advantages: - 100% coverage of lists - Accuracy estimate before purchase - GDPR-compliance as no sensitive data is required. Demografy can work with only first names or masked last names

Use cases: - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You can provide even masked last names keeping personal data in-house. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

Here are a few use cases for this project:

Use Case 1: Gender-Based Retail Analytics By analyzing customer demographics in retail stores, the "man vrouw dataset 1" can help retailers understand the gender distribution of their shoppers, empowering them to make informed decisions on store layout, marketing strategies, and product placements.

Use Case 2: Crowd Monitoring and Event Management This model can help enhance safety and optimize visitor experience at crowded events, such as concerts or festivals, by identifying the gender distribution of attendees, enabling promoters to customize services, restrooms allocation, and security measures accordingly.

Use Case 3: Digital Advertising and Marketing Using the "man vrouw dataset 1" model, businesses can better target their digital advertisements by understanding the key demographic visiting specific websites or engaging with specific content, allowing for tailored ad campaigns designed to target male or female audiences.

Use Case 4: Smart Surveillance and Security Systems The model can be used in surveillance and security systems to help identify and track people by their HU classes (man or vrouw) in premises like airports or corporate buildings, allowing security teams to analyze patterns and prevent potential threats.

Use Case 5: Social Media Image Analysis The "man vrouw dataset 1" model can be used to analyze the gender composition of social media images, providing insights into trends, preferences, and behaviors of different gender groups on social platforms. This information can then be used for targeted marketing or social research purposes.

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 ---

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.

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

CE Vision USA is the premier data set tracking consumer spend on credit and debit cards. Private investors and corporate clients use CE Vision retail commerce data for competitor analysis, market share, cross-shopping, demographics, and market share data by industry and channel.

Food And Non-Food Retail Market size was valued at USD 12 Trillion in 2024 and is projected to reach USD 16.55 Trillion by 2031, growing at a CAGR of 3.6% from 2024 to 2031Food And Non-Food Retail Market DriversConsumer Preferences and Behavior: Changing consumer preferences, including preferences for healthier food options, organic products, and ethically sourced goods, influence the retail market. Non-food retailers also respond to shifts in consumer behavior, such as the growing demand for online shopping and omnichannel retail experiences.Economic Factors: Economic indicators such as GDP growth, disposable income levels, unemployment rates, and consumer confidence affect consumer spending patterns in both food and non-food retail sectors. During economic downturns, consumers may prioritize essential goods over discretionary purchases, impacting retail sales.Population Demographics: Population demographics, such as aging populations, urbanization trends, and changes in household sizes, influence retail market dynamics. For example, aging populations may drive demand for healthcare products and services, while urbanization can lead to increased demand for convenience foods and online shopping options.

Retail Sector Market Outlook

According to our latest research, the global retail sector market size reached USD 28.3 trillion in 2024, driven by robust consumer demand, digital transformation, and evolving shopping behaviors. The market is poised to grow at a CAGR of 5.7% from 2025 to 2033, reaching an estimated USD 46.9 trillion by 2033. This expansion is underpinned by significant investments in omnichannel strategies, rapid e-commerce penetration, and the increasing adoption of advanced retail technologies worldwide.

One of the primary growth factors fueling the retail sector market is the accelerated shift toward digitalization and the integration of cutting-edge technologies. Retailers are leveraging artificial intelligence, machine learning, and data analytics to enhance customer experiences, streamline operations, and personalize marketing efforts. The proliferation of smartphones and increased internet penetration have made online shopping more accessible, prompting even traditional brick-and-mortar retailers to invest heavily in digital platforms. Additionally, the adoption of contactless payment systems and advanced inventory management solutions has played a crucial role in improving operational efficiency and customer satisfaction, further propelling market growth.

Another significant growth driver is the evolution of consumer preferences and the rising demand for convenience and personalization. Modern consumers are increasingly seeking seamless, flexible, and personalized shopping experiences, both online and offline. Retailers are responding by offering a wider range of products, implementing omnichannel retail strategies, and enhancing last-mile delivery services. The growing popularity of subscription services, click-and-collect models, and same-day delivery options exemplifies this shift. Furthermore, the expansion of emerging product categories such as health and wellness, sustainable goods, and smart home devices has contributed to the diversification and growth of the retail sector market.

Globalization and the expansion of retail infrastructure in emerging economies have also played a pivotal role in driving market growth. Countries across Asia Pacific, Latin America, and the Middle East & Africa are witnessing rapid urbanization, rising disposable incomes, and an expanding middle class. These factors have led to increased consumer spending and heightened demand for diverse retail products and services. Multinational retailers are entering these markets through strategic partnerships, acquisitions, and franchise models, capitalizing on the untapped potential and contributing to the overall growth trajectory of the global retail sector.

Regionally, Asia Pacific continues to dominate the global retail sector market, accounting for the largest share in 2024, driven by the robust growth of economies such as China, India, and Southeast Asian countries. North America and Europe remain mature and highly competitive markets, characterized by advanced retail infrastructure and high consumer spending. Meanwhile, Latin America and the Middle East & Africa are emerging as lucrative markets, supported by favorable demographic trends and increasing digital adoption. The regional outlook for the retail sector market remains optimistic, with all regions expected to contribute significantly to overall market expansion through 2033.

Type Analysis

The retail sector market is segmented by type into online retail and offline retail, both of which play distinct yet complementary roles in the industry’s evolution. Online retail has witnessed exponential growth in recent years, fueled by advancements in e-commerce platforms, increasing internet penetration, and the widespread adoption of smartphones. Consumers are gravitating towards online shopping due to its convenience, extensive product variety, and competitive pricing. E-commerce giants and digital-native brands have set new standards for customer service, delivery speed, and personaliza

The global retail industry, valued at $32.68 billion in 2025, is projected to experience robust growth, driven by a compound annual growth rate (CAGR) of 7.65% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of e-commerce platforms, particularly among younger demographics, is significantly impacting the industry's trajectory. Consumers are increasingly drawn to the convenience, wider selection, and often lower prices offered by online retailers. Furthermore, the rise of omnichannel retail strategies, integrating both online and offline experiences, is enhancing customer engagement and driving sales. Globalization and the expansion of international markets also contribute to the sector's growth, with companies like Walmart and Amazon leading the way in global expansion and market penetration. However, the industry faces challenges such as intense competition, rising logistics costs, and the need to adapt to evolving consumer preferences and technological advancements. Successful players are focusing on data-driven decision-making, personalization of customer experiences, and sustainable practices to remain competitive. Segmentation within the retail sector, encompassing food and grocery, personal care, apparel, furniture, and pharmaceuticals, provides diverse growth avenues, with each segment responding differently to broader economic trends and technological innovations. The geographical distribution of market share also reveals regional variations, with North America and Asia Pacific expected to maintain leading positions, propelled by strong consumer spending and robust infrastructure development. The retail landscape is becoming increasingly dynamic, characterized by a shift in consumer behavior and technological disruption. The integration of artificial intelligence (AI) and machine learning (ML) in areas like inventory management, supply chain optimization, and personalized marketing is transforming operational efficiency and enhancing customer experience. The rise of subscription models and the growth of the gig economy are also impacting the retail workforce and delivery mechanisms. Competition is particularly fierce among major players, necessitating strategic partnerships, acquisitions, and a focus on innovation to maintain market share. Maintaining a strong brand reputation, incorporating robust cybersecurity measures, and adhering to evolving consumer privacy regulations are critical for long-term success in this competitive and ever-changing industry. The next decade will likely see further consolidation within the sector, with larger companies acquiring smaller competitors and enhancing their market dominance through technology and efficient operations. Recent developments include: October 2023: Amazon announced that it provides online shopping services in South Africa to assist independent retailers in starting, expanding, and growing their enterprises.August 2023: Italian luxury fashion brand Gucci and Chinese e-commerce giant JD.com, popularly known as Jingdong, have partnered digitally. With the launch of a new digital flagship shop on the e-commerce retailer's platform, the partnership will reach a significant milestone.May 2023: Walmart announced the launch of over 28 healthcare facilities in its Walmart Supercenters, providing value-based and dental care services, among others.. Key drivers for this market are: Rapid Expansion of Urban Areas, Rise of E-commerce and Omnichannel Retailing. Potential restraints include: Rapid Expansion of Urban Areas, Rise of E-commerce and Omnichannel Retailing. Notable trends are: E-commerce is the Fastest-growing Segment in the Retail Industry.

The global food and non-food retail market is experiencing robust growth, driven by several key factors. The rising disposable incomes in developing economies, coupled with the increasing urbanization and changing consumer lifestyles, are fueling demand for a wider variety of products across both food and non-food categories. E-commerce continues to be a significant driver, with online sales channels witnessing substantial growth, particularly among younger demographics. The rise of omnichannel retail strategies, integrating online and offline experiences, is further enhancing consumer convenience and driving sales. Competitive pricing strategies, loyalty programs, and personalized marketing initiatives are also contributing to market expansion. However, challenges remain, including fluctuating commodity prices impacting food retail margins and increasing competition from both established players and new entrants leveraging innovative technologies and business models. Supply chain disruptions and geopolitical uncertainties also pose risks to overall market stability. Segmentation analysis reveals that the food segment dominates the market, although non-food retail is witnessing faster growth due to increasing discretionary spending and the adoption of new technologies like augmented and virtual reality for enhanced shopping experiences. Within the non-food segment, apparel and electronics remain prominent categories, while the food segment shows strong growth in ready-to-eat meals and organic products driven by consumer health consciousness. Regional variations are apparent, with North America and Europe holding significant market shares, while Asia-Pacific displays substantial growth potential owing to its expanding middle class. Key players like Walmart, Amazon, and other global retailers are constantly innovating to improve supply chain efficiency, expand their reach, and meet evolving consumer expectations. The future will likely see further consolidation within the retail industry, with larger players acquiring smaller chains to enhance scale and market share. Sustainable practices and ethical sourcing are also gaining importance, influencing consumer purchasing decisions and shaping industry strategies.