Demographical Shopping Purchases Data

Analyzing customer purchasing patterns and preferences

By Joseph Nowicki [source]

About this dataset

This dataset contains demographic information about customers who have made purchases in a store, including their name, IP address, region, age, items purchased, and total amount spent. Furthermore, this data can provide insights into customer shopping behaviour for the store in question - from their geographical information to the types of products they purchase. With detailed demographic data like this at hand it is possible to make strategic decisions regarding target customers as well as developing specific marketing campaigns or promotions tailored to meet their needs and interests. By gaining deeper understanding of customer habits through this dataset we unlock more possibilities for businesses seeking higher engagement levels with shoppers

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How to use the dataset

This dataset includes information such as customer's names, IP address, age, items purchased and amount spent. This data can be used to uncover patterns in spending behavior of shoppers from different areas or regions across demographics like age group or gender.

Research Ideas

• Analyze customer shopping trends based on age and region to maximize targetted advertising.
• Analyze the correlation between customer spending habits based on store versus online behavior.
• Use IP addresses to track geographical trends in items purchased from a particular online store to identify new markets for targeted expansion

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Demographic_Data_Orig.csv | Column name | Description | |:---------------|:------------------------------------------------------------------------------------------------| | full.name | The full name of the customer. (String) | | ip.address | The IP address of the customer. (String) | | region | The region of residence of the customer. (String) | | in.store | A boolean value indicating whether the customer made the purchase in-store or online. (Boolean) | | age | The age of the customer. (Integer) | | items | The number of items purchased by the customer. (Integer) | | amount | The total amount spent by the customer. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Joseph Nowicki.

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 ...

Shopper Demographics Analytics Market Outlook

As per our latest research, the global shopper demographics analytics market size in 2024 is valued at USD 5.3 billion, with a robust CAGR of 14.7% projected through the forecast period. By 2033, the market is expected to reach USD 17.2 billion, reflecting the accelerating adoption of advanced analytics solutions in retail and related sectors. The primary growth driver is the increasing need for retailers and brands to understand and predict consumer behavior in an era characterized by omnichannel shopping and intense competition.

The growth of the shopper demographics analytics market is significantly propelled by the retail sector’s digital transformation. Retailers are increasingly leveraging analytics to gain granular insights into customer demographics, preferences, and purchasing behavior. The integration of artificial intelligence (AI) and machine learning (ML) into analytics platforms has enabled businesses to process vast amounts of data in real time, offering actionable insights that drive personalized marketing and operational efficiency. As consumer expectations for tailored experiences continue to rise, retailers are investing heavily in shopper analytics to enhance customer engagement, improve inventory management, and optimize store layouts, further fueling market expansion.

Another key growth factor is the proliferation of e-commerce and the corresponding surge in online data generation. E-commerce platforms are uniquely positioned to collect detailed demographic and behavioral data, which can be analyzed to segment customers, predict purchasing trends, and personalize marketing campaigns. The adoption of cloud-based analytics solutions has further democratized access to advanced analytics, allowing even small and medium-sized enterprises (SMEs) to harness the power of shopper demographics analytics. Moreover, the integration of analytics with customer relationship management (CRM) and point-of-sale (POS) systems has streamlined data collection and analysis, enabling businesses to respond swiftly to changing consumer preferences.

The increasing focus on omnichannel retail strategies is also driving demand for shopper demographics analytics. Retailers are striving to provide a seamless shopping experience across physical stores, online platforms, and mobile applications. Analytics solutions help bridge the gap between different channels by offering a unified view of customer behavior, enabling businesses to deliver consistent and personalized experiences. The rise of smart stores and the deployment of Internet of Things (IoT) devices have further enriched the data ecosystem, providing real-time insights into foot traffic, dwell times, and purchase patterns. These advancements are expected to sustain the market’s high growth trajectory over the coming years.

From a regional perspective, North America currently dominates the shopper demographics analytics market, owing to the presence of major technology providers and early adoption by leading retailers. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding retail infrastructure, and increasing digital adoption among consumers. Europe also holds a significant market share, supported by strong regulatory frameworks and a mature retail sector. The Middle East & Africa and Latin America are emerging as promising markets, as retailers in these regions invest in analytics to stay competitive and cater to evolving consumer demands. These regional dynamics underscore the global relevance and growth potential of shopper demographics analytics.

Component Analysis

The shopper demographics analytics market by component is bifurcated into software and services, with software solutions representing the larger share in 2024. The software segment encompasses a wide range of analytics platforms, including proprietary and open-source solutions designed to collect, process, and visualize demographic data. These platforms leverage advanced technologies such as AI, ML, and big data analytics to deliver actionable insights in real time. The growing adoption of cloud-based analytics software has further accelerated market growth, enabling retailers to scale their analytics capabilities without significant upfront investment in IT infrastructure. The continuous evolution of analytics software, with features such as predictive modeling, data v

This dataset provides a snapshot of prices for various products across different stores over a span of several months. Here's a summary of the data:

Products: The dataset includes prices for a range of products including rice, dried beans, bottled water, canned vegetables, milk, rope, flashlight, duct tape, and water filters.

Stores: Prices are recorded from three different stores: Walmart, Costco, and Target.

Price Variation: Prices vary across stores and over time

Date: Prices are recorded on specific dates, allowing for the analysis of price trends over time.

This data can be analyzed to identify patterns in pricing, compare prices between stores, and assess how prices change over time for different products. Such analysis can inform purchasing decisions and provide insights into consumer behavior and market dynamics.

This dataset is obtained from https://github.com/AlexTheAnalyst/Power-BI/blob/main/Apocolypse%20Food%20Prep.xlsx. This is a guided project by @AlexTheAnalyst

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

This dataset contains simulated retail transaction data from various branches in Jordan. It includes information about customer demographics, purchase details, product categories, and payment methods. The data is designed to reflect typical retail transactions, capturing customer behavior and preferences across different regions and professions.

This dataset is an good resource for data mining and data analysis training, as it encompasses a diverse range of trends and patterns that can be uncovered through analysis. With detailed customer demographics, purchase behavior, product preferences, and payment methods across various branches, it provides a rich ground for exploring consumer insights and sales performance. The inclusion of business rules and realistic trends such as income-based spending patterns, branch-specific gender preferences, and payment method preferences ensures that trainees can practice identifying and interpreting meaningful patterns in the data. Furthermore, the dataset's comprehensive nature, with data points spread over several years and incorporating fluctuations due to external factors like the COVID-19 pandemic, allows for the development of robust analytical skills, making it an ideal tool for anyone looking to enhance their expertise in data mining and analysis.

Table of Fields

Detailed Descriptions

• order_id: A unique identifier assigned to each order, used to distinguish different transactions.
• branch_name: The name of the branch where the transaction occurred, indicating the geographic location.
• is_customer: A boolean value (TRUE or FALSE) indicating whether the individual making the purchase is a registered customer of the store.
• customer_id: A unique identifier assigned to each customer, used to track customer-specific transactions and demographics.
• customer_profession: The profession of the customer, providing insight into the customer's job and potential buying power.
• customer_income: The monthly income of the customer, expressed in Jordanian Dinar (JOD), used to analyze spending patterns relative to income.
• purchase_date: The date on which the purchase was made, used to track sales over time.
• purchase_time: The specific time at which the purchase was made, providing information on peak shopping hours.
• product_line: An identifier for the product purchased, linked to the product catalog for detailed product information.
• unit_price: The price per unit of the product purchased, in Jordanian Dinar (JOD).
• quantity: The number of units of the product purchased in the transaction.
• total_price: The total price for the quantity of products purchased, calculated as unit_price multiplied by quantity.
• tax_amount: The tax amount ap...

GapMaps premium demographic 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.

Demographic Data attributes include:

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.

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.

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.

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

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.

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

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

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 AGS Demographic 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

This dataset contains 150,000 retail interaction records representing customer journeys in both e-commerce and in-store environments. It captures detailed behavioral, demographic, and product-related information to support research in product sales history, customer demographics, purchase patterns, personalized shopping experiences, customer behavior analysis, and predictive modeling.

Each row corresponds to a unique customer–product interaction, including session details, browsing or purchasing behavior, and applied discounts. The purchase column serves as the binary target variable (1 = purchased, 0 = not purchased), making the dataset suitable for various classification and recommendation tasks.

Key Features

Size: 150,000 rows × 19 columns

Target Column: purchase (binary: 1 = purchased, 0 = not purchased)

Data Types:

Categorical: User ID, product ID, interaction type, device type, product category, brand, location, gender

Numerical: Price, discount, age, loyalty score, previous purchase count, average purchase value

Temporal: Timestamp (to study trends and patterns)

Text: Search keywords

Behavioral Data: Interaction type (view, click, add to cart, purchase), purchase history statistics

Product Metadata: Category, brand, price, discount percentage

User Demographics: Age, gender, loyalty score

Applications:

Retail personalization

Purchase prediction

Customer segmentation

Behavioral pattern 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

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.

1) Data Introduction • The Retail Sales Dataset is data designed to analyze retail sales and customer behavior in a virtual retail environment, including transaction history, customer demographics, and product information.

2) Data Utilization (1) Retail Sales Dataset has characteristics that: • This dataset details retail sales and customer characteristics such as transaction ID, date, customer ID, gender, age, product category, purchase volume, unit price, total amount. (2) Retail Sales Dataset can be used to: • Customer Segmentation and Marketing Strategy: By analyzing purchase patterns by age, gender, and product category, you can use them to establish a customized marketing strategy. • Sales Trends and Inventory Management: It can be used to streamline retail operations such as inventory management and promotion planning by analyzing sales trends by period and product.

This dataset provides a comprehensive view of customer product reviews, including detailed ratings, review text, sentiment analysis, and user demographics such as age, gender, and location. It enables advanced sentiment analysis, natural language processing, and customer satisfaction research, making it ideal for businesses seeking to understand product perception and improve customer experience.

🛒 E-Commerce Customer Behavior and Sales Dataset 📊 Dataset Overview This comprehensive dataset contains 5,000 e-commerce transactions from a Turkish online retail platform, spanning from January 2023 to March 2024. The dataset provides detailed insights into customer demographics, purchasing behavior, product preferences, and engagement metrics.

🎯 Use Cases This dataset is perfect for:

Customer Segmentation Analysis: Identify distinct customer groups based on behavior Sales Forecasting: Predict future sales trends and patterns Recommendation Systems: Build product recommendation engines Customer Lifetime Value (CLV) Prediction: Estimate customer value Churn Analysis: Identify customers at risk of leaving Marketing Campaign Optimization: Target customers effectively Price Optimization: Analyze price sensitivity across categories Delivery Performance Analysis: Optimize logistics and shipping 📁 Dataset Structure The dataset contains 18 columns with the following features:

Order Information Order_ID: Unique identifier for each order (ORD_XXXXXX format) Date: Transaction date (2023-01-01 to 2024-03-26) Customer Demographics Customer_ID: Unique customer identifier (CUST_XXXXX format) Age: Customer age (18-75 years) Gender: Customer gender (Male, Female, Other) City: Customer city (10 major Turkish cities) Product Information Product_Category: 8 categories (Electronics, Fashion, Home & Garden, Sports, Books, Beauty, Toys, Food) Unit_Price: Price per unit (in TRY/Turkish Lira) Quantity: Number of units purchased (1-5) Transaction Details Discount_Amount: Discount applied (if any) Total_Amount: Final transaction amount after discount Payment_Method: Payment method used (5 types) Customer Behavior Metrics Device_Type: Device used for purchase (Mobile, Desktop, Tablet) Session_Duration_Minutes: Time spent on website (1-120 minutes) Pages_Viewed: Number of pages viewed during session (1-50) Is_Returning_Customer: Whether customer has purchased before (True/False) Post-Purchase Metrics Delivery_Time_Days: Delivery duration (1-30 days) Customer_Rating: Customer satisfaction rating (1-5 stars) 📈 Key Statistics Total Records: 5,000 transactions Date Range: January 2023 - March 2024 (15 months) Average Transaction Value: ~450 TRY Customer Satisfaction: 3.9/5.0 average rating Returning Customer Rate: 60% Mobile Usage: 55% of transactions 🔍 Data Quality ✅ No missing values ✅ Consistent formatting across all fields ✅ Realistic data distributions ✅ Proper data types for all columns ✅ Logical relationships between features 💡 Sample Analysis Ideas Customer Segmentation with K-Means Clustering

Segment customers based on spending, frequency, and recency Sales Trend Analysis

Identify seasonal patterns and peak shopping periods Product Category Performance

Compare revenue, ratings, and return rates across categories Device-Based Behavior Analysis

Understand how device choice affects purchasing patterns Predictive Modeling

Build models to predict customer ratings or purchase amounts City-Level Market Analysis

Compare market performance across different cities 🛠️ Technical Details File Format: CSV (Comma-Separated Values) Encoding: UTF-8 File Size: ~500 KB Delimiter: Comma (,) 📚 Column Descriptions Column Name Data Type Description Example Order_ID String Unique order identifier ORD_001337 Customer_ID String Unique customer identifier CUST_01337 Date DateTime Transaction date 2023-06-15 Age Integer Customer age 35 Gender String Customer gender Female City String Customer city Istanbul Product_Category String Product category Electronics Unit_Price Float Price per unit 1299.99 Quantity Integer Units purchased 2 Discount_Amount Float Discount applied 129.99 Total_Amount Float Final amount paid 2469.99 Payment_Method String Payment method Credit Card Device_Type String Device used Mobile Session_Duration_Minutes Integer Session time 15 Pages_Viewed Integer Pages viewed 8 Is_Returning_Customer Boolean Returning customer True Delivery_Time_Days Integer Delivery duration 3 Customer_Rating Integer Satisfaction rating 5 🎓 Learning Outcomes By working with this dataset, you can learn:

Data cleaning and preprocessing techniques Exploratory Data Analysis (EDA) with Python/R Statistical analysis and hypothesis testing Machine learning model development Data visualization best practices Business intelligence and reporting 📝 Citation If you use this dataset in your research or project, please cite:

E-Commerce Customer Behavior and Sales Dataset (2024) Turkish Online Retail Platform Data (2023-2024) Available on Kaggle ⚖️ License This dataset is released under the CC0: Public Domain license. You are free to use it for any purpose.

🤝 Contribution Found any issues or have suggestions? Feel free to provide feedback!

📞 Contact For questions or collaborations, please reach out through Kaggle.

Happy Analyzing! 🚀

Keywords: e-c...

Welcome to the Retail Sales and Customer Demographics Dataset! This synthetic dataset has been meticulously crafted to simulate a dynamic retail environment, providing an ideal playground for those eager to sharpen their data analysis skills through exploratory data analysis (EDA). With a focus on retail sales and customer characteristics, this dataset invites you to unravel intricate patterns, draw insights, and gain a deeper understanding of customer behavior.

****Dataset Overview:**

This dataset is a snapshot of a fictional retail landscape, capturing essential attributes that drive retail operations and customer interactions. It includes key details such as Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, and Total Amount. These attributes enable a multifaceted exploration of sales trends, demographic influences, and purchasing behaviors.

Why Explore This Dataset?

• Realistic Representation: Though synthetic, the dataset mirrors real-world retail scenarios, allowing you to practice analysis within a familiar context.
• Diverse Insights: From demographic insights to product preferences, the dataset offers a broad spectrum of factors to investigate.
• Hypothesis Generation: As you perform EDA, you'll have the chance to formulate hypotheses that can guide further analysis and experimentation.
• Applied Learning: Uncover actionable insights that retailers could use to enhance their strategies and customer experiences.

Questions to Explore:

• How does customer age and gender influence their purchasing behavior?
• Are there discernible patterns in sales across different time periods?
• Which product categories hold the highest appeal among customers?
• What are the relationships between age, spending, and product preferences?
• How do customers adapt their shopping habits during seasonal trends?
• Are there distinct purchasing behaviors based on the number of items bought per transaction?
• What insights can be gleaned from the distribution of product prices within each category?

Your EDA Journey:

Prepare to immerse yourself in a world of data-driven exploration. Through data visualization, statistical analysis, and correlation examination, you'll uncover the nuances that define retail operations and customer dynamics. EDA isn't just about numbers—it's about storytelling with data and extracting meaningful insights that can influence strategic decisions.

Embrace the Retail Sales and Customer Demographics Dataset as your canvas for discovery. As you traverse the landscape of this synthetic retail environment, you'll refine your analytical skills, pose intriguing questions, and contribute to the ever-evolving narrative of the retail industry. Happy exploring!

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.

This dataset provides detailed, line-level records of pharmacy retail purchase events, including both over-the-counter and prescription medications. Each record captures transaction details, customer demographics (where available), product specifics, payment method, and prescription information, enabling comprehensive analysis of purchasing patterns, health trends, and market basket behaviors.

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 loyalty programs. Mexican retail law

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Retail Site Selection AI Market Outlook

According to our latest research, the global Retail Site Selection AI market size reached USD 1.46 billion in 2024, with a robust year-on-year growth reflecting the sector’s increasing reliance on data-driven decision-making. The market is projected to expand at a CAGR of 18.7% from 2025 to 2033, reaching a forecasted value of USD 7.27 billion by 2033. This remarkable growth is primarily driven by the rising adoption of artificial intelligence solutions to optimize retail location strategies, improve customer targeting, and maximize revenue potential through advanced analytics and predictive modeling.

The primary growth factor for the Retail Site Selection AI market is the intensifying competition in the retail industry, which necessitates more precise and data-driven site selection processes. Traditional methods of site selection, which relied heavily on intuition and basic demographic analysis, are being rapidly replaced by AI-powered solutions that integrate vast datasets, including real-time traffic patterns, consumer behavior analytics, and market trends. Retailers are increasingly leveraging these advanced tools to identify optimal store locations, minimize risks, and enhance return on investment. The ability of AI to process and analyze complex variables at scale has dramatically improved the accuracy and efficiency of site selection, making it a critical asset for both established retail chains and emerging players seeking to expand their footprint.

Another significant driver is the growing volume of available data and advancements in machine learning algorithms. The proliferation of IoT devices, mobile applications, and digital payment systems has resulted in a wealth of granular data points that can be harnessed for site selection analysis. AI solutions can now incorporate real-time consumer movement, seasonal trends, and competitive intelligence to deliver actionable insights. Additionally, cloud-based platforms have democratized access to sophisticated AI tools, enabling even small and medium enterprises to benefit from enterprise-grade analytics without substantial infrastructure investments. As data privacy regulations evolve, AI providers are also enhancing their compliance frameworks, further driving adoption among risk-averse retail organizations.

Furthermore, the shift toward omnichannel retailing and the integration of physical and digital experiences are compelling retailers to rethink their site selection strategies. AI-powered site selection tools help retailers align their brick-and-mortar presence with online customer engagement, ensuring that new locations complement digital channels and drive overall brand performance. Retailers are increasingly using AI to evaluate factors such as proximity to logistics hubs, population density, and local market saturation, allowing for more holistic decision-making. The emphasis on customer-centricity and the need to adapt quickly to changing market dynamics are accelerating the deployment of AI in retail site selection, fostering long-term growth in the market.

Regionally, North America continues to lead the Retail Site Selection AI market, accounting for the largest share due to the presence of major retail chains, advanced technological infrastructure, and a high degree of digitalization. Europe follows closely, driven by the rapid adoption of AI in retail and stringent regulatory standards that promote innovation in site selection practices. The Asia Pacific region is witnessing the fastest growth, propelled by urbanization, expanding middle-class populations, and the digital transformation of retail businesses. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by increasing investments in retail infrastructure and the growing awareness of AI-driven site selection benefits.

Component Analysis

The Retail Site Selection AI market by component is segmented into Software and Services. The so