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

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

More Datasets

For more datasets, click here.

Featured Notebooks

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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.

This dataset offers a detailed view of customer profiles, including demographics, contact information, account status, and summarized purchase history, making it ideal for CRM systems and customer analytics. The data supports segmentation, targeted marketing, and lifecycle analysis, enabling businesses to enhance customer engagement and retention strategies.

The User Profile Data is a structured, anonymized dataset designed to help organizations understand who their users are, what devices they use, and where they are located. Each record provides privacy-compliant linkages between user IDs, demographic profiles, device intelligence, and geolocation data, offering deep context for analytics, segmentation, and personalization.

Built for privacy-safe analytics, the dataset uses hashed identifiers like phone number and email and standardized formats, making it easy to integrate into big-data platforms, AI pipelines, and machine learning models for advanced analytics.

Demographic insights include gender, age, and age group, essential for audience profiling, marketing optimization, and consumer intelligence. All gender data is user-declared and AI-verified through image-based avatar validation, ensuring data accuracy and authenticity.

The dataset’s Device Intelligence Layer includes rich technical attributes such as device brand, model, OS version, user agent, RAM, language, and timezone, enabling technical segmentation, performance analytics, and targeted ad delivery across diverse device ecosystems.

On the location and POI front, the dataset combines GPS-based and IP-based coordinates—including country, region, city, latitude, longitude —to provide high-precision geospatial insights. This enables mobility pattern analysis, market expansion planning, and POI clustering for advanced location intelligence.

Each user record contains onboarding and lifecycle fields like unique IDs, and profile update timestamps, allowing accurate tracking of user acquisition trends, data freshness, and activity duration.

🔍 Key Features • 1st-party, consent-based demographic & device data • AI-verified gender insights via avatar recognition • OS-level app data with 120+ daily sessions per user • Global coverage across APAC and emerging markets • GPS + IP-based geolocation & POI intelligence • Privacy-compliant, hashed identifiers for safe integration

🚀 Use Cases • Audience segmentation & lookalike modeling • Ad-tech and mar-tech optimization • Geospatial & POI analytics • Fraud detection & risk scoring • Personalization & recommendation engines • App performance & device compatibility insights

🏢 Industries Served Ad-Tech • Mar-Tech • FinTech • Telecom • Retail Analytics • Consumer Intelligence • AI & ML Platforms

Description: This dataset includes detailed demographic and behavioral information about restaurant consumers. It is designed to provide insights into consumer profiles, preferences, and habits, which can be useful for improving customer experience and developing targeted marketing strategies.

Features:

Consumer_ID: A unique identifier assigned to each consumer in the dataset. City: The city where the consumer resides. State: The state or province where the consumer is located. Country: The country where the consumer lives. Latitude: The geographical latitude of the consumer’s location. Longitude: The geographical longitude of the consumer’s location. Smoker: Indicates whether the consumer is a smoker (e.g., Yes/No). Drink_Level: The consumer’s level of alcohol consumption (e.g., None, Light, Moderate, Heavy). Transportation_Method: The mode of transportation the consumer uses to travel to the restaurant (e.g., Car, Public Transit, Walking). Marital_Status: The consumer’s marital status (e.g., Single, Married, Divorced, Widowed). Usage:

Consumer Profiling: Understand the demographics and habits of different consumer segments to tailor marketing strategies and restaurant offerings. Location Analysis: Analyze consumer location data to identify key markets and optimize restaurant placement or delivery areas. Behavioral Insights: Study smoking and drinking habits to adjust menu options and enhance customer experience. Transportation Trends: Assess how consumers travel to the restaurant to improve accessibility and convenience. Source: The data is collected from restaurant surveys, customer profiles, and demographic studies.

Notes:

Ensure that personal data is handled securely and in compliance with privacy regulations. Regular updates may be necessary to reflect changes in consumer behavior and demographics.

Dataset Description: Jimrealtex Customer Dataset

This dataset contains customer demographic and behavioral information designed for exploring segmentation, clustering, and predictive analytics in retail and marketing contexts. It provides a simple yet powerful foundation for practicing data science techniques such as K-Means clustering, customer profiling, and recommendation systems.

### Dataset Features - CustomerID: Unique identifier for each customer
- Genre: Gender of the customer (Male/Female)
- Age: Age of the customer (years)
- Annual Income (k$): Annual income in thousands of dollars
- Spending Score: A score assigned by the business based on customer behavior and spending patterns

Notes - Some records contain missing values (nan) in Age, Annual Income, or Spending Score. These can be handled using imputation, removal, or advanced techniques depending on the analysis.
- Spending Score is an arbitrary metric often used in clustering exercises to simulate customer engagement.

### Potential Use Cases - Customer Segmentation: Apply clustering algorithms (e.g., K-Means, DBSCAN) to group customers by income and spending habits.
- Marketing Strategy: Identify high-value customers and tailor promotions.
- Predictive Modeling: Build models to predict spending behavior based on demographics.
- Data Cleaning Practice: Handle missing values and prepare the dataset for machine learning tasks.

** Why This Dataset?**

This dataset is widely used in machine learning tutorials and business analytics projects because it is small, interpretable, and directly applicable to real-world scenarios like retail customer analysis. It’s ideal for beginners learning clustering and for professionals prototyping segmentation strategies.

This product will include topics such as age, sex, race, Hispanic or Latino origin, household type, relationship to householder, group quarters population, housing occupancy and housing tenure.

Ipsos Global @dvisor wave 62 was conducted on September 2 and September 16, 2014. It included the following question sections: [add section letter and name, e.g., A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo, JS: Taking Surveys.

Overview

The India Customer Financial Profiles & Transactions Dataset contains 20,000 fully synthetic records that represent the demographic, financial, credit, and transactional behavior of customers across India. This dataset is ideal for machine learning, financial analytics, risk modeling, fintech simulation, and academic research.

All data is algorithmically generated, ensuring:

No real person is represented

No sensitive or identifiable information is included

Full compliance with privacy and research ethics

📂 Dataset File india_customer_financial_profiles_20000_cleaned.csv

A fully cleaned, validated, and standardized dataset containing demographic, financial, and transaction details.

🧾 Data Dictionary 👤 Demographic Information Demographic fields included in the dataset: id — Unique customer ID current_age — Customer age birth_year — Year of birth birth_month — Month of birth gender — Male / Female / Other address — Synthetic Indian address (City, State, PIN)

💰 Financial Attributes Financial attributes included: per_capita_income — Monthly per-person household income yearly_income — Annual income total_debt — Total outstanding debt credit_score — Score between 300–900 num_credit_cards — Number of credit cards]

🧾 Transaction Details Transaction-related fields: transaction_id — Unique transaction ID date — Transaction date (YYYY-MM-DD) client_id — Synthetic ID linked to customer card_id — Card identifier amount — Transaction amount (INR) use_chip — Chip used (Yes/No) merchant_id — Merchant identifier merchant_city — Transaction location (city) merchant_state — State of merchant zip — 6-digit PIN code

🔍 Key Features ✔ 20,000 synthetic customer profiles ✔ Includes demographic + financial + transaction data ✔ Standardized date format (YYYY-MM-DD) ✔ PIN codes extracted and cleaned ✔ No missing values ✔ Consistent and realistic Indian data patterns ✔ High usability for ML and analytics

🧠 Use Cases 🟦 Machine Learning Credit scoring Loan default prediction Fraud detection Customer segmentation Transaction classification

🟩 Data Analytics Financial behavioral trends Income–debt correlation analysis Merchant-level insights Urban vs rural customer profiles

🟧 Fintech Research Synthetic simulation Risk modeling Customer persona creation Spending behavior research

🟪 Education & Learning Data cleaning practice Feature engineering EDA & visualization projects Full ML pipeline demonstrations

🛠️ Data Generation Methodology The dataset was generated using: Synthetic demographic distribution modeling Indian address & PIN pattern simulation Income and credit-score probability distributions Transaction behavior simulation Randomized merchant profiles Rule-based statistical generation No scraping or real-world data sources were used. This dataset is 100% synthetic.

📜 License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You may: Use Modify Redistribute Adapt As long as you provide citation.

✍️ Citation

Use the following format to cite this dataset: Bedmutha, Kundan (2025). India Customer Financial Profiles & Transactions Dataset (20,000 Records).

🙌 Acknowledgements

This dataset was created to support: Students Researchers Machine learning practitioners Fintech analysts Educators Your feedback and suggestions are welcome for future dataset enhancements.

🎉 Thank You!

During a 2023 survey among senior marketing executives worldwide, ** percent of respondents listed demographics as the most important type of data used for customer profiling. Behavioral data came next, with ** percent, while data about business details rounded up the top three with ** percent.

Premium B2C Consumer Database - 269+ Million US Records

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

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

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

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

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

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

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

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

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

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

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

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

Delivery Methods: Secure FTP, API integration, direct download

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

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

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

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

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

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

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

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

Contact our team to discuss your specific ta...

This dataset encompasses deterministic consumer demographics, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Included are age, gender, ethnicity, location, employment, education, income, pet ownership, having kids/children, relationship, military status and more.

Dataset available only to University of Arizona affiliates. To obtain access, you must log in to ReDATA with your NetID. Data is for research use by each individual downloader only. Sharing and/or redistribution of any portion of this dataset is prohibited.This ReferenceUSA dataset from Data Axle (formerly Infogroup) contains household data about US consumers in annual snapshots from 2006-2021. It includes details such as family demographics, income, home ownership status, lifestyle, location and more, which can help users to create marketing plans and conduct competitive analyses.Consumer profiles are described with 58-66 indicators. Data for all states are combined into single files for each year between 2006 and 2012 while there is a file for each state in 2013-2021. The Layout - Consumer DB Historical 2006-2012.xlsx in Documentation.zip applies to 2006-2012. Codebooks for 2013, 2014, 2015, 2017, 2018, 2019 and 2021 are not included but files in 2013-2021 have similar layouts therefore 2016 Historical Residential File Layout.xlsx and 2020 Historical Residential File Layout.xlsx in Documentation.zip apply to 2013-2021.The University of Arizona University Libraries also subscribe to Data Axle Reference Solutions which provides this data in a searchable, online database with historical data available going back to 2003.NOTE: The uncompressed datasets are very large.Detailed file descriptions and MD5 hash values for each file can be found in the README.txt file.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Ipsos Global @dvisor wave 32 was conducted on April 3 and April 17, 2012. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, EQ: Global Retail Intended Purchase Assessment, ET: Languages Used in Business, EU: Online Dating, X: Corporate/Business Risks, C: Corporate Social Responsibility.

Ipsos Global @dvisor wave 17 was conducted on January 14 and January 24, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, BY: Consumer Goods Questions.

Ipsos Global @dvisor wave 72 was conducted from July 24 - August 7, 2015. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo; KN: Euro Questions; BM: Social Issues.

Ipsos Global @dvisor wave 24 was conducted on August 5 and August 18, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, BD: Retail Confidence.

Ipsos Global @dvisor wave 23 was conducted on July 5 and July 18, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, H: Economy/Spending/Purchasing, CW: Media Questions

Selected demographic and housing estimates data citywide and by borough. Five year estimates of population data from the Census Bureau's American Community Survey.

Ipsos Global @dvisor wave 64 was conducted on December 2 and December 16, 2014. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo, IK: Christmas Question.