Context

This real-world customer dataset with 31 variables describes 83,590 instances (customers) from a hotel in Lisbon, Portugal.

Content

The data comprehends three full years of customer personal, behavioral, demographic, and geographical information.

Acknowledgements

Additional information on this dataset can be found in the article A Hotel's customers personal, behavioral, demographic, and geographic dataset from Lisbon, Portugal (2015-2018), written by Nuno Antonio, Ana de Almeida, and Luis Nunes for Data in Brief (online November 2020).

Inspiration

This dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

Sourcing accurate and up-to-date demographic 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 demographic 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 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 Demographic 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

Consumer Insurance Experience & Demographic Profile

This dataset provides a detailed view of how individuals engage with insurance products, paired with demographic and lifestyle attributes to enable powerful segmentation, behavioral analysis, and customer journey mapping. By combining real-world insurance experiences with contextual information about each respondent’s background and preferences, this dataset supports a wide range of data-driven decision-making for insurance providers, policy designers, marketing teams, and product strategists.

Value of the Dataset Understanding how consumers perceive and interact with insurance offerings is critical to building products that resonate and services that retain. This dataset offers that visibility across multiple dimensions—capturing not only what type of insurance consumers hold and how they purchased it, but also what drives their satisfaction, loyalty, and likelihood to switch. Paired with demographic details like income, education, family status, and lifestyle, this information becomes a foundation for more personalized outreach, better-designed offerings, and improved customer experiences.

Because the data reflects lived experiences across diverse markets, it is particularly valuable for benchmarking consumer sentiment in emerging economies, identifying service delivery gaps, or evaluating potential uptake of new policy formats such as digital or personalized insurance.

Example Use Cases 1. Targeted Product Design A health insurer looking to launch short-term, digital-first plans could filter this dataset for consumers with low policy tenure, high digital communication preference, and dissatisfaction with current providers. This segment would inform feature design and positioning.

1. Competitive Analysis A provider evaluating churn risk can identify patterns among users who have filed claims but report dissatisfaction—indicating operational areas that may be driving customer loss and where improvements could increase retention.

2. Communication Channel Optimization By analyzing preferred communication methods across different demographic segments, insurers can tailor outreach strategies (e.g., SMS vs. in-app chat) to improve engagement and reduce support costs.

3. Market Expansion & Localization International insurers can explore regional variations in satisfaction drivers, awareness levels, and price sensitivity to refine go-to-market strategies in countries like Senegal, Tanzania, or the UAE.

4. Personalized Policy Offer Design Using data on interest in personalized policies and lifestyle indicators, providers can build customizable offerings for consumers more likely to value flexibility, such as frequent travelers or those with irregular incomes.

Insurance-Specific Fields & Descriptions Current Insurance Type Captures the kind of insurance the individual currently holds, with a focus on health insurance in this dataset.

Purchase Method Indicates how the insurance was obtained—through an agent, online, employer, etc.—to understand acquisition channels.

Policy Length Duration of the current policy, categorized (e.g., less than 1 year, 1–3 years, more than 5 years) to analyze tenure-based behaviors.

Satisfaction Self-reported satisfaction with the current insurance provider, useful for benchmarking sentiment.

Top Factor in Choosing Provider Highlights what influenced the purchase decision most—such as coverage options, customer service, pricing, or brand reputation.

Policy Review Frequency Shows how often individuals revisit their policy details or compare with alternatives, revealing levels of engagement or passive behavior.

Filed Claim A yes/no indicator showing whether the consumer has ever filed a claim, useful for analyzing downstream service experiences.

Claim Satisfaction Measures satisfaction with how past claims were handled, providing insight into operational effectiveness.

Primary Value Sought Captures what consumers value most from their insurance—e.g., peace of mind, financial protection, access to quality care.

Likelihood to Recommend Acts as a proxy for Net Promoter Score (NPS), indicating brand advocacy and potential referral behavior.

Biggest Areas for Improvement Open-ended or multi-select responses identifying where insurers can do better—lower premiums, faster claims, more digital tools, etc.

Preferred Method of Communication Indicates how consumers want to be contacted—via online chat, phone, email, SMS—supporting channel strategy optimization.

Preferred Services Details the types of updates or services consumers want—such as claims status, policy changes, or coverage recommendations.

Insurance Awareness Score Self-reported awareness of how insurance works, including policy options, rights, and terms.

Interest in Personalized Policies Captures whether the individual is open to customized insurance plans, an important indicator for usage-ba...

For the purpose of our partners and the community to find demographic information on individual member of households that applied for services provided by the Office of Resilience and Community services. Updated Quarterly. Data includes: Client IndexHousehold IndexRaceGenderEthnicityDisability StatusMilitary StatusHealth Insurance (Y/N)Employment StatusEducation StatusHead of Household (Y/N)Age

This dataset includes the following variables: client county; number, percentage, average, and age of clients served, number and percentage of adolescent client served, number and percentage of male clients served , and clients served by race and ethnicity (Latino, White, African American, Asian and Pacific Islander, Other (including Native American); and clients served by primary language (Spanish, English, Other).

As of January 2025, users aged 25 to 34 years made up Facebook's largest audience in the United States, accounting for 24.2 percent of the social network's user base, with 12.3 percent of those users being women. Overall, 9.7 percent of users aged 35 to 44 years were women, and 9.3 percent were men. How many people use Facebook in the United States? Facebook is by far the most used social network in the world and finds a huge share of its audience in the United States. Facebook’s U.S. audience size comes second only to India. In 2023, there were over 246 million Facebook users in the U.S. By 2028, it is estimated that around 263 million people in the U.S. will be signed up for the platform. How do users in the United States view the platform? Although Facebook is widely used and very popular with U.S. consumers, there are issues of trust with its North American audience. As of November 2021, 72 percent of respondents reported that they did not trust Facebook with their personal data. Despite having privacy doubts, a May 2022 survey found that 20 percent of adults had a very favorable opinion of Facebook, and one-third held a somewhat positive view of the platform.

The demographic data displayed in this theme of Florida’s Roadmap to Living Healthy are quantitative measures that exhibit the socioeconomic state of Florida’s communities. The data sets comprising this themed map include topics such as population, race, income level, age, education, housing, and lifestyle data for all of Florida’s 67 counties, and other basic demographic characteristics. The Florida Department of Agriculture and Consumer Services has utilized the most current demographic statistical data from trusted sources such as the U.S. Census Bureau, U.S. Department of Housing and Urban Development, U.S. Department of Labor Bureau of Labor Statistics, Florida Department of Children and Families, and Esri to craft this custom visualization. Demographics provide profound perspective to your data analytics and will help you recognize the distinctive characteristics of a population based on its location. This demographic-themed mapping tool will simplify your ability to identify the specific socioeconomic needs of every community in Florida.

Apple Card owners in the United States in 2023 were typically Millennials who tended to have a relatively high income. This is according to a survey held among Americans who either owned or did not own Apple's credit card. The source adds this demographic was in line with other surveys they held for other Apple products. Statista's Consumer Insights also noted that U.S. Apple iOS users are typically high income. The source of this particular survey, however, does not state how many of its 4,000 respondents owned Apple Card. All statistics on Apple Pay - and services that rely on it, such as Apple Card and Apple Cash - are estimates, typically based on survey information. Apple Inc. does not share figures on individual services, whereas financial providers who offer Apple Pay, Apple Card, etc. are contractually forbidden to share such information.

The most significant cohorts of users on Instagram are aged 18 – 24.

There are more male LinkedIn users than females – although it is pretty balanced.

This dataset displays demographic information for all Boulder Parks and Recreation members and visitors. The dataset includes customer age, gender, resident status, location (city, state, and zipcode), entry date, and membership package type(s).

Please note that due to the nature of open-ended data entry for many customer detail fields, some customer data (e.g. city) will need to be cleaned and normalized before analysis.

Introduction

Yelp Statistics: Yelp isn't just for restaurants and cafes anymore; it's now used for all kinds of businesses. It helps companies show up in local searches and build trust with customers. Good reviews attract new customers and increase visits. Plus, Yelp has features like photos, questions, and check-ins that help businesses connect with their customers on a personal level.

For any business wanting to succeed in their area, using Yelp well is a smart move! This article will shed more light on Yelp Statistics.

There were 279 809 300 Facebook users in United States of America in April 2025, which accounted for 81.3% of its entire population. The majority of them were women - 53.8%. People aged 25 to 34 were the largest user group (67 700 000). The highest difference between men and women occurs within people aged 65 and above, where women lead by 13 300 000.

In 2023, the majority of social media users in Thailand were female which amounted to around 53.2 percent of social media users. Among the social media platforms in Thailand, Facebook has become the most popular platform among Thai users.

Abstract

The L2 Consumer Dataset contains information about consumers from all 50 states and the District of Columbia. The data, which is sourced from credit bureaus and other consumer information sources, is generally bought and used by companies for marketing purposes. Updates are expected quarterly.

All tables (except for New Jersey) were last updated on 03-25-2024. New Jersey was updated on 05-11-2024, when about 25,000 records were removed to comply with Daniel's Law.

Methodology

To create this file, L2 processes nationwide consumer data on an ongoing basis for all 50 states and the District of Columbia with refreshes typically at least every ninety days. The data are sourced from credit bureaus and other consumer information sources. Those data are standardized and consist of approximately 240,000,000 records nationwide.

Usage

Each table contains 667 variables. For more information about these variables, see ***2024-04-20-Commercial-Data-Dictionary.xlsx ***(under Supporting files).

The L2 Consumer and L2 Voter and Demographic data can be joined on the Lalvoterid variable.

One can also use the Lalvoterid variable to validate the state. For example, let's look at the Lalvoterid for one row in the CA-Commercial-2024-03-25 table. The characters in the fourth and fifth positions of this identifier, LALCA25840445, are 'CA' (California).

The date appended to each table name represents when the data was last updated. All tables (except for New Jersey) were last updated on 03-25-2024. New Jersey was updated on 05-11-2024 when about 25,000 records were removed to comply with Daniel's Law. For more information about this release, see 2024-03-27-Commercial-Data-Release-Notes.docx* *(under Supporting files).

Bulk Data Access

Data access is required to view this section.

DataMapping Tool

Data access is required to view this section.

There were 6 009 900 Messenger users in Sweden in June 2024, which accounted for 56.9% of its entire population. The majority of them were women - 52.2%. People aged 25 to 34 were the largest user group (1 333 900). The highest difference between men and women occurs within people aged 65 and above, where women lead by 432 900.

The Life and Health (L&H) Insurance industry is experiencing a rapid transformation driven by the increasing adoption of data analytics. The market, valued at $2647.3 million in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 9.2% from 2025 to 2033. This robust growth is fueled by several key factors. Firstly, the need for improved risk assessment and underwriting is pushing insurers to leverage advanced analytics for predictive modeling. This allows for more accurate pricing, reduced fraud, and better customer segmentation. Secondly, demographic profiling enabled by data analytics helps insurers tailor products and services to specific customer needs, leading to increased customer satisfaction and retention. Data visualization tools further enhance decision-making by providing clear and concise insights into complex datasets, facilitating better strategy development and operational efficiency. Finally, the rise of Insurtech companies and the increasing availability of sophisticated software solutions are accelerating the adoption of data analytics across the L&H insurance sector. The competitive landscape is shaped by a mix of established players like Deloitte, SAP AG, and IBM, alongside specialized Insurtech firms offering innovative data analytics solutions. The segmentation of the market reveals significant opportunities across various applications and types. Predictive analysis, demographic profiling, and data visualization are the most prominent application segments, reflecting the industry's focus on risk management, customer understanding, and improved operational efficiency. The service and software segments represent the primary delivery models for data analytics solutions. While North America currently holds a dominant market share, regions like Asia-Pacific are experiencing rapid growth, driven by increasing digitalization and a rising middle class with growing insurance needs. Regulatory changes promoting data sharing and increased customer data privacy awareness are likely to influence market dynamics in the coming years. The key challenges include data security concerns, the need for skilled data scientists, and the integration of legacy systems with new data analytics platforms. Successfully navigating these challenges will be crucial for insurers to fully capitalize on the transformative potential of data analytics.

There are currently over 1.5 billion active users on TikTok worldwide.

According to information from NapoleonCat, the highest share of Facebook users in Indonesia, 38 percent, were between the ages of 25 and 34 years as of December 2024. As of this date, there were approximately 174 million Facebook users in Indonesia, of which 46.2 percent were female.

The provided data asset is relational and consists of four distinct data files.

1. address.csv: contains address information

2. customer.csv: contains customer information.

3. demographic.csv: contains demographic data

4. termination.csv: includes customer termination information.

5. autoinsurance_churn.csv: includes merged customer churn data generated from this notebook.

All data sets are linked using either ADDRESS_ID or INDIVIDUAL_ID. The ADDRESS_ID pertains to a specific postal service address, while the INDIVIDUAL_ID is unique to each individual. It is important to note that multiple customers may be assigned to the same address, and not all customers have demographic information available.

Size of the data set

The data set includes 1,536,673 unique addresses and 2,280,321 unique customers, of which 2,112,579 have demographic information. Additionally, 269,259 customers cancelled their policies within the previous year.

Note

Please note that the data is synthetic, and all customer information provided is fictitious. While the latitude-longitude information can be mapped at a high level and generally refers to the Dallas-Fort Worth Metroplex in North Texas, it is important to note that drilling down too far may result in some data points that are located in the middle of Jerry World, DFW Airport, or Lake Grapevine. The physical addresses provided are fake and are unrelated to the corresponding lat/long.

The termination table includes the ACCT_SUSPD_DATE field, which can be used to derive a binary churn/did not churn variable. The data set is modelable, meaning that the other data available can be used to predict which customers churned and which did not. The underlying logic used to make these predictions should align with predicting auto insurance churn in the real world.