The majority of guests on Airbnb are women. Most Airbnb guests are aged 25 to 34.

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

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

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

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

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

Hotel Guest Arrivals: Foreign: AO: Japan data was reported at 497,000.000 Person in 2016. This records a decrease from the previous number of 847,602.973 Person for 2015. Hotel Guest Arrivals: Foreign: AO: Japan data is updated yearly, averaging 1,163,671.000 Person from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 1,710,413.000 Person in 2000 and a record low of 497,000.000 Person in 2016. Hotel Guest Arrivals: Foreign: AO: Japan data remains active status in CEIC and is reported by Directorate General for Enterprise. The data is categorized under Global Database’s France – Table FR.Q008: Hotels Statistics: Guest Arrivals (Annual).

This survey highlights the areas in which the most frequent errors in guest service occur, failures that have the biggest negative impact on guest loyalty. 33 percent of hotel managers named the food and beverage area as experiencing the most failures.

This statistic shows the results of a representative survey on visitor demographics in theme parks in Germany in 2013. That year, a total of 21 percent of single respondents from 25 to 49 years of age stated that they went to a theme park at least once last year.

The increasing speed of technological innovation in all industries has been a catalyst for the digitalization of the hospitality industry. In early 2021, hoteliers in Asia, Europe, and North America, were surveyed on which aspects of the guest journey they were looking to digitize later that year. The majority of respondents, 70 percent, stated that they were looking to digitize hotel information for their guests. Meanwhile, 57 percent of respondents stated that they were looking to digitize the check-in/check-out process.

How many hospitality companies are looking to digitalize?

The share of travel and hospitality companies with an individual or team directly responsible for digital transformation worldwide varied in 2020. When executives in the industry were surveyed on whether their organization had an individual or team directly responsible for digital transformation, 27 percent of respondents stated that their organization had a cross-functional team for digital transformation. Meanwhile, 16 percent of respondents, respectively, stated that they either had a third-party partner, such as a consultant or agency, or no one responsible for digital transformation.

How confident are hospitality companies in their ability to digitalize?

In 2020, executives in the global travel and hospitality industry were asked about their ability to deliver on digital objectives given the state of their company's budget. The majority of respondents, 39 percent, stated that they were somewhat confident about their company's ability to meet digital objectives given the state of their budget. Meanwhile, 12 percent of respondents stated that they were quite concerned. As a consequence, the confidence level of travel and hospitality companies to deliver on their digital objectives worldwide depended at least in part on the company’s budget.

France Hotel Guest Arrivals: Region: Burgundy-Franche-Comte data was reported at 5,110,000.000 Person in 2016. France Hotel Guest Arrivals: Region: Burgundy-Franche-Comte data is updated yearly, averaging 5,110,000.000 Person from Dec 2016 (Median) to 2016, with 1 observations. France Hotel Guest Arrivals: Region: Burgundy-Franche-Comte data remains active status in CEIC and is reported by Directorate General for Enterprise. The data is categorized under Global Database’s France – Table FR.Q008: Hotels Statistics: Guest Arrivals (Annual).

Provide monthly statistical reports on the number of visitors to the tourism and recreation areas in Taoyuan City, with data recorded from January 2014.

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

Demographic characteristics of those who respond to the Nova Scotia Visitor Exit Survey.

Introduction

McDonald’s Statistics: McDonald's, a global leader in the fast food industry, has been a cornerstone in culinary history since its inception in 1940 in San Bernardino, California. As of 2023, the brand boasts over 41,800 locations worldwide, with its largest market in the United States, where approximately 13,400 restaurants operate.

In Europe, France and the United Kingdom lead with around 1,600 and 1,400 McDonald's respectively, while China dominates the Asia Pacific and Middle East regions in the number of McDonald's locations. Renowned for its Big Mac, which has been used since 1986 in the Economist's Big Mac Index to measure economic purchasing power, McDonald's continues to thrive in a dynamic consumer market. By 2024, McDonald's brand value reached USD 38 billion, securing its position as the second most valuable restaurant brand globally, outpacing competitors like Burger King, KFC, and Subway.

This enduring success is attributed to the company's adaptation to digital trends and consumer preferences, integrating delivery services with platforms like UberEats, DoorDash, and GrubHub. This strategy, part of McDonald's "4Ds" approach—digitalization, delivery, drive-thru, and development—ensures its prominence in the fast food industry continues.

This dataset provided statistics and performance metrics about the volume and responsiveness in engaging with customers via several customer engagement channels. Data was provided for New York City Transit Subway and Bus customer engagement and customer service teams between May 2017 and May 2022.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling and person

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Every separate and independent structure that has been constructed or converted for use as temporary or permanent housing. This includes any class of fixed or mobile shelter used as a place of lodging at the time of enumeration. A dwelling can be a) a private house, apartment, floor in a house, room or group of rooms, ranch, etc. designed to give lodging to one person or a group of people or b) a boat, vehicle, railroad car, barn, shed, or any other type of shelter occupied as a place of lodging at the time of enumeration. - Households: All the occupying members of a family or private dwelling that live together as family. In most cases, a household is made up of a head of the family, relatives of this person (wife or partner, children, grand-children, nieces and nephews, etc.), close friends, guests, lodgers, domestic employees and all other occupants. Households with five or fewer lodgers are considered private,but households with six or more lodgers are considered a non-family group. - Group quarters: Accommodation for a group of people who are not usually connected by kinship ties who live together for reasons of discipline, healthcare, education, mlitary activity, religion, work or other dwellings such as reform schools, boarding schools, barracks, hopsitals, guest houses, nursing homes, workers camps, etc.

Universe

Population in private and communal housing

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics

SAMPLE DESIGN: Systematic sample of every 10th household with a random start, drawn by the Minnesota Population Center

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 268,248

Mode of data collection

Face-to-face [f2f]

Research instrument

Single record that includes housing and population questionnaires

As part of the AIR research project - AI-based recommender for sustainable tourism - guest surveys were conducted in the form of face-to-face interviews in six German use cases: North Sea, Baltic Sea, Sauerland (ski resorts and lakes), Ruhr area, Allgäu Füssen, Allgäu rural area. This form of demand analysis is intended to obtain information about the demographics of the guests, tourist behaviour patterns, reasons for the choice of destination and perception of the destination as well as information behaviour. The surveys were carried out from July to the end of September 2022, for the Sauerland ski resorts in the 2022/23 winter season. 5,975 people were surveyed in total, all of whom are included in this SPSS dataset (sav-format).

The web map displays past, current, and projected population growth within San Diego County, CA. National Visitor Use Monitoring Data is also present for the Cleveland National Forest.

Hotel Guest Arrivals: Resident: Region: Hauts-de-France data was reported at 4,187,000.000 Person in 2016. Hotel Guest Arrivals: Resident: Region: Hauts-de-France data is updated yearly, averaging 4,187,000.000 Person from Dec 2016 (Median) to 2016, with 1 observations. Hotel Guest Arrivals: Resident: Region: Hauts-de-France data remains active status in CEIC and is reported by Directorate General for Enterprise. The data is categorized under Global Database’s France – Table FR.Q008: Hotels Statistics: Guest Arrivals (Annual).

In 2023, the number of international hotel guests in the selected Dutch cities increased significantly compared to the previous year. Overall, Amsterdam recorded by far the highest figure, with just under seven million inbound hotel guests in 2023. Rotterdam ranked second in the list, with 776 thousand inbound hotel guests. Accommodation services in Amsterdam: hotels vs. Airbnb Based on data on the accommodation establishments in Amsterdam broken down by type, there were just over 500 hotels in the popular Dutch destination in 2023. By comparison, the number of Airbnb listings in Amsterdam totaled nearly 9,000 as of March 2024, with entire homes and apartments accounting for most listings. How many inbound tourists visit the Netherlands? In 2023, the Netherlands ranked behind Austria and ahead of Poland in the list of the most visited European countries by inbound visitors, with over 20 million inbound tourist arrivals. Meanwhile, international tourism receipts in the Netherlands amounted to approximately 20 billion U.S. dollars that year, exceeding the figures recorded before the impact of the COVID-19 pandemic.

In 2025, consumers in the United States were asked about their level of satisfaction with different travel and tourism industries. Airlines, car rentals, online travel agencies (OTAs), rideshare, and lodgings were all given an average American Customer Satisfaction score of 75 out of 100.

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.

This dataset contains information on hotel bookings from two different types of hotels: city hotel and resort hotel. The dataset includes details about customer demographics, booking information, and reservation details. It is an excellent resource for businesses in the hospitality industry, researchers, and data scientists interested in analyzing customer behavior, booking patterns, and market trends for these two types of hotels.