The dataset was collected from Kaggle. It includes various features related to customer demographics, purchasing behavior, and other relevant metrics.

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 dataset provides a streamlined approach to customer segmentation using the RFM (Recency, Frequency, Monetary) model, enhanced with geographical data. It includes essential metrics for analyzing customer behavior, helping businesses tailor their marketing strategies and improve customer relationships.

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

How high is the brand awareness of Kohl's in the United States?When it comes to fashion store customers, brand awareness of Kohl's is at **% in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Kohl's in the United States?In total, **% of U.S. fashion store customers say they like Kohl's. However, in actuality, among the **% of U.S. respondents who know Kohl's, **% of people like the brand.What is the usage share of Kohl's in the United States?All in all, **% of fashion store customers in the United States use Kohl's. That means, of the **% who know the brand, **% use them.How loyal are the customers of Kohl's?Around **% of fashion store customers in the United States say they are likely to use Kohl's again. Set in relation to the **% usage share of the brand, this means that **% of their customers show loyalty to the brand.What's the buzz around Kohl's in the United States?In the summer-fall period of 2024, about **% of U.S. fashion store customers had heard about Kohl's in the media, on social media, or in advertising over the past three months. Of the **% who know the brand, that's **%, meaning at the time of the survey there's some buzz around Kohl's in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Abstract

The 1998 Ghana Demographic and Health Survey (GDHS) is the latest in a series of national-level population and health surveys conducted in Ghana and it is part of the worldwide MEASURE DHS+ Project, designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1998 GDHS is to provide current and reliable data on fertility and family planning behaviour, child mortality, children’s nutritional status, and the utilisation of maternal and child health services in Ghana. Additional data on knowledge of HIV/AIDS are also provided. This information is essential for informed policy decisions, planning and monitoring and evaluation of programmes at both the national and local government levels.

The long-term objectives of the survey include strengthening the technical capacity of the Ghana Statistical Service (GSS) to plan, conduct, process, and analyse the results of complex national sample surveys. Moreover, the 1998 GDHS provides comparable data for long-term trend analyses within Ghana, since it is the third in a series of demographic and health surveys implemented by the same organisation, using similar data collection procedures. The GDHS also contributes to the ever-growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The major focus of the 1998 GDHS was to provide updated estimates of important population and health indicators including fertility and mortality rates for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of key variables for the ten regions in the country.

The list of Enumeration Areas (EAs) with population and household information from the 1984 Population Census was used as the sampling frame for the survey. The 1998 GDHS is based on a two-stage stratified nationally representative sample of households. At the first stage of sampling, 400 EAs were selected using systematic sampling with probability proportional to size (PPS-Method). The selected EAs comprised 138 in the urban areas and 262 in the rural areas. A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second stage selection of households. At the second stage of sampling, a systematic sample of 15 households per EA was selected in all regions, except in the Northern, Upper West and Upper East Regions. In order to obtain adequate numbers of households to provide reliable estimates of key demographic and health variables in these three regions, the number of households in each selected EA in the Northern, Upper West and Upper East regions was increased to 20. The sample was weighted to adjust for over sampling in the three northern regions (Northern, Upper East and Upper West), in relation to the other regions. Sample weights were used to compensate for the unequal probability of selection between geographically defined strata.

The survey was designed to obtain completed interviews of 4,500 women age 15-49. In addition, all males age 15-59 in every third selected household were interviewed, to obtain a target of 1,500 men. In order to take cognisance of non-response, a total of 6,375 households nation-wide were selected.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the GDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. These questionnaires were based on model survey instruments developed for the international MEASURE DHS+ programme and were designed to provide information needed by health and family planning programme managers and policy makers. The questionnaires were adapted to the situation in Ghana and a number of questions pertaining to on-going health and family planning programmes were added. These questionnaires were developed in English and translated into five major local languages (Akan, Ga, Ewe, Hausa, and Dagbani).

The Household Questionnaire was used to enumerate all usual members and visitors in a selected household and to collect information on the socio-economic status of the household. The first part of the Household Questionnaire collected information on the relationship to the household head, residence, sex, age, marital status, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. For this purpose, all women age 15-49, and all men age 15-59 in every third household, whether usual residents of a selected household or visitors who slept in a selected household the night before the interview, were deemed eligible and interviewed. The Household Questionnaire also provides basic demographic data for Ghanaian households. The second part of the Household Questionnaire contained questions on the dwelling unit, such as the number of rooms, the flooring material, the source of water and the type of toilet facilities, and on the ownership of a variety of consumer goods.

The Women’s Questionnaire was used to collect information on the following topics: respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunisation and health, marriage, fertility preferences and attitudes about family planning, husband’s background characteristics, women’s work, knowledge of HIV/AIDS and STDs, as well as anthropometric measurements of children and mothers.

The Men’s Questionnaire collected information on respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, as well as knowledge of HIV/AIDS and STDs.

Response rate

A total of 6,375 households were selected for the GDHS sample. Of these, 6,055 were occupied. Interviews were completed for 6,003 households, which represent 99 percent of the occupied households. A total of 4,970 eligible women from these households and 1,596 eligible men from every third household were identified for the individual interviews. Interviews were successfully completed for 4,843 women or 97 percent and 1,546 men or 97 percent. The principal reason for nonresponse among individual women and men was the failure of interviewers to find them at home despite repeated callbacks.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of shortfalls made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 1998 GDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 1998 GDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 1998 GDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1998 GDHS is the ISSA Sampling Error Module. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.

This dataset contains customer satisfaction scores collected from a survey, alongside key demographic and behavioral data. It includes variables such as customer age, gender, location, purchase history, support contact status, loyalty level, and satisfaction factors. The dataset is designed to help analyze customer satisfaction, identify trends, and develop insights that can drive business decisions.

File Information: File Name: customer_satisfaction_data.csv (or your specific file name)

File Type: CSV (or the actual file format you are using)

Number of Rows: 120

Number of Columns: 10

Column Names:

Customer_ID – Unique identifier for each customer (e.g., 81-237-4704)

Group – The group to which the customer belongs (A or B)

Satisfaction_Score – Customer's satisfaction score on a scale of 1-10

Age – Age of the customer

Gender – Gender of the customer (Male, Female)

Location – Customer's location (e.g., Phoenix.AZ, Los Angeles.CA)

Purchase_History – Whether the customer has made a purchase (Yes or No)

Support_Contacted – Whether the customer has contacted support (Yes or No)

Loyalty_Level – Customer's loyalty level (Low, Medium, High)

Satisfaction_Factor – Primary factor contributing to customer satisfaction (e.g., Price, Product Quality)

Statistical Analyses:

Descriptive Statistics:

Calculate mean, median, mode, standard deviation, and range for key numerical variables (e.g., Satisfaction Score, Age).

Summarize categorical variables (e.g., Gender, Loyalty Level, Purchase History) with frequency distributions and percentages.

Two-Sample t-Test (Independent t-test):

Compare the mean satisfaction scores between two independent groups (e.g., Group A vs. Group B) to determine if there is a significant difference in their average satisfaction scores.

Paired t-Test:

If there are two related measurements (e.g., satisfaction scores before and after a certain event), you can compare the means using a paired t-test.

One-Way ANOVA (Analysis of Variance):

Test if there are significant differences in mean satisfaction scores across more than two groups (e.g., comparing the mean satisfaction score across different Loyalty Levels).

Chi-Square Test for Independence:

Examine the relationship between two categorical variables (e.g., Gender vs. Purchase History or Loyalty Level vs. Support Contacted) to determine if there’s a significant association.

Mann-Whitney U Test:

For non-normally distributed data, use this test to compare satisfaction scores between two independent groups (e.g., Group A vs. Group B) to see if their distributions differ significantly.

Kruskal-Wallis Test:

Similar to ANOVA, but used for non-normally distributed data. This test can compare the median satisfaction scores across multiple groups (e.g., comparing satisfaction scores across Loyalty Levels or Satisfaction Factors).

Spearman’s Rank Correlation:

Test for a monotonic relationship between two ordinal or continuous variables (e.g., Age vs. Satisfaction Score or Satisfaction Score vs. Loyalty Level).

Regression Analysis:

Linear Regression: Model the relationship between a continuous dependent variable (e.g., Satisfaction Score) and independent variables (e.g., Age, Gender, Loyalty Level).

Logistic Regression: If analyzing binary outcomes (e.g., Purchase History or Support Contacted), you could model the probability of an outcome based on predictors.

Factor Analysis:

To identify underlying patterns or groups in customer behavior or satisfaction factors, you can apply Factor Analysis to reduce the dimensionality of the dataset and group similar variables.

Cluster Analysis:

Use K-Means Clustering or Hierarchical Clustering to group customers based on similarity in their satisfaction scores and other features (e.g., Loyalty Level, Purchase History).

Confidence Intervals:

Calculate confidence intervals for the mean of satisfaction scores or any other metric to estimate the range in which the true population mean might lie.

Project Overview: Customer Segmentation Using K-Means Clustering

Introduction In this project, I analysed customer data from a retail store to identify distinct customer segments. The dataset includes key attributes such as age, city, and total sales of the customers. By leveraging K-Means clustering, an unsupervised machine learning technique, I aim to group customers based on their age and sales metrics. These insights will enable the creation of targeted marketing campaigns tailored to the specific needs and behaviours of each customer segment.

Objectives - Cluster Customers: Use K-Means clustering to group customers based on age and total sales. - Analyse Segments: Examine the characteristics of each customer segment. - Targeted Marketing: Develop strategies for personalized marketing campaigns targeting each identified customer group.

Data Description The dataset comprises:

• Age: The age of the customers.
• City: The city where the customers reside.
• Total Sales: The total sales generated by each customer.

Methodology - Data Preprocessing: Clean and preprocess the data to handle any missing or inconsistent entries. - Feature Selection: Focus on age and total sales as primary features for clustering. - K-Means Clustering: Apply the K-Means algorithm to identify distinct customer segments. - Cluster Analysis: Analyse the resulting clusters to understand the demographic and sales characteristics of each group. - Marketing Strategy Development: Create targeted marketing strategies for each customer segment to enhance engagement and sales.

Expected Outcomes - Customer Segments: Clear identification of customer groups based on age and purchasing behaviour. - Insights for Marketing: Detailed understanding of each segment to inform targeted marketing efforts. - Business Impact: Enhanced ability to tailor marketing campaigns, potentially leading to increased customer satisfaction and sales.

By clustering customers based on age and total sales, this project aims to provide actionable insights for personalized marketing, ultimately driving better customer engagement and higher sales for the retail store.

As of January 2025, ** percent of social media users in the United States aged 40 to 49 years were users of Facebook, as were ** percent of ** to ** year olds in the country. Overall, ** percent of those aged 18 to 29 years were using Instagram in the U.S. The social media market in the United States The number of social media users in the United States has shown continuous growth in the past years, and it is forecast to continue increasing to reach *** million users in 2029. As of 2023, the social network user penetration in the United States amounted to an impressive ***** percent, meaning that more than nine in ten people in the country engaged with online platforms. Furthermore, Facebook was by far the most popular social media platform in the United States, accounting for ** percent of all social media visits in 2023, followed by Pinterest with **** percent of visits. The global social media landscape As of April 2024, **** billion people were social media users, accounting for **** percent of the world’s population. Northern Europe was the region with the highest social media penetration rate with a reach of **** percent, followed by Western Europe with **** percent and Eastern Asia **** percent. In contrast, less than one in ten people in Middle Africa used social networks. Facebook’s popularity is not limited to the United States: this network leads the market on a global scale, and it accumulated more than three billion monthly active users (MAU) as of 2024, which is far more any other social media platform. YouTube, Instagram, and WhatsApp followed, all with *** billion or more MAU.

Comprehensive demographic dataset for Means, KY, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

For more information about this layer please see the GIS Data Catalog.This layer was originally created to support the STAR Rating System objective EE-4: Equitable Services & Access Community Level Outcomes for King County. Three demographics are combined into one attribute to determine King County services access analysis by census tract. Each demographic: English proficiency, people of color, and household income, was applied to census tracts separately. This demographic information is based on the 2016 - 2020 American Community Survey from the Census Bureau. The demographic was sorted into five classes using the Natural Breaks classification. Then each class was given a score: 1 for the first class, 2 for the second class, 3 for the third class, etc. Next, each census tract received a score based on the class it was in: 1 if it was in the first class, 2 if it was in the second class, 3 if it was in the third class, etc. So each census tract will have a score for English proficiency (ESL_Score), a score for people of color (RE_Score), and a score for household income (Income_Score). These scores were added up (TotalScore) and an evenly weight average was determined (WeightedTotal333333). Thus each score for the census tracts represents a combination of demographics where lower scores mean a wealthier, less diverse community and higher scores mean more diverse, less wealthy community.

How high is the brand awareness of Stop & Shop in the United States?When it comes to grocery store customers, brand awareness of Stop & Shop is at *** in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Stop & Shop in the United States?In total, ** of U.S. grocery store customers say they like Stop & Shop. However, in actuality, among the *** of U.S. respondents who know Stop & Shop, *** of people like the brand.What is the usage share of Stop & Shop in the United States?All in all, ** of grocery store customers in the United States use Stop & Shop. That means, of the *** who know the brand, *** use them.How loyal are the customers of Stop & Shop?Around ** of grocery store customers in the United States say they are likely to use Stop & Shop again. Set in relation to the ** usage share of the brand, this means that *** of their customers show loyalty to the brand.What's the buzz around Stop & Shop in the United States?In August 2022, about ** of U.S. grocery store customers had heard about Stop & Shop in the media, on social media, or in advertising over the past three months. Of the *** who know the brand, that's ***, meaning at the time of the survey there's little buzz around Stop & Shop in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Pandemics such as Covid-19 pose tremendous public health communication challenges in promoting protective behaviours, vaccination, and educating the public about risks. Segmenting audiences based on attitudes and behaviours is a means to increase the precision and potential effectiveness of such communication. The present study reports on such an audience segmentation effort for the population of England, sponsored by the United Kingdom Health Security Agency (UKHSA) and involving a collaboration of market research and academic experts. A cross-sectional online survey was conducted between 4 and 24 January 2022 with 5525 respondents (5178 used in our analyses) in England using market research opt-in panel. An additional 105 telephone interviews were conducted to sample persons without online or smartphone access. Respondents were quota sampled to be demographically representative. The primary analytic technique was k means cluster analysis, supplemented with other techniques including multi-dimensional scaling and use of respondent ‐ as well as sample-standardized data when necessary to address differences in response set for some groups of respondents. Identified segments were profiled against demographic, behavioural self-report, attitudinal, and communication channel variables, with differences by segment tested for statistical significance. Seven segments were identified, including distinctly different groups of persons who tended toward a high level of compliance and several that were relatively low in compliance. The segments were characterized by distinctive patterns of demographics, attitudes, behaviours, trust in information sources, and communication channels preferred. Segments were further validated by comparing the segmentation variable versus a set of demographic variables as predictors of reported protective behaviours in the past two weeks and of vaccine refusal; the demographics together had about one-quarter the effect size of the single seven-level segment variable. With respect to managerial implications, different communication strategies for each segment are suggested for each segment, illustrating advantages of rich segmentation descriptions for understanding public health communication audiences. Strengths and weaknesses of the methods used are discussed, to help guide future efforts.

📊 Welcome to the Customer Dataset! 🤝👥

This dataset provides valuable insights into customer demographics, income, spending habits, and purchase behavior. 🎯💰 It's designed to support your analysis, prediction, and customer segmentation tasks. 📈🔍

Let's dive in and uncover the patterns that drive customer behavior! 🕵️‍♂️💡💼

Feel free to explore, experiment, and apply various data analysis techniques to unlock meaningful insights. 🧐💡✨

Happy exploring! 🚀🔬🔍

Guaranteed Income Product Market Outlook

According to our latest research, the global guaranteed income product market size reached USD 2.13 trillion in 2024, with a robust compound annual growth rate (CAGR) of 7.4% projected through the forecast period. By 2033, the market is expected to reach USD 4.06 trillion, driven by rising demand for financial security, demographic shifts, and product innovation. The guaranteed income product market is experiencing notable expansion, fueled by increased consumer awareness, evolving regulatory frameworks, and a growing emphasis on retirement planning and wealth protection.

The guaranteed income product market is witnessing significant growth due to the heightened need for financial stability in an increasingly volatile global economic environment. As individuals and organizations seek to mitigate risks associated with market fluctuations and uncertain income streams, demand for products like annuities, structured settlements, and pension plans has surged. Furthermore, the ongoing shift from defined benefit to defined contribution retirement schemes has placed greater responsibility on individuals to secure their post-retirement income, amplifying the appeal of guaranteed income solutions. The market is also benefiting from the proliferation of product options that cater to diverse risk profiles and financial goals, making guaranteed income products more accessible and customizable than ever before.

Another key growth factor for the guaranteed income product market is the rapid digital transformation within the financial services sector. The integration of advanced technologies such as artificial intelligence, machine learning, and blockchain has streamlined the product distribution process, enhanced customer engagement, and improved the transparency and efficiency of transactions. Online platforms are playing a pivotal role in educating consumers, offering tailored advice, and simplifying the purchase journey. This digital evolution has not only broadened the reach of guaranteed income products but has also enabled providers to develop innovative solutions that address the unique needs of a diverse clientele, including younger demographics who are increasingly prioritizing long-term financial planning.

Demographic trends, particularly the aging global population, are significantly influencing the guaranteed income product market. As life expectancy rises and birth rates decline in many developed and emerging economies, the proportion of retirees seeking stable income streams is increasing rapidly. Governments and employers are also encouraging private retirement savings, further boosting the adoption of guaranteed income products. Additionally, heightened awareness of longevity risk—the risk of outliving one’s savings—has prompted both individuals and institutions to explore these products as a means of securing predictable, lifelong income. This demographic shift is particularly pronounced in regions such as North America, Europe, and parts of Asia Pacific, where pension reforms and social security limitations are reinforcing the importance of personal retirement planning.

Regionally, the guaranteed income product market exhibits strong growth potential across Asia Pacific, North America, and Europe, with each region displaying unique drivers and challenges. North America remains the largest market, supported by a mature insurance and pension landscape, while Asia Pacific is witnessing the fastest growth due to rapid urbanization, rising middle-class affluence, and proactive government initiatives to promote retirement savings. Europe, on the other hand, is characterized by a sophisticated regulatory environment and high levels of consumer awareness. Latin America and the Middle East & Africa are emerging markets, gradually increasing their market shares as financial literacy improves and new distribution channels are developed. The global marketÂ’s expansion is thus underpinned by both mature and emerging economies, each contributing to the sectorÂ’s dynamic growth trajectory.

Annuities, as a financial instrument, have long been a cornerstone of retirement planning, offering individuals a reliable means to secure a steady income stream during their post-worki

How high is the brand awareness of Bath & Body Works in the United States?When it comes to beauty and health online shop users, brand awareness of Bath & Body Works is at ** percent in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Bath & Body Works in the United States?In total, ** percent of U.S. beauty and health online shop users say they like Bath & Body Works. However, in actuality, among the ** percent of U.S. respondents who know Bath & Body Works, ** percent of people like the brand.What is the usage share of Bath & Body Works in the United States?All in all, ** percent of beauty and health online shop users in the United States use Bath & Body Works. That means, of the ** percent who know the brand, ** percent use them.How loyal are the customers of Bath & Body Works?Around ** percent of beauty and health online shop users in the United States say they are likely to use Bath & Body Works again. Set in relation to the ** percent usage share of the brand, this means that ** percent of their customers show loyalty to the brand.What's the buzz around Bath & Body Works in the United States?In September 2023, about ** percent of U.S. beauty and health online shop users had heard about Bath & Body Works in the media, on social media, or in advertising over the past three months. Of the ** percent who know the brand, that's ** percent, meaning at the time of the survey there's some buzz around Bath & Body Works in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

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About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in New Market, MD, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 57,198, while the mean income for the highest quintile (20% of households with the highest income) is 475,161. This indicates that the top earners earn 8 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 964,978, which is 203.08% higher compared to the highest quintile, and 1687.08% higher compared to the lowest quintile.

https://i.neilsberg.com/ch/new-market-md-mean-household-income-by-quintiles.jpeg" alt="Mean household income by quintiles in New Market, MD (in 2022 inflation-adjusted dollars))">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Market median household income. You can refer the same here

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18