This dataset provides comprehensive customer data suitable for segmentation analysis. It includes anonymized demographic, transactional, and behavioral attributes, allowing for detailed exploration of customer segments. Leveraging this dataset, marketers, data scientists, and business analysts can uncover valuable insights to optimize targeted marketing strategies and enhance customer engagement. Whether you're looking to understand customer behavior or improve campaign effectiveness, this dataset offers a rich resource for actionable insights and informed decision-making.

Key Features:

Anonymized demographic, transactional, and behavioral data. Suitable for customer segmentation analysis. Opportunities to optimize targeted marketing strategies. Valuable insights for improving campaign effectiveness. Ideal for marketers, data scientists, and business analysts.

Usage Examples:

Segmenting customers based on demographic attributes. Analyzing purchase behavior to identify high-value customer segments. Optimizing marketing campaigns for targeted engagement. Understanding customer preferences and tailoring product offerings accordingly. Evaluating the effectiveness of marketing strategies and iterating for improvement. Explore this dataset to unlock actionable insights and drive success in your marketing initiatives!

According to a survey conducted by CSP Magazine in 2019, ** percent of urban consumers stated that they are visiting convenience stores more often than they were two years ago, versus only ** percent of rural consumers and ** percent of suburban customers.

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.

With Versium REACH Demographic Append you will have access to many different attributes for enriching your data.

Basic, Household and Financial, Lifestyle and Interests, Political and Donor.

Here is a list of what sorts of attributes are available for each output type listed above:

Basic: - Senior in Household - Young Adult in Household - Small Office or Home Office - Online Purchasing Indicator
- Language - Marital Status - Working Woman in Household - Single Parent - Online Education - Occupation - Gender - DOB (MM/YY) - Age Range - Religion - Ethnic Group - Presence of Children - Education Level - Number of Children

Household, Financial and Auto: - Household Income - Dwelling Type - Credit Card Holder Bank - Upscale Card Holder - Estimated Net Worth - Length of Residence - Credit Rating - Home Own or Rent - Home Value - Home Year Built - Number of Credit Lines - Auto Year - Auto Make - Auto Model - Home Purchase Date - Refinance Date - Refinance Amount - Loan to Value - Refinance Loan Type - Home Purchase Price - Mortgage Purchase Amount - Mortgage Purchase Loan Type - Mortgage Purchase Date - 2nd Most Recent Mortgage Amount - 2nd Most Recent Mortgage Loan Type - 2nd Most Recent Mortgage Date - 2nd Most Recent Mortgage Interest Rate Type - Refinance Rate Type - Mortgage Purchase Interest Rate Type - Home Pool

Lifestyle and Interests: - Mail Order Buyer - Pets - Magazines - Reading
- Current Affairs and Politics
- Dieting and Weight Loss - Travel - Music - Consumer Electronics - Arts
- Antiques - Home Improvement - Gardening - Cooking - Exercise
- Sports - Outdoors - Womens Apparel
- Mens Apparel - Investing - Health and Beauty - Decorating and Furnishing

Political and Donor: - Donor Environmental - Donor Animal Welfare - Donor Arts and Culture - Donor Childrens Causes - Donor Environmental or Wildlife - Donor Health - Donor International Aid - Donor Political - Donor Conservative Politics - Donor Liberal Politics - Donor Religious - Donor Veterans - Donor Unspecified - Donor Community - Party Affiliation

This factsheet breaks down Camden’s population by looking at health conditions, and then by their age, sex, ethnicity, and deprivation. Understanding the size and characteristics of each segment helps us plan healthcare resources and service delivery effectively for each group, as well as the population in general.

This profile is designed to accompany the Joint Strategic Needs Assessment (JSNA) chapter on Demographics, which looks at segmenting the borough’s population by their most significant health and social care need. This supplement looks at adults (aged 18 and over) instead of the overall population, because the health and social care need segments covered in this section are more common in adults.

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

All data in Population and Demographic Census Data, grouped by reporting segment. For data grouped by overall segment, see Overall Segments for Population and Demographic Census Data.

GapMaps Segmentation Data from Applied Geographic Solutions (AGS) consists of 68 segments across the US and Canada. Panorama is paired with the industry leading GfK MRI survey and AGS Demographics to provide the essential link between neighborhood demographics and consumer preferences and attitudes.

GapMaps premium demographic data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

Demographic Data attributes include:

Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

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

Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

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

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

Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for AGS Demographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

This forms part of Camden’s Joint Strategic Needs Assessment, focussing on the demographics of our population. This data shows breakdowns of Camden’s population by health conditions, age and sex, and by Camden ward, as supplementary information of the 2015 Camden population segmentation profile (https://opendata.camden.gov.uk/Health/Camden-Demographics-Population-Segmentation-2015/v6fr-wght). It provides the number of people, percentage of the whole population (prevalence) and Camden average for each breakdown. It only focuses on the population aged 18 and over and doesn’t show breakdowns for those diagnosed with learning disability or those aged under 65 who are diagnosed with dementia due to small numbers.

This service offers Esri's Updated Demographics, Census Data, Tapestry Segmentation, and Business Summary data for the United States. Updates are based on the decennial census, Infogroup business data, other public and proprietary data sources, and proprietary models.

All attributes are available at all geography levels: country, state, county, tract, block group, ZIP code, place, county subdivision, congressional district, core-based statistical area (CBSA), and designated market area (DMA).

There are over 2,100 attributes in categories such as: population, households, race and ethnicity, educational attainment, marital status, employment by industry and occupation, income, net worth, housing and home value, number of businesses and employees, sales, and many others. Key attributes from the 2010 Census such as population, are presented for reference. Some attributes such as population, income, and home value, are also projected five years to 2021.

Esri offers Updated Demographics for 2019 and 2024 and Tapestry Segmentation for 2019. Esri provides Census Data for geographies not supplied by the Census Bureau including ZIP Codes and DMAs.

To view ArcGIS Online items using this service, including the terms of use, visit http://goto.arcgisonline.com/demographics9/USA_Demographics_and_Boundaries_2019.

Abstract

The 1988 Egypt Demographic and Health Survey (EDHS) is part of the worldwide Demographic and Health Surveys (DHS) Program, which is designed to collect data on fertility, family planning and maternal and child health.

The 1988 EDHS is the most recent in a series of surveys carried out in Egypt to provide the information needed to study fertility behavior and its determinants, particularly contraceptive use. The EDHS findings are important in monitoring trends in these variables and in understanding the factors which contribute to differentials in fertility and contraceptive use among various population subgroups. The EDHS also provides a wealth of health-related information for mothers and their children, which was not available in the earlier surveys. These data are especially important for understanding the factors that influence the health and survival of infants and young children. In addition to providing insights into population and health issues in Egypt, the EDHS also hopefully will lead to an improved global understanding of population and health problems as it is one of 35 internationally comparable surveys sponsored by the Demographic and Health Surveys program.

The Egypt Demographic and Health Survey (EDHS) has as its major objective the provision of current and reliable information on fertility, mortality, family planning, and maternal and child health indicators. The information is intended to assist policy makers and administrators in Egyptian population and health agencies to: (1) assess the effect of ongoing family planning and maternal and child health programs and (2) improve planning for future interventions in these areas. The EDHS provides data on topics for which comparable data are not available from previous nationally representative surveys, as well as information needed to monitor trends in a number of indicators derived from earlier surveys, in particular, the 1980 Egypt Fertility Survey (EFS) and the 1980 and 1984 Egypt Contraceptive Prevalence Surveys (ECPS). Finally, as part of the worldwide Demographic and Health Surveys (DHS) program, the EDHS is intended to add to an international body of data, which can be used for cross-national research on these topics.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

Geographical Coverage: The EDHS was carried out in 21 of the 26 governorates in Egypt. The Frontier Governorates (Red Sea, New Valley, Matrouh, North Sinai and South Sinai), which represent around two percent of the total population in Egypt, were excluded from coverage because a disproportionate share of EDHS resources would have been needed to survey the dispersed population in these governorates.

The EDHS sample was designed to provide separate estimates of all major parameters for: the national level, the Urban Governorates, Lower Egypt (total, urban and rural) and Upper Egypt (total, urban and rural). In addition, the sample was selected in such a fashion as to yield a sufficient number of respondents from each governorate to allow for governorate-level estimates of current contraceptive use. In order to achieve the latter objective, sample takes for the following governorates were increased during the selection process: Port Said, Suez, Ismailia, Damietta, Aswan, Kafr El-Sheikh, Beni Suef and Fayoum.

Sampling Plan: The sampling plan called for the EDHS sample to be selected in three stages. The sampling units at the first stage were shiakhas/towns in urban areas and villages in rural areas. The frame for the selection of the primary sampling units (PSU) was based on preliminary results from 1986 Egyptian census, which were provided by the Central Agency for Public Mobilization and Statistics. During the first stage selection, 228 primary sampling units (108 shiakhas/towns and 120 villages) were sampled.

The second stage of selection called for the PSUs chosen during the first stage to be segmented into smaller areal units and for two of the areal units to be sampled from each PSU. In urban PSUs, a quick count operation was carried out to provide the information needed to select the secondary sampling units (SSU) while for rural PSUs, maps showing the residential area within the selected villages were used.

Following the selection of the SSUs, a household listing was obtained for each of the selected units. Using the household lists, a systematic random sample of households was chosen for the EDHS. All ever-married women 15-49 present in the sampled households during the night before the interviewer's visit were eligible for the individual interview.

Quick Count and Listing: As noted in the discussion of the sampling plan, two separate field operations were conducted during the sample implementation phase of the EDHS. The first field operation involved a quick count in the shiakhas/towns selected as PSUs in urban areas. Prior to the quick count operation, maps for each of the selected shiakhas/towns were obtained and divided into approximately equal-sized segments, with each segment having well-defined boundaries. The objective of the quick count operation was to obtain an estimate of the number of households in each of the segments to serve as the measures of size for the second stage selection.

A review of the preliminary 1986 Census population totals for the selected shiakhas/towns showed that they varied greatly in total size, ranging from less than 10,000 to more than 275,000 residents. Experience in the 1984 Egypt Contraceptive Prevalence Survey, in which a similar quick count operation was carried out, indicated that it was very time-consuming to obtain counts of households in shiakhas/towns with large populations. In order to reduce the quick count workload during the EDHS, a subsample of segments was selected from the shiakhas/towns, with 50,000 or more population. The number of segments sub-sampled depended on the size of the shiakha. Only the sub-sampled segments were covered during the quick count operation in the large shiakhas/towns. For shiakhas with less than 50,000 populations, all segments were covered during the quick count.

Prior to the quick count, a one-week training was held, including both classroom instruction and practical training in shiakhas/towns not covered in the survey. The quick count operation, which covered all 108 urban PSUs, was carried out between June and August 1988. A group of 62 field staff participated in the quick count operation. The field staff was divided into ten teams each composed of one supervisor and three to four counters.

As a quality control measure, the quick count was repeated in 10 percent of the shiakhas. Discrepancies noted when the results of the second quick count operation were compared with the original counts were checked. No major problems were discovered in this matching process, with most differences in the counts attributed to problems in the identification of segment boundaries.

The second field operation during the sample implementation phase of the survey involved a complete listing of all of the households living in the 456 segments chosen during the second stage of the sample selection. Prior to the household listing, the listing staff attended a one-week training course, which involved both classroom lectures and field practice. After the training, the 14 supervisors and 32 listers were organized into teams; except in Damietta and Ismailia, where the listers work on their own, each listing team was composed of a supervisor and two listers. The listing operation began in the middle of September and was completed in October 1988.

Segments were relisted when the number of households in the listing differed markedly from that expected based on: (1) the quick count in urban areas or (2) the number of households estimated from the information on the size of the inhabited area for rural segments. Few discrepancies were noted for urban segments. Not surprisingly, more problems were noted for rural segments since the estimated size of the segment was not based on a recent count as it was for the urban segments. All segments where major differences were noted in the matching process were relisted in order to resolve the problems.

Note: See detailed description of sample design in APPENDIX B of the report which is presented in this documentation.

Mode of data collection

Face-to-face

Research instrument

The EDHS involved both a household and an individual questionnaire. These questionnaires were based on the DHS model "A" questionnaire for high contraceptive prevalence countries. Additional questions on a number of topics not covered in the DHS questionnaire were included in both the household and individual questionnaires. The questionnaires were pretested in June 1988, following a one-week training for supervisors and interviewers. Three supervisors and seven interviewers participated in the pretest. Interviewer comments and tabulations of the pretest results were reviewed during the process of modifying the questionnaires.

The EDHS household questionnaire obtained a listing of all usual household members and visitors and identified those present in the household during the night before the interviewer's visit. For each of the individuals included in the listing, information was collected on the relationship to the household head, age, sex, marital status, educational level, occupation and work status. In addition, questions were included on the mortality experience of sisters of all household members age 15 and over in order to obtain data to estimate the level of maternal mortality. The maternal mortality questions were administered in a

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.

Bank Customer Segmentation

Most banks have a large customer base - with different characteristics in terms of age, income, values, lifestyle, and more. Customer segmentation is the process of dividing a customer dataset into specific groups based on shared traits.

According to a report from Ernst & Young, “A more granular understanding of consumers is no longer a nice-to-have item, but a strategic and competitive imperative for banking providers. Customer understanding should be a living, breathing part of everyday business, with insights underpinning the full range of banking operations.

About this Dataset

This dataset consists of 1 Million+ transaction by over 800K customers for a bank in India. The data contains information such as - customer age (DOB), location, gender, account balance at the time of the transaction, transaction details, transaction amount, etc.

Interesting Analysis Ideas

The dataset can be used for different analysis, example -

1. Perform Clustering / Segmentation on the dataset and identify popular customer groups along with their definitions/rules
2. Perform Location-wise analysis to identify regional trends in India
3. Perform transaction-related analysis to identify interesting trends that can be used by a bank to improve / optimi their user experiences
4. Customer Recency, Frequency, Monetary analysis
5. Network analysis or Graph analysis of customer data.

WorldView segments has been developed to segment the global population into 10 consistent consumer types by analysing data including: demographics, value orientation, attitudes, consumer behaviour and consumption volume. The segments have been identified and validated in detailed international primary reserach. They enable the identification of customer target groups and the segmentation of markets consistently across multiple countries. The data is built using a combination of WorldView Demographics enhanced with consumer survey panel data across a number of regions where available.

In 2020, the kits and assays segment represented **** percent of the overall population sequencing market worldwide, and platforms accounted for **** percent. Both these segments were forecast to decrease slightly in the global market share by 2030, while software tools was forecast to climb to approximately a **** percent market share.

This dataset provides detailed insights into customer payment method preferences, usage frequency, and transaction values across various demographic and business segments. It enables financial service providers to analyze trends, optimize product offerings, and tailor marketing strategies based on customer behavior and segment characteristics.

Data-driven segmentation methods for population segmentation based on healthcare utilization