This simulated customer dataset provides a practical foundation for performing segmentation analysis and identifying distinct customer groups. The dataset encompasses a blend of demographic and behavioral information, equipping users with the necessary data to develop targeted marketing strategies, personalize customer experiences, and ultimately drive sales growth.

Dataset Schema: Customer Demographics and Behavior

This dataset is structured to provide a comprehensive view of each customer, combining demographic information with detailed purchasing behavior. The columns included are:

• id: A unique identifier assigned to each customer.
• age: The customer's age in years.
• gender: The gender of the customer (e.g., Male, Female).
• income: The customer's annual income, denominated in USD.
• spending_score: A score ranging from 1 to 100 that reflects a customer's spending habits and loyalty.
• membership_years: The total number of years the customer has held a membership.
• purchase_frequency: The total number of purchases the customer has made in the last 12 months.
• preferred_category: The shopping category most frequently chosen by the customer (e.g., Electronics, Clothing, Groceries, Home & Garden, Sports).
• last_purchase_amount: The monetary value (in USD) of the customer's most recent transaction.

Potential Applications and Use Cases

The insights derived from this dataset can be applied to several key business areas:

• Customer Segmentation: Group customers into distinct segments by analyzing their demographic and behavioral data to better understand the composition of your customer base.
• Targeted Marketing: Craft and execute bespoke marketing campaigns tailored to the specific characteristics and preferences of each customer segment.
• Customer Loyalty Programs: Develop and implement loyalty initiatives that are designed to reward desirable spending behaviors and align with customer preferences.
• Sales Analysis: Examine sales data to identify purchasing patterns, understand trends, and forecast future sales performance.

Success.ai’s Retail Data for the Retail Sector in North America offers a comprehensive dataset designed to connect businesses with key players across the diverse retail industry. Covering everything from department stores and supermarkets to specialty shops and e-commerce platforms, this dataset provides verified contact details, business locations, and leadership profiles for retail companies in the United States, Canada, and Mexico.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, marketing, and business development efforts are powered by accurate, continuously updated, and AI-validated data.

Backed by our Best Price Guarantee, this solution empowers businesses to thrive in North America’s competitive retail landscape.

Why Choose Success.ai’s Retail Data for North America?

1. Verified Contact Data for Precision Outreach

   • Access verified phone numbers, work emails, and LinkedIn profiles of retail executives, store managers, and decision-makers.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and efficient campaign execution.

2. Comprehensive Coverage Across Retail Segments

   • Includes profiles of retail businesses across major markets, from large department stores and grocery chains to boutique retailers and online platforms.
   • Gain insights into the operational dynamics of retail hubs in cities such as New York, Los Angeles, Toronto, and Mexico City.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, new store openings, market expansions, and shifts in consumer preferences.
   • Stay aligned with evolving industry trends and emerging opportunities in the North American retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other privacy regulations, ensuring responsible and lawful use of data in your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, marketing directors, and operations managers across the North American retail sector.
• 30M Company Profiles: Access firmographic data, including revenue ranges, store counts, and geographic footprints.
• Store Location Data: Pinpoint retail outlets, regional offices, and distribution centers to refine supply chain and marketing strategies.
• Leadership Contact Details: Connect with CEOs, CMOs, and procurement officers influencing retail operations and vendor selections.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles

   • Identify and engage with store owners, category managers, and marketing directors shaping customer experiences and product strategies.
   • Target professionals responsible for inventory planning, vendor contracts, and store performance.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (luxury, grocery, e-commerce), geographic location, company size, or revenue range.
   • Tailor outreach to align with regional market trends, customer demographics, and operational priorities.

3. Market Trends and Operational Insights

   • Analyze trends such as online shopping growth, sustainability practices, and supply chain optimization.
   • Leverage insights to refine product offerings, identify partnership opportunities, and design effective campaigns.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technology solutions to retail procurement teams, marketing departments, and operations managers.
   • Build relationships with retailers seeking innovative tools, efficient supply chain solutions, or unique product offerings.

2. Market Research and Consumer Insights

   • Analyze retail trends, customer behaviors, and seasonal demands to inform marketing strategies and product launches.
   • Benchmark against competitors to identify gaps, emerging niches, and growth opportunities.

3. E-Commerce and Digital Strategy Development

   • Target e-commerce managers and digital transformation teams driving online retail initiatives and omnichannel integration.
   • Offer solutions to enhance online shopping experiences, logistics, and customer loyalty programs.

4. Recruitment and Workforce Solutions

   • Engage HR professionals and hiring managers in recruiting talent for store operations, customer service, or marketing roles.
   • Provide workforce optimization tools, training platforms, or staffing services tailored to retail environments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality retail data at competitive prices, ensuring strong ROI for your marketing and outreach efforts in North America.

2. Seamless Integration
   ...

Predicting Credit Card Customer Segmentation

Exploring Key Customer Characteristics

By [source]

About this dataset

This dataset contains a wealth of customer information collected from within a consumer credit card portfolio, with the aim of helping analysts predict customer attrition. It includes comprehensive demographic details such as age, gender, marital status and income category, as well as insight into each customer’s relationship with the credit card provider such as the card type, number of months on book and inactive periods. Additionally it holds key data about customers’ spending behavior drawing closer to their churn decision such as total revolving balance, credit limit, average open to buy rate and analyzable metrics like total amount of change from quarter 4 to quarter 1, average utilization ratio and Naive Bayes classifier attrition flag (Card category is combined with contacts count in 12months period alongside dependent count plus education level & months inactive). Faced with this set of useful predicted data points across multiple variables capture up-to-date information that can determine long term account stability or an impending departure therefore offering us an equipped understanding when seeking to manage a portfolio or serve individual customers

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to analyze the key factors that influence customer attrition. Analysts can use this dataset to understand customer demographics, spending patterns, and relationship with the credit card provider to better predict customer attrition.

Research Ideas

• Using the customer demographics, such as gender, marital status, education level and income category to determine which customer demographic is more likely to churn.
• Analyzing the customer’s spending behavior leading up to churning and using this data to better predict the likelihood of a customer of churning in the future.
• Creating a classifier that can predict potential customers who are more susceptible to attrition based on their credit score, credit limit, utilization ratio and other spending behavior metrics over time; this could be used as an early warning system for predicting potential attrition before it happens

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: BankChurners.csv | Column name | Description | |:---------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| | CLIENTNUM | Unique identifier for each customer. (Integer) | | Attrition_Flag | Flag indicating whether or not the customer has churned out. (Boolean) | | Customer_Age | Age of customer. (Integer) | | Gender | Gender of customer. (String) | | Dependent_count | Number of dependents that customer has. (Integer) | | Education_Level ...

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

About this dataset

Context

The dataset tabulates the New Market population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for New Market. The dataset can be utilized to understand the population distribution of New Market by age. For example, using this dataset, we can identify the largest age group in New Market.

Key observations

The largest age group in New Market, IN was for the group of age 70 to 74 years years with a population of 80 (14.71%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in New Market, IN was the Under 5 years years with a population of 7 (1.29%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the New Market is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of New Market total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Age. You can refer the same here

The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann.

The dataset utilized comes from a german bank in 2016 collected by Professor Hoffman of the University of Califonia.

In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes.

The original dataset required extensive cleaning and variable selection I due to its complicated system of categories and symbols. Several columns are simply ignored, because they were viewed as not important or their descriptions are obscure. The selected attributes are:

• Age (numeric)
• Sex (text: male, female)
• Job (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled)
• Housing (text: own, rent, or free)
• Saving accounts (text - little, moderate, quite rich, rich)
• Checking account (numeric, in DM - Deutsch Mark)
• Credit amount (numeric, in DM)
• Duration (numeric, in month)
• Purpose (text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others).

The objective of this analysis is to segment the German bank's customers based on the various factors (variables) available in their database.

The library makes use of the following packages:

• pandas - to manipulate data frames
• numpy - providing linear algebra
• seaborm - to create visualizations
• matplotlib - basic tools for visualizations
• scikit-learn - machine learning library

Conclusion.

The analysis found that the most optimal clusters were 4 as explained below:

Cluster 0 – high mean of credit amount, long duration, younger customers

Cluster 1 – low mean of credit amount, short duration, younger customers

Cluster 2 - low mean of credit amount, short duration, older customers

Cluster 3 - high mean of credit amount, middle-time duration, older customers

Segmenting bank customers through clustering techniques offers significant benefits for both the bank itself and its various stakeholders. Here are some key advantages:

For Banks:

• Improved Customer Targeting and Marketing: Clustering allows banks to identify distinct customer segments with similar characteristics and needs. This enables them to tailor marketing campaigns and product offerings to specific segments, resulting in greater effectiveness and efficiency.
• Enhanced Customer Relationship Management (CRM): By understanding customer segments better, banks can personalize their interactions and communications, fostering stronger relationships and improving customer satisfaction.
• Risk Management: Customer segmentation can help identify high-risk segments, allowing banks to implement strategies to mitigate potential risks, such as fraud or credit defaults.
• Resource Optimization: Banks can allocate resources, such as personnel and marketing budgets, more efficiently by directing them towards segments with the highest potential for profitability.

• New Product Development: By analyzing the needs and preferences of different segments, banks can develop new products and services that cater to their specific requirements, increasing customer loyalty and driving revenue growth.

  For Stakeholders:

• Improved Customer Experience: Segmented communication and personalized offerings lead to a more satisfying and relevant experience for customers, boosting overall satisfaction and trust in the bank.

• Increased Value Perception: By providing products and services aligned with their needs, customers perceive greater value from the bank's offerings, leading to strengthened relationships and increased loyalty.

• Enhanced Financial Inclusion: Customer segmentation can help banks identify underserved segments and develop strategies to offer them tailored financial products and services, promoting greater financial inclusion.

• Improved Regulatory Compliance: By understanding customer behavior and risk profiles better, banks can better comply with regulations and mitigate potential regulatory risks.

  Overall, customer segmentation via clustering empowers banks to make data-driven decisions, optimize their operations, and deliver a more personalized and satisfying experience for their customers. This ultimately leads to increased profitability, stronger stakeholder relationships, and a competitive advantage in the market.

Some additional examples of how customer segmentation can benefit other stakeholders:

• Investors: By analyzing the performance of different customer segments, investors can gain valuable insights into the bank's future growth potential and make informed investment decisions.
• Regulators: Customer segmentation can help regulators identify systemic risks within the financial system and develop targeted policies to maintain financial stability.
• Employees: By understanding the needs an...

The Human Identification Market is estimated to be valued at USD 1.7 billion in 2025 and is projected to reach USD 5.5 billion by 2035, registering a compound annual growth rate (CAGR) of 12.5% over the forecast period.

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?

About this dataset

Context

The dataset tabulates the Non-Hispanic population of East New Market by race. It includes the distribution of the Non-Hispanic population of East New Market across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of East New Market across relevant racial categories.

Key observations

Of the Non-Hispanic population in East New Market, the largest racial group is White alone with a population of 250 (77.88% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the East New Market
• Population: The population of the racial category (for Non-Hispanic) in the East New Market is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of East New Market total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 East New Market Population by Race & Ethnicity. You can refer the same here

Snapshot img

The miso market size will grow up to USD 64.59 mn at a CAGR of 4% during 2021-2025.

This miso market analysis report entails exhaustive statistical qualitative and quantitative data on Product (white miso, yellow miso, and red miso) and Geography (APAC, North America, Europe, South America, and MEA) and their contribution to the target market. View our sample report to gather market insights on the segmentations. Furthermore, with the latest key findings on the post COVID-19 impact on the market, available in this report, you can create successful business strategies to generate new sales opportunities.

What will the Miso Market Size be in 2021?

Browse TOC and LoE with selected illustrations and example pages of Miso Market

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Miso Market: Key Drivers and Trends

According to our research output, there has been a positive impact on the market growth post COVID-19 era. Key drivers such as the increasing soy production are notably supporting the miso market growth. On the other hand, factors such as product contamination have been identified as market challenges that limit the growth of market vendors. This report offers detailed insights on the challenges to stay prepared for the obstacles in the future, which will help companies analyze and develop growth strategies.

This post-pandemic miso market report has assessed the shift in consumer behavior and identified trends and drivers that will help market players outmaneuver challenges. Technology innovations, implementation, and improvisation scope identified in the miso market trends is essential for building new business opportunities across segmentations and geographies.

Who are the Major Miso Market Vendors?

The miso market forecast report provides insights on complete key vendor profiles and their business strategies to reimage themselves. The leading companies included in the report are as follows:

Eden Foods Inc. Great Eastern Sun HIKARI MISO CO. LTD. Ichibiki Co. Ltd. MARUSAN-AI CO. LTD. Miyako Oriental Foods Inc. Miyasaka USA Saikyo-Miso Co. Ltd. Urban Platter Yamato Soysauce & Miso Co. Ltd.

From our Porter’s five forces analysis study, get detailed insights on the functional involvement of the buyers and suppliers to form well-rounded knowledge about the supply chain and create cost reduction plans. The miso market analysis report also contains exhaustive observation on the organic and inorganic growth strategies deployed by the vendors. Click here to uncover details of successful business strategies adopted by the vendors.

Furthermore, our research experts have outlined the magnitude of the economic impact on each segment and recovery expectations post pandemic. To recover from post COVID-19 impact, market vendors should create strategies to grab business opportunities from the fast-growing segments, while refining their scope of growth in the slow-growing ones.

For insights on complete key vendor profiles, download a free sample of the miso market forecast report. The profiles include information on the production, sustainability, and prospects of the leading companies. The report's vendor landscape section also provides industry risk assessment in terms of labor cost, raw material price fluctuation, and other parameters, which is crucial for effective business planning.

Which are the Key Regions for Miso Market?

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Japan, US, China, South Korea (Republic of Korea), and UK are the key markets for miso market in APAC. Learn about the key, emerging, and untapped markets from our miso market size, share, & trends analysis report for targeting your business efforts toward promising growth regions. 62% of the market’s growth will originate from APAC during the forecast period.

APAC has been recording significant growth rate and is expected to offer several growth opportunities to market vendors during the forecast period. drivers.2 has been identified as one of the chief factors that will drive the miso market growth in APAC over the forecast period. To garner further competitive intelligence and regional opportunities in store for vendors, view our sample report.

What are the Revenue-generating Product Segments in the Miso Market?

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The miso market share growth by the _ segment has been significant. The miso market report provides comprehensive understanding of the subsegments of the target market to identify niche customer groups and demographic requirements. Furthermore, the report provides insights on the impact of COVID-19 on market segments, which can be used to deduce transformation patterns in consumer behavior in the coming years and improvise business plans.

Request for a free sample of the report to get an exclusive glimpse of actionable market insights on post

About this dataset

Context

The dataset tabulates the population of New Market by race. It includes the population of New Market across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of New Market across relevant racial categories.

Key observations

The percent distribution of New Market population by race (across all racial categories recognized by the U.S. Census Bureau): 87.45% are white, 4.45% are Black or African American, 0.09% are American Indian and Alaska Native, 0.46% are Asian, 3.15% are some other race and 4.40% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the New Market
• Population: The population of the racial category (excluding ethnicity) in the New Market is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of New Market total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the New Market township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for New Market township. The dataset can be utilized to understand the population distribution of New Market township by age. For example, using this dataset, we can identify the largest age group in New Market township.

Key observations

The largest age group in New Market Township, Minnesota was for the group of age 60 to 64 years years with a population of 406 (11.42%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in New Market Township, Minnesota was the 85 years and over years with a population of 23 (0.65%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the New Market township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of New Market township total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 township Population by Age. You can refer the same here

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.

Here are a few use cases for this project:

Use Case 1: Gender-Based Retail Analytics By analyzing customer demographics in retail stores, the "man vrouw dataset 1" can help retailers understand the gender distribution of their shoppers, empowering them to make informed decisions on store layout, marketing strategies, and product placements.

Use Case 2: Crowd Monitoring and Event Management This model can help enhance safety and optimize visitor experience at crowded events, such as concerts or festivals, by identifying the gender distribution of attendees, enabling promoters to customize services, restrooms allocation, and security measures accordingly.

Use Case 3: Digital Advertising and Marketing Using the "man vrouw dataset 1" model, businesses can better target their digital advertisements by understanding the key demographic visiting specific websites or engaging with specific content, allowing for tailored ad campaigns designed to target male or female audiences.

Use Case 4: Smart Surveillance and Security Systems The model can be used in surveillance and security systems to help identify and track people by their HU classes (man or vrouw) in premises like airports or corporate buildings, allowing security teams to analyze patterns and prevent potential threats.

Use Case 5: Social Media Image Analysis The "man vrouw dataset 1" model can be used to analyze the gender composition of social media images, providing insights into trends, preferences, and behaviors of different gender groups on social platforms. This information can then be used for targeted marketing or social research purposes.

Access high-fidelity consumer data powered by our proprietary modeling technology that provides the most comprehensive consumer intelligence, accurate targeting, first-party data enrichment, and personalization at scale. Our deterministic dataset, anchored in the purchasing habits of over 140 million U.S. consumers, delivers superior targeting performance with proven 70% increase in ROAS.

Core Data Assets Transactional Data Foundation: Real purchasing behavior from over 140 million U.S. consumers with 8.5 billion behavioral signals across 250 million adults. Seven years of daily credit card and debit card purchase data aggregated from all major credit cards sourced from more than 300 national banks, capturing $2+ trillion in annual discretionary spending.

Consumer Demographics & Lifestyle: Comprehensive profiles including age, income, household composition, geographic distribution, education, employment, and lifestyle indicators. Our proprietary taxonomy organizes consumer spending across 8,000+ brands and 2,500+ merchants, from major retailers to emerging direct-to-consumer brands.

Behavioral Segmentation: 150+ custom consumer communities including demographic groups (Gen Z, Millennials, Gen X), lifestyle segments (Health & Fitness Enthusiasts, Tech Early Adopters, Luxury Shoppers), and behavioral categories (Deal Seekers, Brand Loyalists, Premium Service Users, Streaming Subscribers). Purchase Intelligence: Deep insights into consumer spending patterns across entertainment, fitness, fashion, technology, travel, dining, and retail categories. Our models identify cross-category purchasing behaviors, seasonal trends, and brand switching patterns to optimize targeting strategies. Advanced Modeling Technology

Our proprietary consumer intelligence engine combines deterministic transaction-based data with Smart Audience Engineering that transforms first-party signals from anonymized website traffic, behavioral indicators, and CRM enrichment into precision-modeled segments. Unlike traditional data providers who sell static lists, our AI-powered predictive modeling continuously learns and optimizes for unprecedented precision and superior conversion outcomes.

Performance Advantages: Audiences built on user-level transactional data deliver 70% increase in ROAS compared to traditional targeting methods. Weekly-optimized audiences with performance narratives eliminate wasted ad spend by 20-30%, while our deterministic AI models analyze hundreds of attributes and conversion-validated signals to identify prospects with genuine purchase intent, not just lookalike behaviors.

Territory White Space Analysis AI Market Outlook

According to our latest research, the global market size for Territory White Space Analysis AI reached USD 1.87 billion in 2024, propelled by a robust digital transformation across industries and increasing demand for actionable market intelligence. The market is expected to grow at a CAGR of 23.9% from 2025 to 2033, reaching an estimated USD 15.08 billion by 2033. This impressive growth is primarily driven by the rising adoption of AI-powered analytics for optimizing sales territories, identifying untapped market opportunities, and enhancing customer segmentation strategies.

One of the primary growth factors for the Territory White Space Analysis AI market is the accelerating shift toward data-driven decision-making in sales and marketing functions. Organizations across sectors such as BFSI, healthcare, retail, and manufacturing are leveraging AI to analyze vast datasets, uncover hidden market opportunities, and optimize resource allocation. The ability of AI-powered territory analysis tools to deliver granular insights into market potential, competitor activities, and customer behaviors is transforming how businesses approach expansion and growth. The integration of advanced machine learning algorithms enables companies to identify lucrative white spaces within their territories, thereby reducing inefficiencies and maximizing revenue generation. As businesses increasingly recognize the value of precision targeting and predictive analytics, the demand for sophisticated Territory White Space Analysis AI solutions is expected to surge.

Another significant driver is the rapid evolution of cloud computing and scalable AI infrastructure, which has democratized access to advanced analytics for organizations of all sizes. Cloud-based deployment models offer flexibility, scalability, and cost-efficiency, making it easier for companies to implement AI-driven territory analysis without significant upfront investments in hardware or IT resources. This trend is particularly beneficial for small and medium enterprises (SMEs), which can now compete with larger players by leveraging cutting-edge analytics to identify new markets and optimize sales strategies. Furthermore, the increasing convergence of AI with other emerging technologies such as big data, IoT, and geospatial analytics is enhancing the capabilities of territory analysis platforms, enabling real-time insights and more dynamic market segmentation.

The growing emphasis on personalized customer engagement and hyper-local marketing is also fueling the adoption of Territory White Space Analysis AI. As consumer expectations for tailored experiences rise, businesses are turning to AI to segment customers more effectively and deliver targeted offerings. Advanced AI models can analyze demographic, behavioral, and transactional data to uncover micro-segments and identify unmet needs within specific regions or customer groups. This level of precision not only improves customer satisfaction but also drives higher conversion rates and customer loyalty. Additionally, the integration of competitive analysis features allows organizations to benchmark their performance against industry peers and swiftly adapt to shifting market dynamics, further cementing the role of AI in strategic territory management.

From a regional perspective, North America continues to dominate the Territory White Space Analysis AI market, accounting for the largest revenue share in 2024 due to widespread digital adoption, strong presence of AI solution providers, and significant investments in sales optimization technologies. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid economic development, expanding retail and e-commerce sectors, and increasing digitalization initiatives across emerging markets. Europe follows closely, with robust adoption in industries such as BFSI, healthcare, and manufacturing. Latin America and the Middle East & Africa are also witnessing a gradual uptick in adoption, supported by growing awareness and government-led digital transformation programs. As the global landscape evolves, regional market dynamics are expected to play a pivotal role in shaping the future trajectory of the Territory White Space Analysis AI market.

Component Analysis

The Territory White Space Analysis AI market is primarily segmented by component into software and services

Overview:

This dataset provides a comprehensive overview of customer interactions with an online retail store, aiming to predict customer churn based on various behavioral and demographic features. It includes data on customer demographics, spending behavior, satisfaction levels, and engagement with marketing campaigns. The dataset is designed for analysis and development of predictive models to identify customers at risk of churn, enabling targeted customer retention strategies.

Description of Columns:

• Customer_ID: A unique identifier for each customer.
• Age: The customer's age.
• Gender: The customer's gender (Male, Female, Other).
• Annual_Income: The annual income of the customer in thousands of dollars.
• Total_Spend: The total amount spent by the customer in the last year.
• Years_as_Customer: The number of years the individual has been a customer of the store.
• Num_of_Purchases: The number of purchases the customer made in the last year.
• Average_Transaction_Amount: The average amount spent per transaction.
• Num_of_Returns: The number of items the customer returned in the last year.
• Num_of_Support_Contacts: The number of times the customer contacted support in the last year.
• Satisfaction_Score: A score from 1 to 5 indicating the customer's satisfaction with the store.
• Last_Purchase_Days_Ago: The number of days since the customer's last purchase.
• Email_Opt_In: Whether the customer has opted in to receive marketing emails.
• Promotion_Response: The customer's response to the last promotional campaign (Responded, Ignored, Unsubscribed).
• Target_Churn: Indicates whether the customer churned (True or False).

In October 2021, more than *** companies in Japan identified Rakuten Bank as their main online bank. The Japan Net Bank, which was renamed PayPay Bank in April 2021, ranked second, with close to *** businesses that perceived the bank as their main bank.

Leading direct banks in Japan

Direct banks offer online-only banking services without operating any physical branches. Rakuten Bank, which is part of the e-commerce group Rakuten Group, was one of the leading direct banks in Japan with more than ** million bank accounts in 2021, followed by PayPay Bank and SBI Sumishin Net Bank with five and four million bank accounts respectively. Partly due to a shift towards digital banking services following the outbreak of the coronavirus (COVID-19), direct banks like PayPay Bank recorded a steep increase in the number of bank accounts and transactions conducted by customers in fiscal year 2020.

Customer demographics of direct banks

According to a survey, the share of men who conduct financial transactions online was, on average, higher than that of women. This trend is also reflected in the customer demographics of direct banks. Looking at the customers of the three direct banks mentioned above, men accounted for ** to ** percent of customers in all cases. Broken down by age group, customers aged between 40 to 49 years made up the largest proportion of customers.

This dataset provides detailed logs of checking account overdraft fee events, capturing transaction details, account and customer identifiers, fee amounts, and customer segmentation. It enables financial institutions to identify high-risk customer segments, analyze patterns of overdraft occurrences, and develop targeted strategies to reduce fee events and improve customer financial health.

Radiofrequency Identification (RFID) technology has emerged as a transformative force across various industries, revolutionizing the way businesses manage inventory, enhance supply chain efficiency, and improve operational accuracy. RFID utilizes electromagnetic fields to automatically identify and track tags attach