In marketing and selling products or services, it is essential to put in mind that different customers have different preferences, needs, and behaviors, and it's crucial to understand these differences to effectively reach and engage with them. One powerful way to do this is by segmenting customers by age. By doing so, you can tailor your marketing strategies to better resonate with each group and ultimately drive more sales and customer loyalty. This dataset is intended for analysis to identify the effects of different Age Group on revenue and profit

Acknowledgements

https://skillsforall.com/

This statistic shows the United States alternative medicine industry market segmentation in 2011, by client age and gender. Women aged 30 to 69 make up ** percent of the alternative medicine industry.

This is a sample customer datasets for segmentation by unsupervised learning (K-Means Cluster). https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18406763%2F78b0d182c8823595f641c089af2ab859%2FAge_vs_score.png?generation=1706811838720183&alt=media" alt="">

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

Blockchain data query: User Address Age Segmentation

21,300 Images - Human Body Segmentation Data. The data includes indoor scenes and outdoor scenes. The data covers female people and male people. The race distribution includes Asian, black race and Caucasian. The age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The dataset diversity includes multiple scenes, ages, races, postures, and appendages. In terms of annotation, we adpoted pixel-wise segmentation annotations on human body. The data can be used for tasks such as human body segmentation.

The User Profile Data is a structured, anonymized dataset designed to help organizations understand who their users are, what devices they use, and where they are located. Each record provides privacy-compliant linkages between user IDs, demographic profiles, device intelligence, and geolocation data, offering deep context for analytics, segmentation, and personalization.

Built for privacy-safe analytics, the dataset uses hashed identifiers like phone number and email and standardized formats, making it easy to integrate into big-data platforms, AI pipelines, and machine learning models for advanced analytics.

Demographic insights include gender, age, and age group, essential for audience profiling, marketing optimization, and consumer intelligence. All gender data is user-declared and AI-verified through image-based avatar validation, ensuring data accuracy and authenticity.

The dataset’s Device Intelligence Layer includes rich technical attributes such as device brand, model, OS version, user agent, RAM, language, and timezone, enabling technical segmentation, performance analytics, and targeted ad delivery across diverse device ecosystems.

On the location and POI front, the dataset combines GPS-based and IP-based coordinates—including country, region, city, latitude, longitude —to provide high-precision geospatial insights. This enables mobility pattern analysis, market expansion planning, and POI clustering for advanced location intelligence.

Each user record contains onboarding and lifecycle fields like unique IDs, and profile update timestamps, allowing accurate tracking of user acquisition trends, data freshness, and activity duration.

🔍 Key Features • 1st-party, consent-based demographic & device data • AI-verified gender insights via avatar recognition • OS-level app data with 120+ daily sessions per user • Global coverage across APAC and emerging markets • GPS + IP-based geolocation & POI intelligence • Privacy-compliant, hashed identifiers for safe integration

🚀 Use Cases • Audience segmentation & lookalike modeling • Ad-tech and mar-tech optimization • Geospatial & POI analytics • Fraud detection & risk scoring • Personalization & recommendation engines • App performance & device compatibility insights

🏢 Industries Served Ad-Tech • Mar-Tech • FinTech • Telecom • Retail Analytics • Consumer Intelligence • AI & ML Platforms

Description The gender distribution includes female and male, the race distribution is Asian, Caucasian and black race, the age distribution is teenager, young and middle-aged. The data diversity includes multiple scenes, multiple light conditions, multiple types of costume (upper garment, lower garment, and shoes), and multiple apparel accessories (bag, glasses, accessories, etc.). In terms of annotation, semantic segmentation of 47 categories object (including background, costume and apparel accessory) was adopted. The dataset can be used for tasks such as human costume & apparel accessory segmentation and fashion recommendation. For more details, please visit: https://www.nexdata.ai/datasets/computervision/975?source=Kaggle

Specifications Data size 50,022 images Population distribution race distribution: 22,642 images of Asian, 19,396 images of Caucasian and 7,984 images of black race; gender distribution: 22,937 images of male, 27,085 images of female; age distribution: 7,569 images aged from 0 to 18, 39,023 images aged from 19 to 45, 3,430 images over 45 years old Collecting environment 11,996 images in indoor scenes, 38,026 images in outdoor scenes Data diversity including multiple scenes, multiple light conditions, multiple types of costume (upper garment, lower garment, and shoes), and multiple apparel accessories (bag, glasses, accessories, etc.) Data format the image data format is .jpg and .png, the annotation file format is .json Accuracy the accuracy of labels of race, gender, age group and collecting environment is over 97%; segmentation annotation accuracy is over 97%

Get the Dataset This is just an example of the data. To access more sample data or request the price, contact us at info@nexdata.ai

E-Commerce Customer Segmentation Dataset This synthetic dataset contains information about 20 customers of an e-commerce platform, designed for customer segmentation and classification tasks.

Dataset Overview Each record represents a unique customer with demographic and behavioral features that help classify them into different customer segments.

Features: customer_id: Unique identifier for each customer

age: Age of the customer (years)

annual_income_k$: Annual income in thousands of dollars

spending_score: A score between 0 and 100 indicating customer spending habits (higher means more spending)

membership_years: Length of membership in years

segment: Customer segment label; possible values are:

Low (low-value customers)

Medium (medium-value customers)

High (high-value customers)

Potential Use Cases Customer segmentation

Targeted marketing campaigns

Customer lifetime value prediction

Behavioral analytics and profiling

Clustering and classification algorithm testing

Dataset Size 20 samples

6 columns

License This dataset is provided under the Apache 2.0 License.

The market for middle-aged and elderly women's clothing is experiencing significant growth, driven by several key factors. The increasing global population of women aged 50 and above, coupled with rising disposable incomes and a greater emphasis on personal well-being in this demographic, are fueling demand. This segment is demonstrating a shift towards more stylish, comfortable, and functional clothing, moving beyond traditional perceptions of "seniors' fashion." Online sales channels are experiencing rapid expansion, offering convenience and wider product choices to this target audience. However, challenges remain, including maintaining consistent brand image and appeal across different age sub-groups within the target market, and adapting designs to accommodate diverse body types and preferences. The preference for natural fabrics, sustainable practices and ethical sourcing is also becoming increasingly important and influencing purchasing decisions. Competition remains high, with a diverse range of both established and emerging brands vying for market share. Geographic variations in purchasing power and cultural preferences also influence market performance, with regions like North America and Europe demonstrating stronger initial market penetration due to higher disposable income and established e-commerce infrastructure. The Asia-Pacific region, especially China and India, shows immense growth potential as increasing affluence and changing lifestyle patterns drive demand. A focus on providing personalized experiences and targeted marketing will be crucial for brands aiming to maximize success in this expanding market. Successful brands within this market segment are leveraging targeted marketing strategies to highlight the comfort, quality, and style of their products. They are also prioritizing ethical and sustainable practices, increasingly important to environmentally and socially conscious consumers. Product innovation, such as adaptive clothing and specialized designs addressing specific needs (e.g., arthritis-friendly closures), represents a significant opportunity for growth. The integration of technology, such as virtual try-on tools and personalized recommendations, is enhancing the online shopping experience. Future growth will depend on brands' ability to effectively utilize data analytics to understand customer preferences and tailor their offerings, while adapting to evolving fashion trends and maintaining sustainable business practices. A key challenge lies in addressing the diverse needs and preferences across different age subgroups within the middle-aged and elderly women's apparel market, requiring sophisticated segmentation and targeting approaches.

Semantic segmentation masks for hands on scanned X-Rays from the RSNA Bone Age dataset.

Mask were obtained manually using thresholding and edge detection and all masks were quality checked.

Based on this two models (Tensormask and Efficient-UNet) were trained to obtain the masks on the full RSNA Bone Age dataset.

This dataset encompasses a rich collection of 248 images, all featuring Asian faces spanning an age range from 0 to 80 years. With a focus on diversity, this dataset offers a comprehensive representation of facial features and aging characteristics across different life stages.

Additionally, the dataset includes a file named "**DeepFace_analyze.csv**" which encapsulates the results of analyzing the dataset using the DeepFace library. This analysis focuses on evaluating the accuracy and performance of the DeepFace library when applied to Asian faces, specifically individuals of Vietnamese ethnicity. The CSV file serves as a valuable resource for gauging the library's effectiveness in handling facial analysis tasks within the context of the dataset.

Dataset Details:

• Total Images: 248
• Age Groups: 0 - 80
• Ethnicity: Asian
• Image Resolution: Varies (Good, Normal, Poor, or Hidden - such as when facial features are obscured by items like facemarks)
• Image Format: JPEG
• Face Angle: Straight, Left, Right

We hope that this dataset contributes to the advancement of research, technology, and understanding related to Asian facial characteristics, age progression, and the intricacies of facial analysis algorithms.

The Age Related Molecular Degeneration Market is estimated to be valued at USD 12.9 million in 2025 and is projected to reach USD 25.4 million by 2035, registering a compound annual growth rate (CAGR) of 7.0% over the forecast period.

General Information:

Total Rows: 53,503 Total Columns: 20 File Size: ~8.2 MB

Data Types:

Integer: 5 columns Object (String): 15 columns

Column Details:

Customer ID: Unique identifier for each customer (Integer). Age: Age of the customer (Integer). Gender: Gender of the customer (Male/Female) (String). Marital Status: Marital status of the customer (e.g., Single, Married) (String). Education Level: Highest education level attained (e.g., Bachelor's Degree) (String). Geographic Information: Location information (State/Region) (String). Occupation: Customer's profession (e.g., Manager, Entrepreneur) (String). Income Level: Annual income of the customer in local currency (Integer). Behavioral Data: Categorical data on behavior patterns (String). Purchase History: Date of the last purchase (Date format). Interactions with Customer Service: Preferred method of communication with customer service (e.g., Phone, Chat) (String). Insurance Products Owned: Insurance policies owned by the customer (String). Coverage Amount: Total insurance coverage amount (Integer). Premium Amount: Monthly premium payment (Integer). Policy Type: Type of insurance policy (e.g., Family, Group) (String). Customer Preferences: General preferences (e.g., Email, Text) (String). Preferred Communication Channel: Method of communication preferred (e.g., In-Person Meeting, Mail) (String). Preferred Contact Time: Most suitable time for contact (e.g., Morning, Afternoon) (String). Preferred Language: Language preference for communication (e.g., English, French) (String). Segmentation Group: Customer segmentation group assigned (e.g., Segment2, Segment3) (String).

Key Observations: Comprehensive customer segmentation data, ideal for demographic, behavioral, and financial analysis. Mixture of categorical, numerical, and date-related attributes. Useful for marketing analysis, predictive modeling, and customer insights.

Objective: To perform Exploratory Data Analysis (EDA) on the customer segmentation dataset to uncover insights into customer demographics, purchasing behaviors, and transaction patterns. These insights will guide the company in identifying potential segments for targeted marketing.

Retirement Notice: This item is in mature support as of June 2023 and will be retired in December 2025. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.This map displays the dominant LifeMode Summary Group in the USA by country, state, county, ZIP Code, tract, and block group, based on Esri's Tapestry Segmentation system. The popup refers to state, county, ZIP Code, tract, and block group values depending on scale. Each popup is configured to display the following information within each geography level:Dominant Tapestry SegmentLink to more information about the predominant Tapestry SegmentTotal populationMedian age (Median Age web map)Diversity Index (Diversity Index web map)Median household income (Median Household Income web map)Median disposable income (Median Disposable Income web map)Count of households by Tapestry LifeMode Summary GroupCount of population by race/ethnicityLink to more information about Esri's Demographics Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Context and Objective:

This dataset is developed as part of a business analysis project aimed at exploring sales performance and customer demographics. It is inspired by real-world scenarios where companies strive to enhance their marketing strategies by understanding consumer behavior. The project focuses on the year 2023 and provides insights into how targeted marketing impacts sales while emphasizing demographic characteristics such as age and gender.

Source:

The dataset is synthetically generated, designed to simulate real-world sales scenarios for 20 products. It includes data points that mirror industry practices, ensuring a realistic and comprehensive foundation for analysis. The structure and data content are informed by common business intelligence practices and hypothetical yet plausible marketing scenarios.

Inspiration:

This dataset is inspired by the challenges businesses face in balancing targeted and broad marketing strategies. Companies frequently debate whether niche marketing for specific demographics or campaigns targeting a wider audience yields better outcomes. The dataset serves as a sandbox for exploring these questions, combining data analytics, visualization, and storytelling to drive actionable business insights.

Key Features:

Sales Data: Includes monthly sales records for 20 products, categorized by revenue, units sold, and discounts applied.

Demographic Information: Covers customer age, gender, and location to enable segmentation and trend analysis.

Applications:

Business Insights: Explore product popularity trends across different demographic groups. Revenue Analysis: Understand revenue patterns throughout 2023 and their correlation with customer age and gender.

Marketing Strategy Optimization: Evaluate the effectiveness of targeted vs. broad campaigns, particularly those targeting specific gender or age groups.

Visualization and Storytelling: Build dashboards and presentations to communicate insights effectively. This dataset is ideal for analysts and students seeking hands-on experience in SQL, exploratory data analysis, and visualization tools like Power BI. It bridges the gap between data science and practical business decision-making.

BackgroundAbnormal brain development is common in children with cerebral palsy (CP), but there are no recent reports on the actual brain age of children with CP.ObjectiveOur objective is to use the brain age prediction model to explore the law of brain development in children with CP.MethodsA two-dimensional convolutional neural networks brain age prediction model was designed without segmenting the white and gray matter. Training and testing brain age prediction model using magnetic resonance images of healthy people in a public database. The brain age of children with CP aged 5–27 years old was predicted.ResultsThe training dataset mean absolute error (MAE) = 1.85, r = 0.99; test dataset MAE = 3.98, r = 0.95. The brain age gap estimation (BrainAGE) of the 5- to 27-year-old patients with CP was generally higher than that of healthy peers (p < 0.0001). The BrainAGE of male patients with CP was higher than that of female patients (p < 0.05). The BrainAGE of patients with bilateral spastic CP was higher than those with unilateral spastic CP (p < 0.05).ConclusionA two-dimensional convolutional neural networks brain age prediction model allows for brain age prediction using routine hospital T1-weighted head MRI without segmenting the white and gray matter of the brain. At the same time, these findings suggest that brain aging occurs in patients with CP after brain damage. Female patients with CP are more likely to return to their original brain development trajectory than male patients after brain injury. In patients with spastic CP, brain aging is more serious in those with bilateral cerebral hemisphere injury than in those with unilateral cerebral hemisphere injury.

The IPCTI dataset comprises 2,648 CBCT-derived dental images from 662 patients, with each patient contributing four images: sagittal and coronal views of the upper central incisors. The dataset includes 452 female and 210 male subjects, and its primary objective is to support age estimation based on these image sets. In addition to age and sex labels, the dataset provides object detection and semantic segmentation annotations. This set of annotations makes the dataset suitable for a variety of tasks, including research in forensic odontology, dental imaging, and anatomical studies, primarily those focused on age estimation based on radiographic features. Therefore, IPCTI offers a valuable benchmark for advancing deep learning research in computer vision, particularly in the context of multi-view learning and multi-task learning.