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

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.

Shoppers aged 35 to 44 years made up the largest share of *************** buyers in the United States in 2023. In the same period, buyers aged 21 to 34 years were the largest share of ************ buyers.

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

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 Segment Tool provides information on the causes of death and age groups that are driving inequalities in life expectancy at local area level. Targeting the causes of death and age groups which contribute most to the life expectancy gap should have the biggest impact on reducing inequalities.

The tool provides data tables and charts showing the breakdown of the life expectancy gap in 2020 to 2021 for 2 comparisons:

• England: the gap between each local area or region as a whole and England as a whole
• within area: the gap between the most deprived quintile of each area and the least deprived quintile of the area

The tool contains data for England, English regions and upper tier local authorities.

Success.ai’s Consumer Marketing Data API empowers your marketing, analytics, and product teams with on-demand access to a vast and continuously updated dataset of consumer insights. Covering detailed demographics, behavioral patterns, and purchasing histories, this API enables you to go beyond generic outreach and craft tailored campaigns that truly resonate with your target audiences.

With AI-validated accuracy and support for precise filtering, the Consumer Marketing Data API ensures you’re always equipped with the most relevant data. Backed by our Best Price Guarantee, this solution is essential for refining your strategies, improving conversion rates, and driving sustainable growth in today’s competitive consumer landscape.

Why Choose Success.ai’s Consumer Marketing Data API?

1. Tailored Consumer Insights for Precision Targeting

   • Access verified demographic, behavioral, and purchasing data to understand what consumers truly value.
   • AI-driven validation ensures 99% accuracy, minimizing wasted spend and improving engagement outcomes.

2. Comprehensive Global Reach

   • Includes consumer profiles from diverse regions and markets, enabling you to scale campaigns and discover emerging opportunities.
   • Adapt swiftly to new markets, product launches, and shifting consumer preferences with real-time data at your fingertips.

3. Continuously Updated and Real-Time Data

   • Receive ongoing updates that reflect evolving consumer behaviors, interests, and market trends.
   • Respond quickly to seasonal changes, competitor moves, and industry disruptions, ensuring your campaigns remain timely and relevant.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, guaranteeing responsible and lawful data usage.

Data Highlights:

• Detailed Demographics: Age, gender, location, and income levels to refine targeting and messaging.
• Behavioral Insights: Interests, browsing patterns, and content consumption habits to anticipate consumer needs.
• Purchasing History: Understand consumer spending, brand loyalty, and product preferences to tailor promotions effectively.
• Real-Time Updates: Keep pace with evolving consumer tastes, ensuring your strategies remain forward-focused and competitive.

Key Features of the Consumer Marketing Data API:

1. Granular Targeting and Segmentation

   • Query the API to segment consumers by demographics, interests, past purchases, or engagement patterns.
   • Focus campaigns on the most receptive audiences, enhancing conversion rates and ROI.

2. Flexible and Seamless Integration

   • Easily integrate the API into CRM systems, marketing automation tools, or analytics platforms.
   • Streamline workflows and eliminate manual data imports, freeing resources for strategic initiatives.

3. Continuous Data Enrichment

   • Refresh consumer profiles with the latest data, ensuring every decision is backed by current insights.
   • Reduce data decay and maintain top-notch data hygiene to maximize long-term marketing effectiveness.

4. AI-Driven Validation

   • Rely on advanced AI validation techniques to guarantee high-quality data accuracy and reliability.
   • Increase confidence in your campaigns and decrease budget wasted on irrelevant targets.

Strategic Use Cases:

1. Highly Personalized Marketing Campaigns

   • Deliver tailored offers, recommendations, and content that align with individual consumer preferences.
   • Boost engagement and loyalty by making every touchpoint relevant and meaningful.

2. Market Expansion and Product Launches

   • Identify segments most receptive to new products or services, ensuring successful market entry.
   • Stay ahead of consumer demands, evolving your product line and marketing mix to meet changing preferences.

3. Competitive Analysis and Trend Forecasting

   • Leverage consumer insights to anticipate emerging trends and outpace competitors in capturing new markets.
   • Adjust marketing strategies proactively to capitalize on seasonal, cultural, or economic shifts.

4. Customer Retention and Loyalty Programs

   • Use historical purchase and engagement data to identify at-risk customers and implement retention strategies.
   • Cultivate brand advocates by delivering personalized offers and exclusive perks to loyal consumers.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality consumer marketing data at unmatched prices, ensuring maximum ROI for your outreach efforts.

2. Seamless Integration

   • Easily incorporate the API into existing workflows, eliminating data silos and manual data management.

3. Data Accuracy with AI Validation

   • Depend on 99% accuracy to guide data-driven decisions, refine targeting, and elevate your marketing initiatives.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus on specific demog...

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.

Project Overview: Customer Segmentation Using K-Means Clustering

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

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

Data Description The dataset comprises:

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

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

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

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

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.

According to estimates of the Statista Digital Market Outlook, people aged 18 to 24 years were the largest share of matchmaking service users in Russia in 2021. At the same time, people aged 35 to 44 years were the largest share of casual dating users at **** percent.

The Old-age Facilities Construction market is an essential segment of the broader construction industry, focusing on the design and building of specialized facilities that cater to the growing population of seniors. As global demographics shift, with a significant increase in life expectancy and an aging population,

The Age Verification Software market has emerged as a crucial component of various industries, particularly in sectors where compliance with age-related regulations is essential, such as online gaming, e-commerce, and streaming services. This technology serves as a solution to verify the age of individuals accessing

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.