Context:

Digital marketing platforms today rely heavily on user profiling to decide which advertisements should be displayed to which audience. Social networks collect demographic information such as age, gender, and income to understand user behavior and improve ad targeting. This dataset captures how different user demographics respond to online advertisements, making it valuable for studying customer behavior, marketing strategies, and purchase prediction.

The dataset is widely used in machine learning education and projects because it is simple, clean, and ideal for building classification models. It helps beginners and professionals understand how demographic features influence a user’s decision to purchase a product after viewing an ad.

Content :

This dataset contains user demographic information and their response to an advertisement. Each row represents one individual from a social media platform, including:

Age : The age of the user

Estimated Salary : Approximate annual salary of the user

Purchased : Target variable indicating whether the user bought the advertised product

0 = No purchase

1 = Purchase

The dataset can be used for:

• Predicting purchase behavior using machine learning models

• Understanding how age and income affect ad response

• Performing exploratory data analysis (EDA)

• Demonstrating classification algorithms such as Logistic Regression, KNN, SVM, Trees, etc.

• Practicing feature scaling, model training, evaluation, and visualization

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

GIS Data attributes include:

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

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

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

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

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

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

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

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

Primary Use Cases for GapMaps GIS Data:

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

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

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

8. Network Planning

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

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

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

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

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

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

This dataset contains household level transactions over two years from a group of 2,500 households who are frequent shoppers at a retailer. It contains all of each household’s purchases, not just those from a limited number of categories. For certain households, demographic information as well as direct marketing contact history are included.

Due to the number of tables and the overall complexity of The Complete Journey, it is suggested that this database be used in more advanced classroom settings. Further, The Complete Journey would be ideal for academic research as it should enable one to study the effects of direct marketing to customers.

The following are examples of questions that could be submitted to students or considered for academic research:  - How many customers are spending more over time? Less over time? Describe these customers.  - Of those customers who are spending more over time, which categories are growing at a faster rate?  - Of those customers who are spending less over time, with which categories are they becoming less engaged?  - Which demographic factors (e.g. household size, presence of children, income) appear to affect customer spend? -Engagement with certain categories?  - Is there evidence to suggest that direct marketing improves overall engagement?

Demographic Analysis of Shopping Behavior: Insights and Recommendations

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

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

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

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

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

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

Geolocet offers a rich repository of European demographic data, providing you with a robust foundation for data-driven decisions. Our datasets encompass a diverse range of attributes, but it's important to note that the attributes available may vary significantly from country to country. This variation reflects the unique demographic reporting standards and data availability in each region.

Attributes include essential demographic factors such as Age Bands, Gender, and Marital Status, as a minimum. In some countries, we provide cross-referenced attributes, such as Marital Status per Age Band, Marital Status per Gender, or even intricate combinations like Marital Status per Gender and Age. Additionally, for select countries, we offer insights into income, employment status, household composition, housing status, and many more.

🌐 Trusted Source Data

Our demographic data is derived exclusively from official census sources, ensuring the highest level of accuracy and reliability. We take pride in using data that is available under open licenses for commercial use. However, it's important to note that our data is not a direct representation of the original census data. Instead, we use this source data to create comprehensive demographic models that are tailored to your needs.

🔄 Annual Data Updates

To keep your insights fresh and accurate, our data is updated once per year. We offer annual subscriptions, allowing you to access the latest demographic information and maintain the relevance of your analyses.

🌍 Geographic Coverage

While our demographic data spans across the majority of European countries and their administrative divisions' boundaries, it's important to inquire about specific attributes and coverage for each region of interest. We understand that your data needs may vary depending on your target regions, and our team is here to assist you in selecting the most relevant datasets for your objectives.

Contact us to explore our offerings and learn how our data can elevate your decision-making processes.

🌐 Enhanced with Spatial Insights: Administrative Boundaries Spatial Data

Geolocet's demographic data isn't limited to numbers; it's brought to life through seamless integration with our Administrative Boundaries Spatial Data. This integration offers precise boundary mapping, allowing you to visualize demographic distributions, patterns, and densities on a map. This spatial perspective unlocks geo patterns and insights, aiding in strategic decision-making. Whether you're planning localized marketing strategies, optimizing resource allocation, or selecting ideal expansion sites, the geographic context adds depth to your data-driven strategies. Contact us today to explore how this spatial synergy can enhance your decision-making.

🌍 Enhanced with Robust Aggregated POI Data

Geolocet doesn't stop at demographics; we enhance your analysis by offering Geolocet's POI Aggregated Data. This data source provides a comprehensive understanding of local areas, enabling you to craft detailed local area profiles. It's not just about numbers; it's about uncovering the essence of each locality.

🔍 Crafting Local Area Profiles

When you combine our POI Aggregated Data with our Demographics Data, you have the tools to craft insightful local area profiles. Dive into the specific data points for various sectors, such as the number of hospitals, schools, hotels, restaurants, pubs, casinos, groceries, clothing stores, gas stations, and more within designated areas. This level of granularity allows you to paint a vivid picture of each locality, understanding its unique characteristics and offerings.

Contact us today to explore how this synergy can elevate your strategic decision-making and enrich your insights into local communities.

🔍 Customized Data Solutions with DaaS

Geolocet's Data as a Service (DaaS) offers flexibility tailored to your needs. Our transparent pricing model ensures cost-efficiency, allowing you to pay only for the data you require.

The dataset consists of Consumer buying behaviour of FMCG products in connection with cause-related marketing.Dataset is based on questionnaire having thirteen five point scale likert scale statements along with the demographic variables.The questionnaire is drafted based on factors contributing to consumer buying behaviour of cause-related marketing such as information available on product packaging,Brand image and Celebrity endorsement.The responses of likert scale statements were in the form of 'Strongly Agree', 'Agree', Neutral', 'Disagree', Strongly Disagree', and they were coded as 5,4,3,2,1 respectively for positive statements and 1,2,3,4,5 respectively for negative statements.

Shopper Demographics Analytics Market Outlook

As per our latest research, the global shopper demographics analytics market size in 2024 is valued at USD 5.3 billion, with a robust CAGR of 14.7% projected through the forecast period. By 2033, the market is expected to reach USD 17.2 billion, reflecting the accelerating adoption of advanced analytics solutions in retail and related sectors. The primary growth driver is the increasing need for retailers and brands to understand and predict consumer behavior in an era characterized by omnichannel shopping and intense competition.

The growth of the shopper demographics analytics market is significantly propelled by the retail sector’s digital transformation. Retailers are increasingly leveraging analytics to gain granular insights into customer demographics, preferences, and purchasing behavior. The integration of artificial intelligence (AI) and machine learning (ML) into analytics platforms has enabled businesses to process vast amounts of data in real time, offering actionable insights that drive personalized marketing and operational efficiency. As consumer expectations for tailored experiences continue to rise, retailers are investing heavily in shopper analytics to enhance customer engagement, improve inventory management, and optimize store layouts, further fueling market expansion.

Another key growth factor is the proliferation of e-commerce and the corresponding surge in online data generation. E-commerce platforms are uniquely positioned to collect detailed demographic and behavioral data, which can be analyzed to segment customers, predict purchasing trends, and personalize marketing campaigns. The adoption of cloud-based analytics solutions has further democratized access to advanced analytics, allowing even small and medium-sized enterprises (SMEs) to harness the power of shopper demographics analytics. Moreover, the integration of analytics with customer relationship management (CRM) and point-of-sale (POS) systems has streamlined data collection and analysis, enabling businesses to respond swiftly to changing consumer preferences.

The increasing focus on omnichannel retail strategies is also driving demand for shopper demographics analytics. Retailers are striving to provide a seamless shopping experience across physical stores, online platforms, and mobile applications. Analytics solutions help bridge the gap between different channels by offering a unified view of customer behavior, enabling businesses to deliver consistent and personalized experiences. The rise of smart stores and the deployment of Internet of Things (IoT) devices have further enriched the data ecosystem, providing real-time insights into foot traffic, dwell times, and purchase patterns. These advancements are expected to sustain the market’s high growth trajectory over the coming years.

From a regional perspective, North America currently dominates the shopper demographics analytics market, owing to the presence of major technology providers and early adoption by leading retailers. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding retail infrastructure, and increasing digital adoption among consumers. Europe also holds a significant market share, supported by strong regulatory frameworks and a mature retail sector. The Middle East & Africa and Latin America are emerging as promising markets, as retailers in these regions invest in analytics to stay competitive and cater to evolving consumer demands. These regional dynamics underscore the global relevance and growth potential of shopper demographics analytics.

Component Analysis

The shopper demographics analytics market by component is bifurcated into software and services, with software solutions representing the larger share in 2024. The software segment encompasses a wide range of analytics platforms, including proprietary and open-source solutions designed to collect, process, and visualize demographic data. These platforms leverage advanced technologies such as AI, ML, and big data analytics to deliver actionable insights in real time. The growing adoption of cloud-based analytics software has further accelerated market growth, enabling retailers to scale their analytics capabilities without significant upfront investment in IT infrastructure. The continuous evolution of analytics software, with features such as predictive modeling, data v

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.

During a survey carried out in November 2021 among marketers from ** countries worldwide, ** percent stated their organizations used past purchases to define target consumer segments. Consumer demographics, such as age, gender, income, or location, were used most often, named by ** percent of respondents.

Individual poverty status using Market Basket Measure (MBM) by visible minority groups, age, and gender.

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

Demographic Data attributes include:

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

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

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

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

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

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

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

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

Primary Use Cases for AGS Demographic Data:

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

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

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

8. Network Planning

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

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

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

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

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

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific ta...

Demographic characteristics of analytic sample.

Association of PSA airings and market-level factors, overall and by pandemic wave.

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, MD was for the group of age 5 to 9 years years with a population of 219 (13.79%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in New Market, MD was the 80 to 84 years years with a population of 3 (0.19%). 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

Live streaming e-commerce emphasizes the role of live streaming influencers and the dynamic interactions between viewers and live streaming influencers. Utilizing data collected from 400 questionnaires, this study delves into the mechanisms through which characteristics of live streaming influencers influence consumer purchase intentions, with a focus on consumer emotional trust as a mediating variable as well as consumer education level, age, perceived risk, and live-stream engagement as moderating factors. The findings indicate that the traits of live streaming influencers have a positive effect on consumers’ intent to purchase. Emotional trust mediates the influence of live streaming influencer characteristics on purchase intention. Consumers’ educational level positively moderates the relationship between the professionalism and homogeneity of influencers and their purchase intentions, while it negatively moderates the relationship between influencers’ attraction and interactivity with purchase intentions. Additionally, the age of consumers positively moderates the link between the professionalism of influencers and purchase intentions, but negatively moderates the links between homogeneity, attraction, and interactivity of influencers and purchase intentions. Furthermore, both consumers’ educational level and age positively moderate the impact of emotional trust on purchase intentions. Lastly, perceived risk and live-stream engagement respectively exert negative and positive moderating effects on the influence of influencer professionalism and attraction on purchase intentions. The study contributes to influencer marketing theory by adopting an innovative approach to systematically investigate the collective influence of all four live streaming influencer characteristics (professionalism, homogeneity, attraction, and interactivity) within a comprehensive framework.

Overview

This dataset provides a comprehensive view of customer interactions with digital marketing campaigns. It includes demographic data, marketing-specific metrics, customer engagement indicators, and historical purchase data, making it suitable for predictive modeling and analytics in the digital marketing domain.

Features

Demographic Information

• CustomerID: Unique identifier for each customer.
• Age: Age of the customer.
• Gender: Gender of the customer (Male/Female).
• Income: Annual income of the customer in USD.

Marketing-specific Variables

• CampaignChannel: The channel through which the marketing campaign is delivered (Email, Social Media, SEO, PPC, Referral).
• CampaignType: Type of the marketing campaign (Awareness, Consideration, Conversion, Retention).
• AdSpend: Amount spent on the marketing campaign in USD.
• ClickThroughRate: Rate at which customers click on the marketing content.
• ConversionRate: Rate at which clicks convert to desired actions (e.g., purchases).
• AdvertisingPlatform: Confidential.
• AdvertisingTool: Confidential.

Customer Engagement Variables

• WebsiteVisits: Number of visits to the website.
• PagesPerVisit: Average number of pages visited per session.
• TimeOnSite: Average time spent on the website per visit (in minutes).
• SocialShares: Number of times the marketing content was shared on social media.
• EmailOpens: Number of times marketing emails were opened.
• EmailClicks: Number of times links in marketing emails were clicked.

Historical Data

• PreviousPurchases: Number of previous purchases made by the customer.
• LoyaltyPoints: Number of loyalty points accumulated by the customer.

Target Variable

• Conversion: Binary variable indicating whether the customer converted (1) or not (0).

Potential Applications

• Predictive modeling of customer conversion rates.
• Analyzing the effectiveness of different marketing channels and campaign types.
• Identifying key factors driving customer engagement and conversion.
• Optimizing ad spend and campaign strategies to improve ROI.

Usage

This dataset is ideal for data scientists and marketing analysts looking to explore and model customer behavior in response to digital marketing efforts. It can be used for machine learning projects, A/B testing analysis, and more.

Dataset Usage and Attribution Notice

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

Geodemographic Segmentation Market Outlook

According to our latest research, the global Geodemographic Segmentation market size reached USD 5.12 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.7% expected from 2025 to 2033. This growth trajectory will drive the market to an estimated USD 15.34 billion by 2033. The surge in demand for location-based analytics, targeted marketing, and data-driven decision-making across various industries is a key growth factor propelling the market forward. As per our latest research, the adoption of advanced analytics and artificial intelligence in geodemographic segmentation is transforming how organizations understand consumer behavior and optimize operational strategies.

The primary growth factor for the geodemographic segmentation market is the increasing need for personalized marketing and customer-centric business models. Organizations across industries such as retail, banking and financial services, and telecommunications are leveraging geodemographic data to understand consumer preferences, purchasing power, and lifestyle choices. This enables highly targeted campaigns and product offerings, resulting in improved customer engagement and higher conversion rates. The proliferation of digital channels and the growing volume of location-based data have further fueled the adoption of geodemographic segmentation solutions. As businesses strive to remain competitive in a crowded marketplace, the ability to deliver tailored experiences based on geographic and demographic insights is becoming a critical differentiator.

Another significant driver is the technological advancements in data analytics, artificial intelligence, and machine learning. Modern geodemographic segmentation solutions integrate big data analytics with sophisticated algorithms to deliver actionable insights in real time. The integration of geospatial data with demographic, psychographic, and behavioral information enables organizations to create comprehensive customer profiles. This not only enhances marketing effectiveness but also supports strategic decision-making in areas such as site selection, risk assessment, and resource allocation. The cloud-based deployment of these solutions has further democratized access to advanced analytics, making it feasible for small and medium-sized enterprises (SMEs) to leverage geodemographic segmentation without significant upfront investments in IT infrastructure.

The expanding application of geodemographic segmentation in non-traditional sectors such as healthcare, real estate, and transportation is also contributing to market growth. In healthcare, for instance, providers use geodemographic data to identify underserved communities and tailor health interventions accordingly. Real estate companies analyze demographic trends to predict property demand and optimize investment decisions. Similarly, logistics firms utilize geodemographic insights to streamline supply chain networks and enhance last-mile delivery efficiency. This cross-industry adoption underscores the versatility and value proposition of geodemographic segmentation, driving its continued expansion in the coming years.

Regionally, North America remains the largest market for geodemographic segmentation, driven by the high adoption of analytics technologies and the presence of leading solution providers. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, digital transformation initiatives, and increasing investments in smart city projects. Europe also holds a significant share, supported by stringent data privacy regulations and a mature retail sector. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, with rising demand for data-driven solutions in sectors such as retail, banking, and logistics. These regional dynamics highlight the global relevance and growth potential of the geodemographic segmentation market.

Component Analysis

The geodemographic s

Context

This dataset provides insights into the daily mobile usage patterns of 1,000 users, covering aspects such as screen time, app usage, and user engagement across different app categories.

It includes a diverse range of users based on age, gender, and location.

The data focuses on total app usage, time spent on social media, productivity, and gaming apps, along with overall screen time.

This information is valuable for understanding behavioral trends and app usage preferences, making it useful for app developers, marketers, and UX researchers.

This dataset is useful for analyzing mobile engagement, app usage habits, and the impact of demographic factors on mobile behavior. It can help identify trends for marketing, app development, and user experience optimization.

Outcome

This dataset enables a deeper understanding of mobile user behavior and app engagement across different demographics.

Key outcomes include insights into app usage preferences, daily screen time habits, and the impact of age, gender, and location on mobile behavior.

This analysis can help identify patterns for improving user experience, tailoring marketing strategies, and optimizing app development for different user segments.