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

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...

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.

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.

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?

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.

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.

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.

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

Demographic factors of participants (n = 680).

Summary of demographic and behavioral characteristics (n = 402).

The Bank Marketing dataset is a commonly used dataset in the fairness literature, containing information about individuals' demographic and economic characteristics.

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.

With the rapid development of AI intelligent technology, AIGC can bring an innovative revolution to art creation, providing designers with unlimited possibilities but also challenges. These challenges affect the willingness to adopt and constrain the sustainable development of AIGC. The purpose of this study is to analyse the factors of designers’ adoption intention behaviours. This study reconstructed the research model by combining the factors of AIGC technology characteristics and interactivity, technology acceptance model, technology readiness model, etc. The empirical study was conducted from the dual perspectives of AIGC application characteristics and designers’ psychology, in order to predict the factors that predict designers’ behavioural intentions to use AIGC. In this study, a questionnaire survey was conducted among designers in China and 462 valuable responses were received. Through structural equation modelling (SEM) analysis, the study found that: (1) AIGC’s technical features and interactivity positively affect perceived ease of use, and perceived usefulness, but the interactive features do not directly affect perceived usefulness; perceived ease of use and perceived usefulness positively affect designers’ intention to adopt AIGC applications; (2) optimism and innovation positively affect technical features and designers’ intention to adopt; Insecurity negatively affects designers’ willingness to adopt, and insecurity does not affect technical features; discomfort does not affect designers’ technical features and willingness to adopt. This study further extends the theoretical models of TAM(Technology Acceptance Model) and TRI(Technology Readiness Model), provides a theoretical basis for studying designers’ adoption behaviour of AIGC, and enriches the application groups and domains of the theoretical models of TAM and TRI. The results of this study provide inspiration for the development, design, and marketing of AIGC applications, contributing to the realisation and further adoption of AIGC applications, as well as to the professional development of designers.

The dataset comprises 788 entries with 37 columns, providing demographic, behavioral, attitudinal, and environmental data likely centered around consumer behaviors related to organic products. Demographic variables include age, sex, and education level, capturing essential background information on each respondent. Behavioral beliefs are represented across ten items (BB1 to BB10), suggesting specific beliefs or behaviors related to the topic. Additionally, variables such as frequency (Freq), volume of purchases (Vol), and average purchase amount (AvePurch) detail purchasing behaviors. The dataset also includes five belief items (Belief1 to Belief5) along with an aggregated Belief score, and similarly, five attitude items (Att1 to Att5) with an overall Attitude score. Environmental concerns are captured through five items (Env1 to Env5), with a combined Environ score that may represent an overall environmental attitude. Notably, the last two columns (Environ and Unnamed: 36) have numerous missing values, which may need addressing for analysis.

The survey was conducted from August 1 to September 30, 2024. Respondents are from different parts of Metro Manila and the province of Cavite.

Demographic characteristics of respondents in private-owned sports center.

This dataset contains simulated customer data that can be used for segmentation analysis. It includes demographic and behavioral information about customers, which can help in identifying distinct segments within the customer base. This can be particularly useful for targeted marketing strategies, improving customer satisfaction, and increasing sales.

Columns: id: Unique identifier for each customer. age: Age of the customer. gender: Gender of the customer (Male, Female, Other). income: Annual income of the customer (in USD). spending_score: Spending score (1-100), indicating the customer's spending behavior and loyalty. membership_years: Number of years the customer has been a member. purchase_frequency: Number of purchases made by the customer in the last year. preferred_category: Preferred shopping category (Electronics, Clothing, Groceries, Home & Garden, Sports). last_purchase_amount: Amount spent by the customer on their last purchase (in USD). Potential Uses: Customer Segmentation: Identify different customer segments based on their demographic and behavioral characteristics. Targeted Marketing: Develop targeted marketing strategies for different customer segments. Customer Loyalty Programs: Design loyalty programs based on customer spending behavior and preferences. Sales Analysis: Analyze sales patterns and predict future trends.

The swift growth of e-commerce has markedly changed how consumers shop, especially among Generation Z, which is called Digital Natives. This study examines how product presentation videos on the Shopee video platform influence impulse buying behaviors in this group, focusing on how internal stimuli, including entertainment experience (ET), educational experience (ED), escapist experience (ES), and esthetic experience (EH) influence online impulse buying (OIB) through the mediation of arousal (AR) and pleasure (PL). In addition, demographic factors, including age, gender, and income, are treated as control variables. This research adopts a quantitative methodology, and data was gathered using a Likert scale questionnaire and a non-probability sampling method, while the SmartPLS statistical tool was used to analyze the interactions of these stimuli and their effect on the impulse buying behavior of Generation Z on digital platforms. Research indicates that entertainment and recreational activities boost emotional engagement by eliciting arousal and pleasure. Educational experiences increase knowledge and also stimulate these feelings. Escapist activities provide temporary relief from daily stresses, increasing arousal, but can also highlight personal insecurities, possibly reducing pleasure. Esthetic experiences, subject to personal tastes, provoke emotional reactions that may vary in pleasure. For Generation Z, arousal and pleasure significantly influence impulsive buying decisions. The insights indicate that effectively managing internal factors can trigger emotions leading to impulsive purchases, offering strategic marketing tactics for optimizing e-commerce on platforms like Shopee video. This research advances the understanding of consumer behavior theories in the digital era, emphasizing the intricate roles of arousal and pleasure in online impulse buying.