WiserBrand's Comprehensive Customer Call Dataset: A Decade of Insights

WiserBrand offers an unparalleled dataset comprising over 16 million customer call records, meticulously gathered over the past 10 years and updated daily. This extensive dataset includes:

• User ID and Firm Name: Identify and categorize calls by unique user IDs and company names.
• Call Duration: Analyze engagement levels through call lengths.
• Geographical Information: Detailed data on city, state, and country for regional analysis.
• Call Timing: Track peak interaction times with precise timestamps.
• Call Reason and Group: Categorized reasons for calls, helping to identify common customer issues.
• Device and OS Types: Information on the devices and operating systems used for technical support analysis.

We can build a dataset based on your request, by category, industry, company, date, etc.

Our dataset is designed for businesses aiming to enhance customer service strategies, develop targeted marketing campaigns, and improve product support systems. Gain actionable insights into customer needs and behavior patterns with this comprehensive collection, particularly useful for Consumer Data and Consumer Behavior applications.

The more you purchase, the lower the price will be.

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

Success.ai’s Consumer Behavior Data for Consumer Goods & Electronics Industry Leaders in Asia, the US, and Europe offers a robust dataset designed to empower businesses with actionable insights into global consumer trends and professional profiles. Covering executives, product managers, marketers, and other professionals in the consumer goods and electronics sectors, this dataset includes verified contact information, professional histories, and geographic business data.

With access to over 700 million verified global profiles and firmographic data from leading companies, Success.ai ensures your outreach, market analysis, and strategic planning efforts are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is ideal for businesses aiming to navigate and lead in these fast-paced industries.

Why Choose Success.ai’s Consumer Behavior Data?

1. Verified Contact Data for Precision Engagement

   • Access verified email addresses, phone numbers, and LinkedIn profiles of professionals in the consumer goods and electronics industries.
   • AI-driven validation ensures 99% accuracy, optimizing communication efficiency and minimizing data gaps.

2. Comprehensive Global Coverage

   • Includes profiles from key markets in Asia, the US, and Europe, covering regions such as China, India, Germany, and the United States.
   • Gain insights into region-specific consumer trends, product preferences, and purchasing behaviors.

3. Continuously Updated Datasets

   • Real-time updates capture career progressions, company expansions, market shifts, and consumer trend data.
   • Stay aligned with evolving market dynamics and seize emerging opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible use and legal compliance for all data-driven campaigns.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with industry leaders, marketers, and decision-makers in consumer goods and electronics industries worldwide.
• Consumer Trend Insights: Gain detailed insights into product preferences, purchasing patterns, and demographic influences.
• Business Locations: Access geographic data to identify regional markets, operational hubs, and emerging consumer bases.
• Professional Histories: Understand career trajectories, skills, and expertise of professionals driving innovation and strategy.

Key Features of the Dataset:

1. Decision-Maker Profiles in Consumer Goods and Electronics

   • Identify and engage with professionals responsible for product development, marketing strategy, and supply chain optimization.
   • Target individuals making decisions on consumer engagement, distribution, and market entry strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (consumer electronics, FMCG, luxury goods), geographic location, or job function.
   • Tailor campaigns to align with specific industry trends, market demands, and regional preferences.

3. Consumer Trend Data and Insights

   • Access data on regional product preferences, spending behaviors, and purchasing influences across key global markets.
   • Leverage these insights to shape product development, marketing campaigns, and customer engagement strategies.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing and Demand Generation

   • Design campaigns tailored to consumer preferences, regional trends, and target demographics in the consumer goods and electronics industries.
   • Leverage verified contact data for multi-channel outreach, including email, social media, and direct marketing.

2. Market Research and Competitive Analysis

   • Analyze global consumer trends, spending patterns, and product preferences to refine your product portfolio and market positioning.
   • Benchmark against competitors to identify gaps, emerging needs, and growth opportunities in target regions.

3. Sales and Partnership Development

   • Build relationships with key decision-makers at companies specializing in consumer goods or electronics manufacturing and distribution.
   • Present innovative solutions, supply chain partnerships, or co-marketing opportunities to grow your market share.

4. Product Development and Innovation

   • Utilize consumer trend insights to inform product design, pricing strategies, and feature prioritization.
   • Develop offerings that align with regional preferences and purchasing behaviors to maximize market impact.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality consumer behavior data at competitive prices, ensuring maximum ROI for your outreach, research, and ma...

Market research and public opinion polling companies offer valuable insights into consumer behaviour, values and opinions for businesses, media and public-sector departments. Demand for market research is heavily reliant on business investment and technological advancements, with research companies examining target markets to help businesses tailor their products, services and marketing strategies. The advent of digital platforms and advanced analytics is reshaping the market landscape, providing more efficient data collection methods and insights essential for strategic decision-making. Revenue is expected to expand at a compound annual rate of 2.3% over the five years through 2025 to €2.1 billion, despite being forecast to dip by 2.2% in 2025. The COVID-19 pandemic brought adverse economic conditions, reducing business spending on non-essential services like market research. A decline in advertising spend further hit demand in 2020. However, as restrictions eased, revenue shot up in 2021, as businesses aimed to understand how consumer behaviour was changing in the post-pandemic landscape. Digital advertising's recovery also supported this rebound. Following this, though, inflationary pressures and geopolitical tensions in late 2022 and 2023 curbed spending, as businesses grappled with higher operational costs, leading to a downturn in revenue for market research companies. Prolonged uncertainty and political instability further subdued business investment through 2024 and into 2025, reflected in stagnant demand for market research services. Improving economic conditions are anticipated to bolster the industry’s prospects in the coming years. As inflation eases and interest rates fall, businesses will increase R&D activities, renewing demand for detailed market insights. Opportunities will arise from strengthened collaborations with advertising agencies, aligning strategies based on data-driven insights. Technological advancements will continue to drive innovation, with AI and mobile technologies reshaping data collection and interpretation processes. However, advancing technology will also create challenges as more businesses leverage advanced tech like Gen AI to set up in-house market research solutions, reducing spending on external research companies. At the same time, social media will continue to offer real-time public opinion insights, optimising audience segmentation and sentiment analysis. Data privacy regulations like the GDPR will necessitate balancing innovation with compliance, presenting operational burdens.

Market research companies have benefited from research and development (R&D) expenditure growth as companies develop new products to satisfy consumer demand. Downstream companies continue to rely on market research to create new products and campaigns that fit evolving consumer preferences. As companies strive to enhance consumer-centric strategies amid increased consumer spending, demand for tailored market research solutions has surged. A 10.7% surge in corporate profit over the past five years enabled businesses to outsource more of their research operations to professional market researchers. The digital shift has further transformed the landscape, with companies pioneering new research tools to tap into the vast potential of big data to enhance accessibility and participation. These trends have led to revenue growing at a CAGR of 3.8% to an estimated $36.4 billion over the past five years, including an estimated 2.1% boost in 2025 alone. Consumers' and advertisers' growing reliance on the internet has led to new metrics market researchers can use to better understand consumers. These have allowed new companies to enter the industry and driven providers to adjust services and implement new technologies. The rising use of social media to advertise and market new products across platforms like TikTok and Instagram also contributed to the growing demand for market research. These technological advancements improved data collection and analysis methods, offering actionable insights that helped companies refine marketing strategies and develop better products. New opportunities continue to drive revenue growth, but expansions to services and onboarding of new technology cut researchers’ profitability. Moving forward, the industry will benefit from acceleration in R&D budgets and technological and a data procurement evolution. Companies will strengthen their R&D budgets as economic conditions improve, further driving demand for advanced market research tools. The proliferation of online commerce and smart technologies will give researchers unprecedented access to consumer data. Technological developments, such as artificial intelligence (AI), are poised to create new metrics based on human reactions, which companies can leverage to better understand consumer behavior and preferences. Access to these metrics, however, will lead to tightening data privacy regulations, which may result in higher compliance costs that eat into profitability. Finally, growing emphasis on ethical practices, transparency and data security will shape consumer trust and research standards, creating new opportunities and challenges in a rapidly evolving marketplace. Revenue is poised to grow at a CAGR of 2.4% to an estimated $41.0 billion through the end of 2030.

This dataset contains 333 responses with 13 features, focusing on consumer insights for marketing strategy in the fashion and apparel industry.

It captures roles within the industry, product interests, supplier preferences, and factors influencing purchasing decisions. The dataset also explores perceptions of product quality, pricing, promotions, and communication channels, making it valuable for strategic marketing analysis.

Data Collection • Method: Online survey • Format: 13 columns, with categorical, ordinal, and textual responses • Prepared for: Market research, statistical analysis, and business strategy development

Key Features • Role in the apparel industry • Geographic location • Product interests in fashion shops • Key values when choosing garment suppliers • Rating of product quality • Perception of pricing • Willingness to pay more (e.g., for faster shipping, sustainability) • First awareness of the fashion shop • Preferred platform for product catalogs • Promotions influencing buying decisions • Likelihood of recommending the shop • Preferred type of content from the shop

Potential Applications • Analyze consumer roles and geographic distribution in the apparel industry • Identify most valued supplier attributes (price, delivery, quality, sustainability) • Understand product interests and market demand trends • Evaluate perceptions of pricing and product quality • Model recommendation likelihood based on satisfaction and values • Assess the effectiveness of promotional strategies • Identify preferred communication and catalog platforms • Inform targeted marketing and content strategy for fashion brands

Question Paper Solutions of chapter Introduction to Consumer Behaviour and Consumer Research of Consumer Behaviour, 5th Semester , Bachelor in Business Administration 2020 - 2021

Analyzing Customer Spending Habits to Improve Sales Performance

A Cross-Country Study

By Vineet Bahl [source]

About this dataset

This Sales Data dataset offers a unique insight into the spending habits of customers from various countries across the globe. With detailed information on customer age, gender, product category, quantity, unit cost and price, as well as revenue generated through sales of products listed in this dataset, you can explore and discover patterns in consumer behavior. Analyze shifts in consumer trends with qualitative data like customer age and gender to know what drives customers’ decisions when shopping online or offline. Compare different markets to analyze pricing strategies for new product launches or promotional campaigns. Also with this dataset you can gain valuable insights about the changes in consumer demand for specific products over time – find out which Products had better margin or however see how different promotions impacted overall sales performance from different categories and sub-categories! Analyzing consumer behavior is key to success when it comes to commerce business models so this Sales Data offers powerful ways into understanding your customer base better!

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How to use the dataset

This dataset presents a great opportunity to actively analyze customer spending habits on products and services to improve sales performance. The data contains information about the date of purchase, year, month, customer age, gender, country, state and product category. Further analysis can reveal insights into different customer segments based on their demographic characteristics such as age and gender as well as location (country & state).

The dataset also includes 3 additional columns at the end: quantity purchased in each transaction, unit cost and unit price for each product or service purchased which can be used to determine if customers are purchasing items in bulk or buying more expensive items than usual. Likewise any discrepancies between the unit cost & price can help establish whether discounts were applied during those transactions which could potentially point towards loyalty or reward programs being put in place for returning customers. Lastly the final column shows total revenue generated from those purchases which we can use to identify any patterns whereby certain groups of customers show higher purchasing power than others based on their spends (unit cost & quantity combination) over various periods/months/years of sales interactions with them.

In summary this dataset allows us to explore numerous dimensions related to ascertaining superior sales performance by studying how its various attributes play out together when it comes down to driving profitability through improved customer acquisition strategies as well increasing purchase rates from existing ones minus any discounts available in-between!

Research Ideas

• Analyzing customer demographics by countries and states to better target future marketing campaigns.

• Tracking changes in customers’ spending habits over time for different product categories.

• Identifying which product categories have the highest average revenue per sale to help prioritize resources for those products or services

Acknowledgements

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

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: SalesForCourse_quizz_table.csv | Column name | Description | |:---------------------|:--------------------------------------------------| | Date | Date of the sale. (Date) | | Year | Year of the sale. (Integer) | | Month | Month of the sale. (Integer) | | Customer Age | Age of the c...

Question Paper Solutions of chapter Consumer Behaviour and Marketing Research of Marketing Management, 2nd Semester , Master of Business Administration

Market research is crucial for companies in the beauty industry to stay up-to-date with consumer needs and trends. This article covers the two main types of market research, understanding consumer preferences for natural and organic products, the impact of social media on consumer behavior, and identifying new distribution channels. By conducting research, companies can develop products and strategies that resonate with consumers and drive sales.

During a May 2022 survey, ** percent of responding customers stated that a positive customer service experience made them more likely to purchase again. Moreover, ** percent of customers would recommend a company based solely on excellent customer service.

The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. This type of information is essential for forecasting changes in aggregate consumer behavior. Since the late 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. Each monthly survey probes a different aspect of consumer confidence. Open-ended questions are asked concerning evaluations and expectations about personal finances, employment, price changes, the national business climate, present market conditions for the the purchasing of houses, automobiles, personal computers, and other durables, familiarity with and expected use of the Internet, views on credit card company offers and credit card usage, and information about family and company vehicles. Demographic information includes race, ethnic origin, sex, age, education, marital status, and household size and income.

Market researchers investigate clients' target markets' behaviour, values and opinions, providing insights that allow them to tailor their products, services and marketing. Researchers rely on hefty European research and development expenditure to fuel demand for market research. The surge in digitalisation has opened new doors for market research providers while intensifying competition. Artificial intelligence is increasingly important in analysing, identifying and generating research insights from social media posts using a flood of data. Meanwhile, digital surveys have allowed research companies to expand their outreach, save resources and costs and often attain more accurate and comprehensive insights for clients. Over the five years through 2025, industry revenue is expected to contract at a compound annual rate of 1.1% to reach €25.2 billion. The high inflationary environment in recent years has taken a toll on market research budgets. A sharp contraction in business sentiment squeezed corporate profit in 2022, discouraging companies from investing in research and development activities and negatively affecting professional research providers. A greater availability of data and alternative research methods means that researchers are competing more and more with in-house research departments. In 2025, industry revenue is expected to drop by 0.3% as consumers are finding their research needs met by AI tools such as ChatGPT, however, this trend is expected to be short-lived as research companies will strive to prove their value to clients. Over the five years through 2030, industry revenue is forecast to swell at a compound annual rate of 3.7% to reach €30.3 billion. Over the coming years, market research companies will face higher external competition from technology specialists leveraging insights internally, constraining revenue growth. Nonetheless, researchers will benefit from expanding online advertising activity. Those incorporating advanced data analytics systems and digital market research technology will remain competitive and benefit from greater digitalisation. Smart mobile surveys will also become an invaluable tool for consumer research companies.

Market researchers investigate clients' target markets' behaviour, values and opinions, providing insights that allow them to tailor their products, services and marketing. Researchers rely on hefty European research and development expenditure to fuel demand for market research. The surge in digitalisation has opened new doors for market research providers while intensifying competition. Artificial intelligence is increasingly important in analysing, identifying and generating research insights from social media posts using a flood of data. Meanwhile, digital surveys have allowed research companies to expand their outreach, save resources and costs and often attain more accurate and comprehensive insights for clients. Over the five years through 2025, industry revenue is expected to contract at a compound annual rate of 1.1% to reach €25.2 billion. The high inflationary environment in recent years has taken a toll on market research budgets. A sharp contraction in business sentiment squeezed corporate profit in 2022, discouraging companies from investing in research and development activities and negatively affecting professional research providers. A greater availability of data and alternative research methods means that researchers are competing more and more with in-house research departments. In 2025, industry revenue is expected to drop by 0.3% as consumers are finding their research needs met by AI tools such as ChatGPT, however, this trend is expected to be short-lived as research companies will strive to prove their value to clients. Over the five years through 2030, industry revenue is forecast to swell at a compound annual rate of 3.7% to reach €30.3 billion. Over the coming years, market research companies will face higher external competition from technology specialists leveraging insights internally, constraining revenue growth. Nonetheless, researchers will benefit from expanding online advertising activity. Those incorporating advanced data analytics systems and digital market research technology will remain competitive and benefit from greater digitalisation. Smart mobile surveys will also become an invaluable tool for consumer research companies.

Ethical Clearance : 2018FBREC560The purpose of the study was to describe and understand the theoretical application of the interlinked relationship between opinion variables, interest variables, and activity variables, which form part of consumer decision-making internal and external influencing variables model. An explanatory research design was employed to explain the correlation between the variables. The study was conducted at a rooibos tea company in Gauteng province targeting a purposive sample using an online questionnaire. The respondents were consultants who buy, sell and consume rooibos teas by the company.

Dataset Title: Impact of Social Media Algorithms on Consumer Behavior in Rondon do Pará, Brazil (2025)

General Description:

This dataset provides quantitative information for analyzing the influence of social media algorithms on consumer behavior in the municipality of Rondon do Pará, Brazil. The data were compiled from public sources and complemented by empirical online responses, encompassing variables related to social media usage, exposure to personalized advertisements, and online purchasing decisions. The dataset aims to support research in the fields of digital marketing, consumer behavior, and regional economic development.

Methodology:

The research adopts a quantitative, descriptive, and applied approach, based on the analysis of secondary dataobtained from public databases such as IBGE, SEBRAE, Statista, Ebit/Nielsen, and Meta Business Suite, as well as locally collected online data. Variables are grouped into thematic blocks as follows: 1. Sociodemographic Profile – age, average income, occupation, and internet usage frequency. 2. Use of Social Media – average daily usage time, most accessed platforms, and advertisement exposure frequency. 3. Algorithmic Influence and Personalization – engagement rates, retention time, and targeted content. 4. Role of Digital Influencers – audience reach, credibility, and purchase decision impact. 5. Online Consumer Behavior – purchase frequency, motivations, and comparison between online and physical shopping. 6. Impact on Local Commerce – perception of e-commerce substitution effects and influence on local economic activity.

Data analysis was conducted using Microsoft Excel and IBM SPSS Statistics, applying descriptive statistics, Pearson’s correlation, and regional comparative analysis.

Data Format and Structure:

• File type: .csv • Number of observations: 102 valid records • Number of variables: 21 columns corresponding to the thematic categories above • Encoding: UTF-8 • Delimiter: Comma (,)

Main Variables:

• age (numeric) • gender (categorical) • monthly_income (numeric, in BRL) • daily_social_media_use (numeric, hours/day) • most_used_social_media (categorical) • ad_exposure_frequency (Likert scale 1–6) • ad_influence_level (Likert scale 1–6) • trust_in_influencers (Likert scale 1–6) • online_purchase_preference (binary: 0 = physical store, 1 = online) • impact_on_local_commerce (Likert scale 1–6)

Temporal Coverage: January – October 2025

Geographical Coverage: Rondon do Pará, State of Pará, Brazil.

Categories

Business to Business Marketing

Acknowledgement

Carlos Cesar Santos

This systematic literature review, conducted following the PRISMA 2020 guidelines, synthesizes 117 Scopus-indexed articles (2009–April 4, 2025) to address six research questions on artificial intelligence’s (AI) role in consumer behavior. The study examines research trends (RQ1), prevalent AI technologies (RQ2), key variables (RQ3), methodological approaches (RQ4), future research directions (RQ5), and AI’s transformative impact on consumer behavior (RQ6). Findings report exponential publication surges after 2020, driven by machine learning and natural language processing applications. Consumer attitudes (trust, privacy) and behavioral intentions emerge as primary mediators of AI adoption, while ethical concerns underline the need for open algorithmic architectures. Quantitative survey methods dominate, though longitudinal and cross-cultural investigations remain infrequent. Generative AI is a disruptive technology enabling hyper-personalization but also authenticity threats. Practical implications call for ethical imperatives of AI governance and hybrid human-machine processes to weigh innovation against consumer trust. In noting limitations of geographic coverage and linguistic bias, the review proposes future research agendas like cultural mediation of AI acceptance and longitudinal behavioral impact. By examining these dimensions systematically, the research enhances academic understanding of AI’s multifaceted influence and supports actionable insight to ethically grounded marketing practice in evolving technology contexts.

This is the original data set of related research on consumer behavior.

Makeup market research is crucial for cosmetics companies to stay ahead of competition by understanding trends, preferences, and behaviours of consumers. By analysing data on sales trends, purchase history, and consumer behaviour, companies can develop a marketing strategy that caters to the needs of their target market. This article explains how makeup market research is essential for companies to stay competitive and meet the needs of their consumers in the ever-changing cosmetics industry.

GeoLifestyle delivers 561M+ records of behavioral, lifestyle, and geolocation data across 8 emerging markets. With over 78 attributes per profile—covering shopping behavior, mobility, media habits, income proxies, and more—this dataset supports AI-driven audience targeting, segmentation, and predictive modeling. Ideal for marketers, data scientists, and platforms seeking location-based consumer intelligence. Fully compliant, updated frequently, and stored on-premise.

➤ Optimized For: ・Behavioral and predictive analytics ・Location-based audience segmentation ・AI-driven customer modeling ・Hyper-targeted marketing and personalization ・Data-driven market research and expansion

➤ Designed For: Marketing Tech & Ad Platforms: Build geo-personalized audiences and improve targeting precision

Retail & E-Commerce Companies: Drive local growth with real-world lifestyle and location signals

Financial Services: Segment consumers by affluence, behavior, and mobility insights

AI/ML & Data Science Teams: Train smarter models with structured, labeled behavioral inputs

Agencies & Consulting Firms: Design high-conversion campaigns in emerging markets

➤ Key Highlights ・561M+ profiles across 8 countries ・78 behavioral attributes per profile ・Geo-precision: 2-meter radius lat/lng ・12 months of historical behavior and movement ・Quarterly updates, on-premise delivery ・Fully compliant with GDPR, LGPD, PDPA

Delivered by 1datapipe®, the global leader in structured identity and lifestyle intelligence. Pricing and additional samples available upon request.