1) Data Introduction • The Consumer Behavior and Shopping Habits Dataset is a tabular collection of customer demographics, purchase history, product preferences, shopping frequency, and online and offline purchasing behavior.

2) Data Utilization (1) Consumer Behavior and Shopping Habits Dataset has characteristics that: • Each row contains detailed consumer and transaction information such as customer ID, age, gender, purchased goods and categories, purchase amount, region, product attributes (size, color, season), review rating, subscription status, delivery method, discount/promotion usage, payment method, purchase frequency, etc. • Data is organized to cover a variety of variables and purchasing patterns to help segment customers, establish marketing strategies, analyze product preferences, and more. (2) Consumer Behavior and Shopping Habits Dataset can be used to: • Customer Segmentation and Target Marketing: You can analyze demographics and purchasing patterns to define different customer groups and use them to develop customized marketing strategies. • Product and service improvement: Based on purchase history, review ratings, discount/promotional responses, etc., it can be applied to product and service improvements such as identifying popular products, managing inventory, and analyzing promotion effects.

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...

The Global Audience Segments dataset categorizes people in United States based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse American and global audiences with various interests and demographic profiles.

This statistic shows the different types of stationary stores where Americans did their shopping in the past 12 months in 2024. The results were sorted by income tier. As of March 2024, 52 percent of respondents who stated their income was high said they had shopped at a pharmacy store in the last 12 months. The survey was conducted among 8,217 respondents. Access millions of exclusive survey results with Statista Consumer Insights.

The Global Audience Segments dataset categorizes people in Canada based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse Canadian and global audiences with various interests and demographic profiles.

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 Global Audience Segments dataset categorizes people in France based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse French and global audiences with various interests and demographic profiles.

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. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2016 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' types of savings and financial investments, loan use, family income and retirement planning. Other topics in this series typically include ownership, lease, and use of automobiles, respondents' use of personal computers at home and in the office, and respondents' familiarity with and use of the Internet. Demographic information includes ethnic origin, sex, age, marital status, and education.

This dataset contains information about 3,900 customers and their purchase behavior in an e-commerce setting. The data spans various customer demographics, purchase preferences, and transactional details. It is designed to help analyze customer behavior, shopping patterns, and marketing effectiveness

In today’s rapidly evolving digital landscape, understanding consumer behavior has never been more crucial for businesses seeking to thrive. Our Consumer Behavior Data database serves as an essential tool, offering a wealth of comprehensive insights into the current trends and preferences of online consumers across the United States. This robust database is meticulously designed to provide a detailed and nuanced view of consumer activities, preferences, and attitudes, making it an invaluable asset for marketers, researchers, and business strategists.

Extensive Coverage of Consumer Data Our database is packed with thousands of indexes that cover a broad spectrum of consumer-related information. This extensive coverage ensures that users can delve deeply into various facets of consumer behavior, gaining a holistic understanding of what drives online purchasing decisions and how consumers interact with products and brands. The database includes:

Product Consumption: Detailed records of what products consumers are buying, how frequently they purchase these items, and the spending patterns associated with these products. This data allows businesses to identify popular products, emerging trends, and seasonal variations in consumer purchasing behavior. Lifestyle Preferences: Insights into the lifestyles of consumers, including their hobbies, interests, and activities. Understanding lifestyle preferences helps businesses tailor their marketing strategies to resonate with the values and passions of their target audiences. For example, a company selling fitness equipment can use this data to identify consumers who prioritize health and wellness.

Product Ownership: Information on the types of products that consumers already own. This data is crucial for businesses looking to introduce complementary products or upgrades. For instance, a tech company could use product ownership data to target consumers who already own older versions of their gadgets, offering them incentives to upgrade to the latest models.

Attitudes and Beliefs: Insights into consumer attitudes, opinions, and beliefs about various products, brands, and market trends. This qualitative data is vital for understanding the emotional and psychological drivers behind consumer behavior. It helps businesses craft compelling narratives and brand messages that align with the values and beliefs of their target audience.

The Global Audience Segments dataset categorizes people in Belgium based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse Belgian and global audiences with various interests and demographic profiles.

During a May 2023 survey among the general population of selected countries worldwide, ** percent of respondents said they were more price-conscious compared to the previous year. Additionally, ** percent stated that they were doing more research before each purchase and making fewer impulse purchases altogether.

"Grocery store / supermarket" and "Clothing / apparel / shoe store" are the top two answers among UK consumers in our survey on the subject of "Most common offline purchases by type".The survey was conducted online among 5,368 respondents in the UK, in 2025.

The Global Audience Segments dataset categorizes people in Switzerland based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse Swiss and global audiences with various interests and demographic profiles.

The Consumer Behavior database is derived from an analysis of ‘doublebase’ survey data using geodemographic market segmentation. Each of the approximately 40,000 records in the survey is geocoded then assigned the geodemographic market segment code of the block group. The results are then summarized for each variable over the sixty-eight segments, in effect providing the average value for each market segment. For example, a variable such as “Shopped at Macy’s” is computed by summarizing the records for each segment as a yes/no response, then finding the average percentage of households in each segment who shopped at Macy’s. This is often referred to as a profile.

The profile is then applied to geographic areas by making the assumption that households in demographically similar neighborhoods will tend to have similar consumption patterns as a result of their similar economic means, life stage, and other characteristics. The result is a series of estimates for geographic areas which measure the relative propensity of consumers in each geographic area to shop at particular stores, own various household items, and engage in activities.

Consumer Behavior Categories include; • Apparel • Appliances • Attitudes and Organizations • Advertising • Media Advertising • Media Attitudes • Automobiles • Buying Habits • Consumer Confidence • Financial • Food • Health • Intended Purchases • Political Outlook • Public Activities • Sports • Technology • Vacations • Automotive • Baby • Beverages • Computer • Electronics • Family Restaurants • Fast Food and Drive-In Restaurants • Financial • Groceries • Health & Beauty • Health & Medical • Home Furnishings and Equipment • Insurance • Internet • Leisure • Media Radio • Media Read • Media Television • Pets • Shopping • Sports • Telephone • Travel • Video

This AI-Driven Consumer Behavior Dataset captures key aspects of online shopping behavior, including purchase decisions, browsing activity, customer reviews, and demographic details. The dataset is designed for research in consumer behavior analysis, AI-driven recommendation systems, and digital marketing optimization.

Key Features: ✔ Consumer Purchase Data – Tracks product purchases, prices, discounts, and payment methods. ✔ Clickstream Data – Includes browsing behavior, pages visited, session duration, and cart abandonment. ✔ Customer Reviews & Sentiments – Provides ratings, textual reviews, and sentiment analysis scores. ✔ Demographic Information – Includes age, gender, location, and income levels. ✔ Target Column (purchase_decision) – Indicates whether a customer completed a purchase (1) or not (0).

Observed linkages between consumer and B2B emails and website domains, categorized into IAB classification codes.

This data provides an unprecedented view into individuals' in-market intent, interests, lifestyle indicators, online behavior, and propensity to purchase. It is highly predictive when measuring buyer intent leading up to a purchase being made.

Hashed emails can be linked to plain-text emails to append all consumer and B2B data fields for a full view of the individual and their online intent and behavior.

Files are updated daily. These are highly comprehensive datasets from multiple live sources. The linkages include first and last-seen dates and an "intent intensity" score derived from the frequency of similar intent categories over a period of time.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

The vast majority of consumers in Poland chose less processed foods when shopping in 2023, with *** out of 10 respondents feeling discouraged from buying a product with long and complicated ingredients.

Analysis of economic conduct of consumers under social-psychological aspects in advance of a purchase decision as well as during the purchase itself. Topics: Assessment of consumer influence on price, quality and product offering; perceived influences on one´s own purchase decision; subjective feeling of obligation to buy in the store; criticism of quality and price in the act of buying; attitude to advice and service by sales clerks; criticism of sales personnel; preferred type of business; shopping behavior and shopping habits; knowledge about the market; knowledge about and attitudes to name-brand articles and trade names; purchase behavior regarding sell-out; price and quality consciousness; classification of products according to luxury and consumer goods; social assessment of shoppers based on their choice of store; self-assessment of economic extent to which informed; political interest; preferred residential area; media usage; city size; party preference; memberships. Demography: age (classified); sex; marital status; number of children; religious denomination; school education; occupation; employment; income; household income; size of household; self-assessment of social class; state; refugee status; possession of durable economic goods. Interviewer rating: social class of respondent; spontaneity and certainty of answers given; suspected opinion leadership of respondent. Also encoded were the following interviewer characteristics: sex; age; social class; occupation; income and school degree. Analyse des wirtschaftlichen Verhaltens von Verbrauchern unter sozialpsychologischen Aspekten im Vorfeld einer Kaufentscheidung sowie beim Kauf selbst. Themen: Einschätzung des Verbrauchereinflusses auf Preis, Qualität und Warenangebot; wahrgenommene Einflüsse auf die eigene Kaufentscheidung; subjektives Gefühl von Kaufverpflichtungen im Geschäft; Kritik an Qualität und Preis beim Kaufakt; Einstellung zur Beratung und Bedienung durch Verkäufer; Kritik am Verkaufspersonal; bevorzugter Geschäftstyp; Einkaufsverhalten und Einkaufsgewohnheiten; Kenntnis des Marktes; Kenntnisse über und Einstellungen zu Markenartikeln und Handelsmarken; Kaufverhalten bezüglich Ausverkauf; Preis- und Qualitätsbewußtsein; Klassifikation von Waren nach Luxus- und Gebrauchsgütern; soziale Einschätzung von Käufern aufgrund ihrer Wahl des Geschäfts; Selbsteinschätzung der wirtschaftlichen Informiertheit; Selbsteinstufung der Schichtzugehörigkeit; politisches Interesse; bevorzugte Wohngegend; Mediennutzung; Ortsgröße; Parteipräferenz; Mitgliedschaften. Demographie: Alter (klassiert); Geschlecht; Familienstand; Kinderzahl; Konfession; Schulbildung; Beruf; Berufstätigkeit; Einkommen; Haushaltseinkommen; Haushaltsgröße; Selbsteinschätzung der Schichtzugehörigkeit; Bundesland; Flüchtlingsstatus; Besitz langlebiger Wirtschaftsgüter. Interviewerrating: Schichtzugehörigkeit des Befragten; Spontanität und Bestimmtheit der Antwortgebung; vermutete Meinungsführerschaft des Befragten. Zusätzlich verkodet wurden folgende Interviewermerkmale: Interviewergeschlecht; Intervieweralter; Schichtzugehörigkeit; Beruf; Einkommen; Schulabschluss.

The Global Audience Segments dataset categorizes people in Germany based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse German and global audiences with various interests and demographic profiles.