Success.ai is at the forefront of delivering precise consumer behavior insights that empower businesses to understand and anticipate customer needs more effectively. Our extensive datasets provide a deep dive into the nuances of consumer actions, preferences, and trends, enabling businesses to tailor their strategies for maximum engagement and conversion.

Explore the Multifaceted Dimensions of Consumer Behavior:

• Consumer Sentiment Analysis: Decode the emotions and sentiments behind consumer interactions with brands and products to refine messaging and product offerings.
• Web Activity Insights: Monitor and analyze consumer online behaviors, from browsing patterns to engagement metrics, to optimize digital strategies and user experience.
• Geodemographic Segmentation: Utilize detailed demographic and geographic data to segment audiences accurately, enabling personalized marketing approaches that resonate with diverse consumer groups.
• Consumer Purchasing Patterns: Understand the what, when, and why behind consumer purchases to forecast trends and align inventory and marketing efforts accordingly.
• Advanced Consumer Profiling: Build detailed profiles based on consumer behavior data to target or retarget customers with precision.

Why Choose Success.ai for Consumer Behavior Data?

• Comprehensive Data Integration: Seamlessly integrate our rich consumer data into your CRM systems, enhancing your data reservoir with valuable consumer insights.
• Real-Time Updates and Predictive Analytics: Leverage the latest consumer behavior trends powered by AI-driven analytics to stay ahead in a rapidly changing market.
• Precision and Reliability: Count on our meticulous data collection and processing methods, ensuring high accuracy and compliance with international data protection regulations.
• Scalable Solutions: Whether you're a small business or a large enterprise, our flexible data solutions can be scaled to meet your specific needs and budget constraints.
• Competitive Pricing: We offer the most compelling pricing in the industry, guaranteeing you get top-tier data without overspending.

Strategic Applications of Consumer Behavior Data for Business Growth:

• Enhanced Email Marketing: Use detailed consumer profiles to craft personalized email campaigns that increase open rates and conversions.
• Optimized Online Marketing: Apply insights from consumer web activity and search trends to fine-tune your online marketing tactics for better ROI.
• Effective B2B Lead Generation: Identify and engage potential business clients by understanding their industry-specific behaviors and preferences.
• Robust Sales Data Enrichment: Enrich your sales strategies with deep behavioral insights, turning cold calls into informed discussions and increasing sales success.
• Dynamic Competitive Intelligence: Stay competitive by monitoring how consumer behaviors are shifting in your industry and adjust your strategies proactively.

Empower Your Business with Actionable Consumer Insights from Success.ai

Success.ai provides not just data, but a gateway to transformative business strategies. Our comprehensive consumer behavior insights allow you to make informed decisions, personalize customer interactions, and ultimately drive higher engagement and sales.

Get in touch with us today to discover how our Consumer Behavior Intent Data can revolutionize your business strategies and help you achieve your market potential.

Contact Success.ai now and start transforming data into growth. Let us show you how our unmatched data solutions can be the cornerstone of your business success.

The dataset "isoc_ec_ibhv" has been discontinued since 08/02/2024.

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...

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.

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.

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 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.

"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 4,710 respondents in the UK, in 2025.

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.

The GapMaps Consumer Behavior database sourced from Applied Geographic Solutions (AGS) is derived from an analysis of the MRI surveys using Panorama. Each of the approximately 40,000 records in the MRI survey is geocoded then assigned the Panorama 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 Panorama 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.

In most cases, these should be considered as relative indicators, since local differences may result in different behavior. In addition, in some cases, variables must be considered as potential only, since the activity or store may not be locally available.

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.

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.

• Audience Data 1P Data Audience ResolveID™ Platform - Audience Identity Cookieless Technology • Audience Data Identity Global US Graph – 670m + Identity Records • Access to 14 Billion Identity Consumer Profile Data Identifiers • Over 500+ Consumer Attributes, Online & Offline Data Behavior & Signals • IAB™ Seller-Defined Cookieless-Contextual Category – Intent & Behavior Signal Audience Cohorts
• Access to Customer Data Enrichment & Customer Data Ingestion • First-Party Data Ingestion & Data Appending

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.

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

This survey 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 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2003 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Additional questions inquired about buying intentions for automobiles and computers, and the 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 sources of income, and respondents' ownership, lease, and use of automobiles. Other topics typically include respondents' use of personal computers at home and in the office, respondents' familiarity with and use of the Internet, electronic banking, and information on informed consent and confidentiality regarding the survey. Demographic information includes ethnic origin, sex, age, marital status, and education.

Recent research has shown that event-related brain potentials (ERPs) recorded while participants view lists of different consumer goods can be modulated by their preferences toward these products. However, it remains largely unknown whether ERP activity specific to a single consumer item can be informative about whether or not this item will be preferred in a shopping context. In this study, we examined whether single-item ERPs could reliably predict consumer preferences toward specific consumer goods. We recorded scalp EEG from 40 participants while they were viewing pictures of consumer goods and we subsequently asked them to indicate their preferences for each of these items. Replicating previous results, we found that ERP activity averaged over the six most preferred products was significantly differentiated from ERP activity averaged across the six least preferred products for three ERP components: The N200, the late positive potential (LPP) and positive slow waves (PSW). We also found that using single-item ERPs to infer behavioral preferences about specific consumer goods led to an overall predictive accuracy of 71%, although this figure varied according to which ERPs were targeted. Later positivities such as the LPP and PSW yielded relatively higher predictive accuracy rates than the frontal N200. Our results suggest that ERPs related to single consumer items can be relatively accurate predictors of behavioral preferences depending on which type of ERP effects are chosen by the researcher, and ultimately on the level of prediction errors that users choose to tolerate.

Unacast’s Consumer Behavior Data Enrichment provides a robust view of consumers’ affinities, lifestyles, stages of life, and travel patterns. Utilize Consumer Data Enrichment to build multi-dimensional customer profiles, generate look-alike models, extend reach to different marketing channels, and clarify households and connections between devices.

Our on-demand API can enrich opted-in customer data (Mobile Advertiser IDs) or hashed email addresses (HEMs) and FLIPs (frequently leveraged IPs). Based on the query, the API will deliver associated data such as HEM, app data, Unacast Persona Data, demographic data, countries traveled, and more.

Companies use Unacast Consumer Data Enrichment for: - Understanding reach - Identity Graphs - Measuring campaign performance - Measuring return on advertising spend (ROAS) - Addressability - Audience creation - Psychographic analysis - Enhancing customer data - Personalization - Market research - Creating look-alike panels

Make your customer data more powerful, illuminate data gaps, and understand the relationships between devices.

Pricing can vary based on product, scope, region, etc. Reach out to us here to get more details on pricing.

Behavioral Targeting Market Outlook

The global behavioral targeting market size was valued at approximately USD 10.5 billion in 2023 and is expected to reach around USD 29.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This impressive growth is driven by the increasing need for personalized marketing strategies and the rising adoption of advanced analytics technologies across various industry verticals.

One of the primary growth factors for the behavioral targeting market is the rapid advancement and adoption of big data analytics and machine learning technologies. These technologies enable companies to gather and analyze vast amounts of consumer data, thus allowing for more accurate and effective targeting. As companies strive to enhance customer experiences and improve conversion rates, the demand for sophisticated behavioral targeting solutions continues to rise. Additionally, the proliferation of smartphones and other connected devices has resulted in an exponential increase in data generation, further fueling the market growth.

Another significant driver for the growth of the behavioral targeting market is the increasing emphasis on personalized advertising. Traditional advertising methods are becoming less effective as consumers are inundated with generic and irrelevant ads. By leveraging behavioral targeting, companies can deliver personalized and relevant ads to their target audience, significantly improving engagement and conversion rates. This shift towards personalization is evident across various industries, including retail, BFSI, healthcare, media and entertainment, and IT and telecommunications, further propelling the market growth.

Moreover, the growing focus on enhancing customer experiences is also contributing to the expansion of the behavioral targeting market. Businesses are increasingly recognizing the importance of understanding consumer behavior and preferences to deliver tailored experiences. Behavioral targeting solutions enable companies to gain deep insights into consumer behavior, preferences, and buying patterns, allowing them to create highly targeted marketing campaigns and deliver superior customer experiences. This focus on customer-centric strategies is expected to drive the adoption of behavioral targeting solutions across various sectors.

From a regional perspective, North America is expected to dominate the behavioral targeting market during the forecast period, owing to the presence of a large number of key market players and the rapid adoption of advanced technologies in the region. Additionally, the Asia Pacific region is anticipated to witness significant growth, driven by the increasing digitalization and growing e-commerce industry in countries like China and India. Europe, Latin America, and the Middle East & Africa are also expected to contribute to the market growth, albeit at a relatively moderate pace.

Component Analysis

The behavioral targeting market can be segmented by component into software and services. The software segment is anticipated to hold a significant share of the market, driven by the increasing adoption of advanced analytics and machine learning technologies. These software solutions enable businesses to collect, analyze, and interpret vast amounts of consumer data, facilitating the creation of highly targeted marketing campaigns. Additionally, the continuous advancements in software capabilities, such as real-time data processing and predictive analytics, are expected to further drive the demand for behavioral targeting software.

Within the services segment, the market can be further divided into professional services and managed services. Professional services include consulting, integration, and training services, which are essential for the successful implementation and optimization of behavioral targeting solutions. As businesses seek to maximize the benefits of their behavioral targeting investments, the demand for professional services is expected to grow. On the other hand, managed services involve the outsourcing of behavioral targeting operations to third-party service providers, allowing businesses to focus on their core competencies. The increasing trend towards outsourcing and the need for specialized expertise are expected to drive the growth of the managed services segment.

The integration of behavioral targeting solutions with other marketing technologies, such as customer relationship management (CRM) and marketing automation platforms, is also expected to boost the demand for both