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...

Consumer Insurance Experience & Demographic Profile

This dataset provides a detailed view of how individuals engage with insurance products, paired with demographic and lifestyle attributes to enable powerful segmentation, behavioral analysis, and customer journey mapping. By combining real-world insurance experiences with contextual information about each respondent’s background and preferences, this dataset supports a wide range of data-driven decision-making for insurance providers, policy designers, marketing teams, and product strategists.

Value of the Dataset Understanding how consumers perceive and interact with insurance offerings is critical to building products that resonate and services that retain. This dataset offers that visibility across multiple dimensions—capturing not only what type of insurance consumers hold and how they purchased it, but also what drives their satisfaction, loyalty, and likelihood to switch. Paired with demographic details like income, education, family status, and lifestyle, this information becomes a foundation for more personalized outreach, better-designed offerings, and improved customer experiences.

Because the data reflects lived experiences across diverse markets, it is particularly valuable for benchmarking consumer sentiment in emerging economies, identifying service delivery gaps, or evaluating potential uptake of new policy formats such as digital or personalized insurance.

Example Use Cases 1. Targeted Product Design A health insurer looking to launch short-term, digital-first plans could filter this dataset for consumers with low policy tenure, high digital communication preference, and dissatisfaction with current providers. This segment would inform feature design and positioning.

1. Competitive Analysis A provider evaluating churn risk can identify patterns among users who have filed claims but report dissatisfaction—indicating operational areas that may be driving customer loss and where improvements could increase retention.

2. Communication Channel Optimization By analyzing preferred communication methods across different demographic segments, insurers can tailor outreach strategies (e.g., SMS vs. in-app chat) to improve engagement and reduce support costs.

3. Market Expansion & Localization International insurers can explore regional variations in satisfaction drivers, awareness levels, and price sensitivity to refine go-to-market strategies in countries like Senegal, Tanzania, or the UAE.

4. Personalized Policy Offer Design Using data on interest in personalized policies and lifestyle indicators, providers can build customizable offerings for consumers more likely to value flexibility, such as frequent travelers or those with irregular incomes.

Insurance-Specific Fields & Descriptions Current Insurance Type Captures the kind of insurance the individual currently holds, with a focus on health insurance in this dataset.

Purchase Method Indicates how the insurance was obtained—through an agent, online, employer, etc.—to understand acquisition channels.

Policy Length Duration of the current policy, categorized (e.g., less than 1 year, 1–3 years, more than 5 years) to analyze tenure-based behaviors.

Satisfaction Self-reported satisfaction with the current insurance provider, useful for benchmarking sentiment.

Top Factor in Choosing Provider Highlights what influenced the purchase decision most—such as coverage options, customer service, pricing, or brand reputation.

Policy Review Frequency Shows how often individuals revisit their policy details or compare with alternatives, revealing levels of engagement or passive behavior.

Filed Claim A yes/no indicator showing whether the consumer has ever filed a claim, useful for analyzing downstream service experiences.

Claim Satisfaction Measures satisfaction with how past claims were handled, providing insight into operational effectiveness.

Primary Value Sought Captures what consumers value most from their insurance—e.g., peace of mind, financial protection, access to quality care.

Likelihood to Recommend Acts as a proxy for Net Promoter Score (NPS), indicating brand advocacy and potential referral behavior.

Biggest Areas for Improvement Open-ended or multi-select responses identifying where insurers can do better—lower premiums, faster claims, more digital tools, etc.

Preferred Method of Communication Indicates how consumers want to be contacted—via online chat, phone, email, SMS—supporting channel strategy optimization.

Preferred Services Details the types of updates or services consumers want—such as claims status, policy changes, or coverage recommendations.

Insurance Awareness Score Self-reported awareness of how insurance works, including policy options, rights, and terms.

Interest in Personalized Policies Captures whether the individual is open to customized insurance plans, an important indicator for usage-ba...

Automatic Gender recognition

https://www.techgig.com/hackathon/colombiaonline

Introduction Demographic segmentation has been a key part of the marketing strategy for decades, and as more and more buyers conduct their research and make purchase decisions online, it's become even more pivotal to marketing's overall success. From highly targeted ad campaigns to personalized web pages for returning website visitors, it's now easier than ever to reach a specific persona, group, or individual online, and with so many companies competing for buyers' attention online today, knowing just where to find your prospects and how to best communicate with them is crucial. Under Demographic segmentation, marketing professionals divide the population based on demographic variables, such as age, gender, income, etc. The problem that we are trying to solve is Gender segmentation.

Gender Segmentation - Under gender segmentation, the database is divided into male or female. Both men and women have different interest in terms of shopping for various products such as apparel, cosmetics, perfumes, shoes, etc. and even food habits. The segmentation based on gender is important for lots of industries which have portfolios for both male as well as female. For example, Nike as a sportswear company has a separate portfolio for both male as well as female. As a company, Nike will have to come out with different strategies to market products differently for male and female. The company even has to create a separate segment for both men and women in the showroom itself. This is much evident if you go to an apparel showroom such as Zara, Marks & Spencers, etc.

Times Internet Limited is No 1 Premium Digital Publisher in the world with over 400 million monthly unique visitors, consuming 10K+ unique contents which are published daily generating 80 billion monthly pageviews. Since, most of TIL revenue comes from Digital Advertising where TIL focuses on serving relevant ads to its audiences. Hence, Gender Identification is very crucial for us in order to plan and execute successful marketing campaigns for brands and provide maximum ROI to our Advertisers. Unlike other social media platforms where users submit their personal details like gender, age, etc TIL is a publishing company where users consumes content without sharing their personal details. Hence, we need a solution to identify their gender by understanding how they interact with TIL digital contents. Problem Statement Automatic Gender recognition based on digital content reading pattern through Machine Learning.

Data There will be 2 data sets that would be shared with the participants for understanding the structure of the datasets, training their algorithm(s)/model(s) and to test their model and present their findings/results.

***Sample Data (for training) ***- To understand the structure of the data, so, that they can extract the information from sample data. File will contain fields for users from different gender and the content they had consumed, the participants have to analyse, understand and train their algorithm/model on this data.

***Testing Data ***- This is the system data (with certain modified fields, see Privacy Policy section), where the participants have to run their model and submit their results and presentation over the same. Data Set Download Data Set File Name Description Format Size UserIdToUrl.zip Url visited by user zip Urls_Json_Data.zip Urls details zip UserIdToGender_Train.csv User to gender data for training csv UserId_Test.csv User to gender data for testing csv sample_submission.csv Sample submission csv

Data Dictionary Here's a brief version of what you'll find in the data description file.

Variable Description userid User id gender Gender url Content URL title Title of the Content description Short Description of the Content long_description Long Description of the Content alt_titles Alternate Title of the Content brand Content Brand Name language Language of the Content Submission Model Implementation Findings & Insights of the model results of the above Presentation Evaluation Metric The score is calculated with the following formula:

Score = Number of correct predictions / Total records*100

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.

Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

We collect, validate, model, and segment raw data signals from over 900+ sources globally to deliver thousands of mobile audience segments. We then combine that data with other public and private data sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms then clean, enrich, unify and aggregate these data sets for use in our products. We have categorized our audience data into consumable categories such as interest, demographics, behavior, geography, etc. Audience Data Categories:Below mentioned data categories include consumer behavioral data and consumer profiles (available for the US and Australia) divided into various data categories. Brand Shoppers:Methodology: This category has been created based on the high intent of users in terms of their visits to Brand outlets in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Place Category Visitors:Methodology: This category has been created based on the high intent of users visiting specific places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Demographics:This category has been created based on deterministic data that we receive from apps based on the declared gender and age data. Marital Status, Education, Party affiliation, and State residency are available in the US. Geo-Behavioural:This category has been created based on the high intent of users in terms of the frequency of their visits to specific granular places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Interests:This segment is created based on users' interest in a specific subject while browsing the internet when the visited website category is clearly focused on a specific subject such as cars, cooking, traveling, etc. We use a deterministic model to assign a proper profile and time that information is valid. The recency of data can range from 14 to 30 days, depending on the topic. Intent:Factori receives data from many partners to deliver high-quality pieces of information about users’ shopping intent. We collect data from sources connected to the eCommerce sector and we also receive data connected to online transactions from affiliate networks to deliver the most accurate segments with purchase intentions, such as laptops, mobile phones, or cars. The recency of data can range from 7 to 14 days depending on the product category. Events:This category was created based on the high interest of users in terms of content related to specific global events - sports, culture, and gaming. Among the event segments, we also distinguish categories related to the interest in certain lifestyle choices and behaviors. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. App Usage:Mobile category is a branch of the taxonomy that is dedicated only to the data that is based on mobile advertising IDs. It is based on the categorization of the mobile apps that the user has installed on the device. Auto Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of automobile and other automotive attributes via a survey or registration. These audiences are currently available in the USA. Motorcycle Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of motorcycle and other motorcycle-based attributes via a survey or registration. These audiences are currently available for the USA. Household:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on users' declaring their marital status, parental status, and the overall number of children via a survey or registration. These audiences are currently available in the USA. Financial:Consumer Profiles - Available for the US and Australia this audience has been created based on their behavior in different financial services like property ownership, mortgage, investing behavior, and wealth and declaring their estimated net worth via a survey or registration. Purchase/ Spending Behavior:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on their behavior in different spending behaviors in different business verticals available in the USA. Clusters:Consumer Profiles - Available for the US and AustraliaClusters are groups of consumers who exhibit similar demographic, lifestyle, and media consumption characteristics, empowering marketers to understand the unique attributes that comprise their most profitable consumer segments. Armed with this rich data, data scientists can drive analytics and modeling to power their brand’s unique marketing initiatives. B2B Audiences;Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring their employee credentials, designations, and companies they work in, further specifying business verticals, revenue breakdowns, and headquarters locations. Customizable Audiences Data Segment:Brands can choose the appropriate pre-made audience segments or ask our data experts about creating a custom segment that is precisely tailored to your brief in order to reach their target customers and boost the campaign's effectiveness. Location Query Granularity:Minimum area: HEX 8Maximum area: QuadKey 17/City

With extensive coverage nationally and across various languages, our B2C Language Demographic Data provides valuable insights for sales, marketing, and research purposes. Whether you're seeking to expand your client base, enhance lead generation efforts, or conduct market analysis, our dataset empowers you to make informed decisions and drive business growth.

Our B2C Language Demographic Data covers a wide range of languages including but not limited to Chinese, Arabic, Hindi, French, German, Vietnamese and more. By leveraging our dataset, you can identify potential prospects, explore new market opportunities, and stay ahead of the competition. Whether you're a startup looking to establish your presence, a seasoned enterprise aiming to expand your market share or a researcher, our B2C Language Demographic Data offers valuable insights.

Uses

The use cases of our B2C Language Demographic Data are diverse and versatile. From targeted marketing campaigns (e.g., billboard, location-based), to market segmentation and cohort analysis, our dataset serves as a valuable asset for various business and research functions. Whether you're targeting influencers, or specific industry verticals, our B2C Language Demographic Data provides the foundation for effective communication and engagement.

Key benefits of our B2C Language Demographic Data include:

• Enhanced Lead Generation: Identify locations of high-potential prospects
• Improved Targeting: Tailor your marketing efforts based on detailed location- based insights on your target cohort. Our rich set of contact points enable business to direct energies to precisely where they create the most impact.
• Increased ROI: Maximize the efficiency of your marketing campaigns by focusing on the most promising opportunities.
• Data Accuracy: Ensure the reliability and validity of your data with our regularly updated and verified dataset.
• Competitive Advantage: Stay ahead of the competition by accessing comprehensive market intelligence and strategic insights.
• Scalability: Our dataset grows with your business, providing scalability and flexibility to meet evolving needs.
• Compliance: Our B2C Language Demographic Data complies with relevant data privacy regulations

Why businesses partner with us:

Operating for over ten years, innovation is our north star, driving value, fostering collaborative grown and compounding returns for our partners.
Our data is compliant and responsibly collected. We are easy to work with.
We offer products that are cost effective and good value. We work to make an impact for our customers. Talk to us about the solutions you are after

Key Tags:

Data Enrichment, B2C Sales, Analytics, People Data, B2C, Customer Data, Prospect Data, Audience Generation, B2C Data Enrichment, Business Intelligence, AI / ML, Market Intelligence, Segmentation, Audience Targeting, Audience Intelligence, B2C Advertising, List Validation, Data Cleansing, Competitive Intelligence, Demographic Data, B2C Data, Lead Information, Data Append, Data Augmentation, Data Cleansing, Data Enhancement, Data Intelligence, Data Science, Due Diligence, Marketing Data Enrichment, Master Data Enrichment, People-Based Marketing, Predictive Analytics, Prospecting, Sales Intelligence, Sales Prospecting

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

Global Fashion Retail Analytics Dataset

📊 Dataset Overview

This synthetic dataset simulates two years of transactional data for a multinational fashion retailer, featuring:
- 📈 4+ million sales records
- 🏪 35 stores across 7 countries:
🇺🇸 United States | 🇨🇳 China | 🇩🇪 Germany | 🇬🇧 United Kingdom | 🇫🇷 France | 🇪🇸 Spain | 🇵🇹 Portugal

Currencies Covered: Each transaction includes detailed currency information, covering multiple currencies:
💵 USD (United States) | 💶 EUR (Eurozone) | 💴 CNY (China) | 💷 GBP (United Kingdom)

Designed for Detailed and Multifaceted Analysis

🌐 Geographic Sales Comparison
Gain insights into how sales performance varies between regions and countries, and identify trends that drive success in different markets.

👥 Analyze Staffing and Performance
Evaluate store staffing ratios and analyze the impact of employee performance on store success.

🛍️ Customer Behavior and Segmentation
Understand regional customer preferences, analyze demographic factors such as age and occupation, and segment customers based on their purchasing habits.

💱 Multi-Currency Analysis
Explore how transactions in different currencies (USD, EUR, CNY, GBP) are handled, analyze currency exchange effects, and compare sales across regions using multiple currencies.

👗 Product Trends
Assess how product categories (e.g., Feminine, Masculine, Children) and specific product attributes (size, color) perform across different regions.

🎯 Pricing and Discount Analysis
Study how different pricing models and discounts affect sales and customer decisions across diverse geographies.

📊 Advanced Cross-Country & Currency Analysis
Conduct complex, multi-dimensional analytics that interconnect countries, currencies, and sales data, identifying hidden correlations between economic factors, regional demand, and financial performance.

Synthetic Data Advantages

Generated using algorithms, it simulates real-world retail dynamics while ensuring privacy.

• Privacy-Safe: All customer and employee data is artificially generated to ensure privacy and compliance with data protection regulations. Personal details, such as emails and phone numbers, are anonymized.
• Scalable Patterns: The data replicates real-world retail dynamics, ensuring scalability of patterns for testing algorithms and analytics models.
• Controlled Complexity: The dataset introduces intentional complexities (e.g., missing job titles, inconsistent phone number formats) to offer a more realistic and challenging exploration experience for exploratory data analysis.
• Customizable for Various Use Cases: Whether you're performing sales forecasting, employee performance analysis, or customer segmentation, this dataset offers a flexible foundation for diverse analytical tasks.

This dataset is an ideal resource for retail analysts, data scientists, and business intelligence professionals aiming to explore multinational retail data, optimize operations, and uncover new insights into customer behavior, sales trends, and employee efficiency.

Social Media Analytics Market Outlook

The global market size of social media analytics was valued at approximately $5.2 billion in 2023 and is projected to reach around $21.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 17.1% over the forecast period. This remarkable growth can be attributed to the increasing importance of data-driven decision making in modern business strategies. The expansion of social media platforms and the corresponding surge in user-generated data have driven the need for advanced analytics tools to make sense of this information, thereby acting as a significant growth factor for the market.

One of the primary growth factors for the social media analytics market is the increasing adoption of data analytics by organizations to gather meaningful insights from vast amounts of unstructured social media data. Companies across various sectors are now leveraging social media analytics to understand customer behavior, preferences, and trends, which in turn helps in refining marketing strategies and improving customer experience. The proliferation of smartphones and internet penetration has further fueled the frequency and volume of social media interactions, providing a more extensive dataset for analytics.

Another key driver is the integration of artificial intelligence (AI) and machine learning (ML) technologies with social media analytics platforms. These advanced technologies are enabling more accurate sentiment analysis, demographic segmentation, and predictive analytics. AI and ML algorithms can process large datasets more efficiently, allowing businesses to quickly respond to market changes and consumer demands. Moreover, the development of sophisticated natural language processing (NLP) tools is enhancing the capability of social media analytics to understand and interpret human language, making sentiment analysis more precise and actionable.

The increasing demand for personalized marketing is also significantly contributing to the growth of the social media analytics market. Brands are now focusing on delivering highly personalized content to their target audiences to enhance engagement and conversion rates. Social media analytics provides detailed insights into individual user profiles, preferences, and behaviors, enabling marketers to create more targeted and effective campaigns. The shift towards influencer marketing is another trend driving the market, as businesses seek to measure the impact and ROI of their influencer partnerships through analytics.

Social Networking Sites have become integral to the way individuals and businesses interact and communicate. These platforms provide a space for users to share content, connect with others, and engage in discussions. The rise of social networking sites has significantly contributed to the volume of data available for analysis, offering businesses a wealth of information to understand consumer behavior and preferences. As these sites continue to evolve, they are increasingly being used as tools for marketing, brand building, and customer engagement. The ability to analyze data from social networking sites allows companies to tailor their strategies and improve their offerings, ultimately enhancing customer satisfaction and loyalty.

From a regional perspective, North America dominates the social media analytics market, with a substantial share attributed to the early adoption of advanced technologies and the presence of major social media platforms. The Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by the expanding user base of social media platforms and increasing investments in digital marketing. The European market is also growing steadily, supported by stringent data privacy regulations that are compelling organizations to adopt more robust analytics solutions. Latin America and the Middle East & Africa are emerging markets with significant growth potential due to increasing internet penetration and social media usage.

Component Analysis

The social media analytics market can be segmented by component into software and services. The software segment comprises tools and platforms used to collect, analyze, and visualize social media data. These solutions range from basic sentiment analysis tools to comprehensive analytics platforms that offer real-time moni

We'll tailor a Twitch dataset to meet your unique needs, encompassing streamer profiles, viewer engagement metrics, streaming times, demographic data of viewers, follower counts, chat statistics, and other pertinent metrics.

Leverage our Twitch datasets for diverse applications to bolster strategic planning and market analysis. Scrutinizing these datasets enables organizations to grasp viewer preferences and streaming trends, facilitating nuanced content development and engagement initiatives. Customize your access to the entire dataset or specific subsets as per your business requisites.

Popular use cases involve optimizing content strategy based on streamer performance and viewer engagement, enhancing marketing strategies through targeted audience segmentation, and identifying and forecasting trends in the streaming community to stay ahead in the digital entertainment landscape.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases:

360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

WorldView segments has been developed to segment the global population into 10 consistent consumer types by analysing data including: demographics, value orientation, attitudes, consumer behaviour and consumption volume. The segments have been identified and validated in detailed international primary reserach. They enable the identification of customer target groups and the segmentation of markets consistently across multiple countries. The data is built using a combination of WorldView Demographics enhanced with consumer survey panel data across a number of regions where available.

Sure! Let's dive into a case study on customer lifetime value (CLV) analytics.

Case Study: E-commerce Store

Background: ABC Electronics is an online retailer specializing in consumer electronics. They have been in operation for several years and have built a substantial customer base. ABC Electronics wants to understand the lifetime value of their customers to optimize their marketing strategies and improve customer retention.

Objectives: 1. Calculate the customer lifetime value for different segments of customers. 2. Identify the most valuable customer segments. 3. Develop personalized marketing strategies to increase customer retention and maximize CLV.

Data Collection: ABC Electronics collects various data points about their customers, including: - Customer demographics (age, gender, location, etc.) - Purchase history (transaction dates, order values, products purchased, etc.) - Website behavior (pages visited, time spent, etc.) - Customer interactions (customer service inquiries, feedback, etc.)

Data Preparation: To perform CLV analysis, ABC Electronics needs to aggregate and organize the collected data. They merge customer demographic information with purchase history and website behavior data to create a comprehensive dataset for analysis.

Calculating CLV: ABC Electronics uses the following formula to calculate CLV:

CLV = (Average Order Value) x (Purchase Frequency) x (Customer Lifespan)

1. Average Order Value (AOV): Calculated by dividing the total revenue by the number of orders placed during a specific period.

2. Purchase Frequency: Calculated by dividing the total number of orders by the total number of unique customers during a specific period.

3. Customer Lifespan: The average time a customer remains active. It can be calculated by averaging the time between a customer's first and last order.

ABC Electronics calculates the CLV for each customer and then segments them based on their CLV values.

Segmentation and Analysis: ABC Electronics segments their customers into three groups based on CLV:

1. High-Value Customers: Customers with CLV in the top 20% percentile. These customers generate the most revenue for the business.

2. Medium-Value Customers: Customers with CLV in the middle 60% percentile. These customers contribute to the overall revenue and have decent long-term potential.

3. Low-Value Customers: Customers with CLV in the bottom 20% percentile. These customers have low spending patterns and may require additional nurturing to increase their CLV.

ABC Electronics analyzes the behavior, preferences, and characteristics of each customer segment to identify patterns and insights that can inform their marketing strategies.

Marketing Strategies: Based on the analysis, ABC Electronics formulates the following marketing strategies:

1. High-Value Customers:

   • Offer personalized recommendations and exclusive deals based on their purchase history.
   • Provide excellent customer service and priority support to ensure their loyalty.
   • Implement a loyalty program to reward their continued patronage.

2. Medium-Value Customers:

   • Create targeted email campaigns to showcase new products and promotions.
   • Use retargeting ads to remind them of products they have shown interest in.
   • Offer limited-time discounts to encourage repeat purchases.

3. Low-Value Customers:

   • Implement a win-back campaign to re-engage with these customers.
   • Send personalized offers and discounts to encourage them to make additional purchases.
   • Collect feedback and address any concerns to improve their experience.

Monitoring and Evaluation: ABC Electronics continuously monitors the effectiveness of their marketing strategies by tracking CLV over time and assessing changes in customer behavior. They analyze metrics such as repeat purchase rate, average order value, and customer retention rate to evaluate the success of their initiatives.

By leveraging CLV analytics, ABC Electronics can allocate their marketing resources effectively, focus on customer segments with the highest potential, and develop strategies to maximize

customer retention and long-term profitability.

This case study demonstrates the practical application of CLV analytics in a real-world scenario and highlights the importance of data-driven decision-making for optimizing business performance.

The objectives of the Smallholder Household Survey in Mozambique were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Mozambique according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

This dataset comprises a collection of YouTube comment threads primarily from the ABS-CBN Entertainment channel, which is the most-subscribed and most-viewed entertainment channel in Southeast Asia. It offers insights into public engagement and reactions to video content, specifically highlighting discussions around "Cardo Chronicles" related videos. The dataset is ideal for natural language processing (NLP), social media analysis, and understanding audience sentiment within a Filipino cultural context.

Columns

• commentThread.videoId: Unique identifier for the YouTube video to which the comment thread belongs.
• commentThread.id: Unique identifier for the comment thread.
• commentThread.channelId: Identifier for the YouTube channel where the comments were posted.
• commentThread.canReply: Boolean indicating if replies can be made to the top-level comment in the thread.
• commentThread.totalReplyCount: The total number of replies to the top-level comment.
• commentThread.isPublic: Boolean indicating if the comment thread is publicly visible.
• comment.id: Unique identifier for an individual comment.
• comment.textOriginal: The original text content of the comment.
• comment.authorDisplayName: The display name of the comment's author.
• comment.authorChannelId.value: The channel ID of the comment's author.
• comment.canRate: Boolean indicating if the comment can be rated (liked/disliked).
• comment.viewerRating: The viewer's rating of the comment (e.g., 'none').
• comment.likeCount: The number of likes the comment has received.
• comment.publishedAt: The timestamp when the comment was published.
• comment.updatedAt: The timestamp when the comment was last updated.
• topLevelComment: Indicates if the comment is a top-level comment (value '1' for top-level, '0' for replies, though sample shows '1' for all entries).

Distribution

The dataset is typically provided in a CSV (Comma Separated Values) format. A sample file is available separately for review. While the ABS-CBN Entertainment channel hosts over 220,000 videos, this particular dataset contains comments from 655 videos. The structured format allows for straightforward parsing and analysis of individual comments and their associated metadata. Specific numbers for total rows/records are not currently available without the full dataset.

Usage

This dataset is well-suited for various applications, including: * Natural Language Processing (NLP) tasks such as sentiment analysis, topic modelling, and keyword extraction from social media text. * Social media research to analyse audience engagement, trending topics, and user behaviour on YouTube. * AI and Machine Learning model training for tasks like comment classification, content moderation, or audience segmentation. * Market research to understand public opinion and reactions to entertainment content. * Linguistic studies focusing on informal language use in online contexts within a Southeast Asian cultural background.

Coverage

The dataset primarily covers YouTube comments from the ABS-CBN Entertainment channel, a prominent Philippine commercial broadcast network headquartered in Quezon City, Philippines. The sample comments provided are from 13th August 2016. The demographic scope includes YouTube users who engage with Filipino entertainment content globally, with a strong focus on the Philippines. It is important to note that due to API limitations, this dataset does not include comments from all 220,000+ videos available on the channel.

License

CC0

Who Can Use It

• Data Scientists and AI/ML Developers: For building and training models related to text analysis, sentiment prediction, and social media analytics.
• Researchers: Those studying online communication, media consumption, cultural trends, or specific regional demographics (e.g., the Philippines).
• Marketing and Media Analysts: To gauge audience reception, identify popular content, and inform content strategy for entertainment channels.
• Linguists and Sociologists: For qualitative and quantitative analysis of online discourse and language patterns.

Dataset Name Suggestions

• ABS-CBN YouTube Comments (Cardo Chronicles)
• Filipino YouTube Entertainment Comments
• Cardo Chronicles YouTube Discussion Data
• ABS-CBN Fan Engagement Comments
• Philippine Entertainment YouTube Reactions

Attributes

Original Data Source: 🇵🇭 ABS-CBN Entertainment YT Channel Comments

The objectives of the Smallholder Household Survey in Nigeria were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Nigeria according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

The objectives of the Smallholder Household Survey in Tanzania were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Tanzania according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

The objectives of the Smallholder Household Survey in Bangladesh were to: Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; Segment smallholder households in Bangladesh according to the most compelling variables that emerge; Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

We'll customize a Zomato dataset to align with your unique requirements, incorporating data on restaurant categories, customer reviews, pricing trends, popular dishes, demographic insights, sales figures, and other relevant metrics.

Leverage our Zomato datasets for various applications to strengthen strategic planning and market analysis. Examining these datasets enables organizations to understand consumer preferences and dining trends, facilitating refined menu offerings and marketing campaigns. Tailor your access to the complete dataset or specific subsets according to your business needs.

Popular use cases include optimizing menu assortment based on consumer insights, refining marketing strategies through targeted customer segmentation, and identifying and predicting trends to maintain a competitive edge in the restaurant and food service market.