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...

Context:

Digital marketing platforms today rely heavily on user profiling to decide which advertisements should be displayed to which audience. Social networks collect demographic information such as age, gender, and income to understand user behavior and improve ad targeting. This dataset captures how different user demographics respond to online advertisements, making it valuable for studying customer behavior, marketing strategies, and purchase prediction.

The dataset is widely used in machine learning education and projects because it is simple, clean, and ideal for building classification models. It helps beginners and professionals understand how demographic features influence a user’s decision to purchase a product after viewing an ad.

Content :

This dataset contains user demographic information and their response to an advertisement. Each row represents one individual from a social media platform, including:

Age : The age of the user

Estimated Salary : Approximate annual salary of the user

Purchased : Target variable indicating whether the user bought the advertised product

0 = No purchase

1 = Purchase

The dataset can be used for:

• Predicting purchase behavior using machine learning models

• Understanding how age and income affect ad response

• Performing exploratory data analysis (EDA)

• Demonstrating classification algorithms such as Logistic Regression, KNN, SVM, Trees, etc.

• Practicing feature scaling, model training, evaluation, and visualization

Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

Demographical Shopping Purchases Data

Analyzing customer purchasing patterns and preferences

By Joseph Nowicki [source]

About this dataset

This dataset contains demographic information about customers who have made purchases in a store, including their name, IP address, region, age, items purchased, and total amount spent. Furthermore, this data can provide insights into customer shopping behaviour for the store in question - from their geographical information to the types of products they purchase. With detailed demographic data like this at hand it is possible to make strategic decisions regarding target customers as well as developing specific marketing campaigns or promotions tailored to meet their needs and interests. By gaining deeper understanding of customer habits through this dataset we unlock more possibilities for businesses seeking higher engagement levels with shoppers

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How to use the dataset

This dataset includes information such as customer's names, IP address, age, items purchased and amount spent. This data can be used to uncover patterns in spending behavior of shoppers from different areas or regions across demographics like age group or gender.

Research Ideas

• Analyze customer shopping trends based on age and region to maximize targetted advertising.
• Analyze the correlation between customer spending habits based on store versus online behavior.
• Use IP addresses to track geographical trends in items purchased from a particular online store to identify new markets for targeted expansion

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Demographic_Data_Orig.csv | Column name | Description | |:---------------|:------------------------------------------------------------------------------------------------| | full.name | The full name of the customer. (String) | | ip.address | The IP address of the customer. (String) | | region | The region of residence of the customer. (String) | | in.store | A boolean value indicating whether the customer made the purchase in-store or online. (Boolean) | | age | The age of the customer. (Integer) | | items | The number of items purchased by the customer. (Integer) | | amount | The total amount spent by the customer. (Float) |

Acknowledgements

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

Discover the booming Influencer Marketing market, projected to hit $13.8B in 2025 with a 31.95% CAGR. This in-depth analysis explores key trends, drivers, restraints, and regional breakdowns, featuring top players like Upfluence and Aspire. Learn how to leverage this powerful marketing strategy. Recent developments include: August 2024: The Tourism Authority of Thailand (TAT) has unveiled its new influencer marketing platform, 'TAT Connex', marking a significant step in its digital transformation strategy for promoting and developing tourism. 'TAT Connex' invites a diverse range of participants, including local and international Key Opinion Leaders (KOLs), influencers, bloggers, media personalities, and celebrities. They can connect with ten distinct categories of tourism-related businesses. These categories encompass dining establishments, hotels and homestays, transportation services, recreational activities and attractions, travel agencies, health and beauty services, shopping centers, entertainment venues, and souvenir shops.July 2024: Collective Artists Network, a new media entity focused on pop culture, is venturing into AI by acquiring Galleri5. Galleri5 is an AI-driven platform specializing in influencer marketing analytics and content management, catering to brands and influencers. Its offerings encompass AI-generated visual content, creator intelligence, campaign oversight, and trend prediction. By acquiring Galleri5, the firm is making a significant stride in weaving deep tech and AI into the fabric, bolstering its capacity to deliver state-of-the-art solutions to talents, content platforms, and brands.July 2024: Publicis Groupe has agreed to acquire Influential, the leading global influencer marketing platform. Influential specializes in authentically linking brands to their audiences through creating, deploying, and optimizing digital campaigns driven by creators. As the world's largest influencer marketing firm by revenue, Influential boasts a proprietary AI-driven technology platform that analyzes over 100 billion data points. Its expansive network includes over 3.5 million creators, granting access to data on 90% of global influencers with over 1 million followers. Currently, Influential provides its services to more than 300 brands worldwide.June 2024: Qoruz, an influencer marketing platform based in India, has partnered with Dabur, a brand celebrated for its natural and Ayurvedic products. This collaboration enhances Dabur's influencer marketing strategy, fostering more authentic and impactful connections with its audience. Leveraging Qoruz's sophisticated analytics and influencer management tools, Dabur aims to pinpoint influencers that resonate with the brand's fundamental values. Through Qoruz’s platform, Dabur gains data-driven insights into influencer performance and audience engagement, enabling them to craft campaigns that effectively resonate with their target demographic.. Key drivers for this market are: Firms Increasing Necessity to Utilize Influencer Marketing Platforms for Enhanced Consumer Engagement, Increasing Penetration of Social Media Platforms. Potential restraints include: Firms Increasing Necessity to Utilize Influencer Marketing Platforms for Enhanced Consumer Engagement, Increasing Penetration of Social Media Platforms. Notable trends are: Fashion and Lifestyle is Expected to Hold Significant Share.

This dataset captures user engagement with social media advertisements, providing insights into how different demographics interact with online ads. It includes various attributes related to users, ad content, and engagement metrics, making it suitable for machine learning tasks such as ad performance prediction, personalized recommendations, and sentiment analysis.

The target column (engaged) indicates whether a user interacted with an ad (1 for engagement, 0 for no engagement), making it ideal for classification tasks.

Key Features: User Demographics:

user_id: Unique identifier for each user

age: Age of the user

gender: Gender of the user (Male, Female, Other)

location: User’s geographic region

Ad Characteristics:

ad_id: Unique identifier for each ad

ad_type: Type of advertisement (Image, Video, Text, Carousel)

ad_duration: Length of the ad (in seconds, for video ads)

ad_category: Category of the advertisement (e.g., Fashion, Technology, Food)

Engagement Metrics:

clicks: Number of times the user clicked on the ad

likes: Number of likes the ad received from the user

shares: Number of times the ad was shared by the user

view_time: Time spent viewing the ad (in seconds)

Behavioral Attributes:

previous_interactions: Number of past interactions with similar ads

device_type: Device used to view the ad (Mobile, Desktop, Tablet)

time_of_day: Time when the user viewed the ad (Morning, Afternoon, Evening, Night)

Target Column:

engaged: Binary target variable (1 = User engaged, 0 = No engagement)

Demografy is a privacy by design customer demographics prediction AI platform.

Core features: - Demographic segmentation - Demographic analytics - API integration - Data export

Key advantages: - 100% coverage of lists - Accuracy estimate before purchase - GDPR-compliance as no sensitive data is required. Demografy can work with only first names or masked last names

Use cases: - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You can provide even masked last names keeping personal data in-house. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

With the migration of sex workers to online advertising in Canada, a substantial body of research has emerged on how they communicate with prospective clients. However, given the enormous quantity of archival material available, finding representative ways to identify what sex workers say is a difficult task. Numerical analysis of commonly used phrases allows for the analysis of large numbers of documents potentially identifying themes that may be missed using other techniques. This study considers how Canadian sex workers communicate by examining how the word “no” was used by online advertisers over a 15-year period. Source materials consisted of three collections of online classified advertising containing over 4.2 million ads collected between 2007 and 2022 representing 214456 advertisers. Advertisers and demographic variables were extracted from ad metadata. Common terms surrounding the word “no” were used to identify themes. The word “no” was used by 115127 advertisers. Five major themes were identified: client reassurance (54084 advertisers), communication (47130 advertisers), client race (32612 advertisers), client behavior (23863 advertisers), and service restrictions (8545 advertisers). The probability of there being an association between an advertiser and a major theme was found to vary in response to several variables, including: time period, region, advertiser gender, and advertiser ethnicity. Results are compared with previous work on race and risk messaging in sex work advertising and factors influencing client race restrictions are considered. Over time, the restriction related themes of client behavior, service restrictions, and client race became more prominent. Collectives, multi-regional, cis-female, and Black or Mixed ancestry advertisers were more likely to use restrictions.

The largest share of advertising across Meta platforms in Czechia fell on Czech residents aged between 25 and 34 in February 2025. Meta's social media users in Czechia aged 35 to 44 were also a significant segment of the advertising audience.

Snapshot img

Advertising Services Market Size 2024-2028

The advertising services market size is forecast to increase by USD 156 billion at a CAGR of 4.34% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of in-app advertising and the integration of Augmented Reality (AR) technology into marketing campaigns. In-app advertising has become a preferred choice for businesses looking to reach their audience in a more targeted and engaging way. According to recent studies, mobile app usage has d, with users spending an average of 3 hours and 15 minutes per day on mobile apps. This trend presents a substantial opportunity for advertising services providers, as more businesses look to capitalize on this captive audience. However, the market is not without challenges. The growing adoption of ad-blocker solutions by consumers is a major concern for advertising services providers. Ad-blockers are estimated to reach 700 million users worldwide by 2023, posing a significant threat to the effectiveness of traditional digital advertising. To navigate this challenge, advertising services providers must invest in innovative ad formats and targeting strategies that can bypass ad-blockers and deliver personalized and relevant ads to consumers. Additionally, staying abreast of emerging technologies, such as AR and Artificial Intelligence (AI), and integrating them into advertising campaigns will be crucial for companies seeking to differentiate themselves in a crowded market. By focusing on these key trends and challenges, advertising services providers can capitalize on the growing demand for digital advertising and effectively navigate the evolving market landscape.

What will be the Size of the Advertising Services Market during the forecast period?

Request Free SampleThe market in the US continues to experience growth, fueled by the increasing number of mobile phone users and the expansion of digital media. E-commerce platforms have emerged as significant advertisers, driving demand for search engine advertising and display ads. Internet penetration has reached an all-time high, providing advertisers with a vast audience to target. Video advertising, including video ads, has gained popularity due to the increasing consumption of digital content. Microsoft advertising and other ad platforms have adopted data-driven strategies, leveraging artificial intelligence and data analytics to deliver personalized advertisements. However, challenges such as ad fraud and privacy concerns persist, necessitating the development of advanced technologies and regulations. Emerging economies offer significant growth opportunities, particularly in healthcare and other industries. Demographics continue to influence advertising trends, with social media advertising remaining a key channel for reaching younger audiences. Advertisement channels continue to evolve, with email advertising and other forms of digital marketing maintaining their relevance.

How is this Advertising Services Industry segmented?

The advertising services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeDigital advertisingTV advertisingPrint advertisingOOH advertisingOthersGeographyNorth AmericaUSAPACChinaJapanEuropeGermanyUKSouth AmericaMiddle East and Africa

By Type Insights

The digital advertising segment is estimated to witness significant growth during the forecast period.Digital advertising encompasses the utilization of the Internet and advanced digital technologies, including search engine optimization (), pay-per-click, email advertisements, and various digital media and platforms, to promote products or services. The global advertising market is experiencing significant growth during the forecast period, driven by several factors. The increasing global Internet penetration, expanding mobile phone user base, and growing number of user searches are primary contributors to the digital advertisement spending segment. Additionally, the ongoing digital transformation across industries necessitates businesses to enhance their online presence. Programmatic advertising, a data-driven strategy, is gaining popularity due to its efficiency and ability to target specific audience demographics. Microsoft Advertising and other ad platforms employ programmatic advertising, enabling businesses to reach their desired audience more effectively. Digital media, including social media, television, and e-commerce platforms, are increasingly becoming essential advertising channels. Artificial Intelligence (AI) is revolutionizing the advertising industry by enabling personalized and sustainable advertising. AI-driven ad formats, such as smart ads and video ads, cater to individual consumer preferences and enhan

Much research has examined how crime rates vary across urban neighborhoods, focusing particularly on community-level demographic and social characteristics. A parallel line of work has treated crime at the individual level as an expression of certain behavioral patterns (e.g., impulsivity). Little work has considered, however, whether the prevalence of such behavioral patterns in a neighborhood might be predictive of local crime, in large part because such measures are hard to come by and often subjective. The Facebook Advertising API offers a special opportunity to examine this question as it provides an extensive list of “interests” that can be tabulated at various geographic scales. Here we conduct an analysis of the association between the prevalence of interests among the Facebook population of a ZIP code and the local rate of assaults, burglaries, and robberies across 9 highly populated cities in the US. We fit various regression models to predict crime rates as a function of the Facebook and census demographic variables. In general, models using the variables for the interests of the whole adult population on Facebook perform better than those using data on specific demographic groups (such as Males 18-34). In terms of predictive performance, models combining Facebook data with demographic data generally have lower error rates than models using only demographic data. We find that interests associated with media consumption and mating competition are predictive of crime rates above and beyond demographic factors. We discuss how this might integrate with existing criminological theory.

Snapshot img

Internet Advertising Market Size 2024-2028

The internet advertising market size is forecast to increase by USD 651.2 billion at a CAGR of 18.69% between 2023 and 2028. The market is experiencing significant growth, driven by various sectors such as media and entertainment, transport and tourism, and IT and telecom. One of the primary growth factors is the digital transformation that is reshaping industries, leading to increased online presence and advertising spend. Another trend is the advancement of advertising technology, including the emergence of streaming platforms and the widespread use of smartphones and high-speed internet. However, challenges persist, such as the rise of ad fraud and the increasing use of ad blockers. To address these challenges, the industry is embracing automation, with automated bidding strategies, ad copy, PPC reporting, and rules becoming increasingly common. By leveraging these tools, businesses can optimize their ad campaigns and improve their return on investment.

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The market continues to evolve, providing businesses with numerous opportunities to reach their target audiences and drive conversions. By implementing strategic ad formats across various channels, companies can increase website traffic, generate brand exposure, and ultimately, boost sales. Promotional messages play a crucial role in capturing the attention of potential customers. In the digital realm, businesses can utilize banners, pop-ups, and e-newsletters to engage their target consumers. These ad formats offer precision in reaching specific demographics and geographic locations, ensuring that businesses maximize their return on investment.

Simultaneously, search engines are a primary digital channel for advertising, as they cater to users actively seeking information or products. By optimizing ad campaigns for search engines, businesses can reach consumers who are most likely to make a purchase. However, the Internet advertising landscape extends beyond digital channels. Traditional media, such as magazines, newspapers, and television, also offer valuable opportunities for businesses to reach their target audiences. By integrating ad strategies across both digital and print channels, companies can create a cohesive marketing approach that resonates with consumers. The mobile segment represents a significant portion of internet usage, making it an essential channel for businesses to consider.

Moreover, mobile-optimized ads, including banners and pop-ups, can effectively reach consumers on the go, increasing the likelihood of conversions. Demographics and geographic locations are essential factors in determining the most effective ad strategies. By understanding the unique characteristics of their target audiences, businesses can tailor their messaging and channel selection to maximize conversions and sales. Precision is key in the market. Businesses must continually refine their ad strategies to ensure they are reaching the right consumers with the most effective messages. By monitoring ad performance and adjusting tactics as needed, companies can optimize their advertising efforts and drive growth.

In conclusion, the market offers businesses a wealth of opportunities to reach their target audiences and drive conversions. By implementing strategic ad formats across various channels, businesses can increase website traffic, generate brand exposure, and ultimately, boost sales. Effective ad strategies require a deep understanding of consumer demographics, geographic locations, and channel selection, ensuring that businesses maximize their return on investment.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Platform

  Mobile
  Desktop and laptop
  Others


Type

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  APAC

    China
    Japan


  Europe

    Germany


  South America



  Middle East and Africa

By Platform Insights

The mobile segment is estimated to witness significant growth during the forecast period. The market holds significant value in fostering human connection and storytelling for brands, reaching increasingly diverse target audiences through various ad strategies on digital channels. With the dominance of mobile devices, this sector has gained prominence, as mobile phone ownership has reached 78% of the global population aged 10 and above, according to ITU data. In 2023, approximately 5.4 billion people, or 67% of the world population, accessed the internet. European and American regions boasted high internet penetration rates of nearly 90%, while the Arab States and Asia-Pacific regions saw about two-thirds of their populations using the inte

About

This dataset provides insights into user behavior and online advertising, specifically focusing on predicting whether a user will click on an online advertisement. It contains user demographic information, browsing habits, and details related to the display of the advertisement. This dataset is ideal for building binary classification models to predict user interactions with online ads.

Features

• id: Unique identifier for each user.
• full_name: User's name formatted as "UserX" for anonymity.
• age: Age of the user (ranging from 18 to 64 years).
• gender: The gender of the user (categorized as Male, Female, or Non-Binary).
• device_type: The type of device used by the user when viewing the ad (Mobile, Desktop, Tablet).
• ad_position: The position of the ad on the webpage (Top, Side, Bottom).
• browsing_history: The user's browsing activity prior to seeing the ad (Shopping, News, Entertainment, Education, Social Media).
• time_of_day: The time when the user viewed the ad (Morning, Afternoon, Evening, Night).
• click: The target label indicating whether the user clicked on the ad (1 for a click, 0 for no click).

Goal

The objective of this dataset is to predict whether a user will click on an online ad based on their demographics, browsing behavior, the context of the ad's display, and the time of day. You will need to clean the data, understand it and then apply machine learning models to predict and evaluate data. It is a really challenging request for this kind of data. This data can be used to improve ad targeting strategies, optimize ad placement, and better understand user interaction with online advertisements.

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Spreadsheet showing contribution of individual sites.

Generalised polygons of administrative districts of municipalities with extended powers (AD MEP) with selected absolute and ratio demographic indicators in 2019–2023

The Social Media Ad Optimization Dataset provides a comprehensive collection of user interaction data related to digital advertising campaigns. It is designed to support research in predictive modeling, targeted advertising, and AI-driven campaign optimization.

Key Features: User Demographics: Age, gender, location, and interests.

Ad Metadata: Ad ID, category, platform, type, and textual content.

User Engagement Data: Impressions, clicks, conversions, and time spent on ads.

Temporal Information: Interaction timestamps and day of the week.

Device Insights: Device type used for accessing the ad.

Applications: Ad engagement prediction and conversion modeling.

Behavioral analysis for personalized targeting.

Optimization of ad delivery strategies using AI.