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...

Programmatic Creative QA AI Market Outlook

According to our latest research, the global Programmatic Creative QA AI market size was valued at USD 1.42 billion in 2024 and is anticipated to reach USD 8.36 billion by 2033, growing at a robust CAGR of 21.6% during the forecast period. The market's expansion is fueled by the rising adoption of artificial intelligence in digital advertising, as enterprises seek to automate quality assurance and optimize the performance of creative assets across programmatic platforms.

A primary growth driver for the Programmatic Creative QA AI market is the exponential increase in digital advertising spend globally, particularly on programmatic channels. Brands and agencies are increasingly leveraging AI-powered creative QA tools to ensure that their digital advertisements meet quality standards, comply with brand guidelines, and are optimized for diverse audience segments. This surge is further propelled by the demand for real-time, data-driven insights that can enhance creative effectiveness, reduce manual errors, and accelerate campaign turnaround times. With advertisers aiming to maximize ROI and minimize wasted impressions, the integration of AI in creative QA processes has become a strategic imperative, driving consistent market growth.

Another significant factor contributing to market expansion is the evolution of consumer expectations and the proliferation of advertising formats. As audiences engage with content across multiple devices and channels, advertisers are compelled to deliver highly relevant, personalized, and visually consistent creative assets. Programmatic Creative QA AI solutions enable advertisers to automate the validation, adaptation, and optimization of creative assets for various formats, such as display, video, social, and mobile advertising. This capability not only enhances user experience but also ensures compliance with platform-specific requirements and regulatory standards, thereby reducing risk and fostering brand trust.

The increasing sophistication of AI algorithms and advancements in machine learning technologies have also played a pivotal role in shaping the Programmatic Creative QA AI market. Modern AI systems can now detect subtle issues in creative assets, such as inappropriate content, brand safety violations, and technical discrepancies, with high accuracy and speed. Moreover, the integration of natural language processing and computer vision has enabled automated systems to evaluate both visual and textual elements in advertisements. This technological progress is empowering organizations to scale their creative operations efficiently while maintaining high standards of quality and compliance, further accelerating market adoption.

From a regional perspective, North America continues to dominate the Programmatic Creative QA AI market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the presence of major advertising technology vendors, high digital ad spend, and rapid adoption of AI-driven solutions by enterprises. Asia Pacific, on the other hand, is witnessing the fastest growth, driven by the digital transformation of emerging economies, increasing internet penetration, and a burgeoning digital advertising ecosystem. Europe also demonstrates substantial adoption, particularly among multinational brands and agencies that prioritize compliance with stringent data privacy regulations. These regional dynamics are expected to shape market trends throughout the forecast period.

Component Analysis

The Programmatic Creative QA AI market is segmented by component into Software and Services. The software segment commands the majority share, driven by the proliferation of AI-powered platforms that automate the creative quality assurance process. These software solutions integrate seamlessly with programmatic advertising platforms, enabling real-time analysis, validation, and optimization of creative assets. As advertisers increasingly demand scalability and efficiency, software providers are continually innovating to offer advanced features such as automated compliance checks, cross-channel creative optimization, and detailed analytics dashboards. The robust demand for these solutions underscores the pivotal role of software in transforming creative QA workflows.

Within the software se

API Action Integration: Exposes an API action (workflowstatechange) for initiating state transitions, enabling programmatic control and integration with other systems or user interfaces. Native CKAN State Management Example: Includes a native_workflow implementation that demonstrates how the framework can be used to manage CKAN's existing dataset states (private/public) using the extension's toolset, providing a practical starting point for developers. Use Cases: Data Approval Process: A government agency could use the workflow extension to implement a multi-stage approval process for datasets. Datasets might initially be in a "draft" state, then transition to a "review" state, require an administrator's approval to move to "published". The workflow handles ensuring datasets in "draft" or "review" remain private. Data Quality Validation: An organization ingesting data from various sources might use a custom workflow to automatically flag datasets requiring a quality review, ensuring only validated datasets are available for public consumption. Technical Integration: The extension operates by providing a plugin infrastructure where custom workflow implementations can register. These implementations define the available states and transitions for datasets. Triggers can be initiated via API calls, allowing administrators to update a dataset’s workflow status and effect associated permissions changes. The workflowgetstate function allows developers to fetch the current state of a dataset to make decisions within custom code. Benefits & Impact: Implementing the ckanext-workflow extension enables organizations to exert more control over the dataset lifecycle, ensuring that data is managed according to specific organizational policies and workflows. The extension improves data quality, enhances data governance, and facilitates systematic approval processes, resulting in more reliable and trustworthy data assets.

Weak Supervision Platform Market Outlook

According to our latest research, the global Weak Supervision Platform market size was valued at USD 348 million in 2024, and it is anticipated to reach USD 1.52 billion by 2033, expanding at a robust CAGR of 17.8% during the forecast period. The primary growth driver for this market is the increasing need for scalable, cost-effective, and high-quality labeled data to train advanced machine learning models across various industries.

The rising complexity and volume of data in artificial intelligence and machine learning workflows have necessitated more efficient data annotation techniques, propelling the adoption of weak supervision platforms. Traditional data labeling is often labor-intensive and expensive, especially for large datasets. Weak supervision platforms significantly reduce manual effort by leveraging noisy, limited, or imprecise sources to generate training data at scale. This paradigm shift is especially attractive for organizations aiming to accelerate AI deployment without compromising data quality. The growing demand for AI-driven solutions in sectors such as healthcare, finance, and retail is further fueling the uptake of weak supervision technologies, as these industries seek to harness actionable insights from vast, unstructured datasets.

Another significant growth factor is the proliferation of natural language processing (NLP), computer vision, and speech recognition applications. As organizations increasingly adopt AI-powered automation for customer service, fraud detection, medical diagnostics, and personalized marketing, the need for large, accurately labeled datasets has intensified. Weak supervision platforms bridge this gap by enabling organizations to generate high-quality training data from heterogeneous sources, including user feedback, domain heuristics, and pre-existing knowledge bases. This capability not only accelerates model development but also enhances the adaptability of AI systems to new domains and languages, driving broader market penetration.

The ongoing advancements in cloud computing and the growing acceptance of cloud-based deployment models are also catalyzing market growth. Cloud-based weak supervision platforms offer scalability, flexibility, and ease of integration with existing data infrastructures, making them highly attractive for enterprises of all sizes. The ability to access cutting-edge tools and frameworks without significant upfront investment democratizes AI adoption, particularly for small and medium enterprises (SMEs). Moreover, the integration of weak supervision with other AI lifecycle management tools is streamlining end-to-end machine learning workflows, leading to improved productivity and faster time-to-market for AI solutions.

From a regional perspective, North America currently dominates the weak supervision platform market, driven by early adoption of AI technologies, a strong ecosystem of machine learning startups, and significant investments in digital transformation initiatives. However, Asia Pacific is expected to witness the fastest growth, fueled by rapid digitalization, expanding industrial automation, and increasing government support for AI innovation. Europe is also emerging as a key market, with growing emphasis on ethical AI, data privacy, and regulatory compliance shaping platform adoption. Latin America and the Middle East & Africa are gradually catching up, supported by rising awareness and investments in AI infrastructure.

Component Analysis

The weak supervision platform market by component is primarily segmented into software and services, each playing a pivotal role in the overall ecosystem. The software segment encompasses core platforms, frameworks, and tools that enable organizations to build, deploy, and manage weak supervision pipelines. These solutions offer functionalities such as data labeling automation, programmatic labeling, data augmentation, and integration with popular machine learning libraries. The rising demand for customizable, scalable, and interoperable software solutions is driving continuous innovation in this segment. Vendors are increasingly focusing on user-friendly interfaces, support for multiple data types, and seamless integration with cloud and on-premises infrastructures to cater to diverse enterprise requirements.

The services segment, on the other hand, includes consulting, implementation, train

The Latin American programmatic advertising market is experiencing robust growth, driven by increasing digital adoption, expanding mobile penetration, and the rise of e-commerce. While precise market size data for Latin America specifically is not provided, we can extrapolate from the global CAGR of 8.34% and regional market trends. Considering the significant growth in digital advertising across emerging markets, a conservative estimate places the Latin American programmatic advertising market value at approximately $2 billion in 2025. This figure is likely influenced by factors such as Brazil's substantial digital economy and the increasing sophistication of ad tech infrastructure across the region. Key growth drivers include the rising popularity of mobile advertising, the increasing adoption of RTB (Real-Time Bidding) platforms by advertisers seeking targeted campaigns, and the expansion of digital display advertising across various sectors. The market segmentation mirrors global trends, with digital display and mobile display dominating advertising media, and a significant portion of ad spend coming from large enterprises due to their more advanced marketing strategies. However, the SMB (small and medium-sized business) segment shows significant potential for future growth as more businesses adopt digital marketing strategies. Challenges include inconsistencies in data quality and measurement across different markets, limited infrastructure in some areas, and the need for increased digital literacy amongst advertisers. Despite these challenges, the programmatic advertising market in Latin America is expected to maintain a strong growth trajectory over the next decade. The relatively low digital penetration compared to mature markets represents substantial untapped potential, fueling further growth in the coming years. The forecast period from 2025 to 2033 anticipates continued expansion, driven by both increasing ad spend and the ongoing improvement in targeting capabilities. While the precise CAGR for the Latin American market requires more granular data, it's reasonable to expect a CAGR in line with or exceeding the global average, considering the region's growth potential. The market is expected to be shaped by further technological advancements, particularly in areas like AI-powered ad optimization and improved fraud detection measures. This will lead to a more efficient and transparent programmatic ecosystem, further driving investment and growth within the Latin American region. The dominance of specific trading platforms, such as RTB, will likely continue, albeit with increasing competition from newer, more specialized solutions. Recent developments include: July 2022: Place Exchange, a supply-side platform for programmatic out-of-home media, has expanded into major markets in Latin America, including Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Panama, Paraguay, and Peru. The expansion allows marketers access to more than 9,000 digital-out-of-home screens from media companies such as Billboard Planet, Brapex, Doohmain, LatinAD, OLA Media, and many more., April 2022: Hivestack, an independent, programmatic digital out-of-home (DOOH) ad tech company, partnered with Clear Channel Outdoor LatAm, an out-of-home (OOH) media advertising company in Latin America. This partnership enables the integration of Clear Channel LatAm's premium DOOH inventory into the Hivestack Supply Side Platform (SSP), and will be available programmatically via Private Marketplace (PMP) deals through the Hivestack Demand Side Platform (DSP), as well as leading omnichannel DSPs who are integrated into Hivestack's platform. Key drivers for this market are: Growth of Digital Media Advertisement, Better use of Data for Programmatic Advertising. Potential restraints include: Growth of Digital Media Advertisement, Better use of Data for Programmatic Advertising. Notable trends are: Growth of Digital Media Advertisement Due to Increased Use of Data.

A comprehensive dataset analyzing artificial intelligence applications in digital advertising, covering hyper-personalization trends, generative AI creative tools, conversational AI implementations, data quality challenges, privacy regulations, and actionable strategies for businesses to integrate AI advertising technologies effectively in 2025.

The ckanext-gbif extension enhances CKAN by connecting it to the GBIF (Global Biodiversity Information Facility) API. This allows CKAN to retrieve and display data, such as data quality information (DQIs), associated with datasets that have a GBIF ID. This bridge between CKAN and GBIF is designed to improve data accessibility and provide additional context for biodiversity-related datasets. Key Features: GBIF API Integration: Retrieves additional data from the GBIF API for records containing a GBIF ID, bringing in valuable external information to enrich CKAN datasets. Data Quality Information (DQI) Retrieval: Specifically retrieves and displays DQIs from GBIF, providing users with insights into the quality and reliability of biodiversity data. Templating for GBIF Data: Supplies templates for displaying GBIF data within CKAN, ensuring a consistent and user-friendly presentation. Specifically, it modifies the record/specimen.html and record/dwc.html templates. gbifrecordshow Action: Implements a gbifrecordshow action that allows retrieval of GBIF data associated with a specific CKAN record, facilitating programmatic access to GBIF information. Dataset Publisher Linking: Enables linking to the dataset publisher on GBIF using the ckanext.gbif.organisationkey configuration variable. Dataset Linking: Enables linking to the dataset itself on GBIF using the ckanext.gbif.datasetkey configuration variable. Technical Integration: The extension integrates within CKAN by adding 'gbif' to the list of active plugins in the CKAN configuration file (.ini). It utilizes configuration settings to link to GBIF and modifies existing templates to display GBIF data, ensuring seamless integration with CKAN's existing functionality. It provides an action, callable via the CKAN API or other extension code, to fetch data and display it with enhanced templates. Benefits & Impact: By integrating GBIF data into CKAN, the ckanext-gbif extension provides improved data context and accessibility. This reduces the need to navigate between platforms, and allows users to quickly access relevant information from GBIF within CKAN. The inclusion of data quality information from GBIF further enhances the trustworthiness and usability of biodiversity datasets within the CKAN ecosystem.

This study explored the behavioral factors that influence data quality and information use within Ethiopia's health system, particularly in health centers supported by the Capacity Building and Mentorship Program (CBMP). Although Ethiopia has made significant efforts to improve its Health Information System (HIS) through initiatives like the Information Revolution, the behavioral aspects affecting data practices remain underexamined. Using a descriptive qualitative approach, researchers conducted 43 key informant interviews and 15 focus group discussions with health workers, HMIS staff, and facility leaders across five regions. Thematic analysis identified seven key behavioral domains shaping data practices: perceived value of data, self-efficacy, motivation and commitment, competing clinical demands, social support and teamwork, adherence to protocols, and leadership and governance.Findings revealed that while some health workers recognize the importance of data for clinical and programmatic decision-making, others deprioritize it due to high workloads and limited support. Self-efficacy was boosted by basic training but hindered by gaps in advanced data skills. Motivation suffered from a lack of recognition and encouragement, and collaboration was weakened by minimal peer learning opportunities. Adherence to data protocols was inconsistent, and poor leadership, unclear roles, and weak accountability structures further impaired data use. The study recommends integrated strategies such as Telegram-based digital learning tools, PMT optimizers, and leadership strengthening initiatives to build a culture of effective data use. These behavioral interventions are critical to enhancing the performance of HIS and fostering evidence-informed decision-making at the primary healthcare level.

Abstract

In order to provide the Government and international funding agencies with a reliable and up to date integrated assessment of all major aspects of household living conditions in the Union of Myanmar, the United Nations Development Programme (UNDP) and the Government of the Union of Myanmar have agreed on the implementation of an Integrated Household Living Conditions Assessment (IHLCA) in 2003-2005.

The expected outputs of this project include: - A nationwide qualitative study on people’s perceptions of poverty in Myanmar including 224 focus groups in December 2003. The results of this study were published in July 2004 in four volumes; - A nationwide quantitative survey of 18,660 households with two rounds of data collection (November-December 2004 and May 2005); - A Poverty Management Information System (PMIS).

The IHLCA involved two phases: (i) the first phase was a qualitative study which aimed at providing information on the perceptions of the people of Myanmar on living conditions to feed into the final selection of indicators to include in the questionnaire of the second quantitative phase of this baseline survey; (ii) this last phase included two rounds of data collection.

The first analysis of IHLCA data led to the preparation of four reports: - Integrated Household Living Conditions Assessment in Myanmar: Poverty Profile; - Integrated Household Living Conditions Assessment in Myanmar: Vulnerability–Relevant Information; - Integrated Household Living Conditions Assessment in Myanmar: MDG-Relevant Information; - Integrated Household Living Conditions Assessment in Myanmar: Quantitative Survey Technical Report.

SURVEY OBJECTIVES

In order to provide a holistic assessment of living conditions in Myanmar, drawing on reliable data that are representative of the country’s population, the IHLCA was a logical continuation of previous assessments of social and economic conditions and outcomes. On the basis of IHLCA results, it will be possible to better understand the situation of the population in relation to poverty, vulnerability and inequality. The information generated will allow for better planning of policies and programs for improving household living conditions.

The main objectives of the Survey were the following: - To obtain an accurate and holistic assessment of population well-being by measuring a number of indicators related to living conditions from an integrated perspective; - To provide reliable and updated data for identifying different levels of poverty in order to help better focus programmatic interventions and prioritize budget allocations; - To provide quantitative and qualitative data for better understanding the dimensions of wellbeing and poverty in Myanmar and the endogenous and exogenous factors behind the observed patterns and trends in living conditions; - To provide baseline information for monitoring progress towards the achievement of the Millennium Development Goals and other national and international targets; - To develop a rigorous and standardized methodology for establishing a framework for monitoring living conditions and conducting future time-trend analysis.

Given the breadth of information that was to be generated by the integrated survey and the range of stakeholders involved in the project, there were also a number of secondary objectives including:

• The compilation of updated statistics for a series of indicators that were also addressed in previous surveys in Myanmar for comparative time-trend analyses on specific aspects of living conditions where appropriate;
• The compilation of precise statistics on the spatial distribution of poor and non-poor households for poverty mapping;
• For economic and social analysis, improved data for monitoring differentials in living conditions by urban-rural residence, gender and other population sub-groups;
• For policy and programmatic formulation, comprehensive data on the population’s perceptions of living conditions, in particular prioritization in terms of their preferences to improve well-being and reduce poverty across regions of the country.

Geographic coverage

Administratively, the Union of Myanmar is divided into 17 States/Divisions. These in turn are subdivided into 61 Districts. Districts are further subdivided into Townships, Wards, Village Tracts and Villages.

The IHLCA Survey covered both the urban and rural areas at the regional and national levels.

The Survey aimed to produce data at the regional level for each of the 17 States/Divisions. No Township estimates were to be provided as this would necessitate too large a sample size. The sample was large enough to provide good sample estimates of a number of important living conditions characteristics at the national level, and reasonably good sample estimates at the State/Division level.

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to minimise sampling errors, the careful design of a statistically sound sampling plan was deemed of critical importance. The starting point of such a plan was a sampling frame, or complete listing of communities and households from which a sample can be drawn, and the desired precision level for key indicators, to be used in the determination of the expected sample size. The sampling plan was designed to collect representative information from a stratified multiple-stage random sample of around 18888 households across all regions of the country.

A number of factors had to be addressed in the determination of a survey design, including the sampling plan. Factors to be considered with regard to sampling were: - The specific objectives of the survey; - The country’s characteristics, in particular its administrative divisions; - The level of precision desired for the resulting estimates; - The desired timeframe for availability of results; - The availability of human and financial resources.

On the one hand, designing a plan to include a very large sample of households would allow for more precise estimates of the selected indicators and enable greater degrees of disaggregation at the sub-national level.

On the other hand, in favor of a sample size that was not too big were the needs of concerned stakeholders to have results available in a timely manner (within a few weeks or months from the end of fieldwork) as well as the workload and budget constraints. Experience has shown that surveys with very large samples: (i) have a high probability of becoming bogged down, creating delays of several years in results publication; (ii) are prone to poor data quality, in particular due to non-sampling errors; and (iii) represent a major disturbing factor for other statistical operations that technical and reporting agencies must conduct. While from an international perspective the financial costs of conducting surveys may be relatively low in Myanmar, the opportunity cost of the time and resources spent on a very large-scale survey and not on other productive activities was taken into account.

Another consideration was the desired level of disaggregation by the IHLCA main data users. It was decided to ensure collection of representative data for the following spatial units: - National level; - States/divisions (17); - Urban/rural areas by state/division.

This breakdown suggested a total of 34 strata (2 area types * 17 states/divisions).

One significant constraint to the design of the sampling plan for the IHLCA quantitative survey was the absence of a reliable updated sampling frame or complete listing of households across the country from which a sample could be drawn. Usually such frames are based on the results of the most recent population census; however there had been no national count in Myanmar since 1983. Updated population estimates were to be obtained from The Department of Population (DOP) of the Ministry of Population. The frame was imperfect. In addition a number of areas were excluded by PD because of inaccessibility for fieldwork implementation due to transportation/communication problems or ongoing security concerns.

The options for selecting households for questionnaire implementation ranged from simple random sampling of households across the country (the most efficient methodology from a purely statistical viewpoint, but one for which fieldwork costs may be prohibitive), to multi-stage random selection based on probability proportional to size (a more commonly used approach given the costs-benefits tradeoffs). However, considering the lack of reliable population numbers at the lowest levels of geographic disaggregation for Myanmar, the sampling plan had to rely on probability proportional to estimated size (PPES) approaches and the measures of size used were the number of households at different geographical levels.

Another issue that was considered in the determination of the sample size was the desired precision level by the IHLCA main data users. The calculation was based on observed variances for key variables in past survey experiences.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey questionnaires were used for the IHLCA survey3:

1) The household questionnaire, administered at household level, included 9 modules covering different aspects of household living conditions: Module 1: Household Basic Characteristics; Module 2: Housing; Module 3: Education; Module 4: Health; Module 5: Consumption Expenditures; Module 6: Household Assets; Module 7: Labour and Employment; Module 8: Business; Module 9: Finance and Savings.

2) The Community questionnaire, administered to local key informants, which included 4 modules which aimed at providing general information on the village/wards where the survey was being undertaken and at reducing

The Digital Out-of-Home (DOOH) advertising market is experiencing robust growth, projected to reach $20.78 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the increasing adoption of programmatic advertising within the DOOH space allows for more targeted and efficient campaigns, mirroring the success seen in online advertising. Secondly, advancements in display technology, including higher resolution screens and interactive capabilities, are enhancing the visual appeal and engagement potential of DOOH ads, making them more impactful than traditional static billboards. Finally, the integration of data analytics enables advertisers to track performance in real-time and optimize campaigns for maximum ROI. This data-driven approach is making DOOH increasingly attractive to marketers seeking measurable results. The market's growth is not without its challenges. Competition among established players like JCDecaux, Clear Channel Outdoor Holdings, and Lamar Advertising Company is intensifying, necessitating continuous innovation and strategic partnerships. Moreover, the cost of deploying and maintaining advanced DOOH infrastructure can be substantial, potentially hindering smaller market entrants. However, the ongoing shift towards digital advertising formats and the increasing availability of high-quality data are likely to outweigh these restraints, leading to sustained market expansion throughout the forecast period (2025-2033). The geographic segmentation of the market, while currently unspecified, will likely reflect established advertising trends, with North America and Europe holding significant market share.

The Catalogue of Life is building a comprehensive catalogue of all known species on Earth. It offers two types of releases that reflect different levels of quality. The Base Release is curated and verified by experts specifically for COL. The eXtended Release (COL XR) builds on the Base Release 2025-11-12 (COL25.11) by programmatically integrating additional data sources. It integrates information from 59668 overlapping taxonomic and nomenclatural global, regional, national and management data sources (checklists) as well as originating from digitised literature available in Catalogue of Life's infrastructure ChecklistBank. New names and other data included in the COL XR are indicated with the XR icon.

This release addresses several gaps of the Base Release and also enriches the existing names with information such as authorships, references, and vernacular names. It also adds molecular data, such as barcode index numbers or operational taxonomic units, to the Catalogue of Life. Higher taxonomy is being added only in selected groups with important gaps. Meanwhile, the information from the global data sources of the Catalogue of Life Base Release remains unmodified.

This enhanced process is continuously evolving and undergoing quality control checks by COL editors and its community. Due to its programmatic nature as well as the taxonomic and nomenclatural differences among the data sources used, some issues may arise. We therefore caution the user, and invite everyone to help log data issues in the COL’s data GitHub repository. We will do our best to resolve these issues as soon as possible.

The global video advertising software market is experiencing robust growth, driven by the surging popularity of online video content and the increasing sophistication of programmatic advertising. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. Firstly, the rise of over-the-top (OTT) platforms and connected TV (CTV) devices provides advertisers with new avenues to reach audiences, boosting demand for effective video ad management solutions. Secondly, the increasing adoption of programmatic advertising allows for targeted and data-driven campaigns, enhancing ROI and fueling investment in the software solutions that power them. Thirdly, the continuous evolution of video advertising formats, including interactive ads and in-stream video, necessitates sophisticated software capable of managing and optimizing these complex campaigns. Leading players like Sizmek, 4C, DoubleClick, MediaMath, TubeMogul, dataxu, Amobee, BrightRoll, ExactDrive, Liquidus, and Rocket Fuel are constantly innovating to meet these evolving needs, offering features such as advanced analytics, real-time bidding (RTB), and cross-platform campaign management. However, the market also faces challenges. One significant restraint is the increasing complexity of the digital advertising ecosystem, requiring software that can seamlessly integrate with various platforms and data sources. Another challenge lies in maintaining ad quality and brand safety, ensuring that video ads are not associated with inappropriate or harmful content. The ongoing evolution of privacy regulations also presents a hurdle, demanding that software providers adapt to new requirements concerning data collection and usage. Despite these obstacles, the overall outlook for the video advertising software market remains positive, driven by consistent innovation and the continued growth of the online video landscape. Market segmentation is crucial for understanding this dynamic environment. Factors like ad format (in-stream, out-stream, etc.), platform (desktop, mobile, CTV), and target audience are key aspects influencing software selection and market size. The future likely holds even more specialized solutions tailored to the unique needs of specific industry verticals.

Retailer Data Co-Op Platform Market Outlook

According to our latest research, the global Retailer Data Co-Op Platform market size reached USD 4.2 billion in 2024, with a robust CAGR of 13.8% expected through the forecast period. By 2033, the market is projected to achieve a value of USD 13.3 billion, driven by the increasing demand for collaborative data solutions, advanced analytics, and the growing need for unified customer insights. The primary growth factor is the retail sectorÂ’s accelerated digital transformation, compelling retailers, brands, and marketplaces to leverage data co-op platforms for competitive advantage and operational efficiency.

The surge in adoption of Retailer Data Co-Op Platforms is fundamentally propelled by the exponential growth in retail data generation and the critical need for actionable insights. Retailers are increasingly recognizing the value of pooling anonymized data to unlock deeper customer understanding, optimize marketing campaigns, and enhance personalization. The integration of artificial intelligence and machine learning within these platforms enables real-time analytics and predictive modeling, which significantly improves decision-making processes. Furthermore, as consumer expectations evolve towards seamless and personalized shopping experiences, retailers are compelled to harness collective data intelligence to remain agile and relevant in a highly competitive market landscape. This trend is further accelerated by the proliferation of omnichannel retailing, where data from various touchpoints must be aggregated and analyzed for holistic insights.

Another key growth driver for the Retailer Data Co-Op Platform market is the increasing emphasis on data-driven marketing and advertising. Retailers and brands are leveraging these platforms to gain granular insights into customer behavior, preferences, and purchase patterns. By participating in data co-ops, organizations can access enriched datasets that extend beyond their proprietary information, enabling more precise audience segmentation and targeted advertising. This capability not only enhances marketing ROI but also supports dynamic pricing strategies and inventory optimization. The rise of programmatic advertising and real-time bidding further amplifies the need for comprehensive, high-quality data, positioning data co-op platforms as indispensable tools for modern retail operations.

The evolving regulatory landscape and growing concerns around data privacy and security are also shaping the marketÂ’s trajectory. Retailer Data Co-Op Platforms are investing heavily in robust data governance frameworks, consent management, and compliance with regulations such as GDPR and CCPA. These measures are essential to building trust among participants and ensuring the ethical use of shared data. Moreover, advancements in privacy-enhancing technologies, such as differential privacy and federated learning, are enabling secure data collaboration without compromising individual privacy. As regulatory scrutiny intensifies, platforms that prioritize transparency, accountability, and security are likely to gain a competitive edge and foster broader industry adoption.

Regionally, North America remains the largest market for Retailer Data Co-Op Platforms, accounting for approximately 41% of global revenue in 2024. The regionÂ’s dominance is attributed to the presence of major retail chains, advanced digital infrastructure, and a high degree of data maturity among enterprises. Europe follows closely, driven by strong regulatory frameworks and a focus on data-driven retail strategies. The Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, increasing e-commerce penetration, and the digital transformation of traditional retail formats. Latin America and the Middle East & Africa, while currently representing smaller shares, are expected to witness accelerated adoption as digital ecosystems mature and retailers seek innovative solutions to enhance competitiveness.

The Federated Analytics Retail Consortium is playing a pivotal role in shaping the future of data collaboration in the retail sector. By fostering a collaborative environment among retailers, brands, and technology providers, the consortium aims to standardize data sharing practices an

Inventory Time Series for DoubleVerify Holdings Inc. DoubleVerify Holdings, Inc. provides media effectiveness platforms in the United States and internationally. The company provides data analytics that enable advertisers to increase the effectiveness and quality and return on their digital advertising investments. It offers DV Authentic Ad, a metric of digital media quality, which evaluates the existence of fraud, brand safety and suitability, viewability and geography for each digital ad; DV Authentic Attention that provides actionable and comprehensive data to drive campaign performance; Scibids AI, an AI-powered digital campaign optimization solutions. and Custom Contextual solution, which allows advertisers to match their ads to relevant content to maximize user engagement and drive campaign performance. In addition, the company provides DV Publisher suite, a solution for digital publishers to manage revenue and increase inventory yield by improving video delivery, identifying lost or unfilled sales, and aggregate data across all inventory sources; and DV Pinnacle, a service and analytics platform user interface that allows its customers to adjust and deploy controls for their media plan and track campaign performance metrics across channels, formats, and devices. Further, it offers software platform that is integrated across digital advertising ecosystem, including programmatic platforms, social media channels, and digital publishers. The company serves brands, publishers, and other supply-side customers covering various industry verticals, including consumer packaged goods, financial services, telecommunications, technology, automotive, and healthcare. The company was founded in 2008 and is headquartered in New York, New York.