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Customer Analytics Applications Market Size 2024-2028

The customer analytics applications market size is estimated to grow by USD 16.73 billion at a CAGR of 17.58% between 2023 and 2028. The growth of the market depends on several factors, including the increasing number of social media users, the growing need for improved customer satisfaction, and an increase in the adoption of customer analytics by SMEs. Customer analytics application refers to a software or system that analyzes customer data such as behavioral, demographic, and personal information to gain insights into their behavior, preferences, and needs. It uses various techniques such as data mining, predictive modeling, and statistical analysis to gather information and make informed decisions in marketing, sales, product development, and overall customer management. The goal of a customer analytics application is to enhance customer understanding and improve business strategies by allowing companies to make data-driven decisions and provide personalized experiences to their customers.

What will be the Size of the Market During the Forecast Period?

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Market Dynamics

In the evolving internet retail landscape, businesses are increasingly adopting innovative cloud deployment modes to enhance their operational efficiency. Customer Data Platforms (CDPs) like Neustar and Clarity Insight are pivotal in integrating and analyzing customer data to drive personalized experiences and strategic decisions. These platforms leverage cloud deployment modes to offer scalable solutions that support internet retail operations and enhance customer engagement. Data platforms are instrumental in collecting and processing vast amounts of data, providing valuable insights for trailblazers in the industry. By utilizing advanced cloud deployment modes, companies can efficiently manage their data infrastructure and improve their online retail strategies. Integrating Neustar and Clarity Insight into their systems enables businesses to stay ahead of the competition by offering tailored experiences and optimizing their internet retail performance through scalable solutions.

Key Market Driver

An increase in the adoption of customer analytics by SMEs is notably driving market growth. Expanding the efficiency and performance of business operations is critical to achieving the desired set of goals of an organization. Businesses with a customer-centric approach deal with massive amounts of customer data, which is stored, managed, and processed in real-time. SMEs generate numerous forms of customer data related to customer demographics and sales, marketing campaigns, websites, and conversations. Consequently, these businesses must scrutinize all this customer-related data to achieve a competitive edge in the market. SMEs are majorly using these as they enable better forecasting, resource management, and streamlining of data under one platform, lower operational costs, improve decision-making, and expand sales.

In addition, the increase in customer data, along with the companies' need to automate customer data processing, is leading to the increased adoption by SMEs. Hence, customer analytics is being executed across SMEs for better management of their business operations via a centralized management system with enhanced collaboration, productivity, simplified compliance, and risk management. Such factors are the significant driving factors driving the growth of the global market during the forecast period.

Major Market Trends

Advancements in technology are an emerging trend shaping the market growth. AI and ML technologies have revolutionized the way businesses understand and analyze customer data, allowing them to make more informed decisions and deliver customized experiences. Also, AI and ML have played a critical role in fake detection and prevention in the customer analytics market. Algorithms can identify unusual activities that may indicate fraud by analyzing transactional data and behavioral patterns. This allows businesses to secure themselves and their customers from potential financial losses.

Additionally, AI and ML have enhanced customer segmentation capabilities. Businesses can group customers based on their similarities by using clustering algorithms, allowing them to create targeted marketing campaigns for specific segments. This enables enterprises to personalize their messages and offers, resulting in higher customer engagement and conversion rates. These factors are anticipated to fuel the market growth and trends during the forecast period.

Significant Market Restrain

Data integration issues are a significant challenge hindering market growth. To analyze customer data generated from various types of systems, enterprises use these. The expansion in the use of smart devices and Internet penetration is creating

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

Our consumer data is meticulously gathered and aggregated from surveys, digital services, and public sources, ensuring the collection of fresh and reliable data points through powerful profiling algorithms. Our comprehensive data enrichment solution spans a variety of datasets, enabling you to address gaps in customer data, gain deeper insights into your customers, and enhance client experiences.

Data Categories and Attributes:

• Geography: City, State, ZIP, County, CBSA, Census Tract, etc.
• Demographics: Gender, Age Group, Marital Status, Language, etc.
• Financial: Income Range, Credit Rating Range, Credit Type, Net Worth Range, etc.
• Persona: Consumer Type, Communication Preferences, Family Type, etc.
• Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc.
• Household: Number of Children, Number of Adults, IP Address, etc.
• Behaviors: Brand Affinity, App Usage, Web Browsing, etc.
• Firmographics: Industry, Company, Occupation, Revenue, etc.
• Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc.
• Auto: Car Make, Model, Type, Year, etc.
• Housing: Home Type, Home Value, Renter/Owner, Year Built, etc

Data Export Methodology

Our dynamic data collection ensures the most updated insights, delivered at intervals best suited to your needs (daily, weekly, or monthly).

Use Cases

Our enriched consumer data supports a 360-degree customer view, data enrichment, fraud detection, and advertising & marketing, providing valuable insights to enhance your business strategies and client interactions.

Digital Survey Tools Market Outlook

The global Digital Survey Tools market size was valued at approximately $3.2 billion in 2023 and is expected to reach around $8.1 billion by 2032, growing at a CAGR of 10.9% during the forecast period. This robust growth can be attributed to increasing digitalization across various sectors, the necessity for real-time feedback, and advancements in analytical capabilities.

One of the primary growth factors driving the digital survey tools market is the burgeoning need for real-time data collection and analysis. In today’s fast-paced world, businesses require immediate feedback to make informed decisions. Digital survey tools provide an efficient and effective way to gather and analyze data, helping organizations stay agile and responsive. Furthermore, the rise of mobile technology has made it easier to reach a broader audience, enabling more comprehensive data collection across different demographics and geographies.

The growth of the e-commerce sector is another significant driver for the digital survey tools market. As online shopping becomes more prevalent, businesses are increasingly relying on digital surveys to understand customer satisfaction, preferences, and behaviors. This information is crucial for tailoring marketing strategies, improving products or services, and ultimately enhancing customer loyalty. Moreover, the COVID-19 pandemic has accelerated the adoption of digital solutions, including survey tools, as businesses strive to maintain customer engagement and gather feedback in a remote environment.

Additionally, the integration of artificial intelligence and machine learning with digital survey tools is revolutionizing the market. These technologies enable advanced analytics, predictive insights, and personalized survey experiences, making the data collected more valuable and actionable. AI-powered survey tools can automatically analyze open-ended responses, identify trends, and even predict future behaviors, thus enabling organizations to make proactive decisions. As these technologies continue to evolve, their adoption in digital survey tools is expected to soar, further propelling market growth.

On the regional front, North America holds a significant share of the digital survey tools market owing to its advanced technological infrastructure and the presence of major industry players. The region’s high internet penetration rate and widespread use of mobile devices further facilitate the adoption of digital survey tools. Additionally, the Asia Pacific region is expected to witness substantial growth during the forecast period. Rapid digital transformation, increasing internet and smartphone usage, and growing awareness about the benefits of digital surveys are driving the market in this region. Countries like China and India are emerging as key markets due to their large populations and expanding digital ecosystems.

Type Analysis

The digital survey tools market can be segmented by type into online surveys, mobile surveys, email surveys, and others. Online surveys are currently the most popular type, driven by their convenience and broad reach. These surveys can be easily distributed through various online channels such as websites, social media, and email, making them highly accessible. Additionally, online surveys often come with robust analytical tools that allow for real-time data analysis and reporting, enabling organizations to quickly interpret and act on the collected information. The versatility and efficiency of online surveys make them a preferred choice for many businesses and researchers.

Mobile surveys are gaining traction due to the widespread use of smartphones and mobile internet. These surveys are designed to be completed on mobile devices, offering convenience and flexibility to respondents. The mobility factor allows businesses to reach respondents anytime and anywhere, increasing the response rate and the diversity of the sample population. Moreover, mobile surveys often include features like push notifications and location-based services, which can enhance respondent engagement and data accuracy. As mobile technology continues to evolve, the adoption of mobile surveys is expected to rise significantly.

Email surveys, while traditional, remain a valuable tool in the digital survey toolkit. They are particularly effective for reaching a specific, targeted audience, as they can be sent directly to individuals' inboxes. Email surveys often offer higher completion rates compared to other types due to their personalized nature. They also allow fo

The size of the Customer Data Platform Market was valued at USD 1.75 USD Billion in 2023 and is projected to reach USD 8.07 USD Billion by 2032, with an expected CAGR of 24.4% during the forecast period. A Customer Data Platform (CDP) is a unified software system that consolidates and organizes customer data from various sources to create a comprehensive, single view of each customer. This data can include behavioral, transactional, and demographic information collected from multiple channels such as websites, mobile apps, social media, and email interactions. A CDP enables businesses to collect, store, and analyze this data to gain insights into customer preferences, behaviors, and trends. With a centralized customer profile, companies can deliver personalized marketing campaigns, improve customer service, and enhance customer experiences across different touchpoints, ultimately driving better customer engagement and business outcomes. The Customer Data Platform (CDP) market is experiencing rapid growth due to increasing customer-centricity, advancements in data analytics, the need for personalized marketing campaigns, and the proliferation of data sources and touchpoints. These platforms enable businesses to collect, unify, and analyze customer data to gain valuable insights, enhance customer experiences, and drive growth. Key drivers for this market are: Increased Use of Advanced Data Pipeline Tools for Cloud Flexibility among Organizations to Bolster Market Growth. Potential restraints include: Customer Data Privacy Concerns to Obstruct Product Adoption. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

The FR 3033p is the first part of a two-stage survey series, which has been conducted at regular five-year intervals since 1955. It is a census survey designed to identify the universe of finance companies eligible for potential inclusion in the FR 3033s. It gathers limited information including total assets, areas of specialization, and information on the corporate structure of such companies. The second part of these information collections, the FR 3033s, collects balance sheet data on major categories of consumer and business credit receivables and major liabilities, along with income and expenses, and is used to gather information on the scope of a company's operations and loan and lease servicing activities. In addition, additional questions were added to collect lending information related to the COVID-19 impacts.

Audience Analytics Market Outlook

The global audience analytics market size was valued at USD 3.2 billion in 2023 and is projected to reach USD 10.8 billion by 2032, growing at a CAGR of 14.5% during the forecast period. The surge in digital advertising and the growing need to understand consumer behavior are significant factors contributing to this market's growth.

The increasing adoption of data-driven decision-making processes across various industries is one of the primary growth factors driving the audience analytics market. Companies are increasingly relying on audience analytics to gain insights into consumer preferences, behaviors, and trends. This data enables firms to tailor their marketing strategies more effectively, thus boosting engagement and conversion rates. Moreover, the rise of social media platforms has created a massive amount of user-generated data, which is a goldmine for audience analytics. These platforms allow businesses to track consumer sentiments and preferences in real-time, providing a competitive edge in the market.

Another crucial growth factor is the technological advancements in artificial intelligence (AI) and machine learning (ML). These technologies are pivotal in enhancing the capabilities of audience analytics tools. AI and ML algorithms can process vast amounts of data at unprecedented speeds, offering deeper and more accurate insights. This not only improves the precision of audience analysis but also makes real-time analytics feasible. As a result, businesses can respond to market changes and consumer needs more promptly, thereby improving customer satisfaction and loyalty.

The rise of mobile and digital media consumption has also significantly contributed to the growth of the audience analytics market. With the proliferation of smartphones and high-speed internet, consumers are more connected than ever before. This has led to a surge in digital content consumption, providing a wealth of data for analytics. Companies are leveraging this data to understand viewing patterns, content preferences, and engagement levels across different demographics. This information is invaluable for content creators, advertisers, and marketers to optimize their strategies and maximize ROI.

Regionally, North America holds a dominant position in the audience analytics market, followed by Europe and the Asia Pacific. The presence of key market players, coupled with high adoption rates of advanced technologies, contributes to North America's leadership. Europe is also a significant player due to the stringent data protection regulations which enhance the reliability of audience data. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid digital transformation and increasing internet penetration in emerging economies like China and India.

In the retail and e-commerce sector, the use of Retail Analytics Software is becoming increasingly prevalent. This software enables retailers to gain a comprehensive understanding of consumer behavior by analyzing data from various sources such as online shopping patterns, in-store purchases, and customer feedback. By leveraging these insights, retailers can optimize their inventory management, personalize marketing strategies, and enhance the overall shopping experience. The ability to predict trends and customer preferences allows retailers to stay ahead of the competition and meet the ever-evolving demands of their customers. As the retail landscape continues to evolve, the integration of advanced analytics tools is essential for driving growth and maintaining a competitive edge.

Component Analysis

The audience analytics market is segmented by component into software and services. The software segment includes various analytics platforms and tools that businesses use to gather, process, and analyze audience data. These software solutions are pivotal in transforming raw data into actionable insights. They include real-time analytics, predictive analytics, and sentiment analysis tools, among others. The increasing demand for comprehensive analytics solutions is driving the growth of this segment. As businesses aim to gain a competitive edge, the adoption of advanced software tools that offer in-depth insights into audience behavior is on the rise.

Services, the other component segment, involve consulting, implementation, and support services. These services enable bu

Abstract

Census of Population and Housing (CPH) refers to the entire process of collecting, compiling, evaluating, analyzing, publishing, and disseminating data about the population and the living quarters in a country. It entails the listing and recording of the characteristics of each individual and each living quarter as of a specified time and within a specified territory. In other words, the CPH offers a “snapshot” of the entire population on a specific date, that is, how many people reside within the national borders, who they are, and where they live during such specified date. Also, included are the characteristics of the housing units where they reside.

The 2010 CPH is designed to take an inventory of the total population and housing units in the Philippines and collect information about their characteristics. The census of population is the source of information on the size and distribution of the population, as well as their demographic, social, economic, and cultural characteristics. The census of housing, on the other hand, provides information on the stock of housing units and their structural characteristics and facilities which have bearing on the maintenance of privacy and health, and the development of normal family living conditions. These information are vital for making rational plans and programs for local and national development.

Specifically, the 2010 CPH aims to: - obtain comprehensive data on the size, composition, and distribution of the population of the Philippines; - gather data on birth registration, literacy, school attendance, place of school, highest grade/year completed, residence 5 years ago, overseas worker, usual occupation, kind of business or industry, class of worker, place of work, fertility, religion, citizenship, ethnic group, disability, and functional difficulty, and determine their geographic distribution; - take stock of the housing units existing in the country and to get information about their geographic location, structural characteristics, and facilities, among others; - obtain information on the characteristics of the barangay, which will be used as basis for urban-rural classification; and - serve as sampling frame for use in household-based surveys.

Data collected in this census were compiled, evaluated, analyzed, published, and disseminated for the use of government, business, industry, social scientists, other research and academic institutions, and the general public. Among the important uses of census data are the following:

In government: - redistricting and apportionment of congressional seats; - allocation of resources and revenues; - creation of political and administrative units; - formulation of policies concerning population and housing; and - formulation of programs relative to the delivery of basic services for health, education, housing, and others

In business and industry: - determination of sites for establishing businesses; - determination of consumer demands for various goods and services; and - determination of supply of labor for the production of goods and services

In research and academic institutions: - conduct of researches on population and other disciplines; and - study of population growth and distribution as basis in preparing projections

Geographic coverage

National coverage Regions Provinces Cities and Municipalities Barangays

Analysis unit

household questionnaire: individuals (household members), households, housing units institutional questionnaire: individuals (institutional population), institutional living quarters barangay questionnaire: barangay

Universe

Census-taking in the Philippines follows a de-jure concept wherein a person is counted in the usual place of residence or the place where the person usually resides. Information on the count of the population and living quarters were collected with 12:01 a.m. of May 1, 2010 as the census reference time and date.

The following individuals were enumerated:

• Those who were present at the time of visit and whose usual place of residence is the housing unit where the household lives.

• Those whose usual place of residence is the place where the household lives but are temporarily away at the time of the census.

• Boarders/lodgers of the household or employees of household-operated businesses who do not usually return/go to their respective homes weekly.

• Overseas workers and who have been away at the time of the census for not more than five years from the date of departure and are expected to be back within five years from the date of last departure.

• Filipino "balikbayans" with usual place of residence in a foreign country but have resided or are expected to reside in the Philippines for at least a year from their arrival.

• Citizens of foreign countries who have resided or are expected to reside in the Philippines for at least a year from their arrival, except members of diplomatic missions and non-Filipino members of international organizations.

• Persons temporarily staying with the household who have no usual place of residence or who are not certain to be enumerated elsewhere.

Kind of data

Census/enumeration data [cen]

Sampling procedure

In the 2010 CPH, there are basically two types of questionnaires used for the enumeration of household members. These are CPH Form 2 or the Common Household Questionnaire and CPH Form 3 or the Sample Household Questionnaire. CPH Form 3 contains more questions than CPH Form 2.

The 2010 CPH was carried out through a combination of complete enumeration and sampling. For this census, systematic cluster sampling was adopted. This sampling method is designed in such a way that efficient and accurate estimates will be obtained at the city/municipality level.

The sampling rate or the proportion of households to be selected as samples depends on the size of the city/municipality where the Enumeration Area (EA) is located. For the cities/municipalities with estimated number of households of 500 and below, 100 percent sampling rate was used. While for those cities/municipalities with estimated number of households of 501 and above, a sampling rate of 20 percent was implemented.

In this sampling scheme, each city/municipality was treated as a domain. For city/municipality with 100 percent sampling rate, all households in all the EAs within this city/municipality were selected as samples. For those with a 20 percent sampling rate, systematic cluster sampling was adopted. That is, sample selection of one in five clusters with the first cluster selected at random. Thus in effect, the EAs belonging to the city/municipality with 20 percent sampling rate are divided into clusters of size 5. Random start is pre-determined for each EA.

If the sampling rate applied to a city/municipality is 100 percent, it means that all households in that municipality were administered with CPH Form 3. If it is 20 percent, it means that 20 percent of all households used CPH Form 3 while 80 percent used CPH Form 2.

The random start used by EA is a number from 1 to 5 which was used to select the cluster where the first sample households in an EA, and subsequently the other sample households, were included.

Clusters are formed by grouping together households that have been assigned consecutive serial numbers as they were listed in the Listing Booklet. For a 20 percent sampling rate, clusters were formed by grouping together five households.

Mode of data collection

Face-to-face [f2f]

Research instrument

CPH Form 1 - Listing Booklet This form is a booklet used to list the buildings, housing units, households, and the Institutional Living Quarters (ILQs) within an EA. This form also records other important information such as the name of household heads and name and type of institutions and their addresses, population totals, and counts of males and females.

CPH Form 2 - Common Household Questionnaire This is the basic census questionnaire, which was used to interview and record information about the common or nonsample households. This questionnaire gathered information on the following demographic and socio-economic characteristics of the population: relationship to household head, sex, date of birth, age, birth registration, marital status, religion, ethnicity, citizenship, disability, functional difficulty, highest grade/year completed, residence 5 years ago, and overseas worker. It also contains questions on the type of building/house, construction materials of the roof and outer walls, state of repair of the building/house, year the building/house was built, floor area of the housing unit, and tenure status of the lot.

CPH Form 3 - Sample Household Questionnaire This is the basic census questionnaire, which was used to interview and record information about the sample households. This questionnaire contains ALL questions asked in CPH Form 2 PLUS additional population questions: literacy, school attendance, place of school, usual occupation, kind of business or industry, class of worker, place of work, and some items on fertility. Moreover, there are additional questions on household characteristics: fuel for lighting and cooking, source of water supply for drinking and/or cooking and for laundry, and bathing, tenure status of the housing unit, acquisition of the housing unit, source of financing of the housing unit, monthly rental of the housing unit, tenure status of the lot, usual manner of garbage disposal, kind of toilet facility, and land ownership. It also asked questions on the language/dialect generally spoken at home, residence five years from now, and presence of household conveniences/devices, and access to internet.

CPH Form 4 -

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Argentina in 2006, 2010 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2006-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2006-2017 Argentina Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors (group D), construction (group F), services (groups G and H), and transport, storage, and communications (group I). Groups are defined following ISIC revision 3.1. Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, excluding sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)

Three levels of stratification were used in every country: industry, establishment size, and region.

Industry stratification was designed in the following way: In small economies the population was stratified into 3 manufacturing industries, one services industry - retail-, and one residual sector as defined in the sampling manual. Each industry had a target of 120 interviews. In middle size economies the population was stratified into 4 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. For the manufacturing industries sample sizes were inflated by 25% to account for potential non-response in the financing data.

For the Argentina ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposed, the number of employees was defined on the basis of reported permanent full-time workers. This resulted in some difficulties in certain countries where seasonal/casual/part-time labor is common.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies:

a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don't know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from "Don't know" responses.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Peru in 2006, 2010 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2006-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2006-2017 Peru Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors (group D), construction (group F), services (groups G and H), and transport, storage, and communications (group I). Groups are defined following ISIC revision 3.1. Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, excluding sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)

Three levels of stratification were used in every country: industry, establishment size, and region.

Industry stratification was designed in the following way: In small economies the population was stratified into 3 manufacturing industries, one services industry - retail-, and one residual sector as defined in the sampling manual. Each industry had a target of 120 interviews. In middle size economies the population was stratified into 4 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. For the manufacturing industries sample sizes were inflated by 25% to account for potential non-response in the financing data.

For the Peru ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposed, the number of employees was defined on the basis of reported permanent full-time workers. This resulted in some difficulties in certain countries where seasonal/casual/part-time labor is common.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies:

a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

Abstract

The survey was conducted in Nicaragua between October 2016 and June 2017 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 333 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15- 37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

For the Nicaragua ES, size stratification was defined as follows: small (4 to 20 employees), medium (21 to 50 employees), and large (51 or more employees). These categories differ from the global ES size definitions - small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification was done across four regions: Managua (department), East (departments of Masaya, Granada, and Carazo), West (departments of Chinandega and Leon), and North (departments of Esteli, Jinotega, and Matagalpa). Due to several cells without any realized interviews, the stratification regions East, West, and North were combined in one.

The sample frame consisted of listings of firms from two sources: For panel firms the list of 336 firms from the Nicaragua 2010 ES was used, and for fresh firms (i.e., firms not covered in 2010) the sample frame was comprised of a list randomly drawn from the Economic Census, provided by the Banco Central de Nicaragua. Standardized size categories provided by the Census were used.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone/fax numbers so the local contractor had to screen the contacts by visiting them. Due to response rate and ineligibility issues, additional sample had to be extracted by the World Bank in order to obtain enough eligible contacts and meet the sample targets.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 22.8% (326 out of 1,430 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that two different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions).

Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Each variation of the questionnaire is identified by the index variable, a0.

The last complete fiscal year is January to December 2015. For questions pertaining to monetary amounts, the unit is the Nicaraguan Córdoba (NIO).

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.233. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.393.

Release Date: 2020-12-15.Release Schedule:.The data in this file come from the 2017 Economic Census data files released in December 2020. For more information about economic census planned data products releases, see Economic Census: About: 2017 Release Schedule...Key Table Information:.Includes only establishments of firms with payroll. .Data may be subject to employment-and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records:.Kind-of-business construction code.Sales, value of shipments, or revenue ($1,000)..Geography Coverage:.The data are shown for employer establishments and firms for the U.S., States, and Region levels at the U.S. that vary by industry. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies. ..Industry Coverage:.The data are shown at the 2- through 6-digits NAICS code levels. For information about NAICS, see Economic Census: Technical Documentation: Code Lists. ..Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector23/EC1723KOB.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census...Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...To comply with the changes in tabulation methodology, all detail estimates are not displayed in the tabulation. Therefore, all details displayed may not sum to totals displayed within tabulations...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

Abstract

SDES in Kabul was launched in June 2013, jointly by the Central Statistics Organization (CSO) and the United Nations Population Fund (UNFPA) where the latter provided the technical assistance to the entire survey operations. SDES data serve as the benchmark for demographic information at the district level and to some extent, group of villages/enumeration areas. It is the only survey that addresses the need of local development planners for information at the lower level of disaggregation. There are other surveys that CSO has conducted but these are available only at the national and provincial levels.

To achieve a responsive and appropriate policymaking, statistics plays a vital role. In Afghanistan, there has been a longstanding lack of reliable information at the provincial and district levels which hinders the policy making bodies and development planners to come up with comprehensive plans on how to improve the lives of Afghans. With SDES data, though it is not complete yet for the whole country, most of the important indicators in monitoring the progress towards the achievement of Afghanistan's Millennium Development Goals (MDGs) are being collected.

The main objectives of the survey were: · Gathering data for evidence based decision making, policy, planning and management · Providing data for business and industries · Providing policy and planning for residence housing · Providing data about vulnerable populations · Providing data for the basis of humanitarian assistance · Availability of data for research and analysis

Geographic coverage

Kabul Province Kabul Districts Kabul Villages

Analysis unit

Individuals, households

Universe

The survey covered all de jure household members (usual residents)

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey consisted of two related activities: a) the extensive listing and mapping of houses, establishments and institutions (conducted before the household survey) and b) the household survey.

The listing and mapping covered all houses, businesses and institutions in every village and urban area in Kabul Province and included the preparation of sketch maps on which the physical location of each building structure was marked during the canvassing. The locations of important public services, establishments and institutions such as schools, hospitals, banks, etc., were pinpointed using global positioning system (GPS) devices at a later date.

The surveyors used the mapping outputs to guide them in conducting the survey and ensure complete coverage. In total, 16 nahias, and around 843 villages in 14 districts in Kabul Province were canvassed, divided into 3,068 enumeration areas.

The survey first involved a listing of every household in each village. Half of these listed households (i.e. every other household) were taken as samples and asked questions on education, literacy, employment, migration, functional difficulty, fertility, mortality, parents’ living status, birth registration and household and housing characteristics.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect the survey data. - Listing sheet for village/enumeration area - Household questionnaire - Summary sheets for village/enumeration area

Cleaning operations

Central Statistics Organization (CSO) and UNFPA technical staff were responsible for editing the questionnaires, spot-checking, re-interviewing and recording observations during household interviews in all 16 nahias and 14 districts. This helped to ensure errors were corrected at an early stage of enumeration.

Data encoding and cleaning were also done in Karte-char where 178 encoders were hired and four CSO supervisors were detailed to oversee the whole data processing stage.

Our Location Intelligence Data connects people's movements to over 14M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world.

Location Intelligence Data Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated based on Factori’s Mobility & People Graph data aggregated from multiple data sources globally. To achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 40 data records possible for one POI based on the combination of these attributes.

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

Use Case:

Consumer Insights Gain a complete 360-degree view of the customer to detect behavioral changes, assess patterns, and forecast business effects.

Data Enrichment Leverage O2O consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment.

Sales Forecasting Analyze consumer behavior to predict sales and monitor performance of investments

Retail Analytics Analyze footfall trends in various locations and gain understanding of customer personas.

Geofencing: Geofencing involves creating virtual boundaries around physical locations, enabling businesses to trigger actions when users enter or exit these areas

Geo-Targeted Advertising: Utilizing location-based insights, businesses can deliver highly personalized advertisements to consumers based on their proximity to relevant POIs.

Marketing Campaign Strategy: Analyzing visitor demographics and behavior patterns around POIs, businesses can tailor their marketing strategies to effectively reach their target audience.

Site Selection: By assessing the proximity to relevant POIs such as competitors, customer demographics, and economic indicators, organizations can make informed decisions about opening new locations.

OOH/DOOH Campaign Planning: Identify high-traffic locations and understand consumer behavior in specific areas, to execute targeted advertising strategies effectively.

Data Attributes Included: Anonymous id poi_id name description category category_id full_address address city state zip country_code phone url domain rating price_level rating_distribution is_claimed photo_url attributes brand_name brand_id status total_photos popular_times places_topics people_also_search work_hours local_business_links contact_info reviews count naics_code naics_code_description sic_code sic_code_description shape_type shape_polugon geometry_location_type geometry_viewport_northeast_lat geometry_viewport_northeast_lng geometry_viewport_southwest_lat geometry_viewport_southwest_lng geometry_location_lat geometry_location_lng calculated_geo_hash_8 building_id building_name building_type id_type gender age carrier make model os os_version home_country home_geohash work_geohash affluence brands_visited places_categories geo_behaviour interests device_age device_price travelled_countries

Abstract

The Enterprise Survey 2008 was conducted by GSO and its sub-institutions to collect information on enterprises operating in Viet Nam at the end of the year 2007. It is the eighth year of the annual enterprise surveys. All business entities existing at the end of the year were surveyed. Objectives of the survey: - To collect information of enterprises' productive factors (labor, capital, assets,…) and business results in 2007, to assess the situation and capacity of enterprises in different industries and economic sectors. - To collect necessary information for aggregating officially reported indicators in the year 2007 for specialities (number of enterprises, numbers of employees, capital, assets, business result indicators, indicators of the national account such as production value, intermediate cost, value added...) and calculating statistical weights for 2007 as the based year. - To update the enterprise database to meet requirements of statistics of enterprises and other statistics.

Survey Implementation The survey were organized and steered by GSO and principally conducted by Provincial Statistical Offices. Data were gathered by two methods, direct and indirect ones. Details of the two methods and their applicable objects are:

Direct data gathering: enumerators interview respondents directly, ask for data, explanations of circumstance. Based on that, the enumerators fill out the questionnaire. This method is applied for the business environment questionnaire and survey units which have not fully followed accounting standards, unable to self-fill out the questionnaire (small-size enterprises, enterprises under preparation for dissolving, enterprises under investigation,…)

Indirect data gathering: Organization of meeting of chief accountants, accountants or statistical staffs of survey units, or enumerators instruct directly how to fill out the questionnaires as well as information of where to send, how to send, time for sending, so that the survey units fill out the questionnaire on their own and send filled questionnaires to the survey organizer.

Geographic coverage

National

Analysis unit

Enterprise

Universe

They are enterprises independently keeping business account, they were established and under regulations of the State Enterprise Law, Cooperative Law, Enterprise Law, Foreign Investment Law, began business operation before 1st January 2008 and currently exist. They may include seasonal operation enterprises which did not operate on all 12 months of year 2006, enterprises which suspend operation for renovation investment, repairing, construction, production extension, enterprises which stop operation for merging or dissolving but still have the managerial system for answering questions in the questionnaire (exclude enterprises that do not have the managerial system for answering the questions in the questionnaire).

Kind of data

Sample survey data [ssd]

Sampling procedure

The Enterprise Survey 2008 is conducted according to census and sample survey methods. There are three cases that the sample survey are applied. The first case is for surveying non-state enterprises having less than 10 labors, the second case is for surveying of production and business cost, the third case is for business environment survey. Sample selection of these cases are described as follows:

Sample Selection of non-state enterprises having less than 10 employees for application of the Questionnaire No. 1A-DTDN (general questionnaire) (1) The Sampling Frame: The sampling frame is established based on the list of non-state enterprises having less than 10 employees from the Enterprise Survey in the year 2005 (except the enterprises in hotel industry which are all selected). The sampling frame is stratified according to the 2-digit-level industrial classification; in each 2-digit-level industry, enterprises are listed with descending order based on net revenues of production and business in the year 2007. Commercial industry or service industries are stratified into 4-digit-level industries or groups of 4-digit-level industries. (2) Sample selection: The number of non-state enterprises selected for applying the Questionnaire No 1A-DTDN is 15% of enterprises which have less than 10 employees in the list of enterprises from which were collected filled questionnaires in the year 2007. The principle for sample selection is to have reprentativeness of each 2-digit-level industry (for commercial or service industries), representativeness is at 4-digit-level industries or groups of industries in provincies. The selection method is systematic sampling with fixed intervals after a ramdom start. Because the numbers of enterprises having less than 10 employees in provinces, cities are significantly differential, a number of provinces do not have numerous enterprises, no choice of enterprises having less than 10 employees for applying Questionnaire No. 1A-DTDN is only done in 15 provinces/cities: they are Lai Chau, Ha Giang, Dien Bien, Bac Kan, Son La, Cao Bang, Tuyen Quang, Lao Cai, Yen Bai, Ha Nam, Hoa Binh, Ninh Thuan, Kon Tum, Dak Nong, Hau Giang.

Sample selection of enterprises which are surveyed for production, business cost (Questionnaire No. 2A-DTDN) (1) Establishing the Sampling Frame The sampling frame is established based on the list of enterprises from which were collected filled questionnaires in the 2007 Enterprise Survey. (2) Sample Selection The sample is representative for 8 regions and 2-digit level economic industries in each region with sample size of about 10000 enterprises (the sample ratio is approximately 10%). Sample allocation to each region or each 2-digit-level economic industry is based on share of production value of each region to that of the whole country and share of each 2-digit-level economic industry in production value of the region. In each 2-digit-level economic industry, enterprises are listed with descending order based on net revenues of production and business in the year 2004. After having number of sample in each region and each 2-digit-level economic industry in the region, sample selection is done for each 2-digit- level industry in regions by systematic sample with fixed interval of K (K= total number of the 2-digit-level industry divided by the allocated sample to the industry) after a random start.

Sample selection of enterprises conducted business environment survey (Questionnaire No. 4- DTDN) The sample for business environment survey is selected to be respresentative for 1-digit-level industries in each province/city with the sample about 10000 enterprises (Approximate 10%).

Sampling deviation

Procedure for selecting enterprises when sampled enterprises When sample enterprises are missing, Statistical Office of Provinces/ Cities have rights to complete the sample with following priority order: - Enterprises in the province/city and the same 4-digit-level industry having the closest net revenue to the missing enterprises in the sample. - Enterprises in the province/city and the same 2-digit-level industry having the closest net revenue to the missing enterprises in the sample. There was no reports of missing cases, however, with the above procedure, it is expected that there is no major deviations from sample design

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three general types of questionnaires: 1) Questionnaires for basic indicators about production, business of enterprises; 2) Questionnaire about energy; 3) Questionnaire on business environment. In the first and the second general types of questionnaire, there were several specific questionnnaires for specific enterprises. In the current database, only data collected from the first general type of questionnaire is available.

(1) Questionnaires for basic indicators about production, business of enterprises a. Questionnaire No. 1A-DTDN: this questionnaire is to collect information of enterprises in the year 2007 (Applicable to all state enterprises, non-state enterprises having more than 10 employees, 15% of non-state enterprises having less than 10 employees which are selected to be surveyed, all foregin invested enterprises of all industries in the national economy). This questionnaire was filled in for each enterprise as survey object to collect information on screening, labor including labor compensation, activities, production... b. Questionnaire No. 1B-DTDN: this questionnaire is to collect information of enterprises in the year 2007. (Applicable to non-state enteprises having less than 10 employees of all industries in the national economy, but not being selected to be surveyed with Questionnaire No. 1A-DTDN). This is short version of question No.1A-DTDN.
c. Questionnaire No. 1C-DTDN: Production, Selling and Inventory of some industrial products in year 2007. (Applicable to all enterprises having industrial activity as the main activity). This questionnaire is to gather information on production, selling and inventory if industrial products.
d. Questionnaire No. 2B-DTDN: Results of financial intermediate and financial assistance activities in year 2007. (Applicable to all enterprises which are credit institution: Banks, financial comparies, people's credit funds... ). Information on detail revenue and cost items and business result of financial intermediate and financial assistance activities were collected with this questionnaire.
e. Questionnaire No 2C- DTDN: Results of insurance activity and insurance brokerage in year 2007. Information on detail revenue and cost items and business result of insurance activity and insurance brokerage

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Nicaragua in 2003, 2006, 2010 and 2016, as part of Latin America and the Caribbean Enterprise Surveys rollout, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Nicaragua ES 2010 was conducted in August 2010- May 2011, Ecuador ES 2016 was carried out in October 2016 - June 2017. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 1,599 establishments was analyzed: 211 businesses were from 2003 only, 153 firms were from 2006 only, 119 - from 2010 only, 213 - from 2016 only, 146 firms were from 2010 and 2016, 110 - from 2006 and 2010, 72 firms were from 2003, 2006, 2010 and 2016.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15- 37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

For the Nicaragua ES 2016, size stratification was defined as follows: small (4 to 20 employees), medium (21 to 50 employees), and large (51 or more employees). These categories differ from the global ES size definitions - small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

The sample frame consisted of listings of firms from two sources: For panel firms the list of 336 firms from the Nicaragua 2010 ES was used, and for fresh firms (i.e., firms not covered in 2010) the sample frame was comprised of a list randomly drawn from the Economic Census, provided by the Banco Central de Nicaragua. Standardized size categories provided by the Census were used.

In 2010, regional stratification was defined in two locations (city and the surrounding business area): Managua and the Rest of the Country.

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that two different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions).

Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

Release Date: 2021-04-15.Release Schedule:.The data in this file come from the 2017 Economic Census. For information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments of firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records:.Number of establishments.Sales, value of shipments, or revenue ($1,000).Distribution of sales, value of shipments, or revenue (%) .Response coverage of class of customer inquiry (%)..Each record includes a code which represents a specific class of customer...For Wholesale Trade (42), data are published by Type of Operation (All establishments, Merchant Wholesalers, and Manufacturers' Sales Branches and Offices)...For Professional, Scientific, and Technical Services (54), and Other Services (except Public Administration) (81), data are published by Tax Status (Establishments subject to federal income tax) only...Geography Coverage:.The data are shown for employer establishments of firms at the U.S. level for the Utilities (22), Wholesale Trade (42), Retail Trade (44-45), Information (51), and Real Estate and Rental and Leasing (53) sectors. Data are shown for employer establishments of firms at the U.S. and state level for the Professional, Scientific, and Technical Services (54), Administrative and Support and Waste Management and Remediation Services (56), Accommodation and Food Services (72), and Other Services (except Public Administration) (81) sectors. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown for select 4-digit 2017 NAICS codes for Utilities (22); 2- through 7-digit, and selected 8-digit 2017 NAICS code levels for Wholesale Trade (42); 2- through 6-digit 2017 NAICS code levels for Retail Trade (44-45) and Accommodation and Food Services (72); selected 3- through 6-digit 2017 NAICS code levels within subsector 517 for Information (51); selected 4- through 6-digit 2017 NAICS codes levels for Real Estate and Rental and Leasing (53) and Professional, Scientific, and Technical Services (54); and selected 3- through 6-digit 2017 NAICS code levels for Administrative and Support and Waste Management and Remediation Services (56) and Other Services (except Public Administration) (81). For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector00/EC1700CLCUST.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Conta...

Email Capture Tool Market Outlook

The global email capture tool market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 3.5 billion by 2032, growing at a CAGR of 12.5%. One of the primary growth factors driving this market is the increasing need for businesses to enhance their customer engagement and retention strategies amidst rising competition.

Growth in the email capture tool market is primarily driven by the ever-increasing emphasis on data-driven marketing. Businesses today are more focused on collecting, analyzing, and utilizing customer data to create personalized marketing campaigns. Effective email capture tools facilitate the collection of valuable customer information, which can then be analyzed to tailor marketing efforts. This has led to a surge in the adoption of these tools, especially among businesses aiming to improve their customer engagement and retention rates.

Additionally, the rise of e-commerce and digital platforms has created a significant demand for efficient lead generation tools. Email capture tools serve as a critical component in lead generation strategies, allowing businesses to amass potential customer data effectively. This data can be used to nurture leads through targeted marketing campaigns, ultimately driving conversions and sales. The increasing investment in digital marketing technologies by businesses across various sectors further propels the market growth.

The integration of advanced technologies such as AI and machine learning into email capture tools is another essential growth factor. These technologies enable more sophisticated data analysis and segmentation, allowing businesses to create even more targeted and effective marketing campaigns. For instance, AI-powered email capture tools can predict customer behavior and preferences, providing businesses with actionable insights to enhance their marketing strategies. This technological advancement is expected to drive the market exponentially in the coming years.

The Email Enrichment Tool is becoming an essential component for businesses looking to maximize the value of their email capture efforts. By integrating these tools with email capture solutions, companies can enrich the data they collect, providing deeper insights into customer demographics, preferences, and behaviors. This enriched data allows for more personalized and effective marketing campaigns, enhancing customer engagement and retention. As businesses strive to stand out in a competitive market, the ability to offer tailored experiences through enriched email data becomes a significant advantage. Consequently, the adoption of Email Enrichment Tools is expected to rise, further driving the growth of the email capture tool market.

Regionally, North America holds a significant share of the email capture tool market, attributed to the high adoption of digital marketing practices and advanced technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is fueled by the rapid digital transformation occurring in developing economies such as India and China, the proliferation of internet users, and the rising number of small and medium enterprises (SMEs) adopting digital marketing tools to enhance their business operations.

Component Analysis

The email capture tool market is segmented by component into software and services. The software segment dominates the market, accounting for a significant share due to the extensive adoption of various email capture software solutions by businesses. These software solutions offer a wide range of features including form builders, pop-ups, embedded forms, and integrations with other marketing tools, providing businesses with the flexibility to capture emails through multiple channels effectively.

Furthermore, the services segment, which includes consulting, implementation, and support services, is also experiencing substantial growth. Businesses often require professional assistance to implement and optimize their email capture strategies effectively. Service providers offer expertise in customizing and integrating email capture tools with existing systems, ensuring seamless operation and maximizing the return on investment for businesses. The rise in demand for these services is particularly notable among SMEs, which may lack the in-house expertise required to deploy these tools independently.

This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.

This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.

The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.

For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: All businesses identified as victims in CPD data have been removed from this dataset.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”

Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

Our proprietary People Data is a mobile user dataset that connects anonymous IDs to a wide range of attributes, including demographics, device ownership, audience segments, key locations, and more. This rich dataset allows our partner brands to gain a comprehensive view of consumers based on their personas, enabling them to derive actionable insights swiftly.

People Data Graph

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