The tables presents indices (2005=100) and changes on twelve months previously (%) of production, turnover and orders in industry (excluding construction), by sector of industry.

Data available: January 2000 till December 2012

Table has been discontinued as from 22 March 2013 due to change of the base year from 2005 to 2010. Statistics Netherlands has started a new table, Industry; production, sales and orders, changes and index (2010 = 100). For more information see sections 3 and 4.

Status of the figures: Production: three most recent months: Provisional. The figures within a reporting year are revised provisional figures until publication in December of the year concerned. Turnover: three most recent months: Provisional. Orders: three most recent months: Provisional.

Changes as of 8 July 2011. Due to new regulations (European System for National Accounts, 2010, Balance of Payments Manual 6) for National Accounts and Balance of Payment, the turnover definition has been adapted. These results in adjustments in production index and other short term statistics. The adaptation of the turnover definition is related to a change in registration of enterprises that (partially) contract out of their production abroad. The adjustment means that goods deal with foreign subsidiaries of Dutch parent companies do count for Dutch production. Goods dealt with in the Netherlands by Dutch subsidiaries of foreign parent companies that remain property of these parent companies do no longer count as Dutch production. However, they count as export of services for the sum that has been added to value in the Netherlands. Until December 2009, index figures for manufacturing turnover are based on the previous turnover definition. From January 2010 onwards, the turnover figures are based on the new turnover definition. Therefore, turnover changes 2010 on 2009 are not accurate.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

This table contains information on the balance sheet of the general government sector. The information is limited to financial assets and liabilities. For each reporting period the opening and closing stocks, financial transactions and other changes are shown. Transactions are economic flows that are the result of agreements between units. Other changes are changes in the value of assets or liabilities that do not result from transactions such as revaluations or reclassifications. The figures are consolidated which means that flows between units that belong to the same sector are eliminated. As a result, assets and liabilities of subsectors do not add up to total assets or liabilities of general government. For example, loans of the State provided to social security funds are part of loans of the State. However, these are not included in the consolidated assets of general government, because it is an asset of a government unit with a government unit as debtor. Financial assets and liabilities in this table are presented at market value. The terms and definitions used are in accordance with the framework of the Dutch national accounts. National accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date.

Data available from: Yearly figures from 1995, quarterly figures from 1999.

Status of the figures: The figures for the period 1995-2022 are final. The figures for 2023 and 2024 are provisional.

Changes as of 24 December 2024: Figures on the third quarter of 2024 are available. The figures for the second quarter of 2024 have been adjusted.

When will new figures be published? Provisional quarterly figures are published three months after the end of the quarter. In September the figures on the first quarter may be revised, in December the figures on the second quarter may be revised and in March the first three quarters may be revised. Yearly figures are published for the first time three months after the end of the year concerned. Yearly figures are revised two times: 6 and 18 months after the end of the year. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the most actual figures. Revised yearly figures are published in June each year. Quarterly figures are aligned to the three revised years at the end of June. More information on the revision policy of Dutch national accounts and government finance statistics can be found under 'relevant articles' under paragraph 3.

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

What information do you include in your sales and marketing database?

When you buy one of our sales and marketing databases, all records we supply to you hold the below information (where it is available):

-Company name -Address (including town, county and postcode) -Phone number (including area code) -Website address -Company registration number and date of incorporation -Financial information (for example, net worth, turnover band, profit and loss, profit percentage change, sales increase or decrease) -Senior decision maker name -Senior decision maker email address (if we do not have this information, we will provide the next best email for example, a departmental email address) -Top-level market sector and granular industry classification -Standard Industrial Classification (SIC) code -Number of employees (both onsite and nationally) -Premise type and description -Number of branches -Legal status (for example, sole trader, partnership, private limited company)

Why use Metric Central?

There are several marketing database providers out there, but these are the reasons why you should choose us for your prospecting requirements.

The most up to date information on the market: We refresh our database every day, meaning when you buy from us, you will have the most up to date and accurate details, without having to worry about database decay, duplicated records

Reach out directly to the key decision-maker: Unlike some lists which will only give you a generic company email address, we will provide you with the name and details of the company’s key decision-maker, meaning you can directly reach out to the person with purchasing power, without being held back by the company gatekeeper

Buy online and use straightaway: With a lot of companies providing a similar product to ours, you have to reach out to them for a quote before you receive your product. Our sales and marketing database can be bought online and downloaded straight away, meaning you can start reaching out to decision-makers the very same day you buy. We even tell you how many companies your data will contain before you commit to purchasing!

A wide range of contacts: We have contact details for prospective customers across a wide range of different industries meaning no matter what your product or service is, we have your needs covered. Check out our website for a full list of databases we can provide CRM ready data: Our list can be uploaded straight into your CRM system of choice, meaning your sales team can start reaching out to prospective customers immediately.

Competitive pricing: We’re one of the most cost-effective companies on the market, combining a high-quality product with an accessible price point. Decades of experience: With several years of experience in the sales agency, our team at Metric Central is well-equipped to understand your pain points and provide you with the data you need to help you sell your product or service

If you would like to know more about the data services we offer and how we can help you grow your business, please don’t hesitate to get in touch with us today, and one of our experienced team reach out to you with the information you need.

Please note: All data is supplied under legitimate interest. The term of supply is as standard of a 12-month multi licence agreement which allows unlimited postal and telephone communication with 12 cold email sends.

Weather significantly impacts sales and eCommerce, influencing consumer behavior and purchasing patterns. By analyzing weather forecast data alongside sales data, we have identified trends so that businesses can make strategic decisions to optimize their operations.

This data includes the forecast of weather-based demand for up to 10 days on daily level for a given ZIP code. In comparison to the full data set, this data sample provides information for one ZIP code.

The data can be found here: "PUBLIC"."FORECAST_GFK_VIEW_EXAMPLE”

The dataset has the following fields:

• date_forecast: Date on which the forecast was created
• country: country of the zip_code.
• zip_code: Zip code for which the index was calculated for a pollen type
• date: date for which the index was calculated
• model: industry / product type for which the indices were calculated
• value: actual impact of the forecasted weather on the given date and zip code. value of 1.15 means the demand is 15 % higher due to the weather than normally.
• class: the class to which level the weather actually influences the demand more generally

The definition of the class is: 1: weather reduces the demand on 10 % of the days 2: weather reduces the demand on 20 % of the days 3: weather has no influence on the demand on 40 % of the days 4: weather increased the demand on 30 % of the days

We offer the following models in this dataset:

• Car-Tyres
• DIY-Activity
• Fashion-Stationary
• Fashion-Ecommerce
• Fashion-Swimwear
• Fashion-Athletic Apparel
• Fashion-Sneakers
• Fashion-Outerwear
• Fashion-Umbrella
• FMCG-Beverages: Beer
• FMCG-Beverages: Coke
• FMCG-Beverages: Coffee
• FMCG-Beverages: Juice
• FMCG-Beverages: Tea
• FMCG-Beverages: Water
• FMCG-Beverages: Wine
• FMCG-Food: Bakery Goods
• FMCG-Food: BBQ
• FMCG-Food: Ice Cream
• FMCG-Food: Snacks
• FMCG-Food: Chocolate Goods
• FMCG-Food: Frozen Goods
• FMCG-Personal Care: Deodorant
• FMCG-Personal Care: Dry Skin
• FMCG-Personal Care: Insect Skin Protection
• FMCG-Personal Care: Body Care
• FMCG-Personal Care: Sun Protection
• FMCG-Pharma: Cold Medicines
• Garden Outdoor: Garden Furniture
• Garden Outdoor: Garden Tools
• Garden Outdoor: Grills Accessories
• Garden Outdoor: Outdoor Plants

Our Market Analysis dataset uncovers consumer movement patterns across brands and categories, helping you define your true trade area and optimize location strategy.

Using foot traffic data tied to specific POIs, this GDPR-compliant, non-PII dataset highlights where your visitors also shop — enabling smarter site selection, lease renegotiation, and competitive market analysis.

Key data points include: - Cross-visitation trends by brand/category - Consumer reach and trade area definition - Weekly, monthly, and quarterly aggregations - Cleaned, normalized, and updated data - Non-PII and fully GDPR-compliant

Focused on the U.S. market, this dataset is ideal for retailers, landlords, and consultants looking to map behavior, refine market coverage, and drive informed decisions.

The Market Saturation and Utilization Core-Based Statistical Areas (CBSA) dataset provides monitoring of market saturation as a means to help prevent potential fraud, waste, and abuse (FWA). CBSAs are geographical delineations that are Census Bureau-defined urban clusters of at least 10,000 people. Market saturation, in the present context, refers to the density of providers of a particular service within a defined geographic area relative to the number of beneficiaries receiving that service in the area. The data can be used to reveal the degree to which use of a service is related to the number of providers servicing a geographic region. There are also a number of secondary research uses for these data, but one objective of making these data public is to assist health care providers in making informed decisions about their service locations and the beneficiary population they serve. The interactive dataset can be filtered and analyzed on the site or downloaded in Excel format.

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates) - Continental Europe - USA

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

This file contains data dictionaries for the following datasets within LMIP (http://lmip.gov.au/): Summary Data Employment by Industry Employment by Industry Time Series Employment Projections by …Show full descriptionThis file contains data dictionaries for the following datasets within LMIP (http://lmip.gov.au/): Summary Data Employment by Industry Employment by Industry Time Series Employment Projections by Industry Employment by occupation Unemployment Rate, Participation Rate & Employment Rate Time Series for States/Territories Unemployment Duration Population by Age Group Population by Age Group Time Series Population by Labour Force Status

The Measurable AI Temu & Fast Fashion E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan, Thailand, Malaysia, Vietnam, Indonesia, Singapore, Hong Kong, Phillippines) - EMEA (Spain, United Arab Emirates, Saudi, Qatar) - Latin America (Brazil, Mexico, Columbia, Argentina)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more - Email ID (can work out user overlap with peers and loyalty)

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Key Table Information.Table Title.Manufacturing: E-Commerce Statistics for the U.S.: 2022.Table ID.ECNECOMM2022.EC2231ECOMM.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Manufacturing: E-Commerce Statistics for the U.S.: 2022.Release Date.2025-01-23.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Sales, value of shipments, or revenue ($1,000)E-Shipments value ($1,000) E-Shipments as percent of total sales, value of shipments, or revenue (%) Range indicating imputed percentage of total sales, value of shipments, or revenueDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. level only. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 3-digit 2022 NAICS code levels for the U.S. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector31/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - 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 applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of symbols, see Economic Census Data Dictionary..Data-Specific Notes.Data users who create their own es...

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Residential market value estimates and most recent sales values for owned properties at a parcel level within Fairfax County as of the VALID_TO date in the attribute table.

For methodology and a data dictionary please view the IPLS data dictionary

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

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 take 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

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 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 purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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.

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 as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 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. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. 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 relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. 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 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. 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 share of rejections per contact was 75.2%.

Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2023. It show business establishments with their business address, industry (ANZSIC4) classification, location and CLUE block and small area allocation.

A business establishment is defined as a • Commercial occupant in a building • Separate land use • Any permanent presence of economic activity in accordance with standard Industry classification (ANZSIC).

Hence, if one organisation has its presence in several buildings in the CLUE area, each time it will be counted as a separate establishment. Consequently, the count of establishments presented in CLUE represents the number of locations, rather than 'enterprises'.

For more information about CLUE see http://www.melbourne.vic.gov.au/clue

For more information about the ANZSIC industry classification system see http://www.abs.gov.au/ausstats/abs@.nsf/mf/1292.0

The Measurable AI FoodPanda Food & Grocery Transaction dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Hong Kong, Taiwan, Singapore, Thailand, Malaysia, Philippines, Pakistan)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the FoodPanda food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Release Date: 2016-02-23.[NOTE: Includes firms with paid employees and firms with no paid employees. Data are based on the 2012 Economic Census, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2012 Survey of Business Owners. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status or that were publicly held or not classifiable by gender, ethnicity, race, and veteran status. Percentages are for respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for All U.S. Firms That Were Family-Owned by Industry, Gender, Ethnicity, Race, and Veteran Status for the U.S.: 2012. ..Release Schedule. . The data in this file was released in February 2016.. ..Key Table Information. . This data is related to all other 2012 SBO files.. Refer to the Methodology section of the Survey of Business Owners website for additional information.. ..Universe. . The universe for the 2012 Survey of Business Owners (SBO) includes all U.S. firms operating during 2012 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown at the U.S. level only.. ..Industry Coverage. . The data are shown for the total of all sectors (NAICS 00) and at the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for All U.S. Firms That Were Family-Owned by Industry, Gender, Ethnicity, Race, and Veteran Status for the U.S.: 2012 contains data on:. . Number of firms, firms with paid employees, and firms with no paid employees. Sales and receipts for all firms, firms with paid employees, and firms with no paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of all respondent firms, respondent firms with paid employees, and respondent firms with no paid employees. Percent of sales and receipts of all respondent firms, respondent firms with paid employees, and respondent firms with no paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are published by whether the business was family-owned in 2012 and by gender, ethnicity, race, and veteran status.. ..Sort Order. . Data are presented in ascending levels by:. . NAICS code (NAICS2012). Gender, ethnicity, race, and veteran status (CBGROUP). Whether the business was family-owned in 2012 (FAMOWN). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SB1200CSCB04 table at: https://www2.census.gov/programs-surveys/sbo/data/2012/SB1200CSCB04.zip. ..Contact Information. . To contact the Survey of Business Owners staff:. . Visit the website at www.census.gov/programs-surveys/sbo.html.. Email general, nonsecure, and unencrypted messages to ewd.survey.of.business.owners@census.gov.. Call 301.763.3316 between 7 a.m. and 5 p.m. (EST), Monday through Friday.. Write to:. U.S. Census Bureau. Survey of Business Owners. 4600 Silver Hill Road. Washington, DC 20233. . . ...Source: U.S. Census Bureau, 2012 Survey of Business Owners.Note: The data in this file are based on the 2012 Economic Census, Survey of Business Owners (SBO). To maintain confidentiality...

Release Date: 2016-02-23.[NOTE: Includes firms with payroll at any time during 2012. Employment reflects the number of paid employees during the March 12 pay period. Data are based on the 2012 Economic Census, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2012 Survey of Business Owners. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status or that were publicly held or not classifiable by gender, ethnicity, race, and veteran status. Percentages are for respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or employment size group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for All U.S. Firms With Paid Employees by Percent of Total Sales of Goods/Services Exported Outside the United States by Employment Size of Firm, Gender, Ethnicity, Race, and Veteran Status for the U.S.: 2012. ..Release Schedule. . The data in this file was released in February 2016.. ..Key Table Information. . This data is related to all other 2012 SBO files.. Refer to the Methodology section of the Survey of Business Owners website for additional information.. ..Universe. . The universe for the 2012 Survey of Business Owners (SBO) includes all U.S. firms operating during 2012 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown at the U.S. level only.. ..Industry Coverage. . The data are shown for the total of all sectors (NAICS 00).. ..Data Items and Other Identifying Records. . Statistics for All U.S. Firms With Paid Employees by Percent of Total Sales of Goods/Services Exported Outside the United States by Employment Size of Firm, Gender, Ethnicity, Race, and Veteran Status for the U.S.: 2012 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of respondent firms with paid employees. Percent of sales and receipts of respondent firms with paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are published by percent of total sales of goods and services that were exported outside the U.S. in 2012 and employment size of firm and by gender, ethnicity, race, and veteran status.. ..Sort Order. . Data are presented in ascending levels by:. . Gender, ethnicity, race, and veteran status (CBGROUP). Employment size of firm (EMPSZFI). Percent of total sales of goods and services that were exported outside the U.S. in 2012 (PEXPORT). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SB1200CSCB36 table at: https://www2.census.gov/programs-surveys/sbo/data/2012/SB1200CSCB36.zip. ..Contact Information. . To contact the Survey of Business Owners staff:. . Visit the website at www.census.gov/programs-surveys/sbo.html.. Email general, nonsecure, and unencrypted messages to ewd.survey.of.business.owners@census.gov.. Call 301.763.3316 between 7 a.m. and 5 p.m. (EST), Monday through Friday.. Write to:. U.S. Census Bureau. Survey of Business Owners. 4600 Silver Hill Road. Washington, DC 20233. . . ...Source: U.S. Census Bureau, 2012 Survey of Business Owners.Not...