What’s the right email frequency? What’s the potential increase in the number of conversions your email campaigns generate if you add an extra message to your schedule? The data in this table should help you find the right answers.

Abstract

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

Geographic coverage

National Coverage

Analysis unit

The main statistical unit to be used for the survey is the establishment. For simple businesses that undertake a single activity at a single location there is a one-to-one relationship between the establishment and the enterprise. For large and complex enterprises, however, it is desirable to separate each activity of an enterprise into establishments to provide the most detailed information possible for industrial analysis. The business register will need to be developed in such a way that records the links between establishments and their parent enterprises. The business register will be created from administrative records and may not have enough information to recognize all establishments of complex enterprises. Large businesses will be contacted prior to the survey post-out to determine if they have separate establishments. If so, the extended structure of the enterprise will be recorded on the business register and a questionnaire will be sent to the enterprise to be completed for each establishment.

SBS has decided to follow the New Zealand simplified version of its statistical units model for the 2009 BAS. Future surveys may consider location units and enterprise groups if they are found to be useful for statistical collections.

It should be noted that while establishment data may enable the derivation of detailed benchmark accounts, it may be necessary to aggregate up to enterprise level data for the benchmarks if the ongoing data used to extrapolate the benchmark forward (mainly VAGST) are only available at the enterprise level.

Universe

The BAS's covered all employing units, and excluded small non-employing units such as the market sellers. The surveys also excluded central government agencies engaged in public administration (ministries, public education and health, etc.). It only covers businesses that pay the VAGST. (Threshold SAT$75,000 and upwards).

Kind of data

Sample survey data [ssd]

Sampling procedure

-Total Sample Size was 1240 -Out of the 1240, 902 successfully completed the questionnaire. -The other remaining 338 either never responded or were omitted (some businesses were ommitted from the sample as they do not meet the requirement to be surveyed) -Selection was all employing units paying VAGST (Threshold SAT $75,000 upwards)

WILL CONFIRM LATER!!

OSO LE MEA E LE FAASA...AEA :-)

Mode of data collection

Mail Questionnaire [mail]

Research instrument

1. General instructions, authority for the survey, etc;
2. Business demography information on ownership, contact details, structure, etc.;
3. Employment;
4. Income;
5. Expenses;
6. Inventories;
7. Profit or loss and reconciliation to business accounts' profit and loss;
8. Fixed assets - purchases, disposals, book values
9. Thank you and signature of respondent.

Supplementary Pages Additional pages have been prepared to collect data for a limited range of industries. 1.Production data. To rebase and redevelop the Industrial Production Index (IPI), it is intended to collect volume of production information from a selection of large manufacturing businesses. The selection of businesses and products is critical to the usefulness of the IPI. The products must be homogeneous, and be of enough importance to the economy to justify collecting the data. Significance criteria should be established for the selection of products to include in the IPI, and the 2009 BAS provides an opportunity to collect benchmark data for a range of products known to be significant (based on information in the existing IPI, CPI weights, export data, etc.) as well as open questions for respondents to provide information on other significant products. 2.Tourism. There is a strong demand for estimates of tourism value added. To estimate tourism value added using the international standard Tourism Satellite Account methodology requires the use of an input-output table, which is beyond the capacity of SBS at present. However, some indicative estimates of the main parts of the economy influenced by tourism can be derived if the necessary data are collected. Tourism is a demand concept, based on defining tourists (the international standard includes both international and domestic tourists), what products are characteristically purchased by tourists, and which industries supply those products. Some questions targeted at those industries that have significant involvement with tourists (hotels, restaurants, transport and tour operators, vehicle hire, etc.), on how much of their income is sourced from tourism would provide valuable indicators of the size of the direct impact of tourism.

Cleaning operations

Partial imputation was done at the time of receipt of questionnaires, after follow-up procedures to obtain fully completed questionnaires have been followed. Imputation followed a process, i.e., apply ratios from responding units in the imputation cell to the partial data that was supplied. Procedures were established during the editing stage (a) to preserve the integrity of the questionnaires as supplied by respondents, and (b) to record all changes made to the questionnaires during editing. If SBS staff writes on the form, for example, this should only be done in red pen, to distinguish the alterations from the original information.

Additional edit checks were developed, including checking against external data at enterprise/establishment level. External data to be checked against include VAGST and SNPF for turnover and purchases, and salaries and wages and employment data respectively. Editing and imputation processes were undertaken by FSD using Excel.

Sampling error estimates

NOT APPLICABLE!!

Here, we’ve gathered email marketing benchmarks by industry. You can see how your average email open, click-through, click-to-open, unsubscribe, and spam complaint rates compare against other companies in your industry.

Abstract

The purpose of the Household Income and Expenditure Survey (HIES) survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in Kiribati. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people. Some more specific outputs from the survey are listed below:

a) To obtain expenditure weights and other useful data for the revision of the consumer price index; b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts; c) To supply basic data needed for policy making in connection with social and economic planning; d) To provide data for assessing the impact on household living conditions of existing or proposed economic and social measures, particularly changes in the structure of household expenditures and in household consumption; e) To gather information on poverty lines and incidence of poverty throughout Kiribati.

In addition, newly developed modules were incorporated in the 2019 HIES including: -Person Details; -Anaemia & Diabetic Test; -Food Recall; -Food Away From Home; -Partaker; -Non-Food Recall; -Household Details; -Dietary Recall; -Disability, Healthy Living & Time-Use; -Deprivation And Financial Inclusion; -Migrant Worker; -Geographic Information + Photo; -Market Survey; -Village Resource Survey (Vrs).

Geographic coverage

National coverage

Analysis unit

Households

Universe

The survey covered all persons resident in private households.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE SIZE:

In determining an appropriate sample size for a survey of this nature, numerous factors come into the equation. These include:

a) The degree of accuracy required for key estimates; b) The population size of the country; c) The manner in which the sample is selected; d) Cost or staffing constraints which may exist; e) Whether or not estimates are required for sub-populations; f) The level of variability in the data being collected.

Each of these factors have different magnitudes of importance, but the major priority should always be on selecting a sample big enough to produce results of suitable accuracy. Many of these issues are generally known as well - for instance:

· A user group may pre-specify what level of accuracy they may wish to achieve for the survey · The population of a country can normally be estimated to a reasonable level of accuracy · The sample selection technique adopted is known · Cost and staff constraints are generally known, and · A user group can once again provide information on whether estimates for sub-populations are required.

The Kiribati 2019 Household Income and Expenditure Survey (HIES) aims to release outputs at the island division level and National level. The targeted sample size has been determined around 2,000 households based on the results of the previous 2006 HIES that provided the following Relative Sampling Error (RSE) at the strata level:

-Sth. Tarawa: 230 with an average cluster size of 10,5 and a Relative Sampling Error (RSE) of: 6,3%; -Northern: 245 with an average cluster size of 11,1 and a Relative Sampling Error (RSE) of: 5,7%; -Central: 217 with an average cluster size of 12,1 and a Relative Sampling Error (RSE) of: 6,7%; -Southern: 244 with an average cluster size of 11,1 and a Relative Sampling Error (RSE) of: 19,0%; -Line Is. & Phoenix: 225 with an average cluster size of 13,2 and a Relative Sampling Error (RSE) of: 14,7%; -TOTAL: 1,161 with an average cluster size of 11,6 and a Relative Sampling Error (RSE) of: 5,4%.

The 2006 Kiribati HIES was based on stratified cluster sampling strategy. The selection of households was based on a three stages selection: island, EA and households. This is the reason why the RSE are above 5% in all domains. In order to improve the quality of the 2019 HIES results, decisions were made to increase the total sample size (making sur we will not over pass the allocated budget) and to use a stratified cluster sampling strategy based on a 2 stage selection (selection of EA and households). The optimal allocation of 1,800 households was used as a first step to the sample allocation, and through several adjustments, the total sample size is 2,180 households with a cluster size of 12 households.

-Sth. Tarawa: 600 with an average cluster size of 12, a number of EAS of 50 and RSE is: 3,8%; -Northern: 400 with an average cluster size of 12, a number of EAS of 33 and RSE is: 4,3%; -Central: 300 with an average cluster size of 12, a number of EAS of 25 and RSE is: 5,2%; -Southern: 480 with an average cluster size of 12, a number of EAS of 40 and RSE is: 13,1%; -Line Is. & Phoenix: 400 with an average cluster size of 12, a number of EAS of 33 and RSE is: 9,3%; -Total: 2,180 with a number of EAS of 182 and RSE is: 2,9%.

SAMPLE SELECTION: The random selection of PSU (EAs) was based on a probability proportional to size selection within each domain. Within each selected EA, a total of 18 households are selected in order to have a replacement list of 6 households (list B) and 12 to contact in priority (list A).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires were published in English. There are 16 sections in the Household Income and Expenditure Survey questionnaire which relate to the following:

-1. Household ID -2. Household member roster -3. Person details -4. Anaemia & diabetic test -5. Food recall -6. Food away from home -7. Partaker -8. Non-food recall -9. Household details -10. Dietary recall -11. Disability, healthy living & time-use -12. Deprivation and financial inclusion -13. Migrant worker -14. Geographic information + photo -15. Market survey -16. Village resource survey (vrs).

Cleaning operations

Data editing was done using the software Stata Version 15. The completed questionnaires were entered into the Survey Solutions CAPI data entry system.

Response rate

Below is a table showing the response rates based on list A (households selected from the sample): -South Tarawa: 85.8%; -Northern: 91.2%; -Central: 80%; -Is. & Phoenix: 87.5%; -TOTAL: 81.1%.

Below is the table showing the completion rates based on valid households from lists A and B (households from the sample + replacements): -South Tarawa: 99.7%; -Northern: 100%; -Central: 100%; -Is. & Phoenix: 100%; -TOTAL: 100%.

What are the average email marketing results in different countries? Here’s what we’ve found.

How many emails should you put into your autoresponder cycle? We’ve analyzed how the average engagement metrics change depending on the number of emails our customers used in their autoresp onder cycles.

The heteroscedastic and volatile characteristics of stock price data have attracted the interest of researchers from various disciplines, particularly in the realm of price forecasting. The stock market’s non-stationary and volatile nature, driven by complex interrelationships among financial assets, economic developments, and market participants, poses significant challenges for accurate forecasting. This research aims to develop a robust forecasting model to improve the accuracy and reliability of stock price predictions using machine learning. A two-stage forecasting model is introduced. First, a random forest subset-based (RFS) feature selection with repeated -fold cross-validation selects the best subset of features from eight predictors: highest price, lowest price, closing price, volume, change, price change ratio, and amplitude. These features are then used as input in a bidirectional gated recurrent unit with an attention mechanism (BiGRU-AM) model to forecast daily opening prices of ten stock indices. The proposed model exhibits superior forecasting performance across ten stock indices when compared to twelve benchmarks, evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination, . The improved prediction accuracy enables financial professionals to make more reliable investment decisions, reducing risks and increasing profits.

Average rents for private row houses and apartments in urban centres with 2,500 to 10,000 people. Organized by centre and number of bedrooms to help rental market professionals make informed business decisions. Note: Data in this series is updated every 5 years in advance of each Census year. This table has been updated from January 28, 2021 to now reflect final data.

Average rents for rental townhomes and apartments in urban centres with at least 10,000 people. Organized by province and number of bedrooms. Drawn from CMHC’s Rental Market Survey, this data table helps industry professionals make informed rental market decisions.

Benchmark regressions of PKU-DFIIC on household participation in risky financial market and IV estimation.

Here, we’re looking at other elements that may play a role in how you run your email marketing campaigns and the average metrics you could expect.

Supermarkets and grocery store outcomes have been a tale of dealing with volatile prices at their purchase and sales points. The continued expansion of Aldi and Amazon has forced the two established industry giants, Woolworths and Coles, to remain price-competitive on both the physical store and online service fronts. To differentiate themselves from low-cost supermarkets, Coles and Woolworths have leant into attracting customers with convenient locations and expanded online shopping capabilities. These supermarket giants also rely on loyalty programs and promotions. Coles and Woolworths have displayed interest in data analytics, strengthening their relationships with analytics firms like Palantir to optimise their marketing and operational processes. The ACCC and Treasury have taken the lead on addressing supplier and customer concerns relating to deceptive discounting practices and supplier contract bargaining exploitation. Supermarket and grocer revenue rose significantly following the COVID-19 outbreak. Household expenditure shifted towards retail industries amid restrictions on many services industries, with this imbalance remaining as high costs limit eating out. A combination of panic buying, along with the suspension of many specials and promotions in supermarkets, boosted grocery turnover at the beginning of the period, spiking revenue for 2019-20. This high benchmark at the start of the period has resulted in an industry correction and an annualised revenue decline of 0.6% to $148.7 billion over the five years to 2024-25. However, stores have largely managed to pass on upstream costs to customers, steadying their profit margins while suppliers and consumers bear the brunt of inflation-driven costs. Revenue is estimated to climb by 0.2% in 2024-25, reflecting the price-driven industry growth more indicative of the overall revenue trend that was drowned out by the pandemic revenue spike and correction. Supermarkets and grocery stores are set to continue performing well with industry revenue slated to climb at an annualised 0.4% over the five years through 2029-30 to $142.8 billion. Population growth and stubborn inflationary pressures, despite rate hikes, are set to keep store prices inching upwards. The results of the Treasury and the ACCC's investigations will shine a light on new regulations and potential penalties in store for large supermarkets. Eventually, when inflationary pressures subside and consumer sentiment returns to a positive level, supermarkets and grocers will be well-positioned to take advantage of consumer appetite for value-added and premium goods. Strong growth in online sales is set to continue.

In this table, we’re looking at whether adding video content (including links to your video hosting platforms) could help you boost your engagement metrics, primarily the average click-th rough and click-to-open rates.

In this table, you’ll see the average landing page conversions based on the subscription rate they generated across industries.

Do individual phrases in email subject lines correlate with email campaign performance? Here we explore whether individual words have the power to make or break your email campaigns.

The average rents for private row houses and apartments in urban centres with 2,500 to 10,000 people. Organized by province and number of bedrooms to help rental market professionals make informed business decisions. Note: Data in this series is updated every 5 years in advance of each Census year. This table has been updated from January 28, 2021 to now reflect final data.

During a November 2022 survey among marketing professionals worldwide, 25 percent of respondents stated they spent between one thousand and five thousand U.S. dollars on an average video. Four percent of respondents said they spent more than 20 thousand dollars on an average video.

During a 2025 survey among chief marketing officers (CMOs) from for-profit companies in the United States, respondents reported that, on average, corporations selling consumer packaged goods (CPG) allocated approximately ** percent of their total budgets to marketing expenses. The consumer services and real estate segments followed, both with average shares above ** percent. The CPG market on the spotlight CPG marketing promotes perishable consumer goods such as food, beverages, or household products. As these items are used and replenished regularly, the CPG industry is known as a highly competitive playing field, and brands rely on effective marketing campaigns to stand out among the crowd. Top advertising spenders Amazon was the top advertiser in the U.S. in 2023, with over ** billion U.S. dollars in spending. Procter & Gamble was the leading advertiser from the CPG industry that year, which comes as no surprise considering the conglomerate's size and extensive brand portfolio. Many of the world’s most popular cleaning and personal care brands, such as Pampers, Braun, Gillette, and Pantene, fall under the P&G umbrella, making the company a multinational CPG giant.