This statistic demonstrates the shares of Russians feeling confident in different types of home improvement activities in the year 2014. While the majority stated feeling confident in wallpapering, accumulating to 80 percent, only roughly one third felt confidence at plumbing.

Healthcare Cost and Utilization Project (HCUP) Fast Stats provides easy access to the latest HCUP-based statistics for health care information topics. HCUP Fast Stats uses visual statistical displays in stand-alone graphs, trend figures, or simple tables to convey complex information at a glance. Fast Stats is updated regularly for timely, topic-specific national and State-level statistics. Fast Stats topics and graphics on hospital stays and emergency department visits, including information at the national, and state levels, trends over time, and selected priority topics such as: State Trends in Hospital User by Payer National Hospital Utilization and Costs Hurricane Impact on Hospital Use Opioids & Neonatal Abstinence Syndrome Severe Maternal Morbidity

Kickstarter, the popular crowdfunding platform, has seen a significant number of projects fall short of their funding goals. As of January 2025, 376,698 projects failed to reach their targets, with the majority (246,351) achieving only 1-20 percent of their funding objectives. This failure rate underscores the challenges creators face in securing financial backing for their ideas, despite Kickstarter's global reach and billions in pledged funds. Crowdfunding's growing impact Since its launch in 2009, Kickstarter has become a major player in the crowdfunding industry. The number of projects hosted on the platform exceeded 651,000 projects, with pledges surpassing 8.5 billion U.S. dollars. Notably, the most successful project to date, "Surpise! Four Secret Novels by Brandon Sanderson", raised an impressive 41 million U.S. dollars in 2022. These figures highlight the platform's potential for creators to secure substantial funding for their projects. Success rates vary by category While many projects struggle to meet their funding goals, success rates differ significantly across categories. As of January 2025, comics boasted the highest success rate at 67.65 percent, followed by dance at 61.11 percent and theater at 59.72 percent. These statistics suggest that certain creative fields may resonate more strongly with Kickstarter's backer community, potentially offering better odds for project success in these areas.

Abstract copyright UK Data Service and data collection copyright owner.

These teaching datasets, comprising a sub-set of a large-scale longitudinal study, the Longitudinal Study of Young People in England (LSYPE), were created as part of the NCRM Developing Statistical Modelling in the Social Sciences: Lancaster-Warwick-Stirling Node Phase 2 project, funded by the Economic and Social Research Council (ESRC). During the project, a web site was created with the aim to provide a web-based training resource about the use of statistical regression methods in educational research. The content is designed to teach users how to perform a variety of regression analyses using SPSS, starting with foundation material in basic statistics and working through to more complex multiple linear, logistic and ordinal regression models. Along with illustrated modules the site contains demonstration videos, interactive quizzes and SPSS exercises and examples that use these LSYPE teaching data. Further information and documentation may be found at the web site, Using Statistical Methods in Education Research. Throughout the site modules users are invited to use the datasets for either following the examples or performing exercises. Prospective users of the data will be directed to register an account in order to download the data.

The full LSYPE study is held at the Archive under SN 5545. The teaching datasets include information drawn from Wave 1 of LSYPE, conducted in 2004, with GCSE results matched from Wave 3 (2006). Further information about the NCRM Node project covering this study may be found on the Developing Statistical Modelling in the Social Sciences ESRC award web page.

Documentation
There is currently no discrete documentation currently available with these teaching datasets; users should consult the web site noted above. Documentation covering the main LSYPE study is available with SN 5545.

For the second edition (July 2011), updated versions of the SPSS data files were deposited to resolve minor anomalies.

Main Topics:

The teaching datasets include variables covering LSYPE respondents' educational test results, academic achievement and school life, and demographic/household characteristics including ethnic group, gender, social class and socio-economic status, computer ownership, private education, and mothers' occupational status and educational background.

Around 55 percent of U.S. veterans and active service members who incurred a physical or mental injury, illness, or wound while serving in the military on or after September 11, 2001 who responded to the annual Wounded Warrior Project Survey in 2020 required assistance or were completely dependent on assistance from another person for one or more daily activities. The Wounded Warrior Project (WWP) is a U.S. charity and veteran organization for veterans and service members who incurred a physical or mental injury, illness, or wound while serving in the military on or after September 11, 2001. This statistic shows the percentage distribution of U.S. veterans and active service members of the Wounded Warrior Project with injuries or health problems related to their service who required assistance with daily activities during an average week in 2020, by activity and level of assistance.

This statistic shows information on some of the fastest project launches on crowdfunding website Kickstarter as of November 2016, based on the amount of time several million dollar projects took to surpass the 1 million dollar funding mark. On Black Friday 2016, board game Kingdom Death: Monster 1.5, a follow-up to the 2012 game Kingdom Death, generated 1 million U.S. dollars in pledges within 19 minutes. On February 23, 2015, smartwatch Pebble Time surpassed the 1 million U.S. dollar mark within 49 minutes. The Veronica Mars movie project had been the fastest Kickstarter movie project to accumulate 1 million U.S. dollars, taking only 4 hours and 24 minutes to do so. The fastest gaming project to reach 1 million U.S. dollars in funding was Shenmue 3 as of June 2015. Successful Kickstarter campaigns - additional information
As of 2015, Kickstarter is one of the largest crowdfunding platforms in the world, having reportedly received more than 1.6 billion U.S. dollars in pledges from 8.9 million individuals since its 2009 launch. According to industry experts, global crowdfunding campaigns have raised a total of 16.2 billion U.S. dollars in 2014, a 167 percent growth from the previous year’s 6.1 billion. In 2015, the industry is expected to reach 34.4 billion U.S. dollars in pledges from all over the world. However, crowdfunding, financing of a project through small donations from a great number of individuals, is not in itself a new idea and has been used in history before. A notable example is the completion of the Statue of Liberty, which was backed by more than 120,000 contributors, most of whom gave less than a dollar, following a campaign initiated by famed journalist Joseph Pulitzer.

While other websites, such as GoFundMe, allow people to raise money for anything from graduations to medical bills, trips or charity, Kickstarter focuses on mainly creative projects, from craft ideas to music albums or technological innovations. As of June 2015, the most popular category of projects featured on the platform is games, with some 360 million U.S. dollars pledged, followed by technology, design, film & video, and music. As of April 2015, some 38 percent of the campaigns posted on Kickstarter have reached or even exceeded their funding goal. The most successful Kickstarter campaign of all time is the one supporting Pebble Time, a smartwatch developed by Pebble Technology Corporation, which had an initial fundraising target of 100 thousand U.S. dollars, but received pledges worth over 10 million U.S. dollars from almost 70 thousand backers. It is also the campaign fastest to reach pledges worth 1 million U.S. dollars, in a record 30 minutes. The popular smartwatch went into production and was released in 2013, selling its one millionth unit in December 2014.

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Coverage. ESS2 and ESS3 covered all regional states including the capital, Addis Ababa. The majority of the sample comprises rural areas as it was carried over from ESS1. The ESS2 and ESS3 were implemented in 433 enumeration areas (EAs) out of which 290 were rural, 43 were small town EAs from ESS1, and 100 were EAs from major urban areas.

Analysis unit

Households Communities

Universe

ESS uses a nationally representative sample of over 5,000 households living in rural and urban areas. The urban areas include both small and large towns.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is a two-stage probability sample. The first stage of sampling entailed selecting primary sampling units, or CSA enumeration areas (EAs). A total of 433 EAs were selected based on probability proportional to size of the total EAs in each region. For the rural sample, 290 EAs were selected from the AgSS EAs. A total of 43 and 100 EAs were selected for small town and urban areas, respectively. In order to ensure sufficient sample size in the most populous regions (Amhara, Oromiya, SNNP, and Tigray) and Addis Ababa, quotas were set for the number of EAs in each region. The sample is not representative for each of the small regions including Afar, Benshangul Gumuz, Dire Dawa, Gambella, Harari, and Somalie regions. However, estimates can be produced for a combination of all smaller regions as one “other region” category. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey consisted of five questionnaires. These questionnaires are similar with the questionnaires used during in the ESS1 and ESS2 with revisions based both on the results of the ESS2 and also on identified areas of need for new data (see Section 7 of the Basic Information Document provided under the Related Materials tab). The household questionnaire was administered to all households in the sample. The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.3 The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on basic demographics; education; health (including anthropometric measurement for children); labor and time use; saving; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; credit; and other sources of household income (Table 2.1). Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information (Table 2.2).

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products (Table 2.3). The livestock module implemented in ESS3 is significantly difference from the module implemented in ESS1 and ESS2.

Cleaning operations

The interviews were carried out using pen-and-paper (PAPI) as well as computer-assisted personal interviewing (CAPI) method. A concurrent data entry arrangement was implemented for PAPI. In this arrangement, the enumerators did not wait until all the interviews were completed. Rather, once the enumerators completed approximately 3-4 questionnaires, supervisors collected these interviews from enumerators and brought them to the branch offices for data entry. This process took place as enumerators continued administering interviews with other households. Then questionnaires were keyed at the branch offices as soon as they were completed using the CSPro data entry application software. The data from the completed questionnaires were then checked for any interview or data entry errors using a STATA program. Data entry errors were flagged for the data entry clerks and the interview errors were then sent to back to the field for correction and feedback to the ongoing interviews. Several rounds of this process were undertaken until the final data files were produced. Additional cleaning was carried out, as needed, by checking the hard copies. In ESS3, CAPI (with a Survey Solutions platform) was used to collect the community data in large town areas.

Response rate

During wave 3, 1255 households were re-interviewed yielding a response rate of 85 percent. Attrition in urban areas is 15% due to consent refusal and inability to trace the whereabouts of sample households.

Abstract

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the second phase ESPS panel survey is based on the updated 2018 pre-census cartographic database of enumeration areas by the Ethiopian Statistical Service (ESS). The sample is a two-stage stratified probability sample. The ESPS EAs in rural areas are the subsample of the AgSS EA sample. That means the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e., the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematic PPS. This is designed to automatically result in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS, and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e., systematic random sampling. One important issue to note in ESPS sampling is that the total number of agriculture households per EA remains at 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA. For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA.

The ESPS-5 kept all the ESPS-4 samples except for those in the Tigray region and a few other places. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The ESPS-5 survey consisted of four questionnaires (household, community, post-planting, and post-harvest questionnaires), similar to those used in previous waves but revised based on the results of those waves and on the need for new data they revealed. The following new topics are included in ESPS-5:

a. Dietary Quality: This module collected information on the household’s consumption of specified food items.

b. Food Insecurity Experience Scale (FIES): In this round the survey has implemented FIES. The scale is based on the eight food insecurity experience questions on the Food Insecurity Experience Scale | Voices of the Hungry | Food and Agriculture Organization of the United Nations (fao.org).

c. Basic Agriculture Information: This module is designed to collect minimal agriculture information from households. It is primarily for urban households. However, it was also used for a few rural households where it was not possible to implement the full agriculture module due to security reasons and administered for urban households. It asked whether they had undertaken any agricultural activity, such as crop farming and tending livestock) in the last 12 months. For crop farming, the questions were on land tenure, crop type, input use, and production. For livestock there were also questions on their size and type, livestock products, and income from sales of livestock or livestock products.

d. Climate Risk Perception: This module was intended to elicit both rural and urban households perceptions, beliefs, and attitudes about different climate-related risks. It also asked where and how households were obtaining information on climate and weather-related events.

e. Agriculture Mechanization and Video-Based Agricultural Extension: The rural area community questionnaire covered these areas rural areas. On mechanization the questions related to the penetration, availability and accessibility of agricultural machinery. Communities were also asked if they had received video-based extension services.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESPS-5 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). However, due to the security situation in northern Ethiopia and to a lesser extent in the western part of the country, only a total of 4999 households from 438 EAs were interviewed for both the agriculture and household modules. The security situation in northern parts of Ethiopia meant that, in Tigray, ESPS-5 did not cover any of the EAs and households previously sampled. In Afar, while 275 households in 44 EAs had been covered by both the ESPS-4 agriculture and household modules, in ESPS-5 only 252 households in 22 EAs were covered by both modules. During the fifth wave, security was also a problem in both the Amhara and Oromia regions, so there was a comparable reduction in the number of households and EAs covered there.

More detailed information is available in the BID.

The REACH repository of good practices comprises over a hundred and twenty records of European and extra European participatory activities in the field of cultural heritage, with an emphasis on small-scale, localised examples, but including also larger collaborative projects and global or distributed online initiatives. Located in over twenty different countries, the activities showcased here cover a wide variety of topics and themes, from urban, rural and institutional heritage to indigenous and minority heritage; from preservation, and management to use and re-use of cultural heritage. This easy-to-use collection of good practices offers professionals, practitioners, researchers and citizens useful information about activities which could be transferred, adapted or replicated in new contexts.

This statistic displays the top pharmaceutical R&D projects based on net present value, as of December 2024. According to the source, the top R&D project was Novo Nordisk's fixed-dose combination for type 2 diabetes and obesity Cagrisema, with a net present value of over 87 billion U.S. dollars at that time.

Abstract

The Agriculture Census is envisioned with the following objectives: · To provide data on the structure of agriculture as well as forestry and fisheries in Vanuatu; · To provide data that will be used as benchmark for current agricultural statistics; and · To provide sampling frame for surveys on agriculture (crops and livestock), fisheries and forestry.

Specifically, the Agriculture Census Phase II aims: · To determine the structure and characteristics of the agricultural activities of the households in Vanuatu such as crop gardening, coconut/cocoa/ coffee/kava/vanilla/pepper farming, tending of cattle and other livestock activities, forestry-related activities and fishing operations; · To determine the number and distribution of household engaged in crop gardening, coconut/cocoa/coffee/kava/vanilla/pepper farming, tending of cattle and other livestock activities, forestry-related activities and fishing operations at the island level; and · To provide data on the farm/holding/sub-holding area, quantity of the crops grown/sold, number of cattle and other livestock kept as of the day of enumeration, quantity of fisheries species gathered/caught, etc.

Geographic coverage

The 18 major islands were classified as: 1. Small - number of households engaged in agricultural activities less than 500 (Torres, Paama, Erromango, Aniwa, Aneityum and Futuna); 2. Medium - number of households engaged in agricultural activities 500-1,999 (Banks, Malo, Maewo, Ambrym,Epi and Shepherds); and 3. Large - number of households operating agricultural activities 2,000 or more (Efate, Malekula, Ambae, Pentecost and Tanna).

Analysis unit

Households and individuals

Universe

The Survey covers all rural households

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sampling method The 18 major islands were classified as: • Small - number of households engaged in agricultural activities less than 500 (Torres, Paama, Erromango, Aniwa, Aneityum and Futuna); • Medium - number of households engaged in agricultural activities 500-1,999 (Banks, Malo, Maewo, Ambrym, Epi and Shepherds); and • Large - number of households operating agricultural activities 2,000 or more (Efate, Malekula, Ambae, Pentecost and Tanna).

In determining the number of households to be interviewed in each island and in each enumeration area (EA): - For small islands, all households were listed and the identified households engaged in agricultural activities were enumerated; - For medium-sized islands, one-third of the sample EAs in these islands were selected and all households were listed and those found to be engaged in agricultural activities were interviewed; and - For large islands, one-third of the total EAs were selected in each island and all households listed. Of households found to have a crop garden, coconut sub-holding or kava sub-holding, one-third were selected to be further interviewed. In addition, all households listed and involved in the subholding of cattle and cash crops like cocoa, coffee (for Tanna only), vanilla and pepper (10 or more plants) were also enumerated.

Sampling deviation

No information mentioned about the sample deviation from the sample design

Mode of data collection

Face-to-face [f2f]

Research instrument

Phase I: Census Listing

Phase II: Surveys Form 1.1 - Household Form 1.2 - Crop Garden Form 1.2A - Gardener's Form Form 1.3 - Kava Form 1.4 - Coconut Form 2 - Cocoa Form 3 - Coffee Form 4 - Vanilla Form 5 - Pepper Form 6 - Cattle Form 7 - Commercial Farm Form A - List of Activities Form B1 - Control Sheet for all small and medium sized islands Form B2 - Control Sheet for Santo, Pentecost and Ambae Form B3 - Control Sheet for Ambrym and Malekula Form B4 - Control Sheet for Efate and Tanna

Cleaning operations

Eight data entry operators were hired by the project to do the data encoding of the Phase I of the project. This was the first-hands on as far as the software is concerned for all the data entry operators. Before the actual data entry, the data processing expert had all eight operators plus the supervisors on a training session for a few days. At the end of the training session, they were familiar with the software and then started the actual data encoding. The processing of data for Phase I of the project took the entire month of June 2006 to be completed. During the Phase II of the project, the expert set up the system and trained the local staff on system operation for two weeks and then left for his home country. Since the project staff and the data entry operators who were hired were already familiar with CsPro, the whole data processing was done without the presence of the consultant. The expert later came for his final mission to prepare the data for tabulation and generate the required tables using the table specifications for that purpose.

The machine data processing of the forms was done using CsPro. Data encoding, data cleaning and tabulation were done using data entry, batch edit and cross tab applications respectively. Control and management of the data entry of the forms and data cleaning of the batch files were done using SCIPS (Survey / Census Integrated Processing System), a Visual Basic 6 (VB6) program developed by the expert designed to integrate the different phases of data capture and data cleaning of any survey/census. The program facilitates the assignment of folios to keyers that resulted to automatic recording of the data capture status of each batch/folio and eliminated errors in the encoding of the geographic identification codes. It also made the data cleaning easier since SCIPS enabled the users to correct errors found by the data consistency and completeness check programs without printing the generated error list.

Response rate

100%

Sampling error estimates

The number of households to be interviewed is based on the sampling methodology that is used in the census. The 15 major islands were classified as:

1. Small - if the number of households engaged in agricultural activities is less than 500; in this case, Torres, Paama and Erromango are under this category.
2. Medium - if the number of households engaged in agricultural activities is between 500 - 1,999; Banks, Malo, Maewo, Ambrym, Epi and Shepherds belong to this group.
3. Large - if the number of households operating agricultural activities is 2,000 or more; Santo, Efate, Malekula, Ambae, Pentecost and Tanna were considered to be large islands.

In selecting the number of households to be interviewed in each island, the following was carried out:

a. For Erromango, Torres and Paama, all households were listed and those households engaged in agricultural activities were enumerated; b. For Banks, Malo, Maewo, Ambrym, Epi and Shepherds, 1/3 of the sample EAs in these islands were selected and all households were listed and those engaged in agricultural activities were interviewed for their involvement in these activities; and c. For Santo, Efate, Malekula, Ambae, Pentecost and Tanna, 1/3 of the total EAs were also selected in each island and all households were listed in these islands, after which only 1/3 of the households engaged in agricultural activities were further interviewed if they were involved in crop garden, coconut sub-holding and kava sub-holding. In addition to this, all households in the selected EAs of these islands that were involved in the sub-holding of cattle and cash crops (with 10 trees or more) like cocoa, coffee (for Tanna only), vanilla and pepper were enumerated.

Data appraisal

Consultants have not provided documents regarding this aspect of data quality.

Pinterest, founded in 2009 and headquartered in San Francisco, California, is an image-oriented social media platform. As of April 2024, 69.4 percent of Pinterest audiences were female and over 22 percent were male. Around 40 percent of Pinterest users, or Pinners, as they are affectionately known, are women aged between 18 and 34 years.

The stamp of approval from U.S. consumers

Pinterest generally garners a largely favorable user response. July 2023 saw Pinterest score 73 out of a possible 100 points with the American Customer Satisfaction Index (ACSI), surpassing LinkedIn, X (formerly Twitter), Instagram, and Facebook in terms of user approval. Another achievement that puts the service ahead of Facebook, Snapchat, and X is the 23.2 percent year-on-year growth in users in January 2024.

What are Pinners searching for?

Pinterest is mostly about creative ideas, such as DIY projects, lifestyle ideas, home decor, and recipes. Beauty, travel, wellness, and dating-related terms are topics that users also like to search for. Imaginative hairstyles and hair colors were prominent search terms in 2022, with the term "lavender and pink hair" experiencing a significant year-on-year increase.  In the last few years, interest in train travel and travel photography has also risen on the platform.