NPS

## Overview

NPS is a dataset for object detection tasks - it contains Koala_cookie Black_tea Soap Snap annotations for 535 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Department and specialty stores achieved the highest Net Promotor Score (NPS) in the United States, according to a survey conducted among ****** consumers in 2021. Department and specialty stores recorded an NPS of **. On the other hand, internet service companies registered the lowest NPS, equal to **.The Net Promoter Score is an index used to gauge the customers' overall satisfaction and brand loyalty. The NPS ranges from -100 to 100 and measures the willingness of customers to recommend a company's products or services to others.

In 2023, the Tottus stores in Peru and the Sodimac stores in Colombia were S.A.C.I. Falabella's business format with the highest Net Promoter Score (NPS). Tottus in Chile followed suit, with an NPS score of 65 percent. In May 2021, Falabella closed its retail businesses in Argentina, remaining present in that market only with its home improvement format, the Sodimac stores, which in 2022 amounted to seven.

Abstract

The main objective of the NPS is to provide high-quality household-level data to the Tanzanian government and other stakeholders for monitoring poverty dynamics, tracking the progress of the Five Year Development Plan (FYDP) II poverty reduction strategy and its predecessor plans, and evaluating the impact of other major, national-level government policy initiatives. As an integrated survey covering a number of different socioeconomic factors, it compliments other more narrowly focused survey efforts, such as the Demographic and Health Survey (DHS) on health, the Integrated Labour Force Survey (ILFS) on labour markets, the Household Budget Survey (HBS) on expenditure, and the National Sample Census of Agriculture (NSCA). Secondly, as a panel household survey in which the same households are revisited over time, the NPS allows for the study of poverty and welfare transitions and the determinants of living standard changes.

Geographic coverage

Designed for analysis of key indicators at four primary domains of inference, namely: Dar es Salaam, Other Urban, Rural, Zanzibar,

Analysis unit

Households; Individuals

Sampling procedure

The NPS is based on a stratified, multi-stage cluster sample design which recognizes four analytical strata: Dar es Salaam, Other Urban areas in Mainland, Rural areas in Mainland, and Zanzibar. The sample design for the NPS 2020/21 targeted the sub-sample of households from the initial NPS 2014/15 cohort considered the “Refresh Panel”. These specific households had never previously been a part of the NPS sample design. This sample consisted of 3,352 households from 419 clusters in the NPS 2014/15 that were tracked and interviewed in the NPS 2020/21. An additional “Booster Sample” of 545 households from major cities and urban areas (specifically, Mbeya, Arusha, Mwanza, Tanga, and Dodoma) was also interviewed to allow for improved estimates in urban centres.

In previous NPS rounds, the sample design included complete households that could not be interviewed in a particular year but were found in later rounds, excluding those households that had refused to be interviewed (i.e. a household that was interviewed in Round 1, lost in Round 2, and found again in Round 3). This situation does not exist in the NPS 2020/21 as they have only been included in, at most, two rounds.

The eligibility requirement for inclusion of a household in this round of the NPS and all others is defined as any household having at least one member aged 15 years and above, excluding live-in servants. Households with at least one eligible member were completely interviewed, including any non-eligible members present in the household.

Additionally, the final sample for NPS 2020/21 included any split-off household or eligible members identified during data collection (i.e. a previous NPS member who had moved or started another household in between rounds). Marriage and migration are the most common reasons for households splitting over time. Ultimately, the final sample size for NPS 2020/21 was 23,592 individuals in 4,709 households. Of these, 4,164 households allow for panel analysis as they have been found and interviewed in both NPS 2014/15 and NPS 2020/21, while the remaining 545 (in the “Booster Sample”) will only have data available in the NPS 2020/21. The complete cohort interviewed in NPS 2020/21 will be maintained and tracked in all future waves of the NPS.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NPS 2020/21 consists of four survey instruments: a Household Questionnaire, Agriculture Questionnaire, Livestock Questionnaire, and a Community Questionnaire. A detailed description of the questionnaires is provided in the Survey Instruments section of the Basic Information Document (available under Downloads). All questionnaires are in English and available for download.

The Net Promoter Score (NPS) Tool market is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The surging adoption of customer feedback systems to gauge satisfaction levels and improve customer engagement is a primary driver of market growth. The rise of cloud-based solutions, which offer scalability and flexibility, is further fueling market expansion. The market landscape is characterized by both established players and emerging vendors. Key players include Zonka, Retently, SurveySparrow, Survey Sensum, Qualtrics, GetFeedback, Delighted, NiceReply, and InMoment. These companies are focusing on product innovation and strategic partnerships to gain a competitive edge. The market is segmented by type (cloud-based and on-premises) and application (large enterprises and SMEs). Cloud-based solutions are gaining traction due to their ease of use and lower maintenance costs. Large enterprises are the predominant users of NPS tools, but SMEs are increasingly recognizing their importance for customer retention and growth. The North American region dominates the market due to the presence of numerous technology companies and a high adoption rate of customer feedback systems.

Companies operating in the education and training sector achieved the highest Net Promotor Score (NPS), according to a survey conducted in 2020. The education and training sector recorded an NPS of **. On the other hand, companies in the healthcare sector registered the lowest NPS, equal to **.The Net Promoter Score is an index used to gauge the customers' overall satisfaction and brand loyalty. The NPS ranges from **** to 100 and measures the willingness of customers to recommend a company's products or services to others.

This service depicts National Park Service tract and boundary data that was created by the Land Resources Division. NPS Director's Order #25 states: "Land status maps will be prepared to identify the ownership of the lands within the authorized boundaries of the park unit. These maps, showing ownership and acreage, are the 'official record' of the acreage of Federal and non-federal lands within the park boundaries. While these maps are the official record of the lands and acreage within the unit's authorized boundaries, they are not of survey quality and not intended to be used for survey purposes." As such this data is intended for use as a tool for GIS analysis. It is in no way intended for engineering or legal purposes. The data accuracy is checked against best available sources which may be dated and vary by location. NPS assumes no liability for use of this data. The boundary polygons represent the current legislated boundary of a given NPS unit. NPS does not necessarily have full fee ownership or hold another interest (easement, right of way, etc...) in all parcels contained within this boundary. Equivalently NPS may own or have an interest in parcels outside the legislated boundary of a given unit. In order to obtain complete information about current NPS interests both inside and outside a unit’s legislated boundary tract level polygons are also created by NPS Land Resources Division and should be used in conjunction with this boundary data. To download this data directly from the NPS go to https://irma.nps.gov Property ownership data is compiled from deeds, plats, surveys, and other source data. These are not engineering quality drawings and should be used for administrative purposes only. The National Park Service (NPS) shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on a computer system at the National Park Service, no warranty expressed or implied is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data.

OPEN Data View service. The Wildland Fire Risk Assessment project was developed by the National Park Service's Fire and Aviation Management program as a response to the devastating 2011 wildfire season. This project developed a consistent assessment method that has been applied to NPS units nationwide regardless of variations in climate, fuels, and topography.The assessment, based on Firewise® assessment forms, evaluates access, surrounding environment, construction design and materials, and resources available to protect facilities from wildland fire. The data collected during the assessment process can be used for:Identifying, planning, prioritizing and tracking fuels treatments at unit, regional and national levels, and Developing incident response plans for facilities and communities within NPS units.The original spatial data for the assessments comes from a variety of sources including the NPS Buildings Enterprise Dataset, WFDSS, NPMap Edits, manually digitized points using Esri basemaps as a reference at various scales, and GPS collection using a multitude of consumer and professional grade GPS devices. The facilities that have been assessed and assigned a facility risk rating have been ground-truthed and field verified. (In some rare occasions, facilities have been verified during remote assessments. Those that have been remotely assessed are marked as such). The resulting data is stored in a centralized geodatabase, and this publicly available feature layer allows the user to view that data.The NPS Facilities feature layer includes the following layers and related tables:Facility - A facility is defined by the NPS as an asset that the NPS desires to track and manage as a distinct identifiable entity. In the case of wildland fire risk assessments, a facility is most often a structure but in special instances, a park unit may wish to identify and assess other at-risk features such as a historic wooden bridge or an interpretive display. The facilities are assessed based on access, the surrounding environment, construction design, and protection resources and limitations, resulting in a numerical score and risk adjective rating for each facility. These ratings designate the likelihood of ignition during a wildland fire. The facilities are symbolized by their respective risk rating.Community - A community is a group of five or more facilities, a majority of which are within 600 feet of each other, that share common access and protection attributes. The community concept was developed to facilitate data collection and entry in areas with multiple facilities and where it made sense to apply treatments and tactics at a scale larger than individual facilities. Most of the community polygons are created using models in ArcMap, but some may have been created or edited in the field using a Trimble GPS unit. *The NPS Facilities layer is updated continually as new wildfire risk assessments are conducted and the Wildland Fire Risk Assessment project progresses. The assessment data contained here is the most current data available.*More information about the NPS Wildland Fire Risk Assessment Project, and the NPS Facilities data itself, can be found at the New Wildland Fire Risk Assessments website. This site provides information on the data collection process, additional ways to access the data, and how to conduct assessments yourself (for both NPS and non-NPS facilities).FACILITY ATTRIBUTES
Unit_ID NWCG Unit ID, Two letter state code and three letter unit abbreviation, for example UTZIP for Zion National Park in Utah.

Fire_Bldg_ID User maintained unique ID for Facility layer.

Building ID Unique Id from the NPS Enterprise Buildings dataset.

FMSS ID Unique ID for the facility in the NPS FMSS database.

Community ID Unique ID linking facility to a community

Assess Scale Indicates if the facility is part of a community/ will be included in a community assessment. Communities are pre-defined by regional GIS staff and visible in this map as a blue perimeter.
Answer "Yes" if you are adding a facility point within a predefined community.

Common Name Name of the structure. In most cases, the name comes from the NPS FMSS database.

Map Label Numerical label used for mapping purposes.

Owner Indicates who owns the structure being assessed.

Facilty Type Indicates the facility type OR if the facility has been REMOVED, DESTROYED, has NO WILDLAND RISK, is PRIVATE - NO SURVEY REQUIRED or DOES NOT REQUIRE A SURVEY (because it is planned for removal).

Facility Use What is the primary use of the facility?

Building Occupied Is the building occupied?

Community Name Name of the community the facility is located within, if any.

Field Crew Field crew completing the assessment.

Last Site Visit Date Date which the facility was visited and assessment data reviewed/updated.

Location General location within the unit – may use FMUs, watersheds, or other identifier. One location may contain multiple communities and individual facilities. Locations are used to filter data for reports and map products.

PrimaryAccess Primary method of accessing the facility.

IngressEgress Number of routes into and away from the facility.

AccessWidth Width of the road or driveway used to access the facility.

AccessCond Grade and surface material of the road or driveway used to access the facility.

BridgeCond Condition, based on load limits and construction.

Turnaround Describes how close can a fire apparatus drive to the facility and once there, whether it can turnaround.

BldgNum Is the facility clearly signed or numbered?

FuelLoad Fuel loading within 300 ft of the facility (see appendix D of the Wildfire Risk Assessment User Guide)

FuelType Predominant fuel type within 300 ft of the facility.

DefensibleSpace Amount of defensible space around the facility, see criteria for evaluating defensible space in the Wildfire Risk Assessment User Guide.

Topography Predominant slope within 300 ft of facility.

RoofMat Roofing material used on the facility.

SidingMat Siding material used on the facility.

Foundation Describes the facility’s foundation.

Fencing Indicates presence of any wooden attachments, fencing, decking, pergola, etc. and fuels clearance around those attachments.

Firewood Firewood distance from facility.

Propane Inidicates if a propane tank exists within 200 feet of a structure and if there is any fuels clearance around the propane tank(s).

Hazmat List of hazmat existing on the site.

WaterSupply Water supply available to the facility.

OverheadHaz Identifies the presence of overhead hazards that will limit aerial firefighting efforts.

SafetyZone Identifies the presence of any potential safety zones.

SZRadius Radius of any potential safety zones.

Obstacles Additional obstacles, not already included in assessment, that will limit firefighting efforts- to include items such as UXO, hazmat,etc. If there are additional obstacles, be sure to comment in Assessment Comments or Tactic descriptions where appropriate.

TriageCategory Refer to IRPG for descriptions of each category. This information will be displayed in the NIFS Structure Triage layer for incident response.

Score Sum of attribute values for all assessment elements including access, environment, structure and protection portions of the assessment.

Rating Wildland fire risk rating based on score. Ratings are No Wildland Risk, Low, Moderate and High. Rating indicates likelihood if facility igniting if a wildland fire occurs.

ProtectionLevel Inidcates structures which are priority for protection during a wildfire. For Alaska Region data, indicates identified protection level for structure. For lower 48, enter ‘Unknown’ unless specified by local unit.

ProtLevelApprovalName Name of person who designated Protection Level

ProtLevelApprovalDate Date Protection Level Designated

ResourcesOfConcern Indicates if it is necessary to contact park staff before engaging in suppression activities because special resources (natural, cultural, historic) of concern are present?

AssessComments Explain any aspects of the assessment that require extra detail.

RegionCode NPS Region Code - AKR, IMR, NER, NCR, MWR, PWR or SER

UnitCode

NPS Unit Code

ReasonIncluded Why is the point in the dataset – NPS owned, Treatment Planning, Protection Responsibility, Planning (other than treatments). Intent of the dataset is to document wildfire risk for NPS owned structures. Other structures or facilities may be included at the discretion of the unit's fire management staff.

Restriction How can the data be shared – Unrestricted, Restricted - No Third Party Release, Restricted – Originating Agency Concurrence, Restricted – Affected Cultural Group Concurrence, Restricted - No Release, Unknown. Only unrestricted data is included in this dataset.

Local_ID Field which can be used to store unique ids linking back to any local datasets.

RevisitInterval How many years will it take for the fuels to change significantly enough to change the score and rating for this facility?

IsVisited Use this field to keep track of what you have done during a field session. Filter on this field to see what has been assessed and what still needs visited during a field data collection session.

DeleteThis Users enter yes if this is this a duplicate or was no facility found.
If you know the facility was REMOVED or DESTROYED, go back to Facility Type and enter that information there.

Data_Source

FirewiseZone1 List of treatments needed to

NPS (Net Promoter Score) Tool Market Outlook

In 2023, the global NPS (Net Promoter Score) tool market size is valued at approximately USD 1.5 billion, with projections indicating a substantial growth to USD 3.8 billion by 2032, driven by a strong CAGR of 10.6%. The significant growth factor is the increasing adoption of customer experience management solutions across various industries, seeking to enhance customer satisfaction and loyalty. The evolving competitive landscape mandates organizations to leverage NPS tools to gain insights into customer sentiment and improve their strategic decision-making processes.

One of the primary growth factors for the NPS tool market is the rising emphasis on customer-centric strategies by businesses across various sectors. Companies today recognize the critical importance of understanding customer feedback and aligning their services and products to meet customer expectations. The NPS tool provides a quantifiable measure of customer loyalty and satisfaction, making it an indispensable tool for businesses aiming to enhance customer relations and drive growth. The increasing focus on customer experience (CX) management drives the demand for sophisticated NPS tools, further bolstering market growth.

The proliferation of digital transformation initiatives is another significant growth driver. As businesses continue to transition to digital platforms, there is a growing need for advanced analytical tools to measure and improve customer experiences online. The NPS tool, with its ability to gather and analyze customer feedback efficiently, becomes crucial in this digital era. Additionally, advancements in artificial intelligence (AI) and machine learning (ML) are being integrated into NPS tools, enhancing their predictive capabilities and making them more effective in providing actionable insights.

Moreover, the increasing importance of data-driven decision-making is propelling the adoption of NPS tools. Organizations are increasingly relying on data to inform their strategies and operations. NPS tools provide valuable data on customer sentiment, which can be used to identify areas of improvement, prioritize investments, and measure the impact of changes over time. This reliance on data is not only prevalent in large enterprises but also among small and medium enterprises (SMEs), further driving the demand for NPS tools.

Regionally, North America holds the largest market share due to the high adoption rate of advanced customer experience management solutions and the presence of major market players. The region's technological advancements and the growing significance of customer satisfaction metrics in business strategy contribute to this dominance. Europe and Asia Pacific are also significant markets, with Europe benefitting from stringent customer satisfaction regulations and Asia Pacific experiencing rapid digitalization and increasing competition among businesses seeking to enhance customer loyalty. Latin America and the Middle East & Africa are emerging markets with growing awareness of the benefits of NPS tools in improving business outcomes.

Component Analysis

The NPS tool market is segmented by component into software and services. The software segment holds a dominant share due to the increasing adoption of digital tools for customer feedback analysis. NPS software provides comprehensive features, including real-time feedback collection, sentiment analysis, and integration with other customer relationship management (CRM) systems. These functionalities make the software an integral part of an organization's customer experience strategy.

Furthermore, the software segment is witnessing continuous innovation, with vendors incorporating AI and ML algorithms to enhance the predictive capabilities of NPS tools. These advancements are enabling organizations to not only measure customer loyalty but also predict future behavior, thereby allowing for more proactive customer management. The integration of NPS software with other business applications, such as marketing automation and business intelligence tools, further enhances its utility and drives demand.

The services segment, comprising consulting, implementation, and support services, is also experiencing significant growth. Organizations often require expert guidance to effectively implement and utilize NPS tools. Consulting services help businesses design and deploy NPS strategies tailored to their unique needs, while implementation services ensure seamless integration with existing systems. Continued support and maintenanc

The Nonpoint Source (NPS) Program, is supported with federal Clean Water Act funds for watershed-based planning and implementation, and requires a match with non-federal dollars. Funds are available annually through requests for proposals to support watershed-based planning and implementation of WBPs. The NPS Program also supports in-house activities for water quality protection, education and outreach, protection of ground water, and interagency coordination.

The National Park Service (NPS) Water Resources Division (WRD) compiles the Hydrographic and Impairment Statistics (HIS) Database for all park units. One of the goals of HIS is to track surface water impairments as defined by the Clean Water Act (CWA). Waters within or adjacent to park units that were identified by states as Category 5 (CWA Section 303d) or Category 4a, 4b, or 4c (CWA Section 305b) were included in this NPS-wide impairment GIS coverage. The GIS coverage of CWA impairments within the National Park System is updated on a monthly basis. Updates to this file in IRMA are made annually. Should you require a more updated version between annual updates, please contact Jia Ling at Jia_Ling@contractor.nps.gov or Dean Tucker at Dean_Tucker@nps.gov. Alternatively, you can also view more updated information on the NPS WRD Hydrographic and Impairment Statistics Database website (https://nature.nps.gov/water/HIS/index.cfm).

Net Promoter Score (NPS) Software Market Outlook

The Net Promoter Score (NPS) Software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 4.2 billion by 2032, exhibiting a remarkable CAGR of 12.1% during the forecast period. The primary growth factors propelling this market include increasing emphasis on customer experience management, the rising adoption of cloud-based solutions, and the significant demand for actionable customer feedback across various industries.

The growing importance of customer experience in driving business growth is a significant factor contributing to the expansion of the NPS software market. Companies across various sectors are recognizing that customer satisfaction and loyalty are critical to long-term success. As businesses strive to enhance their customer experience, the demand for tools that provide insights into customer perceptions and loyalty, such as NPS software, is on the rise. This software enables companies to collect, analyze, and act on customer feedback, thereby improving customer retention and driving revenue growth.

Another pivotal growth driver is the increasing adoption of cloud-based solutions. Cloud deployment offers several advantages, including scalability, cost-effectiveness, and ease of access. Small and Medium Enterprises (SMEs), in particular, are gravitating towards cloud-based NPS software due to lower initial investments and the flexibility to scale operations based on demand. Additionally, advancements in cloud technology and the growing prevalence of Software as a Service (SaaS) models have further boosted the adoption of NPS software across various industries.

The rising need for actionable customer feedback is also playing a crucial role in the market's growth. In today's competitive landscape, understanding customer needs and preferences is essential for businesses to stay ahead. NPS software provides a systematic approach to gather real-time feedback, enabling organizations to make data-driven decisions. The ability to quickly identify and address customer issues helps in improving customer satisfaction and fostering loyalty, which in turn contributes to business growth.

Regionally, North America is expected to dominate the NPS software market, owing to the high adoption rate of advanced technologies and the presence of several key market players. Europe and Asia Pacific are also anticipated to experience significant growth during the forecast period, driven by increasing investments in customer experience initiatives and the growing adoption of digital platforms. Latin America and the Middle East & Africa are projected to witness moderate growth, supported by expanding business operations and the rising focus on customer-centric strategies in these regions.

Component Analysis

The Net Promoter Score (NPS) Software market can be segmented by component into software and services. The software segment is expected to hold a significant share of the market, driven by the increasing adoption of advanced analytics solutions that help businesses gain insights into customer loyalty and satisfaction. This segment includes various types of NPS software, such as standalone solutions, integrated platforms, and mobile applications, all designed to streamline the process of collecting and analyzing customer feedback.

Within the software segment, integrated platforms are particularly gaining traction as they offer a comprehensive suite of tools for managing customer experience. These platforms not only facilitate NPS surveys but also integrate with other customer relationship management (CRM) systems, providing a unified view of customer interactions. The seamless integration of NPS software with existing business systems enables organizations to leverage data more effectively, leading to better decision-making and improved customer outcomes.

The services segment, on the other hand, includes consulting, implementation, and support services. This segment is expected to witness substantial growth, driven by the increasing demand for professional services to ensure the successful deployment and optimization of NPS software. Consulting services play a vital role in helping businesses understand the best practices for NPS implementation, while support services ensure the smooth functioning of the software, addressing any technical issues that may arise.

Furthermore, the customization of NPS software to meet specific business needs is another factor contributing to the growth of

Why Prioritize NPS Structures?National Park Service (NPS) owned structures represent a significant investment of taxpayer dollars and many have invaluable historic qualities. The NPS has an existing enterprise spatial database storing authoritative, readily accessible wildfire risk assessment data which can be used to make informed wildland fire management decisions, including how the agency can reduce risk to NPS structures using fuels treatments. Prioritizing this service wide workload will inform the allocation of fuels funds at regional and park levels to support defensible space around structures and help reduce unwanted losses from wildfires.What is the HIP Value?The HIP value was originally created to prioritize NPS structures service wide for fuels treatments. Data from multiple authoritative sources is used to calculate a value for each NPS structure. The value represents the structure's risk from wildland fire, it's monetary value to the NPS as well as its contribution to the mission of the NPS, and the probability of a fire impacting the structure. This number is compared to all other NPS structures agency wide and the structure is given a priority rank. It is important to note, the only component of the HIP value that can be influenced by fuels treatments is the Hazard (score from wildfire risk assessment).What is the tHIP Value?The tHIP value was developed in an attempt to identify which structures within the NPS Structure Prioritization are "treatable". The Wildland Fire Risk Assessment process evaluates multiple elements to produce a structure's risk assessment score. Some elements, such as accessibility and building construction materials, cannot be mitigated through fuels treatments. The tHIP calculation will look at the elements of the Risk Assessment that can be reduced by implementing fuels treatments and highlight the structures where fuels treatments can provide a benefit. *Because the minimum value of the sum of the Fuel Load and Defensible space elements in the assessment is 1 (instead of 0) we can't just divide by 38 (the max score). We need to subtract the min (1) and then divide by 37 (max-min), which will result in a true percentage. If we treat a structure (fuel load moves to 'Low' and defensible space is 'Greater than 100 feet'), the Treatability will then drop to 0 resulting in a tHIP of 0 and the structure will fall off the prioritization list. Otherwise, for example, the Old Faithful in at Yellowstone National Park will always out-rank quite a few structures because of its high importance and probability if there remains a little treatability benefit (tHIP >0), even after we have done everything we can around the structure. Adjusting the Treatability score to make it a true percentage will fix that- so once we have fully treated a structure it will result in tHIP = 0.

Net Promoter Score (NPS) measures customer satisfaction and loyalty using a Likert scale of 0-10. The dataset is used to predict customer churn and mediation effect relationship, which influent either directly or indirectly through a customer's status/ rating change Date Submitted: 2021-08-29

The Net Promoter Score (NPS) software market is experiencing robust growth, driven by the increasing need for businesses to understand and improve customer loyalty. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several factors: the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the growing preference for data-driven decision-making among enterprises of all sizes, and the increasing focus on customer experience management (CEM) strategies. The market is segmented by deployment (on-premise and cloud-based) and user type (SMEs and large enterprises), with the cloud-based segment dominating due to its inherent flexibility and accessibility. North America currently holds the largest market share, attributed to high technological adoption and a strong focus on customer satisfaction. However, rapid digitalization and growing internet penetration in regions like Asia-Pacific are creating significant growth opportunities in these emerging markets. Competitive forces within the market include both established players and emerging niche providers; this dynamic environment is driving innovation in features, pricing models, and integrations with other CRM and analytics platforms. The continued growth of the NPS software market is expected to be influenced by several key trends. These include the integration of AI and machine learning to provide more predictive analytics and personalized customer engagement, increased focus on omnichannel NPS measurement to capture feedback across various touchpoints, and the growing demand for robust reporting and visualization tools to simplify data interpretation and action planning. Despite these positive trends, challenges remain; these include ensuring data accuracy and consistency across different platforms and overcoming concerns about data privacy and security. Addressing these challenges will be crucial for sustaining the market's strong growth trajectory over the forecast period.

Abstract

In 2004, the Bank, jointly with other donors and the Government of Mozambique, prepared a Poverty and Social Impact Analysis on the issue of fee reform in primary school. Partly as a result of the study findings, the Government took the step of abolishing tuition fees in primary education. In 2006, Ministry of Education and Culture (MEC ) requested a repeat of this analysis, as well as a similar baseline study on barriers to enrollment for the poor in secondary education. In particular the MEC sought World Bank assistance in (a) evaluating the success of the reforms in primary education financing to date, and (b) formulating new policies and initiatives to reduce the barriers the poorest households face in accessing primary and secondary education. This panel survey is part of the Bank's response to this request.

Geographic coverage

Nationally representative

Analysis unit

individuals, households

Universe

The survey was designed to target eligible children/student (i.e. children aged 0-17 y.o. in 2003 or members enrolled in school in 2003) from the IAF sample.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Education Outcomes Panel Survey (NPS) was designed as a panel survey based on a subsample of households interviewed in the 2002/03 Inquérito aos Agregados Familiares (IAF), a national household income and expenditure survey conducted in all provinces of Mozambique from July 2002 to June 2003. The NPS data collection took place from September 2008 to February 2009 and it was performed by a contractor in Mozambique (KPMG), with World Bank and UNICEF field supervision.

The NPS sampling frame consists of enumeration areas (EA) that were drawn to correspond to a particular set of months of the 2002/03 IAF, namely March to May 2003, since it is expected that the IAF has a nationally representative subsample of EAs assigned each quarter. It is important to highlight that the NPS data is nationally representative at the rural and urban areas, but not representative below this level. The main reason is that the IAF sample was clustered to maximize efficiency in the data collection process across a 12 month period, while the NPS sample, due to costs constrains, includes only 3 months. Therefore, the NPS sample does not have enough geographic dispersion to be representative at the province level or below.

All IAF households in the enumeration areas during the months of March-May were included in the NPS sample, resulting in 221 EAs and 2,234 households. This sampling strategy was chosen to reduce the effect of seasonality in the panel analysis when comparing the 2002/03 IAF data to the 2008 NPS data for the same sample households. Originally it was planned to interview all the IAF sample households in these EAs during the same month in which they had been interviewed for the 2002/03 IAF. However, because of delays in the survey planning process, the data collection for the NPS was postponed took place from September 2008 to February 2009. The survey was designed to target eligible children/student (i.e. children aged 0-17 y.o. in 2003 or members enrolled in school in 2003) from the IAF sample. The households in the NPS sample were divided into 2 categories based on their status in 2003:

A. Target 2003 households. These are households that meet at least one of the following criteria: · Households that had at least one child 0-17 years-old in 2003 (see question a13 in the questionnaire ) · Households that had someone in primary or secondary school in 2003, in spite of age (see question a14 in the questionnaire) B. Alternate 2003 Households (14% of original NPS sample)

For the households that did not have any children or student in 2003 but were part of the IAF sample and were in the NPS enumeration area, the following two questions were asked to the first person who was found in the alternate household in 2008: · Does this person's 2008 household currently have anyone who is between 5 and 17 years of age? (see question a15 in the questionnaire) · Does this person's 2008 household currently have anyone who attending primary or secondary school? (see question a16 in the questionnaire)

If the answer was YES to either question (a15 or a16), the interviewer proceeded with the entire questionnaire. If the answer was NO to both questions, the interviewer stopped the interview.

In sum, target households are the source for the panel of children, while alternate households were included to supplement sample size.

There were two types of tracking in the NPS, that of households and that of children/students who split from the original 2003 household and joined new households in 2008. If the entire 2003 household moved in 2008, the field team would gather their new contact information with local leaders, neighbors, friend, etc and follow and interview the household at their new location, provided the household moved within the district (the survey only followed households/children that moved within the district level). New members of the household were also included in the interview.

If the 2003 household was split in 2008 and the members who moved out were a target member (children/student in 2003) who had moved within the district, then the team followed the individuals and interviewed both the original household (if a target member still lived there) and the split household.

The screening for tracking are in section B1 of the questionnaire. A member would be tracked if b100a =1 (this variable is an indicator of whether the member was target member, i.e. less than 17 y.o. in 2003 or attending school in 2003), and b108=2 (the member no longer lives in the household), and b111 <=2 (the member moved to the same village or district). If all these conditions were met, questions B112 (should the member be tracked?) should be 1 (YES) and the household should be followed. The variable "sp" indicates whether the household was the original (sp=0) or a split household (sp>=1).

In case all target members (b100a=1) moved out of the household, the interviewer should end the interview with the original household at question B114.

Mode of data collection

Face-to-face [f2f]

Research instrument

Inquérito aos Agregados Familiares (IAF) 2002 -2003

• caraterísticas gerais do agregado familiar
• questionário comunitário
• questionário das despesas diárias
• questionário das despesas e receitas

NPS Survey 2008-2009

General Household Questionnaire: modules A, B0, B1, B2 (demographics), C0 (education), D0 D1, D2, D3 (employment), E (household characteristics), H (education quality perception), I (transfers) - Consumption module: modules F, GA, GB, GC, GD - Education Event History Module: module C1 - Education Expenditure Modules: module C2

In 2024, Aldi was the second-most favorite retailer among Amazon Prime users living in the United States. The net promoter score (NPS) they assigned to the grocery retailer was ****. Unsurprisingly, U.S. Prime shoppers ranked Amazon as their favorite retailer, with a net promoter score of **.

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modeling the complexities of human behavior, the notion of universal panel data – in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated – can further enhance exploitation of the richness of panel information.

This Basic Information Document (BID) provides a brief overview of the Tanzania National Panel Survey (NPS), but focuses primarily on the theoretical development and application of panel data, as well as key elements of the universal panel survey instrument and datasets generated by the four rounds of the NPS. As this Basic Information Document (BID) for the UPD does not describe in detail the background, development, or use of the NPS itself, the round-specific NPS BIDs should supplement the information provided here.

The NPS Uniform Panel Dataset (UPD) consists of both survey instruments and datasets, meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the NPS. The NPS-UPD provides a consistent and straightforward means of conducting not only user-driven analyses using convenient, standardized tools, but also for monitoring MKUKUTA, FYDP II, and other national level development indicators reported by the NPS.

The design of the NPS-UPD combines the four completed rounds of the NPS – NPS 2008/09 (R1), NPS 2010/11 (R2), NPS 2012/13 (R3), and NPS 2014/15 (R4) – into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

Designed for analysis of key indicators at four primary domains of inference, namely: Dar es Salaam, other urban, rural, Zanzibar.

Analysis unit

• Households
• Individuals

Universe

The universe includes all households and individuals in Tanzania with the exception of those residing in military barracks or other institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

While the same sample of respondents was maintained over the first three rounds of the NPS, longitudinal surveys tend to suffer from bias introduced by households leaving the survey over time; i.e. attrition. Although the NPS maintains a highly successful recapture rate (roughly 96% retention at the household level), minimizing the escalation of this selection bias, a refresh of longitudinal cohorts was done for the NPS 2014/15 to ensure proper representativeness of estimates while maintaining a sufficient primary sample to maintain cohesion within panel analysis. A newly completed Population and Housing Census (PHC) in 2012, providing updated population figures along with changes in administrative boundaries, emboldened the opportunity to realign the NPS sample and abate collective bias potentially introduced through attrition.

To maintain the panel concept of the NPS, the sample design for NPS 2014/2015 consisted of a combination of the original NPS sample and a new NPS sample. A nationally representative sub-sample was selected to continue as part of the “Extended Panel” while an entirely new sample, “Refresh Panel”, was selected to represent national and sub-national domains. Similar to the sample in NPS 2008/2009, the sample design for the “Refresh Panel” allows analysis at four primary domains of inference, namely: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar. This new cohort in NPS 2014/2015 will be maintained and tracked in all future rounds between national censuses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The format of the NPS-UPD survey instrument is similar to previously disseminated NPS survey instruments. Each module has a questionnaire and clearly identifies if the module collects information at the individual or household level. Within each module-specific questionnaire of the NPS-UPD survey instrument, there are five distinct sections, arranged vertically: (1) the UPD - “U” on the survey instrument, (2) R4, (3), R3, (4) R2, and (5) R1 – the latter 4 sections presenting each questionnaire in its original form at time of its respective dissemination.

The uppermost section of each module’s questionnaire (“U”) represents the model universal panel questionnaire, with questions generated from the comprehensive listing of questions across all four rounds of the NPS and codes generated from the comprehensive collection of codes. The following sections are arranged vertically by round, considering R4 as most recent. While not all rounds will have data reported for each question in the UPD and not each question will have reports for each of the UPD codes listed, the NPS-UPD survey instrument represents the visual, all-inclusive set of information collected by the NPS over time.

The four round-specific sections (R4, R3, R2, R1) are aligned with their UPD-equivalent question, visually presenting their contribution to compatibility with the UPD. Each round-specific section includes the original round-specific variable names, response codes and skip patterns (corresponding to their respective round-specific NPS data sets, and despite their variance from other rounds or from the comprehensive UPD code listing)4.

Initial and final dataset of NPs. The "all pulications" sheet is our initial dataset (non-deduplicated). The "publications used in manuscript" sheet is our finial dataset (deduplicated). The "removed publications" sheet is the metedata of publications not indexed in Dimensions.

Market Size and Growth: The global NPS Software market is projected to grow at a CAGR of XX% from 2025 to 2033, reaching a market size of XXX million by 2033. The growth is driven by increasing adoption of customer experience management (CXM) solutions to enhance customer satisfaction and loyalty. Key drivers include the rise of digital channels, the need to measure customer sentiment, and the growing emphasis on data-driven decision-making. Competitive Landscape and Key Trends: The competitive landscape of the NPS Software market is highly fragmented, with numerous players offering a wide range of solutions. Major players in the market include Wootric, Lumoa, Zendesk, Qualtrics, and SatisMeter. Key trends in the market include the shift towards cloud-based solutions, the integration of artificial intelligence (AI) and machine learning (ML), and the emergence of vertical-specific solutions. The market is also witnessing strategic partnerships and mergers and acquisitions to gain competitive advantage and expand market share.