This dataset of 289,870 people sampled across TikTok, X, and Reddit reveals statistics of employee engagement in 2024 to find out whether employees consider themselves engaged, why they were engaged, what would make them more engaged, and to learn more about their demographics.

The engagement of employees in the United States has been decreasing over the last number of years. While engagement peaked in 2020, at 36 percent, it has been trending downwards in the four years since then.

In 2024, X (formerly Twitter) posts saw a year-over-year change in impressions of 98.24 percent. Interactions saw a change of 8.85 percent, and overall engagement decreased by -38.05 percent.

This dataset provided statistics and performance metrics about the volume and responsiveness in engaging with customers via several customer engagement channels. Data was provided for New York City Transit Subway and Bus customer engagement and customer service teams between May 2017 and May 2022.

This dataset comes from the Biennial City of Tempe Employee Survey questions related to employee engagement. Survey respondents are asked to rate their level of agreement on a scale of 5 to 1, where 5 means "Strongly Agree" and 1 means "Strongly Disagree".This dataset includes responses to the following statements:Overall, I am satisfied with the level of employee engagement in my Department I have been mentored at work. Overall, how satisfied are you with your current job? Participation in the survey is voluntary and confidential.This page provides data for the Employee Engagement performance measure. The performance measure dashboard is available at 2.13 Employee Engagement.Additional Information Source: Community Attitude SurveyContact:  Wydale Holmes Contact E-Mail:  wydale_holmes@tempe.govData Source Type:  ExcelPreparation Method:  Data received from vendor (Community Survey)Publish Frequency:  AnnualPublish Method:  ManualData Dictionary

Some common types of content used in content marketing include:

• Blog posts and articles
• Videos
• Podcasts
• Infographics
• E-books and whitepapers
• Webinars and online courses
• Social media posts
• Email newsletters
• Case studies
• Interactive content (quizzes, polls, etc.)

Businesses can use content marketing to reach their target audience, establish thought leadership, generate leads, and support various stages of the customer journey.

Businesses can cultivate relationships with their audiences and ultimately influence their purchasing decisions by consistently providing high-quality content.

Employee engagement in the world increased from 2011 to 2020, but dropped slightly the next years. It stood at 67 percent in 2022 and 2023. It was at its highest in 2020 when it reached 69 percent.

Comprehensive dataset of U.S. employee engagement trends and statistics for 2023

Employee engagement data from an employee survey conducted by Pierce County and completed voluntarily by employees. Numeric responses correspond with the following answers: 0=N/A, 1=Strongly Disagree, 2=Disagree, 3=Agree, 4=Strongly Agree.

Collaboratory is a software product developed and maintained by HandsOn Connect Cloud Solutions. It is intended to help higher education institutions accurately and comprehensively track their relationships with the community through engagement and service activities. Institutions that use Collaboratory are given the option to opt-in to a data sharing initiative at the time of onboarding, which grants us permission to de-identify their data and make it publicly available for research purposes. HandsOn Connect is committed to making Collaboratory data accessible to scholars for research, toward the goal of advancing the field of community engagement and social impact.Collaboratory is not a survey, but is instead a dynamic software tool designed to facilitate comprehensive, longitudinal data collection on community engagement and public service activities conducted by faculty, staff, and students in higher education. We provide a standard questionnaire that was developed by Collaboratory’s co-founders (Janke, Medlin, and Holland) in the Institute for Community and Economic Engagement at UNC Greensboro, which continues to be closely monitored and adapted by staff at HandsOn Connect and academic colleagues. It includes descriptive characteristics (what, where, when, with whom, to what end) of activities and invites participants to periodically update their information in accordance with activity progress over time. Examples of individual questions include the focus areas addressed, populations served, on- and off-campus collaborators, connections to teaching and research, and location information, among others.The Collaboratory dataset contains data from 45 institutions beginning in March 2016 and continues to grow as more institutions adopt Collaboratory and continue to expand its use. The data represent over 6,200 published activities (and additional associated content) across our user base.Please cite this data as:Medlin, Kristin and Singh, Manmeet. Dataset on Higher Education Community Engagement and Public Service Activities, 2016-2023. Collaboratory [producer], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-07-07. https://doi.org/10.3886/E136322V1When you cite this data, please also include: Janke, E., Medlin, K., & Holland, B. (2021, November 9). To What End? Ten Years of Collaboratory. https://doi.org/10.31219/osf.io/a27nb

As of December 2020, 52 percent of North American retailer survey respondents stated that their main customer engagement priority for 2021 was offering additional customer delivery options and pickup. Improving and personalizing the customer journey featured in a number of the top priorities for retailers.

Data DescriptionThe DIPSER dataset is designed to assess student attention and emotion in in-person classroom settings, consisting of RGB camera data, smartwatch sensor data, and labeled attention and emotion metrics. It includes multiple camera angles per student to capture posture and facial expressions, complemented by smartwatch data for inertial and biometric metrics. Attention and emotion labels are derived from self-reports and expert evaluations. The dataset includes diverse demographic groups, with data collected in real-world classroom environments, facilitating the training of machine learning models for predicting attention and correlating it with emotional states.Data Collection and Generation ProceduresThe dataset was collected in a natural classroom environment at the University of Alicante, Spain. The recording setup consisted of six general cameras positioned to capture the overall classroom context and individual cameras placed at each student’s desk. Additionally, smartwatches were used to collect biometric data, such as heart rate, accelerometer, and gyroscope readings.Experimental SessionsNine distinct educational activities were designed to ensure a comprehensive range of engagement scenarios:News Reading – Students read projected or device-displayed news.Brainstorming Session – Idea generation for problem-solving.Lecture – Passive listening to an instructor-led session.Information Organization – Synthesizing information from different sources.Lecture Test – Assessment of lecture content via mobile devices.Individual Presentations – Students present their projects.Knowledge Test – Conducted using Kahoot.Robotics Experimentation – Hands-on session with robotics.MTINY Activity Design – Development of educational activities with computational thinking.Technical SpecificationsRGB Cameras: Individual cameras recorded at 640×480 pixels, while context cameras captured at 1280×720 pixels.Frame Rate: 9-10 FPS depending on the setup.Smartwatch Sensors: Collected heart rate, accelerometer, gyroscope, rotation vector, and light sensor data at a frequency of 1–100 Hz.Data Organization and FormatsThe dataset follows a structured directory format:/groupX/experimentY/subjectZ.zip Each subject-specific folder contains:images/ (individual facial images)watch_sensors/ (sensor readings in JSON format)labels/ (engagement & emotion annotations)metadata/ (subject demographics & session details)Annotations and LabelingEach data entry includes engagement levels (1-5) and emotional states (9 categories) based on both self-reported labels and evaluations by four independent experts. A custom annotation tool was developed to ensure consistency across evaluations.Missing Data and Data QualitySynchronization: A centralized server ensured time alignment across devices. Brightness changes were used to verify synchronization.Completeness: No major missing data, except for occasional random frame drops due to embedded device performance.Data Consistency: Uniform collection methodology across sessions, ensuring high reliability.Data Processing MethodsTo enhance usability, the dataset includes preprocessed bounding boxes for face, body, and hands, along with gaze estimation and head pose annotations. These were generated using YOLO, MediaPipe, and DeepFace.File Formats and AccessibilityImages: Stored in standard JPEG format.Sensor Data: Provided as structured JSON files.Labels: Available as CSV files with timestamps.The dataset is publicly available under the CC-BY license and can be accessed along with the necessary processing scripts via the DIPSER GitHub repository.Potential Errors and LimitationsDue to camera angles, some student movements may be out of frame in collaborative sessions.Lighting conditions vary slightly across experiments.Sensor latency variations are minimal but exist due to embedded device constraints.CitationIf you find this project helpful for your research, please cite our work using the following bibtex entry:@misc{marquezcarpintero2025dipserdatasetinpersonstudent1, title={DIPSER: A Dataset for In-Person Student1 Engagement Recognition in the Wild}, author={Luis Marquez-Carpintero and Sergio Suescun-Ferrandiz and Carolina Lorenzo Álvarez and Jorge Fernandez-Herrero and Diego Viejo and Rosabel Roig-Vila and Miguel Cazorla}, year={2025}, eprint={2502.20209}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2502.20209}, } Usage and ReproducibilityResearchers can utilize standard tools like OpenCV, TensorFlow, and PyTorch for analysis. The dataset supports research in machine learning, affective computing, and education analytics, offering a unique resource for engagement and attention studies in real-world classroom environments.

According to a global report conducted between January 2021 and December 2023, TikTok had the highest engagement rate per post when compared to Instagram, Facebook, and X (formerly Twitter). Overall, TikTok's average engagement rate per post was 2.65 percent in 2023. Instagram's engagement rate stood at 0.7 percent in 2023, and Facebook's stood at 0.15 percent.

In 2021, almost 45 percent of professionals worldwide revealed that employee in their organization are highly engaged in delivering customer experience (CX). During the survey, 25.3 percent of customers rated organizations' CX capability seven out of ten (7/10).

The Report Covers Patient Engagement Companies and it is segmented by Component (Hardware, Software, and Service), Delivery Mode (Web-based and Cloud-based, and On-Premise), Application (Social Management, Fitness and Health Management, Home Healthcare Management, and Financial Management), End User (Provider Payer, Patient, and Other End Users), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and South America). The market provides the value (in USD million) for the above-mentioned segments.

Percentage of households that engaged in activities aimed at conservation or protection of the environment or wildlife, and of those that did the type of activity. The data is from the Households and the environment survey.

Customer Engagement Data from Social Media Users

The employee engagement market is projected to be worth US$ 803.0 Million in 2024. The market is anticipated to reach US$ 3,603.9 Million by 2034. The market is further expected to surge at a CAGR of 16.2% during the forecast period 2024 to 2034.

Attributes	Key Insights
Employee Engagement Market Estimated Size in 2024	US$ 803.0 Million
Projected Market Value in 2034	US$ 3,603.9 Million
Value-based CAGR from 2024 to 2034	16.2%

Country-wise Insights

Countries	Forecast CAGRs from 2024 to 2034
The United States	13.1%
Germany	15.7%
Japan	17.4%
China	16.7%
Australia and New Zealand	19.7%

Category-wise Insights

Category	Market Share in 2024
Integrated Employee Engagement Platform	53.5%
Cloud Based	48.0%

Report Scope

Attribute	Details
Estimated Market Size in 2024	US$ 803.0 Million
Projected Market Valuation in 2034	US$ 3,603.9 Million
Value-based CAGR 2024 to 2034	16.2%
Forecast Period	2024 to 2034
Historical Data Available for	2019 to 2023
Market Analysis	Value in US$ Million
Key Regions Covered	North America Latin America Western Europe Eastern Europe South Asia and Pacific East Asia The Middle East & Africa
Key Market Segments Covered	Solution Deployment Enterprise Size Industry Region
Key Countries Profiled	The United States Canada Brazil Mexico Germany France France Spain Italy Russia Poland Czech Republic Romania India Bangladesh Australia New Zealand China Japan South Korea GCC countries South Africa Israel
Key Companies Profiled	Cisco Systems IBM Corporation Microsoft Corporation Google Bitrix24 Atlassian Corporation Plc Xoxoday Connecteam Officevibe (GSoft Group Inc.) ContactMonkey Inc. Lattice Peakon BambooHR Impraise LumApps SAS

Financial overview and grant giving statistics of Asian American Institute for Research & Engagement

In 2024, posts on X (formerly Twitter) generated an average of 31.46 likes, down from 37.82 likes in 2023. Mentions and reposts also declines year-over-year, and in 2024, the average post on the platform received 1.56 likes and 8.47 mentions.