Employee Engagement Survey Best Practices

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Employee engagement is a critical aspect in the success of any organisation. It plays an important role in encouraging a positive work environment, increasing productivity, and reducing turnover.

One effective tool organisations can use to measure and improve employee engagement is the employee engagement survey. This article will explain some best practices for conducting employee engagement surveys and extracting meaningful insights to drive positive organisational change.

So, let’s delve into employee engagement surveys and discover how they can benefit your workforce.

The importance of employee engagement surveys

Employee engagement surveys provide valuable insights into employees’ thoughts, feelings, and experiences within an organisation. Organisations can better understand what motivates and drives their workforce by gathering employee feedback.

These surveys enable organisations to identify areas that require improvement. It thereby facilitates the implementation of targeted strategies to enhance employee engagement.

Furthermore, employee engagement surveys contribute to developing a culture of open communication and collaboration. When employees feel their opinions are valued and their voices are heard, they are more likely to be engaged and committed to their work.

By providing a channel for staff to air their concerns and suggestions, organisations can foster a sense of ownership and empowerment among their workforce.

Employee engagement surveys also play a crucial role in aligning the organisation’s goals with the needs and expectations of its workforce. On top of leveraging learnings from popular hr degrees online, gathering employee insights help organisations can better understand the factors which drive engagement and tailor their strategies accordingly. This alignment creates a sense of purpose and shared vision. It is because employees feel their contributions are valued and connected to the broader organisational objectives.

Moreover, the survey results provide a benchmark for measuring progress and tracking improvements over time. By regularly assessing employee engagement levels through surveys, organisations can identify trends and evaluate the effectiveness of implemented initiatives.

It also helps make informed decisions to enhance employee satisfaction and overall organisational performance continuously.

Now, let’s take a look at some of the best practices in dealing with employee engagement surveys.

Establish clear objectives (objectives matter)

Establishing clear objectives is the first step in conducting an effective employee engagement survey. Clearly define what you aim to achieve through the survey, whether identifying areas of improvement, measuring progress, or evaluating the effectiveness of existing initiatives.

Having well-defined objectives helps design a survey which aligns with your goals and enables you to gather the most relevant data. Without clear objectives, the survey may lack focus and fail to provide meaningful insights.

To establish clear objectives, consider the areas of employee engagement you want to measure. Are you interested in gauging overall satisfaction, evaluating specific aspects of the work environment, or identifying factors contributing to employee motivation?

Once you have identified your objectives, clearly communicate them to the survey team and ensure all survey questions and metrics align with them.

Design a well-crafted survey (crafting the perfect survey)

Designing a well-crafted survey is crucial to encourage maximum employee participation and obtain accurate and meaningful responses. Start by keeping the survey concise and focused.

Long surveys can be overwhelming and may result in incomplete or rushed responses. Use clear and simple language to ensure all employees can comprehend and respond to the questions easily. Avoid jargon or technical terms which may confuse or alienate participants.

Consider also the order and flow of the survey questions. Begin with general and less sensitive topics before moving to more specific and potentially sensitive areas. This helps in building momentum and trust as employees progress through the survey.

Use a mix of question types, such as multiple-choice, rating scales, and open-ended questions, to gather both quantitative and qualitative data. Multiple-choice questions provide structured data which employees can easily analyse.

Meanwhile, open-ended questions allow employees to provide detailed insights and suggestions. Striking the right balance between different question types ensures a comprehensive understanding of employee sentiments.

Communicate the purpose and importance (communicating the why)

To encourage employee participation, it is essential to communicate the purpose and importance of the survey effectively. Employees should understand what the survey is for.

They should know how it will benefit them and how you will use their feedback to drive positive organisational change. Communicate the survey’s objectives and their impact on shaping the work environment.

Use multiple communication channels to create awareness and emphasise the significance of the survey. Send out personalised Emails to employees, highlighting the purpose of the survey. You can also add its potential to improve their experience at work.

Leverage company-wide announcements or internal newsletters to reach a broader audience. Consider hosting informational sessions or town hall meetings to address employees’ questions or concerns.

By emphasising the purpose and importance, employees are more likely to feel motivated to participate and provide honest and thoughtful responses. They need to understand their feedback is valued and will make a difference in shaping the organisation’s future initiatives and policies.

Ensure confidentiality and anonymity (building trust)

Confidentiality and anonymity are critical factors in obtaining honest and authentic employee responses. Assure employees their survey responses will be confidential.

Also, individual responses will not be attributed or traced back to them. This fosters a sense of trust and encourages employees to provide candid feedback without fear of reprisal or judgement.

To maintain confidentiality, utilise a third-party survey platform or software which ensures anonymity. This way, employees can feel comfortable expressing their opinions without worrying about repercussions.

Communicate the steps to safeguard their responses, including data encryption, secure storage, and limited access to survey results.

It’s also important to explain how the data will be aggregated and reported. By sharing which only group-level data will be analysed and reported, you further reinforce confidentiality and alleviate employees’ concerns about their responses.

Provide clear instructions (guidance matters)

When conducting an employee engagement survey, it’s crucial to provide clear instructions to employees regarding completing the survey. Ensure employees understand the purpose of each question and how to respond accurately.

If you use specific terms or concepts in the survey, provide clear definitions or examples to avoid confusion.

Consider including progress indicators or a completion bar within the survey interface. This gives employees a sense of their progress and encourages them to complete the survey. Provide estimated time requirements so employees can plan their participation accordingly.

When you provide clear instructions, you minimise the chances of misinterpretation and ensure employees can provide accurate and meaningful responses.

Determine timing and frequency (timing is everything)

Knowing the right timing and frequency for employee engagement surveys is essential to maximise participation and obtain reliable data. When scheduling the survey, consider organisational events, workload fluctuations, and employee availability.

Avoid peak periods or high stress when employees may not have sufficient time or mental bandwidth to dedicate to the survey.

The frequency of surveys is equally important. While conducting surveys too frequently can lead to survey fatigue, waiting too long between surveys may result in missed opportunities for improvement.

Strike a balance based on your organisation’s unique needs and culture. Annual or biannual surveys are common frequencies which allow for monitoring progress and identifying trends over time.

However, consider implementing pulse surveys or shorter surveys between the larger ones to capture real-time feedback and address emerging issues promptly.

Analyse and interpret survey results (unveiling insights)

The true value of an employee engagement survey lies in the analysis and interpretation of the gathered data. Once you collect the survey responses, it’s crucial to analyse the data thoroughly to uncover meaningful insights.

Start by collating and organising the data using a survey analysis tool or software. Calculate response rates to assess the representativeness of the sample.

Analyse the quantitative data using statistical techniques such as averages, percentages, and correlations to identify patterns and trends.

Pay attention to areas with particularly high or low scores and explore the underlying factors contributing to those outcomes.

In addition to quantitative data, analyse the qualitative responses from open-ended questions. Categorise and summarise common themes and sentiments expressed by employees. These qualitative insights provide valuable context and depth to the quantitative findings.

Once you analyse the data, interpret the results by comparing them to established benchmarks or external data sources.

This helps understand how your organisation’s employee engagement levels compare to industry standards or similar organisations. Identifying areas of strength and areas for improvement becomes more apparent with benchmarking.

Take action and implement changes (driving positive change)

The ultimate purpose of conducting an employee engagement survey is to drive positive change within the organisation. It’s essential to take action based on the survey findings and implement changes addressing the identified improvement areas.

Simply collecting survey data without acting upon it can lead to employees’ disillusionment and undermine their trust in the survey process.

Start by prioritising the key areas for improvement based on the survey results. Identify actionable steps which can be taken to address these areas and improve employee engagement.

Involve key stakeholders, such as managers and department heads, in decision-making to ensure buy-in and effective implementation.

Develop an action plan which outlines specific initiatives, timelines, and responsible parties for each identified area of improvement. Break down the bigger goals into smaller, achievable steps to make progress more manageable.

Assign clear roles and responsibilities to individuals or teams, and establish regular check-ins to monitor progress and provide support where needed.

It’s important to communicate the action plan and progress updates to employees. Transparency and involvement are key to maintaining employee trust and engagement throughout the change process.

Regularly share updates, milestones, and successes with the workforce, demonstrating their feedback drives tangible improvements.

Monitor the impact of the implemented changes on employee engagement over time. Conduct follow-up or pulse surveys to assess whether the initiatives have had the desired effect. Use the feedback received to refine and adjust your strategies as needed.

Evaluate and adjust survey methods (continuous Improvement)

Even the most well-designed employee engagement survey can benefit from continuous evaluation and improvement. Gather employee feedback regarding their survey experiences, such as the clarity of questions, ease of response, and overall satisfaction with the process. Use this feedback to make necessary adjustments and enhancements to the survey methodology.

Consider adopting new survey tools or technologies which streamline data collection, analysis, and reporting. Stay updated on advancements in survey methodologies and explore innovative ways to gather employee feedback.

For example, you could experiment with mobile surveys, real-time feedback mechanisms, or gamification elements to enhance employee participation and engagement.

Regularly assess the effectiveness of your survey methods by evaluating response rates, participation levels, and the quality of the data collected. Compare your survey results over time to identify trends and patterns.

Continuously refining and improving your survey methods ensures the data collected remains relevant, reliable, and valuable in driving organisational change.

Communicate progress and celebrate success (acknowledging achievements)

Throughout the employee engagement journey, it is essential to communicate progress and celebrate success. Regularly update employees on the initiatives and improvements made due to their feedback.

Share success stories and highlight the positive impact their participation has had on the organisation’s culture and performance.

Acknowledge and appreciate individuals or teams which have significantly improved employee engagement. This could include recognising their efforts during team meetings, award ceremonies, or company-wide events.

Celebrating success reinforces the value of employee engagement and encourages continued participation in future surveys and initiatives.

Openly communicate progress and milestones, providing visibility into the positive changes which have occurred as a direct result of the survey feedback. By sharing tangible outcomes, you inspire a sense of employee pride and ownership, fostering a culture of engagement and commitment.

In Summary

Employee engagement surveys are powerful tools for organisations to measure and enhance employee engagement. By following best practices, you can create an impactful approach to improving employee engagement within your organisation.

Remember, employee engagement is an ongoing process, and the surveys are one part of a larger strategy. Continuously communicate with your employees, involve them in decision-making processes, and implement targeted initiatives based on their feedback.

You can create a thriving work environment which fosters productivity by prioritising employee engagement and consistently seeking their input.

Author Bio: Natasha is a content marketing specialist who thinks it’s kind of fun creating content marketing strategies for SaaS businesses. In her free time, she likes spending time watching Netflix.

The post Employee Engagement Survey Best Practices appeared first on The 6Q Blog.

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DynIBaR: Space-time view synthesis from videos of dynamic scenes

Posted by Zhengqi Li and Noah Snavely, Research Scientists, Google Research

A mobile phone’s camera is a powerful tool for capturing everyday moments. However, capturing a dynamic scene using a single camera is fundamentally limited. For instance, if we wanted to adjust the camera motion or timing of a recorded video (e.g., to freeze time while sweeping the camera around to highlight a dramatic moment), we would typically need an expensive Hollywood setup with a synchronized camera rig. Would it be possible to achieve similar effects solely from a video captured using a mobile phone’s camera, without a Hollywood budget?

In “DynIBaR: Neural Dynamic Image-Based Rendering”, a best paper honorable mention at CVPR 2023, we describe a new method that generates photorealistic free-viewpoint renderings from a single video of a complex, dynamic scene. Neural Dynamic Image-Based Rendering (DynIBaR) can be used to generate a range of video effects, such as “bullet time” effects (where time is paused and the camera is moved at a normal speed around a scene), video stabilization, depth of field, and slow motion, from a single video taken with a phone’s camera. We demonstrate that DynIBaR significantly advances video rendering of complex moving scenes, opening the door to new kinds of video editing applications. We have also released the code on the DynIBaR project page, so you can try it out yourself.

Given an in-the-wild video of a complex, dynamic scene, DynIBaR can freeze time while allowing the camera to continue to move freely through the scene.

Background

The last few years have seen tremendous progress in computer vision techniques that use neural radiance fields (NeRFs) to reconstruct and render static (non-moving) 3D scenes. However, most of the videos people capture with their mobile devices depict moving objects, such as people, pets, and cars. These moving scenes lead to a much more challenging 4D (3D + time) scene reconstruction problem that cannot be solved using standard view synthesis methods.

Standard view synthesis methods output blurry, inaccurate renderings when applied to videos of dynamic scenes.

Other recent methods tackle view synthesis for dynamic scenes using space-time neural radiance fields (i.e., Dynamic NeRFs), but such approaches still exhibit inherent limitations that prevent their application to casually captured, in-the-wild videos. In particular, they struggle to render high-quality novel views from videos featuring long time duration, uncontrolled camera paths and complex object motion.

The key pitfall is that they store a complicated, moving scene in a single data structure. In particular, they encode scenes in the weights of a multilayer perceptron (MLP) neural network. MLPs can approximate any function — in this case, a function that maps a 4D space-time point (x, y, z, t) to an RGB color and density that we can use in rendering images of a scene. However, the capacity of this MLP (defined by the number of parameters in its neural network) must increase according to the video length and scene complexity, and thus, training such models on in-the-wild videos can be computationally intractable. As a result, we get blurry, inaccurate renderings like those produced by DVS and NSFF (shown below). DynIBaR avoids creating such large scene models by adopting a different rendering paradigm.

DynIBaR (bottom row) significantly improves rendering quality compared to prior dynamic view synthesis methods (top row) for videos of complex dynamic scenes. Prior methods produce blurry renderings because they need to store the entire moving scene in an MLP data structure.

Image-based rendering (IBR)

A key insight behind DynIBaR is that we don’t actually need to store all of the scene contents in a video in a giant MLP. Instead, we directly use pixel data from nearby input video frames to render new views. DynIBaR builds on an image-based rendering (IBR) method called IBRNet that was designed for view synthesis for static scenes. IBR methods recognize that a new target view of a scene should be very similar to nearby source images, and therefore synthesize the target by dynamically selecting and warping pixels from the nearby source frames, rather than reconstructing the whole scene in advance. IBRNet, in particular, learns to blend nearby images together to recreate new views of a scene within a volumetric rendering framework.

DynIBaR: Extending IBR to complex, dynamic videos

To extend IBR to dynamic scenes, we need to take scene motion into account during rendering. Therefore, as part of reconstructing an input video, we solve for the motion of every 3D point, where we represent scene motion using a motion trajectory field encoded by an MLP. Unlike prior dynamic NeRF methods that store the entire scene appearance and geometry in an MLP, we only store motion, a signal that is more smooth and sparse, and use the input video frames to determine everything else needed to render new views.

We optimize DynIBaR for a given video by taking each input video frame, rendering rays to form a 2D image using volume rendering (as in NeRF), and comparing that rendered image to the input frame. That is, our optimized representation should be able to perfectly reconstruct the input video.

We illustrate how DynIBaR renders images of dynamic scenes. For simplicity, we show a 2D world, as seen from above. (a) A set of input source views (triangular camera frusta) observe a cube moving through the scene (animated square). Each camera is labeled with its timestamp (t-2, t-1, etc). (b) To render a view from camera at time t, DynIBaR shoots a virtual ray through each pixel (blue line), and computes colors and opacities for sample points along that ray. To compute those properties, DyniBaR projects those samples into other views via multi-view geometry, but first, we must compensate for the estimated motion of each point (dashed red line). (c) Using this estimated motion, DynIBaR moves each point in 3D to the relevant time before projecting it into the corresponding source camera, to sample colors for use in rendering. DynIBaR optimizes the motion of each scene point as part of learning how to synthesize new views of the scene.

However, reconstructing and deriving new views for a complex, moving scene is a highly ill-posed problem, since there are many solutions that can explain the input video — for instance, it might create disconnected 3D representations for each time step. Therefore, optimizing DynIBaR to reconstruct the input video alone is insufficient. To obtain high-quality results, we also introduce several other techniques, including a method called cross-time rendering. Cross-time rendering refers to the use of the state of our 4D representation at one time instant to render images from a different time instant, which encourages the 4D representation to be coherent over time. To further improve rendering fidelity, we automatically factorize the scene into two components, a static one and a dynamic one, modeled by time-invariant and time-varying scene representations respectively.

Creating video effects

DynIBaR enables various video effects. We show several examples below.

Video stabilization

We use a shaky, handheld input video to compare DynIBaR’s video stabilization performance to existing 2D video stabilization and dynamic NeRF methods, including FuSta, DIFRINT, HyperNeRF, and NSFF. We demonstrate that DynIBaR produces smoother outputs with higher rendering fidelity and fewer artifacts (e.g., flickering or blurry results). In particular, FuSta yields residual camera shake, DIFRINT produces flicker around object boundaries, and HyperNeRF and NSFF produce blurry results.

Simultaneous view synthesis and slow motion

DynIBaR can perform view synthesis in both space and time simultaneously, producing smooth 3D cinematic effects. Below, we demonstrate that DynIBaR can take video inputs and produce smooth 5X slow-motion videos rendered using novel camera paths.

Video bokeh

DynIBaR can also generate high-quality video bokeh by synthesizing videos with dynamically changing depth of field. Given an all-in-focus input video, DynIBar can generate high-quality output videos with varying out-of-focus regions that call attention to moving (e.g., the running person and dog) and static content (e.g., trees and buildings) in the scene.

Conclusion

DynIBaR is a leap forward in our ability to render complex moving scenes from new camera paths. While it currently involves per-video optimization, we envision faster versions that can be deployed on in-the-wild videos to enable new kinds of effects for consumer video editing using mobile devices.

Acknowledgements

DynIBaR is the result of a collaboration between researchers at Google Research and Cornell University. The key contributors to the work presented in this post include Zhengqi Li, Qianqian Wang, Forrester Cole, Richard Tucker, and Noah Snavely.