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Tvori, Animation in VR | Review Analysis

Review analysis for Tvori, Animation in VR app.

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Tvori, Animation in VR| Review Analysis

 

About the App

Tvori is a VR animation tool that allows to create, animate and record scenes solely in VR.

The tool is currently in early access and has limited functionality. The team are working on developing new features and making Tvori stable and reliable. It is available to get in Oculus Store, Steam and Viveport.

In this project, I conducted review analysis to assess users’ attitudes, and if there are any patterns in given feedback by reviewers who have been using the tool.

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Review Analysis

Review analysis is a qualitative research methodology that gathers raw product reviews, codes them using qualitative techniques, and formats the output into actionable findings. The purpose of this exercise is to capture the structure of how and why a particular topic is described in a particular way by the active users of the product.

For the review analysis, I collected the reviews from Oculus Store and Steam that were given over the past 1 year. While doing this research I formulated a few focus questions:

  • “How easy is it to use Tvori?”

  • “What is the overall sentiment towards Tvori?”

  • “What are the overall expectations from Tvori?”


Reviews coding

I have had 16 responses available to analyse, and conducted an open coding exercise (grouping concepts and speech patterns), keeping in mind the focus questions. Going through this exercise, I identified 6 groups of trends in reviews:

  1. More positive sentiment

  2. Expectations from Tvori

  3. Time, ease of use & efficiency

  4. Feedback on new updates

  5. Communication with creators

  6. More negative sentiment or constructive feedback

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Speech pattern analysis

I have then conducted speech pattern analysis to be able to assess people’s attitude, confidence, and satisfaction levels through their language.

  1. Often use of markers of high expectation, or words that identify development and some sense of direction for future:

    “has potential”, “extremely promising”, “on the right track”, “heading in the right direction”.

  2. Occasional use of markers that point out that the current expectations weren’t met, more specifically words of expectation or desire for a particular feature, like “Hope ... but / was hoping / on my wish list”:

    “I was hoping it will help with 3d puppetry for drawing my mangas, but...”, “Hope there were more model importing feature, but...”, “I hope they might make things more centeralized”, “features on my wish list that I am sending to developers”.

  3. One of the most commonly used language markers is “so far”, which may speak about some level of user’s uncertainty, need to investigate more, or their expectation for the trend to continue:

    “so far impressive”, “pretty solid so far”, “promising so far”

  4. Occasional use of words identifying users’ exploration process (more negative sentiment), and trying to discover features, like “find / found / look for”:

    “I couldn’t find any way to save my features”, “ability to export was nowhere to be found”

  5. Often use of markers of excitement / high arousal, that include exclamation marks, capitalised conjunctions, multiplication of vowels, positive adjectives, etc.:

    “Waaaay ahead of game”, “has been amazing”, “keeps getting better”, “opened up a whole new world for me”, “brings the experience to a whole new level”, “I love Tvori!”, “Excited to see what this will look like over the next year!”, “really looking forward to”, “The option to animate objects in real time AND keyframe (with interpolation!!!) the parameters of any object puts this thing waay ahead of the game.”, “5 stars and two thumbs up!”, “Great work - really looking forward to updates!”

  6. Often use of words that identify speed, like “in / within …[time]”. Considering the context of usage of these words, in the given examples I suggest to refer to them as markers of efficiency:

    “created in a very short time”, “animated over about a minute”, “started animating within hours”, “was animating and creating within a span of 5-10 minutes”, “saved me a full days worth of work”

  7. A number of comments refer to Tvori as a representation of what VR experiences should be like:

    “Creative, interesting apps like this could be the best use of VR”, “one of the best VR apps”, “it really demonstrates the strengths of VR”

  8. A number of comments highlight it is easy to use, with words like “simple / intuitive / clear / easy”:

    “interface is easy to learn and fast to master”, “Simple and fun to use”, “tools are so simple and intuitive”, “Powerful and intuitive”, “the interface is clear and the tutorials make a great job at explaining the majorities of the features to get you up to speed”.

  9. A large number of comments express growing satisfaction with Tvori with each new update:

    ”really looking forward to updates!”, “recent updates has been amazing”, “keeps getting better with each update”, “This new updates brings the experience to a whole new level”.

I then assessed positive and negative comments on certain trends proportionally. This exercise gave an overall picture in reviews trends and demonstrated that more research has to be done around users’ expectations, how to meet them and learn what would motivate users to recommend the tool to their peers.


Conclusion

As a result of this exercise, we were able to answer the focus questions:

  • “How easy is it to use Tvori?”

  • “What is the overall sentiment towards Tvori?”

  • “What are the overall expectations from Tvori?”

Tvori is reviewed an easy to pick up animation tool, a good example of what VR experiences should be about. It is still evolving and at its early stages, and users have high expectations from it in future. Users’ sentiment is overall fairly positive. Users also enjoy the quick and responsive communication with Tvori’s creators.

The review analysis exercise suggests to do more research around target users’ expectations (what they are and how they can be met); as well as do some studies around what would motivate users to recommend the tool to their peers.

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Client: Tvori

Time: March 2019

Role: Review analysis

Hardware: Oculus Rift