Leveraging comprehensive, segmented survey testing sounds like a robust approach to gather targeted feedback and refine the user experience on CRITICICE. By creating customized user profiles and evaluating hypotheses before development, you can make informed decisions that resonate with your users’ behavior and preferences.
Could you elaborate on how CRITICICE plans to create and maintain these customized user profiles? Are there specific data points or metrics that will be used to segment users effectively?
We’re also developing a project on Tron, by the way. Let me tell you a bit about us.
Strongcoin is a tap-to-earn game. In our game, there’s onboarding to the Tron ecosystem, where we publish partner quests. If you’re interested, we can arrange a collaboration.
Currently, we’re running a contest for the best question/suggestion with cool prizes! Come to our topic and read more
Thank you for your question! At CRITICICE, Customized user profiles allowes tracking user interactions, and allowing users to specify their interests and strengths. We’ll segment users based on activity levels, interests, and engagement patterns. Key data points will include user behavioral data, and preferences. Our approach ensures that we can provide a personalized experience, continually updated based on user feedback and engagement.
You seem not to prioritize community engagement and user feedback at all, no offense. My opinion is spurned from the fact that you missed my question by 13days, approx 2 weeks if we’re calculating based on working days, excluding weekends, damn!!
Sure, it’s already tracked - likes, boosts, comments (CRITICICE app analyzes the mood of the comments left by user using Gemini AI from Google - it’s in test mode), user’s specified pool of interest: e.g. community, BD, marketing or similar
It’s ok, it struck me at first as a flaw I needed to point out, I get it how prompt replies can be tedious and inquiries in the forum can be overwhelming but do your very best in the regard of prioritizing community feedback even if it’ll warrant hiring a social media handler, thanks.
This is indeed a great start, leveraging metrics like likes, boosts, comments, and specified interests to track user behavior and preferences. I’m a little bit skeptical about the use of AI to analyze the mood of comments, though undeniably it is particularly innovative and can provide deeper insights into user sentiment.
Do users have the option to opt-out of certain data tracking features if they prefer more privacy?
All clear, and I personally feel this is crucial for tailoring the user experience while maintaining user privacy and transparency.
Just wondering, how specifically do you use this personalized data to tailor content and recommendations?
It could be used to optimize project recommendations as well as recommend users to connect with.
We are planning to have more content-oriented features that will help users discover new projects and find more useful content from users and experts
We are actively working on the Smart Contracts part to achieve the ideas set while providing users with a seamless and transparent experience of app usage
How do you plan to recommend users to connect with others? Will these recommendations be based solely on shared interests and professional positions, or will other factors such as engagement patterns and past interactions also play a role?
correct, firstly it will be related to the specified interests. However, in future updates, it will also be based on the user’s activity, such as overall connecting activity, related project references and so on