Friday 27th Jan 4PM UTC: we're live here - talking about the article below. It's also recorded for future pleasurable listening.
NFT space is an emerging area of human interest and thus an interesting space for observing human behaviour. NFTs offer opportunity for teams and individuals to build projects and gather international interest around themselves easier than it would be possible if such space didn’t exist. Also, NFTs offer platform to artists of any kind to show their work and possibly earn something from it, easier than it would be if such space didn’t exist. With those characteristics - novel space with opportunities of creating and building projects, intertwined with possibilities of earning money - this screams for closer attention and observation. Who thrives? Who fails? What does that space revolve around? What is it all about?
Thesis & Dysmorphia is a project with a main goal of gathering data about CNFT space, mostly data about one CNFT project in particular, and publishing the Scientific study about it. The project in question is our own “Dysmorphia” PFP collection, and by putting scientific lens on while observing it, we are making a “Case Study” out of it. The study will bring conclusions to some important questions such as:
What makes people believe in project's success?
What brings the most eyes to project?
What influences people’s sentiment about it?
What helps building the project?
What aggravates it?
What is hype?
The following text serves as a recap of the past months since the study started (we are around 4 months in). Of course, giving out exact results which we have now is not an option, nor it will be an option at any time before the full study is out. Therefore, what we can provide here is “show you something, while telling you nothing”. Lines of thinking, explaining a bit about data we are collecting and giving you hints of how it will all look like.
It is important to notice that anyone who interacts with T&D project in any way, is most likely being a part of the data collected. Not by their name nor explicitly in any way, but since every like/message/mint/buy/sell/number is a contribution to the statistics we are following, it is included in it. Participants data can be divided in several categories:
- Participants who fill up the surveys (OG-EG, WL-EG, Public participants who decide to fill them up)
- Participants who visit our website/read blogs
- Participants who fill out twitter polls
- Participants who play our games/quizzes
- Participants who mint/buy/sell our NFTs
- Participants who join our AMAs
- Participants who write in our chats
- Participants who are in our discord
- Participants who follow us on twitter
- & more
Main point of stating categories in such way is just to give you idea that each of those data is being measured as a separate entity. For example, we cannot exactly know what percentage of people who join our AMAs are the same people who are minting/buying our NFTs. For some of these categories it might be possible to follow people by name, and observing closely what are all the activities that one person joins in a project, though in this study we will not be doing it that way. Rather, as stated, those categories will be taken as separate entities, from which the main point of interest will be - does the change in numbers of participants happen proportionally in all categories.
Groups which are being followed in a cohort way are the OG-EGs and WL-EGs as it is known who are members of them (by discord/twitter names). Those members will not change till the end, since only those who started with us from September/October 2022 will ever be a part of those groups. That is what’s giving such importance to OG-EGs and WL-EGs in the first place, because it is the only data in this study which measures and follows the change in same people's sentiment pretty accurately.
The exact numbers of participants in each group is something that will be stated only at the end, when the study is published, since that is also a part of results.
Data gathering methods
There are two main logics behind gathering data in this study, pretty commonly used logics for any kind of observational study.
First - qualitative data: participant’s answers to certain questions, their comments, thoughts, suggestions and questions we get back from them. Also, when possible - following unique members and their reuccurence numbers.
Second - purely quantitative data: numbers. Be it discord/twitter numbers, be it numbers of poll answers, be it numbers of mints/buy/sells. Any number which we can find, we will write it down, chew it up, and try to find the connections and correlations between them and all the other information which we have. If you glance back at the participant categories stated above you can observe that each will be followed with one or both of these logics.
For example, surveys will be followed both quantitatively, as in how many participants filled them out and how many participants answered what, but also qualitatively since there are questions which require from participant to explain something in his/her own words, i.e. give a qualitative perspective.
Also, one important combination of qualitative and quantitative data are the moments in which we are measuring impacts of certain catalyst factors to numbers we are measuring.
Question we regularly get from participants is: “Why do we get the same questions every time?”.
Many of the questions are the same every time exactly so we could measure the quantitative change in answers. It is because we have the same question in every survey throughout the year, e.g. “How do you feel about Thesis & Dysmorphia?”, by having the difference in percentages of different answers (bullish, bearish, indifferent) to that question, followed through time, we can get an idea of how is the sentiment of that group changing. When analysed and checked for statistical significance in any of those data, we can gather some powerful conclusions in the end. Important to notice is that the surveys are put out in moments to be able to follow the change of sentiment happening due to some of the events. All those data will then be combined with all the other numbers we are getting from other channels and analyzed to help us get some conclusions.
Details coming at the end of the study. Any pottential casual relationships will be looked for in all the data.
As stated earlier, we cannot share much, if any, of the results at this point in time. Here below is one part of our visual ideas for the final version of the study, though graphic data here do not represent results to our study (all mixed up). In the study itself we wanna make the data as easily digestible as possible hence all the data will be shown visually, whenever possible. Find below simplified example of how certain timelines, events, numbers and results could look like in the final study.
The greatest part of every Scientific paper is discussion. The moment when we will be able to start writing final discussion will be cherry on top to all the work that’s being put into this. As for now, we will not be discussing results per se, though nevertheless we can use these reports which may come at few more points in time before the final study is out, as an opportunity to - discuss.
I keep discussing some questions with myself.
What is success?
Is success - the amount of hype that you’ve got in your project?
Is making the project based on hype - a real valuable thing?
Does it mean that if you have hype - you will succeed?
Is this whole space revolving around hype?
Does making a great technological utility guarantee success?
Is making a project based on technological utility better?
Better for what? Better than what?
Can utility without hype succeed?
Is there any chance for a project with no utility and no hype to succeed?
What is people’s sentiment on projects based on?
Are people in this space just for the money?
Many questions, many answers still to be obtained. They keep me going forward and that’s why we’re doing this in first place. This project gives us ground to observe, research, think and overthink - about this space. Connecting the dots in something so fast-going like this - where it will be like ages have passed from the moment we’ve started the study to the moment we finish it - might be the one of greatest feats we’ve put ourselves through till now.
One question which naturally arises on top of all the others is:
What if this whole research could give us a formula for success?
That surely is a main object of this study - to learn all about how to build a successful project. All the hows, whens, whats, and whys. You may think to yourself - come on, you didn’t mint out your first mint - what can you tell me about building a successful project? There are few factors to this.
Not minting out instantly + the nature of our project (study accumulating data) means that we have data which can point us to reasons why that happened. Of course, at this moment we cannot start all over “smarter” with the new knowledge we have - but we do have that knowledge. And added to that, in a bit tougher situation right now, while dealing with the slow mint and all it’s perks, we are actually learning about whole other side to project building. And it’s again - new knowledge. Report about that new knowledge will come after the first mint is sold out.
Looking forward to the future lab fam.
Thank you for being with us on this journey, if you like what we're doing, support us by minting NFTs from first drop here. Love, Thesis