The below outlines 8 types of users for rugpullindex.com.
Some users may represent one or more of these profiles at any one time however we have segmented them into different groups for easier analysis.
The 8 user profiles are:
Written by Scott Milat & Tim Daubenschuetz
Context
As these users have gotten to know more about Ocean Protocol they have realised it could be beneficial for them to stake their Ocean tokens on data sets. They have discovered RPI as a potential tool to help them with this decision making process.
Motivation
They are trying to maximise their rewards for staking Ocean tokens.
Pain Points
There’s both a high learning barrier to overcome and a lack of accessible, clear information. There also may be some uncertainty when it comes to the quality of information and likelihood of generating an ROI on what is a new and volatile asset.
Mental Models
The user has so many options when it comes to allocating their capital within new crypto projects. If they have gotten this far they probably understand the value proposition and vision of Ocean Protocol quite well and are wanting to get into something in the hopes of generating a good ROI.
What I want from RPI:
Questions I might have when landing on RPI now:
Context
As these users have gotten to know more about Ocean protocol they have realised it could be beneficial for them to invest in data tokens. They have discovered RPI as a potential tool to help them with this decision making process.
Motivation
They are trying to find valuable data tokens to invest in assuming that both Ocean and the data set will do well over the long term.
Pain Points
It’s really time consuming to go through each dataset and try to find the ones that are most valuable. It’s also unclear exactly how to ‘value’ a data set as there are many factors to consider i.e. data provider’s reputation, number of sales, staking rewards, is the data set being maintained, does the dataset have buyers, who is the dataset targeting, is the data actually valuable etc etc...
Mental Models
The user has so many options when it comes to allocating their capital within new crypto projects. If they have gotten this far they probably understand the value proposition and vision of Ocean Protocol quite well and are wanting to get into something in the hopes of generating a good ROI.
What I want from RPI:
Questions I might have when landing on RPI now:
Context
These users are active or thinking about becoming active in the Web3 ecosystem.
Motivation
These users are looking for cool/interesting projects to work on &/or become an early adopter of something which could grow into being much bigger in the future presenting a potentially high economic incentive.
Pain Points
Hard to find quality projects with good teams to collaborate with/active engaged communities that I can become more engaged with.
Mental Models
If this is a cool project or something which I believe could grow into something much bigger, then I would like to keep engaged with the community, potentially help out or get more involved as the project grows.
Context
These users are developers &/or business people looking to integrate the RPI API into their work/product.
Motivation
They want to build a product using our data. RPI has more than 9 months worth of pricing data. By querying this data directly from our API, they can save time and money building a custom solution.
Pain Points
Some users are coming to RPI because they’ve been left behind by Ocean Protocol. We’re a technically driven product that’s quite approachable by developers. They know what they get from RPI when compared to the more advanced-looking tech stack of the Ocean Protocol.
Mental Models
API users's way of thinking is highly utilitarian. They want to build something and so they’re looking for the most cost-effective way of getting it done. RPI’s simple and approachable API gives them a dopamine hit because they immediately get it.
Context
Other platforms have forked/created their own data marketplaces and data platforms (separate to the Ocean ecosystem). They too are looking to acquire users/investors/contributors to their platforms. They see RPI as targeting the same audience they are interested in.
Motivation
They understand the value of data marketplaces naturally and by getting listed by the Rug Pull Index, they see it as a way to acquire new customers and push their data consume revenue up.
Pain Points
Accessing the right target audience can be time consuming and difficult. First you need to find where the target users are, next you need to collaborate with the platform to negotiate terms and execute the plan/integration. The data investor community is very small. So far, only Ocean has managed to grow a little community of early adopters. So anyone that’s in this market too has to either associate themselves with the OCEAN community or seek out to create one by themselves.
Mental Models
Other Data Platforms are motivated to join RPI if the value proposition is right. They are interested, have budget and an aligned interest to be on RPI and to leverage the network that’s already there and that will hopefully be there in the future. Still, currently Other Data Platforms are struggling to partner with RPI as it's not a legally incorporated company and since the value proposition of their investment is unclear.
Context
Ocean DAO members have varying levels of interest/engagement in Ocean Protocol but a number of influential players have a much larger influence relatively speaking.
Motivation
Important OceanDAO members are primarily big holders of Ocean tokens. They want the Ocean Protocol Ecosystem to flourish. They are looking for projects that have a good ROI and will continue to support the ones they believe do.
Pain Points
OceanDAO members are mostly concerned about the OCEANs that they hold. They want as many well-maintained projects arising from the OceanDAO as possible. Their interest is less in the survival of individual projects and more in the correct strategic functioning of the OceanDAO’s funding mechanism. Their ultimate goal is to make a return on their initial OCEAN investment. OceanDAO members have limited time to do due diligence given the sheer amount of projects. Hence it’s often hard for them to keep on top of projects and see which are adding value.
Mental Models
OceanDAO members want to see RPI succeed given that it continues to make technical process and helps guide the Ocean Protocol mission. With the rising number of participants in the OceanDAO, members are starting to get less chance to do proper due diligence. They may also simply vote to burn Oceans if they see no proper engagement.
Context
Data Providers are looking for ways to make their datasets more appealing to potential stakers, investors, buyers. Data Providers want to make money. If RPI is seen as a key source of ‘quality datasets’ within the Ocean ecosystem then they want to know how they can be a part of the action and make their datasets ‘better’ or rank higher.
Motivation
To get more Ocean staked on their datasets, increase the discoverability of their datasets and increase number of sales.
Pain Points
Data providers' biggest problem is that they can't make a good value proposition to data consumers. Data consumers always face the risk of buying a rug pull in disguise.
No Data Provider has made a return on a tokenized piece of data yet. It is difficult to understand if a single sale can make a data set worthless. It's hard to find stakers and buyers for data sets, expecially when purely focus on technical innovation and e.g. not marketing.
Mental Models
RPI allows a data provider to get (relatively speaking) many eyes on their data token. For the top data providers, being listed as a top project on RPI is of significance as it directs lots of attention and clicks to their web presence (Click through rate). Additionally, RPI can serve as an additional benchmark for how well a data provider's token is doing in the overall market.
Context
Data buyers are likely still far and few between but when they do arrive to the ecosystem they will be looking for a quick and easy way to find/browse the datasets that they need (or might need).
Motivation
They will have a specific use case in mind and be looking for data to help address that use case. We speculate that Data Buyers have highly utilitarian motivations too. If they know they can make more money through buying a data set, they'll pay its price.
Pain Points
The problem with finding data is that it almost directly has to be created for your specific use case for it to be relevant or valuable. Already when having a hypothesis, we humans tend to solve it “by proxy”, meaning we answer different questions and not the original one instead. For data sets, this is the same problem. A data set’s value is not universally the same for any one as some buyers are able to extract significantly more value from it than others.
E.g., if you had gotten ahold of the Cambridge Analytical data set before the 2016 US election, would it have provided you the same level of utility as it did to the Trump campaign team?
Finding the data set they require is an immense and time consuming challenge for data buyers.
Mental Models
If I find a dataset that might be useful: How do I know it's useful/trustworthy/valuable? How do I know that the Ocean Protocol works as I expected it? What information can I access to give myself a high level of confidence that this thing is even worth purchasing and that I'm not getting scammed.