The marketing approach within the current OpenGov framework has resulted in a stream of applications that are challenging to assess using only the information provided by the applicants themselves. A logical solution is to learn directly from ecosystem participants (both existing and potential) about their preferences and to compile a dataset that can be used to evaluate potential media outreach. The set of information we propose to explore includes:
We propose conducting this research with a focus on the builder audience. They are the most interesting for multiple reasons, easily identifiable with public contact information and track record, and working with them could provide a broad spectrum of additional useful information.
To conduct research on the current community and gather data about potential members, we propose examining three large clusters of builders:
We estimate around 8,000 entries at the top of the funnel: 2,000 from Dot, 3,000 from other ecosystems (hackathons/ZK hacks, etc.), and 3,000 from new, actively growing ecosystems
The preliminary set of questions includes:
We present three budget models:
The table is dynamic, allowing for changes to the figures as needed, with input fields highlighted yellow for convenience. We believe that a significantly smaller dataset will not provide a consistent, comprehensive data set; similarly, larger datasets also have limited additional value.
Budget estimates are provided in the attached rough calculation, and we invite further refinement. We anticipate that somewhat active information will be available for about 50% of these contacts, with more detailed information obtained from the remainder. A smaller subset will likely reach any potential for communication.
Research goals aim to provide information that can be used for both current media/influencer KPIs and potential media-buying efforts:
We're considering rewarding builders for participating in the survey. This would not only create a proper funnel but also potentially onboard builders from other ecosystems by providing them with on-chain tokens. Feedback on this approach would be particularly appreciated.
We eagerly await your suggestions on what additional insights we can uncover.