[bisq-network/growth] Hold demo events to help users become more comfortable with using Bisq (#240)

m52go notifications at github.com
Sat Feb 20 00:49:54 CET 2021


Bisq is complex software that many new users find confusing. Even users who have been using Bisq for a while are often unaware of certain features and tips to make the experience better. New features and updates can be communicated better.

Perhaps a twice-per-month interactive demo-style event where such features and tips are demonstrated could help with both: make new users more comfortable with the interface and educate all users on the full capabilities of Bisq, tips on how to how to use it better, etc.

## Learning Goal/Objective
> _- What do we need to learn?_
> _- What is our riskiest assumption?_
> _- What is our one priority?_
> _e.g. "To understand the homepage messaging and design theme that drives the most conversions to activated users"_

To determine if holding events designed to help users become more comfortable with Bisq can help them use it more.

## Hypothesis
> _- Is it Relevant to the learning goal?_
> _- Is it falsifiable and specific?_
> _- Is it causal? e.g. if X then Y._
> _e.g. "If the experiment is successful, I predict that we will create 250 new downloads per week because of a +7% increase in the View > Download conversion rate."_

Bisq has relatively good word-of-mouth in the world of Bitcoin, but it seems many people are intimidated by the supposed complexity of the software, so many never end up using it. Also, the software is constantly improving, and it's not clear that updates are communicated as well as they could be.

Hence this proposal to hold "demo" events: could those who enjoy the idea of Bisq be convinced to start using it (or to use it more) through events that demonstrate features, workflows, and other tips?

Is this event format (demo instead of previous attempts of plain talking / presentation) good enough to get more viewers? Getting **50 people on live streams** seems like a good goal.

Could video excerpts from these demos shared afterward on social channels result in better engagement than previous content efforts? Most blog posts and YouTube videos don't get more than a 100-200 views. Exceeding this low bar would be a success. 

Ideally live offers and volume will increase too, but it may be hard to connect this effort with trading metrics.

## Experiment Design
> _- Is it Specific?_
> _- Is it Achievable?_
> _- How will you collect the data?_

We'll try to shoot for 2 of these demo-style events per month. There won't be a hard schedule—partly to avoid the inevitable monotony of an event held at perfect intervals, and because tips worth demonstrating might not be available on a perfect schedule (e.g., if a new release comes out on Friday, calls that strictly take place on Thursday cannot take advantage).

## Resource Estimation and Probability
> _- How much development effort is required?_
> _- How much people are required to run this experiment?_
> _- How is the Probability of this experiment to succeed? Low -20%, Medium - 50%of High - 80%_

I think this experiment has a high probability of success. Desire to use Bisq is only increasing, and people engage much better with video, especially if it's short and focused and helps them do something they want to do. Previous attempts at growth calls grew long and monotonous, and ultimately served as a way to update a small circle of contributors and users on statistical and conceptual elements of the project that usually didn't relate so well to practical usage (at least, compared to the way a hands-on demo would).

Effort required to test this initiative is very low. We just need to determine topics to cover for a call about 1 week in advance and set up a Jitsi screenshare to make it happen.

A little more work would be required to then slice up and share excerpts of these videos across Bisq's social channels afterward, but there shouldn't be a need for much editing, so it shouldn't be too bad.

## Results
> _- What happened?_
> _- Did we succeed or fail?_
> _- What data did we collect?_
> _- Anything unexpected?_
> _- How close have we been to our prediction?_
> _- Why did we see the result that we did?_

TBD

## Learnings
> _-What did we learn by running this experiment?_

TBD

## Action Items
> _- Another experiment for this goal?_
> _- How do we progress from here on?_

TBD

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