Product Design Lead, Project Manager
Redesign a workshop process into a digital data collection process with live insights and analytics
Develop a survey process that helps network members pursue the best solution for their community as a whole
A streamlined data collection process that could be performed and analyzed digitally
Thicket Labs's primary service is data collection workshops. In these workshops, we had been collecting data using post-it notes, stickers, and paper surveys. This is standard for most brainstorming activities, but it has its flaws. This process makes it difficult to have all voices heard, it's time-consuming to collect the data, and work is often incomplete or incorrect. There's no way to check this at scale with one to two facilitators and 50 participants. This is what inspired us to pursue a digital collection process for workshop settings that could develop into a SaaS product of its own.
The workshop process is directly pulled from a research methodology called Fuzzy Cognitive Mapping. The data collection output from this approach allows for predictive simulations, meaning that it can show how a system is affected in various scenarios. For example, if we increase the number of health services in an area, the community's health will improve. This helps communities test for the most effective and efficient solutions for the community to pursue.
Over the past three years, there have been many iterations of this service. Some notable versions of this were The Possibility Engine and Pocket Strategist.
I designed The Possibility Engine to provide an interface for our APIs. This interface was for internal use only and was developed as a proof-of-concept prototype. There were a lot of bugs and hiccups due to constrained development resources and no external testing. We had drastically limited the possibilities (pun intended).
When I prototyped the Pocket Strategist (seen below) I shifted our focus to a more accessible, consumer-facing, and mobile iteration. I conceived a single user survey process to create a personal network map. This was a great way of gathering data directly from individuals, but it lacked the ability to input collective data to show immediate insights.
Moving forward, I took the best from both products – the robust collective insights of The Possibility Engine and the consumer-friendliness and personalization of the Pocket Strategist.
One last item I wanted to address was to allow for live insights and the potential for a non-linear collection process. I accomplished this by allowing the user to return to previous steps or skip to future steps at any point.
I was also able to streamline the collection of their first idea by directing users right from the signup process to the goals process.
The way this research method works is that the more feedback each user provides, the more powerful the predictive simulation. It was extremely important to lead and instruct users on the value and process of each step.
I recommended a landing page with immediately instructional text to navigate the user to each new section and call to action pop-ups that appear as the user explores a new step. This flow gave gentle direction, freedom to explore and helped build trust in the product – versus the previous prototypes with a forced linear path and no explanation as to why the information was important or necessary.
Inspired by other massive visual data sets like Pinterest and Tumblr, we decided to list user input in a masonry grid. This was kept consistent throughout the data collection process and then diverged once a user entered the simulations and other data visualizations.
Using our brand guidelines, I mocked up each step of the process with different states (active, inactive, edit) to hand off to development.