BetterVote
BetterVote is a pre-seed AI political-tech startup building the future of donor research and list building.
Role
Lead Designer
Timeline
4 Weeks
Team
Co-founders
Summary
User Research/Testing
Responsive design
Design/brand Identity
Deck building
A multi-faceted project focused on 0-1 MVP design/development (mobile & Web), engaging in rapid user testing and iterations on core product flows. Supplemented with building investor deck, product tour/demo and landing page ahead of beta launch.
Context
Finding political donors is hard...
The current solution for finance managers to discover, outreach, and convert possible donors for a political campaign is extremely time consuming, unintuitive, and inaccurate. Campaign managers are unable to discover and engage in personalized outreach to possible donors for their campaigns at scale––limiting fundraising potential.
Let’s better understand who we are building for:
How might we simplify the searching process for potential political donors while also gathering accurate and personalized donor information at scale?
Design Process
Ideation and Iteration
Solution
Meet Bettervote
Breakdown
Smart Search Feature
How does it work?
A user simply enters a natural language query and find results that match exactly what they are looking for
This solves for:
Inaccurate and cumbersome process of searching through filters or key word search Having to sift through results that best match your query
There is a discrepancy between what one actually wants to search and is able to search from filters and key word search
Challenges using natural language search:
opens the door to infinite possibilities of search results, which also means the output is entirely dependent on the quality of the input
Therefore, how might we educate the users on crafting optimal prompts?
A user simply enters a natural language query and find results that match exactly what they are looking for
This solves for:
Inaccurate and cumbersome process of searching through filters or key word search Having to sift through results that best match your query
There is a discrepancy between what one actually wants to search and is able to search from filters and key word search
Challenges using natural language search:
opens the door to infinite possibilities of search results, which also means the output is entirely dependent on the quality of the input
Therefore, how might we educate the users on crafting optimal prompts?
Smart Search
User Testing: Different Iterations of Search
Looking Back
Final Thoughts
This project really pushed me to apply everything that I have learned at Ghost. Here my 3 design/product philosophies I learned throughout this project.
1. Uncovering the fundamental truth:
Truly, nothing matters if we don’t understand what and why we are building. In order to design something that users truly want, we need to understand their foundational motivations. Questioning the core drives first principle thinking. First principle thinking allows me to see the problem in modules and patterns, making it easy to stack, build, and test like legos.
2. Methodical testing:
Given our short time frame, testing must be lean but informative. With each test, I offered users 3 iterations of the same user behavior (ie. search) and let the founders observe the raw reactions users have with the menu selection. I find that this structure had the best balance between driving a specific vision while also receiving feedback. I wanted users to provide valuable insights in the form of reaction, but not the power to dictate our creative vision (Faster Horse principle).
3. Radical transparency:
Being the only product-driven designer, it was crucial for me to constantly provide and ask for feedback. I definitely learned a lot on how to be more communicative and organized for with my thoughts and on Figma. It is important to not sugar-coat problems and be direct, as it often reduces future time spent unravelling miscommunications.
1. Uncovering the fundamental truth:
Truly, nothing matters if we don’t understand what and why we are building. In order to design something that users truly want, we need to understand their foundational motivations. Questioning the core drives first principle thinking. First principle thinking allows me to see the problem in modules and patterns, making it easy to stack, build, and test like legos.
2. Methodical testing:
Given our short time frame, testing must be lean but informative. With each test, I offered users 3 iterations of the same user behavior (ie. search) and let the founders observe the raw reactions users have with the menu selection. I find that this structure had the best balance between driving a specific vision while also receiving feedback. I wanted users to provide valuable insights in the form of reaction, but not the power to dictate our creative vision (Faster Horse principle).
3. Radical transparency:
Being the only product-driven designer, it was crucial for me to constantly provide and ask for feedback. I definitely learned a lot on how to be more communicative and organized for with my thoughts and on Figma. It is important to not sugar-coat problems and be direct, as it often reduces future time spent unravelling miscommunications.
Closing
I am incredibly thankful for this project and working with very thought-provoking founders. I loved the 0 to 0.5 product development journey, as it requires so much more philosophical thinking rather than mere execution.
Although I mostly talked about the meaty portion of MVP development, there was actually a lot of work put into branding, decking, landing page, demos, and the mobile app as well. Here’s a quick showcase of everything I worked on in these 4 weeks.