Intro: Transitioning To A Startup Incubator
Methodology: Lean Startup + UX = Lean UX
Because the team was small and we needed to move fast, I wanted to use Lean methodologies in my approach to delivering value quickly. The Lean Startup methodology claims that every startup is a grand experiment that attempts to answer a question. The question is not “Can this product be built?” Instead, the questions are “Should this product be built?” and “Can we build a sustainable business around this set of products and services?”. Lean UX is a call to work iteratively, streamline design, eliminate waste and maintain a customer-centric perspective in decision making.
Problem: Finding Product Market Fit
The product I would be working on was called WhoZoo.io it was rooted in the problem that in large dynamic organizations it’s hard to find people and get skilled help when you need it. WhoZoo had already validated their main “problem” in Seed 1 and 2 with many user interviews and even got early feedback on the potential solution via flat mockups of the app they wanted to build (they had just entered Series A when I joined the team).
Currently at Problem Solution Fit, WhoZoo hired ME to lead the UX and build out the next phases of Lean Startup – Product Market Fit.
Goal: Get Product Out To Users
So, now I had a good understanding of WhoZoo’s journey and reviewed their Lean Canvas (& the user research that informed it), I mapped out my plan to get the product in users hands. But, every delivery needs a quick inspection before it goes out the door. That’s why I jumped in and did a quick once-over of the product.
Assess the app and ensure its usable
- I ran a heuristic assessment and identify top-level issues
- I got a 2nd opinion on usability via UserTesting.com’s paid testers
- Created Beta user journey map from this research
Iterate & launch the Beta
- Fixed only priority usability issues (& documented others)
- Targeted Trial Persona contacts for the invitation
- Launched Beta and invitation experiment
I learned to treat design as a hypothesis, I don’t have to get things right the first time in the very first iteration. It’s all about getting ideas out into the market early and validating them.
Solution: Iterate on Beta & Launch MVP
The Beta launched and we gathered insights from engaged users and users who felt blocked. This issue was already heard from paid testers. The app lacked formal on boarding and communicating the value proposition. However it wasn’t until these Beta testers walked through the product and got stuck in the funnel did we truly hear the signal loud and clear that on boarding and messaging should be fixed.
Another problem we faced was making small edits in the Beta version of the app. Our foundation instability was proving difficult to build iteratively and it was preventing us from making progress for these users. So we rallied on a plan and aimed to improve the following upon the final MVP release:
Create app foundation and design framework
- Our existing app platform was inefficient and needed an improved design/development foundation to scale quickly
- By moving to an Angular framework we could build quickly on a solid app foundation and move forward fast
- By introducing the Material Design framework I could expedite the handoff from design to development & create design cohesion
Improve on boarding and value proposition messaging
- Users were lost in the app both by the navigation and by their understanding of what the app was doing
- By creating a simple illustrated walk thru with material cards I could communicate the value prop quickly
- Using Material Design framework it was easy to implement the on boarding AND navigation improvements
We launched our MVP with an improved platform (Angular + Material Design) that would allow us to scale and iterate with the experiments we needed to run in the coming months.
Process: Build, Measure, Learn from MVP Experiments
So, now we had a fully functional MVP out in users hands. It was time to dig into the process of Lean Startup. The most famous concept of the Lean Startup philosophy is the ‘build-measure-learn-loop’. This process of “validated learning” requires you to move through the feedback loop as quickly and thoughtful as possible. I was already building, measuring and learning through our Beta to MVP launch and I would continue to persist to get to our goal of Product Market fit.
I completed around 18 experiments in about 6 months, and boy did the time just fly by! Some experiments were small bets and aimed to get a signal to iterate on, these were usually quick and manually executed. Other experiments were big bets or big assumptions we had after seeing small user signals. We tried to invest more development time in these big bets to help gather the evidence we needed to move forward confidently. Here are some examples of big and small bets:
Big Bet: Users want to customize their profile
- Our evidence was based in majority from users commenting that they did not find value from the initial setup of their profile (currently it was a hands-off setup)
- Our assumption and hypothesis was if we could offer more than just machine learned insights and allowed a user to customize their profile they could make it more valuable
- I quickly mocked up a version of the ability to add insights to a user profile and worked with developers to get a slim version of the feature in the app.
- As I suspected, users jumped at the chance to refine and customize their profile by adding their own insights. Resulting in a 45% usage increase.
Small Bet: Creating App notifications
- Our evidence was based on a lack of user retention and bounce rate in return app usage
- Our assumption and hypothesis was if we provided some simple information on updates a user would be more inclined to return to the app and check them out
- I created a simple hand-coded personalized email with key information nuggets that I sent to our 2 active pilot user bases and waited to see the impact
- Just as I suspected we did see the attraction from users, resulting in our highest traffic spike in 1 day to be exact.
- This gave us the evidence we needed to add automated notifications to our existing feature backlog
A key experiment was to improve a user’s profile. We were hearing that our Data Science + Machine Learning wasn’t providing “Instant Value” upon the automated creation of a users profile. So we experimented with the idea of customization and saw a 45% increase in usage based on the new features.
Results: Gaining Momentum
If we already know users feel the problem, who feels it the most and who’s willing to pay for the relief?
We’ve created an onboarding model for individuals but who’s going to facilitate an entire organization onboard seamlessly?
What was more valuable to an organization, the data we were able to collect or the service we aimed to deliver?
We were gaining momentum, we were on the path to it. We had validated the problem, found who it resonated with most, who would pay for a solution and what the sales delivery model would need to be. But… we ran out of time and our funding was paused. WhoZoo would not continue.
Learning: Summary My Experience
Resilience is what gets you through. Staying focused and pushing through to understanding is the reward.
During my deep user interviews, I asked a user how she measured success in her job role. She paused then responded, “That’s pretty difficult for me in this chaotic position, but maybe success is progress in motion.” That statement didn’t sink in right then but think I can look back and resonate with it now. I’m going to keep progress in motion, I’m going to keep trying to get to… understanding.