Testing your risky assumptions does not have to be a risky process. A creative, smart approach can uncover similar, if not more significant, insights than those of a traditional, expensive approach. Due to a lack of resources, startups often do not have the choice of which approach to leverage; they must be inexpensive, quick, and iterative.
After attending Demo Days for Cincinnati-based accelerators Hillman and The Brandery, we were inspired to sit down with three of the startups’ founders — Daniel Bedolla (EXCITED), Kobi Wu Pasmore (VisuWall Technologies), and Meg Greenhalgh Pryde (Brandefy) — to learn how they broke down their business models and used lean experiments to test their risky assumptions.
Based out of Mexico, the first gay lifestyle brand focused on beauty, personal care, and fashion.
What were some of the riskiest assumptions that you tested?
What lean tactics did you use to de-risk these assumptions?
To test the market appeal, we conducted A/B tests on Facebook for two separate perfume ads, targeting individuals who identify as gay. The first ad used generic language and the second used language specific to the gay community. After tracking sales, clicks, and engagements, it validated that there is a market for products targeted to the gay man.
To confirm that individuals were willing to buy our products specifically online, we built a Facebook fan page where consumers were able to browse and select what they wanted to purchase. Once they “purchased” it, we would order that specific product from China and have it shipped to me. I would then package and send it to the end consumer. This helped de-risk the assumption and hone in on what products would be the most popular, resulting in a smarter bulk inventory purchase afterward.
What is on the horizon for your company?
As we think about expanding and offering our website in markets outside of Mexico, we plan on testing two additional assumptions:
An online marketplace for sourcing smart outdoor advertising at eye level. Each placement on its platform is optimized with computer vision and digital screens monetizing a rich network for locations and generating a new channel of outdoor that is accessible to brands of all sizes.
What were some of the riskiest assumptions that you tested?
What lean tactics did you use to de-risk these assumptions?
To test the ROI, I set up a nanny cam in a vacant store window to manually count how many people passed by the location, proving that the traffic would be higher or comparable to other advertising options. Observing from this unique location allowed me to recognize first-hand the potential for even more intricate data to be collected down the road. This experiment also shed light on the legal guardrails of my business idea; I needed to learn about and build the technology to be GDPR compliant.
To confirm that people would actually buy this service, I had to get my first client. We had to source locations and simultaneously find our client. We spent about eight months working through the model and shopping it to both users groups before Apple Music said “Yes.” The ongoing conversations were affirming and helped me hone in on my communication strategy of selling analytics first, rather than the window placement.
What is on the horizon for your company?
Digital and programmatic advertising. We’re also planning to expand into other markets, which each come with their own unique challenges. We are currently in NYC and LA, and plan to expand to Chicago and Miami in the next year.
A mobile app that compares value brand beauty care to high end beauty care to help shoppers save time and money.
What were some of the riskiest assumptions that you tested?
What lean tactics did you use to de-risk these assumptions?
To test if people would actually compare products and leave reviews, we created a video, illustrating the service, and attached a signup list to see how many people would sign up to engage with the service.
To find out if people cared about the comparison between drugstore and high end brands, we bought drugstore products and blogged about them in a review style. We slowly but surely gained traffic, proving that people care about this topic.
What is on the horizon for your company?
We would love to talk with brands, manufacturers, and data companies, and ask “At what point would this service become valuable for you?”
If you have the resources to run experiments in a traditional, expensive way, take a second to put yourself in the shoes of a startup founder the next time you need to test risky assumptions. How would you approach the experiment differently? What else could you allocate your resources toward if they are preserved in the beginning of the process?
Thank you Daniel, Kobi, and Meg for sharing your stories and insights. We’re excited to see how you continue to innovate and grow!
