1 – Discovering Inventions with Customers
On wearing clothes, here are the steps:
- Buy clothes online or in-person
- Remove packaging, tags, and sometimes pins
- Put on your outfit based on occassion/context for the next period or day
B: More Steps
- With underwear, you typically put it on one leg at a time
- Dawn socks (if you’re wearing pants, sex should come first)
- Pants are typically pulled up one leg at a time
- Fasten the front
- Add belt (usually optional)
- Put on shirt (important: do this before putting on deodrant)
- (Very optional) wear a hat
D: Pain Points
- Saving for clothese
- Selecting clothes to wear for specific occassions and that look good together
- Washing, drying, and storing clothes
- Clothes wear down after repeated use and cleaning
E: Lean Canvas
- Problem – Globally, people spend $1.9 trillion on clothes each year, and it’s a lot of work after that—from putting on outfits to cleaning clothes to cleaning and storing these everyday items
- Solution – An everyday, stylish robe for all occassions that can be housed in a special container that uses light and specialized air filters to clean it after every use (based on research about using lights to clean materials)
- Key metrics – A) Sales, B) Ratio of production to consumption, and C) customer feedback
- Unique value proposition – A comfortable, stylish clothing line of everyday-wear robes that customers can wear comfortably in contexts ranging from the board room to around the house and in bed
- Unfair advantage – The simple, quick cleaning method means there’s no need to launder clothes, worry about storage, or ever wonder what to wear again
- Channels – Online marketplace that offer limited options to simplify the clothing selection process; there will be three price points (“Convenient,” “Stylish,” and “Elite”) — the different levels will have three color options A) Light gray, B) Light gray with black seems/edges, and C) Color choose (any color with a CMYK code for dying); promotion will be 100% online and leverage influencers’ audiences to get the word out
- Customer segments – general customers will be busy individuals who want to look professional/stylish but are limited on time; early adopters will include folks with tech-based jobs and work remotely, so what they wear has limited impact on how they’re perceived professionally
- Cost structure –
- Product storage
- Digital marketing
- Revenue streams – SmartWear robe sales
F: Customer Discovery
Over time, I’ll need to interview a cross-section likely customers iteratively. First, I’ll interview people who work from home, especially in tech-centered industries. The next segment will be stay-at-home parents who are looking for comfort and convenience. In time, I will need to refine the robe’s features based on what busy office-based professionals are looking for and what they could wear to ensure their appearance in the robe won’t sacrifice credibility and reputational capital.
2 – “BIG Ideas” Continue to Flow
This process, especially the Lean Canvas, could be used as a call to action. Perhaps I could end each podcast episode with a call-to-action encouraging listeners to take one step toward building a more equitable future over the course of the series. So often, calls-to-action are direct and immediate. What if I shared an overview of this process and asked listeners to iteratively identify an everyday technology and explore who they would improve it. The hook would be: Just as systems and structures of oppression have been developed over the past few centuries to marginalize and opress individuals, each of us has the capacity to do the opposite, create systems, processes, and technologies that make life easier, even better, for more people.
3 – Weapons of Math Destruction by Cathy O’Neil (pp. 140 – 160)
This section focuses on how the process of accessing loaned capital has historically been a “good ol’ boy” system. In recent years, with the proliferation of credit scores and related checks, discrimination and bias have codified the worst of previous credit application processes and expedited the rate of marginalization. As O’Neil points out:
Consider the nasty feedback loop that e-scores create. There’s a very high chance that the e-scoring system will give the borrower from the rough section of East Oakland a low score. A lot of people default there. So the credit card offer popping up on her screen will be targeted to a riskier demographic. That means less available credit and higher interest rates for those who are already struggling. (p. 144)
Thus, individuals who would have been previously been denied access to capital because of their race, ethnicity, geography, socioeconomic status continue to be denied credit just at an alarming rate and with more “tells” about who they are based on the troves of data creditors have access to. What’s more, rather than being denied credit, they can fall prey to predatory lending practices that make their current position—one where they are desparate for credit—worse due to limited offerings with exorbitant interest rates.
Taking this a step further, employers increasingly run credit checks on applicants or current employees seeking a promotion. The irony is that employment would likely result in a better handling of financial hardships. And often enough, people applying for jobs are considered “untrustworthy” and “unreliable” because circumstances in life prevent them from meeting their financial obligations. Enter yet another vicious cycle.
Solutions, however, do exist. The algoritms that shape our lives are fallable—their creators know that—and these big data processes can be adjusted through feedback loops. Unfortunately, much of these technologies operate in the shadows or are considered too complex to be wrong.