My startup failed, and this is how it went down…
We went from fashion-tech prodigies to flaming heap of Jichael Meffries-inspired detritus in 1 short, emotional year.
Design and purchase your creations with UDesign™, right from your mobile phone! Available for Apple and Android.
For every 99 start-ups that fail, you only ever actually hear about that remaining marginal powerhouse. It’s fascinating, really. The most interesting thing about defeat is that while nobody likes to talk about it, it turns out that learning from these kinds of experiences is unequivocally the only way to gain an inch in today’s ferociously competitive global marketplace, or anything else in life for that matter. I’ve said it before, and I’ll say it again: while knowledge of the successes of great people and great companies are sufficient for carving out your own trajectory of success throughout life, it is not even remotely necessary for actually achieving what lies just ahead on that same path. In fact, all that you can really glean by examining the crème de la crème is perhaps an understanding that putting in just enough hard work will (probably) generate just enough sheer luck for you to accomplish what you set out to do.
And what we set out to do was nothing short of taking Vegas for all it’s money.
The gambler’s fallacy, also known as the Monte Carlo fallacy or the fallacy of the maturity of chances, is the mistaken belief that, if something happens more frequently than normal during some period, it will happen less frequently in the future, or that, if something happens less frequently than normal during some period, it will happen more frequently in the future (presumably as a means of balancing nature). In situations where what is being observed is truly random (i.e., independent trials of a random process), this belief, though appealing to the human mind, is false.
The gambler’s fallacy lets us understand why we shouldn’t always trust the judgements and actions of our superiors. Suppose you have never once set foot inside a casino. An old friend calls you up and encourages you to come out with him, as he has been losing frequently lately. This, he claims, is a clear signal he is due for a spankin’ new hot streak, so you better take advantage of it if you know what’s good for you. Now suppose you are thrust in front of a Roulette wheel. You pull out the $1000 your grandmother gave you as a reward for graduating college and cave. The chance to earn some easy money has won you over. Your companion urges you to bet red, and reassures you that the odds are actually in your favor, as he has been to the casino all week and has observed a glut of wins on black. Your Hindbrain concurs. You bet red and, in spite of your initial skepticism, you come out on top. Your $1000 win has you ecstatic! So you come back the next day and lose more in 30 minutes than you’ve ever earned in a week your entire life.
The reason you don’t hear about significant losses is that those who lose surely and swiftly don’t stick around long enough to have a story to tell (we made poor decisions, i.e. to gamble in the first place), whereas those who lose an unfair game much later on are granted the luxury of building a rich narrative that only time and lots of luck make possible (i.e. the market was not yet mature, forces other than our own action conspired against us, etc. — all of a typically external locus of control). The same thing can be said of one’s successes, with the reverse being true. Early winners do champions not make.
1) Survivorship bias. An error that comes from focusing only on surviving examples, causing us to misjudge a situation. For instance, we might think that being an entrepreneur is easy because we haven’t heard of all of the entrepreneurs who have failed.
It can also cause us to assume that survivors are inordinately better than failures, without regard for the importance of luck or other factors.
But let’s not get distracted. This is about how we fucked up.
Ironically, with UDesign™, on multiple occasions we either tacitly failed to follow or explicitly ignored most of the extremely prescience advice set about in the years of reading from which the vast majority of our work was based, i.e. behavioral economics, a la Danny Khaneman & Amos Tversky.
So I’ve decided to break up our failures into a hand-picked selection of the top 10 cognitive biases (in no particular order) which best suit our most egregious of errors . I entreat you to learn as much if not more from our mistakes than you hope you might learn from our successes, or the successes of others:
2) Post-purchase rationalization. Making ourselves believe that a purchase was worth the value after the fact.
Bottom-line, we failed to attract high quality talent quickly and early enough. In late August, 2014, we received a USD $10,000 private equity deal for a 5% stake in our company, placing our valuation at $200,000. With the vast majority of our mobile application complete before having even received our first dollar, we thought it pertinent we invest our seed funding into marketing via photography and videography so that when it was done we would have a shot at promoting it rapidly. We spent way too much money. I’ll tell you exactly how much we spent:
• $1000 on a conceptual promotional video for our brand.
• $2000 for a second promotional video for our app.
• $500 for promotional clothing for the models in our videos.
• $Another 50 per model for the 10 models in our videos.
After $4000 spent, there was NO way, in light of the supposed attention to detail and effort we put into our videography, that this could not generate some kind of a media buzz, and in return, contribute to revenue growth…
The outcome? Based on subsequent actuarial, statistical, and financial analyses, we concluded that not a single dollar spent on videography amounted to a single dollar in revenue generated by those investments. In other words, every dollar we spent on videography and on models was, economically speaking, a complete and utter deadweight loss (photography actually proved to be very effective early on, and should we have invested in that we may have been better off. In retrospect, however, it was still difficult to say whether it would have saved us from our subsequent mistakes).
So what was it? Did we just pick the wrong videographer? Did we pay too much for what was being offered? Should we have paid more for a more experienced videographer? Or significantly less for someone of equal calibre? Maybe our product just didn’t need a video? I mean, we’re selling women’s clothing and the items themselves are undoubtedly “out there”. Perhaps leveraging word of mouth would have proved just as effective?
Organic exposure is certainly cheaper. Was it cool enough? Or were all of our failures simply the result of cruel, abject misfortune? No one will ever actually know… so I don’t blame a soul. I just wish the only formula that even remotely applies to this situation wasn’t actually a formula that states there is no formula.
3) Overconfidence. Some of us are too confident about our abilities, and this causes us to take greater risks in our daily lives.
4) Planning Fallacy. The tendency to underestimate how much time it will take to complete a task.
It turns out we underestimated the complexity of the project, and overestimated our ability to complete it on a limited budget should, closer to launch, any complications arise. We thought we could wing it with our existing coding experience (2–3 non-formal years of Java, Objective-C, and XML between the two of us). We shirked on paying for programming because we thought ourselves experts enough, and what ended up happening was that we spent everything we could have spent on polishing the product itself on marketing instead, under the assumption that we could finish whatever tasks remained effectively ourselves.
It just wasn’t true. Looking back, that extra $4000 would have made a world of a difference. If we had hired smart, we could have had a full stack developer working full-time for another 4 weeks at that rate. That was basically our death knell. Flashy videography just doesn’t do anything for a product that isn’t functionally, if not aesthetically, complete (even if you can’t see what that functionality actually does when you pick up your phone and place the app in your hand — so much goes on in the background that nobody but an experienced software developer could possibly understand). Which leads us to:
5) Choice-supportive bias. When you choose something, you tend to feel positive about it, even if the choice has flaws. You think that your dog is awesome — even if it bites people every once in a while — and that other dogs are stupid, since they’re not yours.
Instead of sucking it up, discounting our losses, and moving on, we did what every good economist should never, EVER do: we incorporated our sunk costs into our expected future outlays. We decided, on the basis of our spending, that what we should be doing is spending EVEN MORE on marketing. Since, well, it’s not like we’d have the money for both effective marketing AND effective software development, so we may as well go BIG in the direction of what we’d already spent, so… more marketing? Trust me, it probably makes as little sense to you now as it did for us back then, but we powered through and committed to our own half-assed convolutions out of fear either way, but partly because we actually started believing that what we had was good enough.
6) Confirmation bias. We tend to listen only to the information that confirms our preconceptions — one of the many reasons it’s so hard to have an intelligent conversation about climate change.
7) Ostrich effect. The decision to ignore dangerous or negative information by “burying” one’s head in the sand, like an ostrich.
In our efforts to confirm that what we were doing actually consisted of nobly staying the course, we stuck our heads in the sands and opted for doing market research on a problem that was nearly 3 years old at this point. Shoes of Prey was doing remarkably well, and besides, “Made-To-Order Fashion [Was Going] Mainstream”. Forbes, guys. Forbes! If Forbes got our back, we gotta be on the right track, yeah? We actually started to believe that this was what people wanted, but even more foolish was that we convinced ourselves that there was a presently unfulfilled niche for this kind of a service, which there wasn’t really. That space was packed. There were more bespoke shoes, custom athletic wears, procedural scarves, and many, many more variants of the exact same thing than we could list. But more of the same, really? That wasn’t our intention in the first place. Like our proverbial Roulette virgin at the start of this article, I caved. I took the bait and got burned on my second visit. We were supposed to nurture the growth of a freakin’ spontaneously self-organizing Complex adaptive system from the ground up! We built a shopping cart.
Lesson well learned. Don’t EVER let people convince you, especially investors, and ESPECIALLY media people, to cut corners. Battle them, mark your territory, and prove your point. Don’t release an unfinished product. Period. If you’re not obsessing over the minutiae, you are not a master. People will recognize you as such and the narrative you’ve spent so long building, in your struggle to attract investors and customers alike, will come crashing down all around you. What you don’t see is often much more important than what is tangible, apparent, and flying straight at you in the face. Why pretend you’re something when you can just be it? Go the extra mile and get there. In retrospect, we should have spent 90% of our seed funding on crafting the perfect product, despite how far we thought we had come without it, and the remaining 10% + a shit ton of sweat equity and super-late nights pumping this thing out as organically as possible.
People WILL share the things they love, but we failed to encourage this innate human tendency at the easiest possible juncture to avoid; we failed to make something that people would really, truly love. Something that people would connect with and resonate and hadn’t been done before. What we needed to do was invest more in our back-end server architecture so that our users could, for example, share and mix their creations with their friends, mutate them, like them, and be paid out when you decided you wanted to support your friends’ creations (now that’s a real genetic algorithm if there ever was one; see SnapTee, which started at about the same time as us). What resulted was a souped up product that while somewhat novel, amounted to nothing more than an in-app shopping experience for a traditional clothing brand. You download the app, generate a pattern (they’re animated, wooh!), and drop $60 for a pair of leggings.
Cool, but none of the hallmarks of a contemporary social network, or a collaborative economy, or whatever new form might the next stage in the evolution of mobile applications be. We thought we could trick people now and make up for it later. Wrong.
8) Hyberbolic discounting. The tendency for people to want an immediate payoff rather than a larger gain later on.
Look, it wasn’t that women didn’t want custom clothing. Designer clothing by nature is custom, in a sense. The real problem was that while on average our users spent up to 7 minutes per session (insanely impressive I might add), they weren’t sure if they wanted to purchase the clothes that they spent all that time personalizing. We knew because we asked. We (creepily?) added almost all of our initial customers to Facebook, and reached out to many more of our followers via Instagram.
Perhaps if someone customized the clothing for them? Like a designer? But that’s actually what UDesign™ would have been all about. By employing a naturally occurring genetic algorithm, spurned by the social interactions of users actively generating the plethora of patterns (some bad, some good, and some ugly) available on the edge of our very own little creative “adjacent possible,” to borrow the term from American theoretical biologist Stuart Kauffman, the community as a whole would actually converge upon a globally optimal mix of popular patterns, and hence, come full circle, the average user wouldn’t have to spend time wondering if their personal creation was something they’d actually wear. Based on the seemingly automatically generated feedback from their community, they’d just know.
They’d have something that is both custom, designer, unique, and also something they know they’d actually wear. There would be no ambiguity, and it would be cheap. Now that solves a real problem in women’s fashion…
Anyway, we began to feel as though we were crunched for time, so instead of either investing more of the money, that by God’s good graces had fallen into our lap, into deeper software development, or by gardening to pay off our losses, we went ahead and released our product because we assumed that the short-term payoff from releasing something, anything, would be better than releasing nothing at all. Another mistake, but I digress.
9) Hindsight bias. Of course Apple and Google would become the two most important companies in phones — tell that to Nokia, circa 2003.
I wish I could tell me I told me so. But I can’t, so what else can I really say? For those of you who’ve got this far, I’d say 1) build something that people actually love, or don’t have yet, or need, 2) something that solves a legitimate problem, and 3) something that isn’t sugar-coated as a last ditch attempt to cover up glaring inadequacies along pivotal development junctures, i.e. have a powerful vision so that you aren’t dissuaded by uncertainty or failure along the way. Those are literally the only things you can keep consistent in a world rife with chaos.
Well. We’re on to better things. We have certainly pivoted. Come July 1, my team and I will be launching something totally new. If you’re curious, check out www.suitshare.com when the time comes. So until we meet again, all I’ve got to say is that I’m not stopping now and neither should you. The last thing you want to do as an entrepreneur, as someone who wants to make the world a better place by fixing something real, is to succumb to the following deadly bias:
10) Zero-risk bias. The preference to reduce a small risk to zero versus achieving a greater reduction in a greater risk.
This plays to our desire to have complete control over a single, more minor outcome, over the desire for more — but not complete — control over a greater, more unpredictable outcome.
Because why risk failing and not succeeding when you could just do nothing at all and avoid failure altogether, am I right?