Getting to Product-Market Fit

Updated: Apr 29, 2020


Missing Precision

How do you get from zero to one? There is surprisingly little to help a startup work through the process and get to product-market fit. There is product-market fit as a term but is defined by investors for investors. It does not have the diagnostic capability a startup needs to self-correct. Pivoting as a term is more helpful – it tells you what you should do – but also suffers from a lack of precision. Given ten types of pivots, how do you determine which one is appropriate? Also, how do you know how large or small the pivot should be? If you are not established in the experimental mindset Eric Ries talks about, you could do serious damage to yourself and your company. There is a need for higher precision and safety when going from zero to one as a startup


There is a need for higher precision and safety when going from zero to one as a startup

Beginning at the beginning

My interest is in making it easier to establish this experimental mindset, creating rigor and safety in your execution. Consider this situation: you’ve just received customer feedback, it is not good and you need to decide what to do next. How do you decide to evolve your offering in a way that does not feel like a blind chance? How do you get disciplined about the process, so you feel psychologically safe? How do you tap your intuition for what might work without feeling like you’re “winging it”? Currently, nothing in startup vernacular makes this possible (although there are some promising indicators, Superhuman's approach to getting to product-market fit and Basecamp's notion of bets )


How do you tap your intuition for what might work without feeling like you’re “winging it” ?

The answer, in my mind, is to go back to your foundational concept of building a startup – the hypothesis – and add structure as well as precision to this. A hypothesis needs to evolve from being merely understandable to becoming analyzable. You should be able to define hypotheses at any scale from a feature to a market study it, learn from it, and use it to develop better intuition about your offering, user, and your market. Strictly speaking, as a startup you have two hypotheses: your value hypothesis when going from zero to one and your growth hypothesis when going from one to scale. While my interest is in going from zero to one, the approach I suggest applies equally to your growth hypothesis as well.


A hypothesis needs to evolve from being merely understandable to becoming analyzable

Think of your value hypothesis as a bet. You have an informed hunch, take a bold position, and make a prediction of how this position will play out. If it works out as you thought, your hunch becomes better informed and eventually a reliable intuition. If it does not, it becomes your lesson which equally informs your intuition and you improve. In other words, you learn.

Thinking in bets is a basic survival skill for any startup whether going from zero to one or one to scale. You can master this you start from zero and head towards product-market fit or you can master it mid-stream as you face a pivot and have to decide how small or large it should be :


Think of your value hypothesis as a bet

The Bet on Google Glasses


Here is what a bet looks like : 

Given: people already wear glasses in public If We: augment glasses with real-time information Then: people will wear them in social situations Resulting In:  + a new norm for social conversations -  unequal power dynamics in social conversations -  pushback from society at large


Clearly, it is the Google Glasses bet. Breaking it down to its elements:


A bet has a Given, which speaks to the context you are entering and is your start point for introducing an offering. In this case, it’s the fact that people already wear glasses in public; you can take this as a given. Getting to a powerful Given is the first step. It should represent an unmet user need and is often the hardest part if you’re building product-first.


The If We is your actual bet. By definition, a bet is a bet because you cannot predict its outcome. If you could, it would be an improvement; go ahead and implement it. A bet also has a quality of boldness to it. Boldness is an expression of conviction and belief, essential to a bet. For you to learn from executing a bet, you must start with this emotional quality. Here, it is the idea of augmenting glasses with real-time information. You are not sure if your market will accept it but if they do, the upside is significant.


A bet has a quality of boldness to it

Because you arrive at conviction and belief rather than start with it, you also arrive at bold bets by starting with a more cautious one. The only condition that needs to be true is that you are not sure how your market will receive your bet. Below this threshold, it is simply an improvement. Trust your natural risk appetite in this: if it feels too risky for you, it probably is at this point in time. Make a small bet rather than a big one. Bet on a feature rather than your product. Do not gamble.


Arrive at bold bets by starting with a more cautious ones

The Then is the first-order effect of the bet. This is the predicted upside if the bet pans out. The If We and Then are a cause and effect chain as you see it. In this example, it is the fact that society at large adopts Google Glasses.


The Resulting In: are the eventual consequences or the “systems” effect of the bet — the predicted second and third-order effects. Here you are putting your mind’s natural desire to think in scenarios to work to predict positive and negative unintended consequences.

While this is a retrospective analysis of Google Glasses, it is useful to understand how a bet works. The given is the fact that wearing glasses in public is an accepted norm and can be built up. The bet is that augmenting glasses with real-time information overlay will lead to mass societal adoption of google glasses ( when speaking, it feels most natural to refer to the If We and Then parts together with the bet. e.g. Our bet is: if we augment glasses with real-information overlays then society as a whole will adopt it).

It clearly did not turn out that way, so what went wrong? 


Put your mind’s natural desire to think in scenarios to work

The early adopters used it as expected. But the technology also had an unanticipated, if predictable side-effect: it amplified human ability. Suddenly one party in the conversation had a lot more information than the other. One person could be a creep and look the other person up on social media mid-conversation, turning him instantly into a glasshole. The result unsurprisingly was shaming of people into not using Google glasses altogether as a way to neutralize the power differential. 


The Spectacles Bet

 In contrast, consider the Spectacles by Snapchat. Here is their bet: 

Given :  -  People already wear sunglasses in public  -  People want to take photos and video of social events  -  Having to “take your camera out” take you out of the moment If we: build a camera into a sunglass to work hands-free Then: people will use them in social situations Resulting In:  + hands-free photographs and videos + more spontaneous and interesting shots + Everyone becoming a photographer   -   An explosion of photos and videos from the same event

 -  The need for an “event re-creation” tool


Why does this work? You can see it in the bet. Their context speaks to an existing pain and not just an existing behavior: the limitation of having to use your hands to take photos. Offering less friction is a powerful incentive for changing social behavior. Importantly, it does not change it by much. People already take photographs of themselves and strangers in public places. We are used to this level of creepiness. Seeing a person with a camera on his sunglasses significantly more creepy. In this way, a bet works as a diagnostic tool.


A bet works as a diagnostic tool

Also, it’s a camera and not a computer, so people know what to expect. And if you wanted to, you could build cues into a product indicating that it is recording, for example, a blinking red light, allowing the other party to make an ask not to record. Familiar cues make it easier to adopt new technology.


A bet is inherently flexible. You can formulate it as you get new insights and develop a better-informed hunch. You can apply it at a small scale, for instance to a feature to get a feel for it, at a medium scale, say your pricing strategy to gain confidence in using it, or a large or even very large scale -- your product, your segment, or your market when the fate of your company hangs in the balance.


A bet is inherently flexible. You can apply it at very small scale or very large scales

Here are some examples of bets at different scales, with the example of Google Glass :


The Feature Bet 


What if Google had decided to stay in the consumer market, rather than pivot to the enterprise market? How might they have designed a bet to test the evolution of the Glasses features?


A feature bet might have been:

Given: augmented social conversations feel creepy If We: make it possible to share see what I see with the other person Then: she will feel less creeped out and more assured Resulting In: + increasing tolerance for an augmented conversation

A simple experiment to test this would have been having the glass output mirrored to the user's phone or made available at a private URL for the other party to join in.


Segment Bet 


But they did shift to an entirely new market segment altogether. Here is my version of what their segment bet may have looked like :

Given: the intense push-back from the consumer market If We: aim for a market where visual overlays are an advantage Then: we might be able to repurpose Google Glasses Resulting In :  + market acceptance - rethinking the product’s purpose

Glass Enterprise edition focuses on “fit(ing) into your workflow, improve accuracy, and collaborate in real-time”, an evolution of the product basic purpose.



EnjoyHQ's experience with Bets

This then is a bet. It is a high precision instrument that lets you think of pivoting in single-degree increments. It also directs you to product-market fit. When EnjoyHQ applied this, the effect on the team was dramatic. Their team conversations rapidly changed from why to how, the level of thoughtfulness in their features increased and internal struggle on what to build all but disappeared. The team always knew how to build a great product, they now understood why they should at the granular level and had the language to talk about it. They eventually emerged with two separate languages– an external-facing one for their features and an internal-facing for their bets.


The internal struggle on what to build all but disappeared

So if you’re facing a deluge of feedback and feel uncertain about how to take the next step in going from zero to one, use bets and trust your intuition. Decide to experiment on a size that feels safe to you -- maybe its one key feature that keeps coming up, or the pricing that feels clunky. Formulate a bet and brainstorm with your team on capital-efficient ways to test the bet. At a very basic level, a bet pulls you out of the fight-or-flight reaction into your pre-frontal cortex so you can process information better, as a team.


Move from fight-or-flight into your pre-frontal cortex

As you study your customer’s response, you will grow regardless of the outcome. If your bet panned out, you feel more confident and your intuition is reinforced. If it did not, you will be in a position to reflect on your prediction (and conviction), learn and inform your intuition. Adjust, make a second bet, and try again — when learning from failure, it is the first turn that is the hardest. As you gain in confidence you can then apply this foundational discipline to think in pivots as well as measure your product-market fit.


All the best.


With gratitude to Sofia and Lucasz from EnjoyHQ and Caroline Strzalka from ItsByU

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