“It doesn’t matter how beautiful your theory (idea) is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong” – Richard Feynman
Building a successful product startup is like trying to win a race driving a vehicle that has less than half its fuel tank filled, whose controls you don’t fully understand and moreover don’t know where the finish line is. What is known is the visibility of runway for next few meters and inspiration from stories of how many has won such race to gain riches.
While the analogy might look far-fetched but startups work under the circumstances of extreme uncertainty. They might herald around a great idea that they think will change the world there is many things that are unknown – the problem being solved, if their solution is the right one, they have the right team to make it happen and so on.
Risk
Risk is the common language that is used to describe and address the elements of uncertainty in life. There are few kinds of uncertainty that a startup has to eliminate as it goes forward on its road to success. Some of these risks are the following
Technology Risk – Can the startup build what it is planning to build with the current state of the art technology? In many cases this may not be a question but products that are at bleeding edge of technology has to evaluate this question. For technology entrepreneurs this is where the motivation for them to build the product would have first started and thus they start the journey here and spend their most of the time.
Product Risk – While the aspect of can build or not is one thing, the other element startup faces is what kind of feature to build first. What is must-have & nice to have feature.
Execution Risk – Is the startup staffed with right team to get things done. Are they able to pull off what they plan to do or are just paralyzed while coping with ever changing conditions on almost everything.
Customer Risk – Finally whether what the startup is building will be used by a set of users, if they will or somebody else will pay for the usage, recommend it to friends after they have used it.
Market Risk – This is aggregated customer risk, are there enough number of customers who will use, pay & recommend? Is there a viable way to reach to them, interact with them and also collect from them?
Resources
While startups address these risks an important law of life – “Resource are limited”
‘Time is limited’ – Startups would have setup or planned a certain time duration during which they wanted to try out their startup.
‘Money at disposal is limited’- Regardless of how financing is done (self or external) money is always in short supply
‘Energy is limited’ – Ask any entrepreneur who has been at it for couple of years and has not seen any breakthrough in progress, he would tell how jadedness and fatigue starts to set in.
‘Even a supporter’s patience is limited’ – In initial days many encourage to give support , after not seeing much tangible progress for a while there is degradation in their support in kind or even words.
Given that the resources are limited how startups approach addressing the risk matters.
99% of the time the following is how startups address risks
Many startups try to extend their resources by raising money. But that alone is not the resource that is limited. Moreover even after extension through infusion of money if startups can’t remove customer risk then the same fate applies.
A workable approach however could be trying to address elimination of customer risk first and also broaden it to market risk.
The most important thing for any product startup is to reach product/market fit .
By focusing on eliminating customer risk is the fastest way to reach there.
Over the last few years a lot of learning has been understood on how to eliminate customer risks, these learning are well documented as customer development and lean experimentation.
Few key principles of these are the following
- All statements are assumptions or guess
- All answer lie outside the building
- Change guesses into facts using experiment with customers
- Start by building uncomfortably small prototypes to test with customers.
- Run those experiment and measure on the metrics
- Incorporate learning into next experiment
- Move through the loop quickly
