the right it — Alberto Savoia (Part 1 of 2)
The Beat of Failure is always lurking around, trying to find the next new product ideas that it can devour and bite those who pursue those ideas without first validating them.
Law of Market Failure
Most new Products fail! Hard truth, but we rather believe the hard truth upfront. Even with competent execution most new Products fail. That’s the Law of Market Failure.
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Why do most Products Fail?
Among the many many reasons most new Products fail, like Ran Out Of Cash, Bad Execution, Rift Between Founders, , Poor Marketing, Ahead Of Its Time, the most prominent among them is ‘No Market Need’. That is, the idea itself was a ‘Wrong It’ in the first place. In short it wasn’t the ‘Right It’. Built it the Right Way, but the ‘It’ itself was wrong in the first place.
The Right It is an idea for a new product that if competently executed will succeed in the market.
So why do so many smart and experienced people build the Wrong It?
To make sure they have the right idea, they invest a significant time and money on Market Research. The problem though is that most often this research is done in Thoughtland. Thoughtland is an imaginary place where the idea is hatched.
When the idea stays too long in Thoughtland it attracts opinions and judgements. Experts disagree. Some folks thing the idea is great, others think its lame. Opinions either pro or opposing the ideas are loosely thrown around. Opinions are not Data. They are subjective and biased judgements. Most importantly they are provided by people who have no skin in the game. It is one thing when a potential users says, I’d buy that if it were available and completely another thing when the user actually puts in their $$ when the product is available.
The Right It cannot be deduced or induced while you stay in Thoughtland. It has to be discovered through experimentation in the real world.
Building the Right It, though not completely fool proof towards failure, gives you a high chance of success as opposed to building the wrong it in the first place.
Why Thoughtland is dangerous?
There are 3 problems with the validity of information we collect in Thoughtland.
- The Lost-in-Translation Problem
The problem here is in communication. Until an idea is made concrete or tangible in some form, your idea for a new product or service is just an abstraction. It is something that you imagine or picture in your head in your own unique way
And when you communicate your idea the way you see it, the other person responds on its utility, usage from their perspective, their way of looking at the world. So what you imagine as the usage of the product would look like will be completely different in how the potential user you are surveying would think about. They have their own personal beliefs, preferences and prejudices.
Think of Uber in its earliest days before it was popular. Many users would have been skeptical about getting into a strangers car and driven around by somebody who has no taxi license!
- The Prediction Problem
People are notoriously bad at predicting what their future needs would be. Take Uber for example. In its earliest days, if assumed you were convinced it was a safe thing to do to ride in a strangers car, many of of us would have thought we would use it one off and not many times. That’s prediction problem! We are not quite good at predicting what we may need in the future without experiencing it as well as how often we may need it.
- The Skin-in-the-Game Problem
Skin in the game means having something to gain or lose in an outcome. So when we ask people for their opinions on our idea, they generally have no skin in the game. And then we take their opinions on face value, we mistake their compliments for our idea as a go ahead to build something and they will pay when its ready. Reality is, most often then not, they won’t
The last problem with Thoughtland is how we interpret that information we have received while the idea is merely an abstraction. That is, Confirmation Bias. We often want to see what we want to see. And when users who haven’t understood the idea exactly as we have it in our mind (lost in translation), predict how they may use our product in the future (prediction problem) and give us opinions with no skin in the game (Skin in the game problem), we have our own cognitive biases that interpret it as a signal to build or not build a product without doing a proper market validation of our idea.
To beat opinions, you need Data. And Data needs to be Fresh (no point in having last years data to solve today’s problem); Relevant (To the specific product or decision being evaluated); Known (Not relying on Data collected from other people (OPD — Other People’s Data) in other organizations or for other projects to make your decisions — The methods vary to collect and filter the data, their biases, influences and motivations may have affected them when they compiled and summarized the data)
You should not make a decision about your idea based solely on what other people did or did not do with an idea similar to yours.
Your Own DAta (YODA) is market data collected firsthand by your own team to validate your own idea. YODA must satisfy the criteria of freshness, relevance, trustworthiness and significance.
Part 2 Here