Highest Hill

“Reach the pinnacle of the Highest Hill, where the most farsighted leaders can be found. This is the place to get focused and identify your research target. Establish a sense of understanding and set your objectives to direct your adventure.

We’re 95% sure you skipped over the last stage because you already have an idea. There are very few of you who land on this website without a solution/idea/startup they’ve already though about and probably are already working on or have at least given some thought.

Problem is, we’ve all been doing it wrong. We have a problem, think about a solution and then start building. Rather simple, straight-forward process, but without a lot of substance behind it. There are entrepreneurs out there working on their 16th idea hopping from one failure to the next in the hopes that the next one will be a hit.

The Design Thinking method tried to fix this. It proposes a framework where problems are turned into questions to ask a target that is similar to the people you’re trying to solve the problem for. Then empathizing with them, understanding the problem, formulating a solution, prototyping it and so on.

Lean Startup methodology is also one of the poster-child methods for entrepreneurs to build their businesses. This one proposes a build-learn-adapt cycle and an interative, step-like approach. 

However, the most common approach of organizations around the world is the problem-solution framework. Even in presentations, entrepreneurs usually structure the slides this way: What is the problem, what’s our solution.

None of these are inherently wrong, but they’re all trying to fix a problem that’s already been solved, but lost in translation. All of these methods are trying to do one very important thing: derisk (reduce the risk of failing). 

Let’s think about it more consciously: What are the chances that the solution you proposes fixes the problem you found for a majority of people, and they’re willing to pay for it as well? Realistically, somewhere around 0.05 to 0.9%. 

What are the chances that your solution fixes the problem after you talk to 100 people to clearly define what their problem is? Probably now somewhere between 1 to 5%.

What are the chances that your solution fixes the problem after you build a small (and I mean very small, should take you a week max. We’re going to talk about how to build a prototype in a week with no technical background in a later chapter) prototype and you give it to 25-50 people to try it and give you feedback? Probably 2 to 7%.

What are the chances that your solution fixes the problem after you convince 5 strangers to pay you for it? Probably 5 to 10%.

Truth be told, businesses, especially startups, have a high failure rate. There’s thousands of studies saying that 80-90-95-99% of startups fail. You’re never going to be 100% sure that what you’re building is going to be succesful. Not even at later stages, when you’re generating 100-200K EUR every month. 

Design Thinking, Lean Startup, Problem-Solution and all of the other methodologies (Business Model Canvas is another examples that comes to mind) are just a very complicated way of saying: “Do your research!“.

Take the idea you have right now in your mind and google it. You’d be suprised how many entrepreneurs haven’t done that. We’ll bet you an arm, a leg and an eye that your idea has already been done by someone else. Even if it might now show in the first couple of results, that doesn’t mean it hasn’t been tried. One good place to check is on Google Scholar which holds proper academic research materials from all over the world.

Now, just because it was done before, doesn’t mean you shouldn’t do it. But we have a very limited time on this planet as humans. How much of it do you want to waste just copying something that’s already been done?

The next 7 chapters are dedicated to research. How to identify the real problems, understand your target audience, test your ideas for as cheap as possible and more. That’s ~30% of the entire map/program. This section will probably save you thousands, tens of thousands or maybe even millions of dollars in the future. And it will also help you save a lot of time in the long run, time better spent building something people actually want. 

We know research is not particularly sexy. You’ve probably started doing startups to avoid doing research or desk work. But there’s no skiping this. Some of the world’s most helpful technologies were first a research brief: The team at Bell Labs researched and invented the transistor, the laser, information theory, UNIX, C and C++ programming languages, the photovoltaic cell, radio astronomy and many, many more. None of them called themselves founders, but researchers. What they missed out on however was monetizing their discoveries.

What we’re proposing for this research stage to actually work is a mindset shift: Stop thinking only about your solution. Or only about your problem. Why? When you’re thinking about solutions/problems, even if you’ve done some research, you’re making a lot of assumptions. As Peer Stoop loves to say, when you assume something, you make an ass out of you and me. Assumptions, by definition, are something that you belive is true without proof.

Assumptions will use every opportunity to murder you and your business. Your assumptions will lead you to making decisions without having enough information. Every single decision will basically be a flip of the coin regarding the fate of your business if you don’t map your assumptions (at least the ones you’re aware of) and prove them wrong (or prove them right!).

If we’re going to be researchers, we need to work with hypotheses. What is a hypothesis? A hypothesis is a concept or idea that you test through research and experiments. In other words, it is a prediction that can be tested by research. Most researchers come up with one or more hypothesis statements at the beginning of the study.

Assumptions Diagram

Enough theory though, let’s take a practical example. Let’s say that you’re building a Grocery-rating app based on product labels. Using the traditional problem-solution framework, we get:

What’s the problem? You can’t easily read what the label says and you can’t understand the information. More importantly, you can’t make a decision whether you should buy that product based on it’s health impact.

What’s the solution? An app that scans the label and gives you a clear health rating for the product based on your personal health data.

Sounds pretty simple and straight-forward, right? Nothing sticks out, it sounds like a logical match. Let’s take the Hypotheses-Proof approach:

What’s one hypothesis? People want to more easily understand whether a product is healthy for them or not. 

How can we prove that? Well, one way would be to ask 100 people. If 50/100 say that they’re actively looking for a healthier product, our hypothesis is proven. Another way to prove it would be to spend 2 hours in a store and watch people buying products and document their behaviour, see if they pick up and read (or at least try to read) the labels. Ask them after they bought the products why did they pick those.

And that’s just one hypothesis, regarding your target audience. You’ll realize that not all people are interested in healthy products, but only a niche part. Now you have to define that niche and understand their needs better and test the hypothesis again: “Pregnant women want to more easily understand whether a product is healthy for them or not”. 

Another hypothesis, that focuses on your solution is: My target audience will choose their groceries based on a health score. One way to get proof to validate/invalidate this assumption is to build a prototype of your app and give it to 100 people. Another way to get proof would be to apply the “Concierge/Wizard of Oz” methods (don’t worry, we’ll have a guide on all the methods to validate assumptions). With this method, you would establish a couple of whatsapp groups with your targets and whenever someone wants to make a decision regarding their groceries, they’re encouraged to post a picture of the product on the group. You, as the research, provide the rating of the product yourself through a message on the group. (Notice that for this second method, the costs are basically 0).

The hypothesis-proof relies a lot on the assumptions we make regarding the problem-solution framework. Let’s introduce you to your buddy for the rest of the journey: The Validation Board.

Copy of Validation Board

There’s two sections to this board: The Stages & The Assumptions. At each stage of your business, you will have different assumptions, each with their own validation method, success criteria and other caractheristics. We’ll dive deeper into this board on future chapters.

This board is nothing new or revolutionary. It’s actually a pretty old model we first discovered at Lean Startup Machine and then we saw it improved by the awesome team at Slidebean. They also have an amazing video explaining how to actually use their version of the board right here: 

Whether you’re using our version of the Validation Board, Slidebean’s or Lean Startup Machine’s, it doesn’t really matter. The process of validating or invalidating your assumptions is what really matters. That being said, we do think our model is superior since it’s a bit simpler and more easily digestable than the others, but we’re also pretty subjective regarding this.

Our invitation throughout this journey that we’ve built is to set your idea aside for a bit and start thinking like a researcher. Start thinking “How can I prove to myself that this business will work, for the least amount of money?”. Even if you apply 10% of what you’re going to learn, you’ll still be end up better off than most other entrepreneurs who just throw themselves at a solution and start building. 

One very important point that we have to reiterate though: You’ll never be 100% sure it works. You can, with this method, reach a point of data overload. A point where you have too much information to make a decision and you freeze. A good rule of thumb is to make a decision and execute when you’ve got 50-60% of the data. We’ll talk at length and even offer an example into how much that is in our future chapter, Validation Valley. Until then, let’s get a better understanding of the world around us and the market we’re going to serve in the next chapter: Market Village.