MINIMUM SUCCESS CRITERION FOR VALIDATING HYPOTHESES

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Given that entrepreneurs tend to be born optimists, and to avoid subjectivity, we must define a priori what the correct threshold is for considering a hypothesis to be validated. The Minimum Success Criterion (Minimum Success Criterion) is a key indicator used to validate business assumptions. The minimum criterion for success refers to the metric or set of metrics that must be achieved for an experiment to be considered successful and the hypothesis validated.

The minimum criterion for success is important because it allows entrepreneurs to make informed decisions based on real and objective data. Without a defined minimum criterion for success, entrepreneurs may be prone to subjective interpretation of results and make decisions that are not supported by objective data.

In order to define a minimum criterion for success, some key steps need to be followed

1. Identify key metrics: Identify the metrics that are critical to measuring the success of your experiment. Make sure these metrics are related to the hypothesis you want to validate.
2. Define a minimum threshold for success: defines a minimum threshold of success for each of the identified metrics. This threshold must be reached for the experiment to be considered successful.
3. Evaluate the results: evaluates the results of the experiment and compares them with the minimum success threshold defined for each of the metrics. If the results meet the defined minimum success threshold, the hypothesis is considered validated. If the minimum success thresholds are not met, the hypothesis has not been validated and adjustments and new experiments must be made.

It is important to remember that the minimum success criterion must be realistic and based on objective data. In addition, the minimum success criteria can change as the process of validating the hypothesis progresses. By following these steps, an effective minimum success criterion can be defined that allows entrepreneurs to make informed decisions and build a successful business.

Practical examples of minimum success criteria in hypothesis validation

HERE ARE SOME PRACTICAL EXAMPLES OF HOW THE MINIMUM SUCCESS CRITERION CAN BE APPLIED IN HYPOTHESIS VALIDATION:

1. Price assumptions: An example of a minimum success criterion for validating a hypothesis about the price of a product or service could be that at least 20% of the potential customers interviewed are willing to pay the proposed price. If this threshold is met, the hypothesis is considered validated.
2. Marketing channel hypothesis: to validate a marketing channel hypothesis, an example of a minimum success criterion could be that at least 30% of potential customers coming through a given marketing channel make a purchase. If this threshold is met, the hypothesis is considered validated.
3. Hypotheses about the target audience: to validate a target audience hypothesis, an example of a minimum success criterion could be that at least 50% of the potential customers interviewed identify with the demographic and psychographic profile defined in the hypothesis. If this threshold is met, the hypothesis is considered validated.
4. Hypothesis on monetisation strategy: to validate a monetisation strategy hypothesis, an example of a minimum success criterion could be that the customer acquisition cost (CAC) is less than the lifetime value of the customer (LTV) within 6 months. If this threshold is met, the hypothesis is considered validated.
5. Hypothesis about the product or service: to validate a hypothesis about a product or service, an example of a minimum success criterion could be that at least 50% of potential customers who try the product or service are willing to pay for it after testing it. If this threshold is met, the hypothesis is considered validated.

The minimum success criterion is a valuable tool in validating hypotheses and allows entrepreneurs to make informed decisions based on objective data. By defining a minimum threshold of success and evaluating the results of the experiment against this threshold, it can be determined whether or not the hypothesis has been validated.

This means deciding in advance how many interviews we will do and what threshold we will consider as "success". Therefore, you have to validate your hypotheses as a fundamental part of the process of creating your company. To validate a hypothesis you have to do an experiment, usually, I think an experiment is the best way to validate a hypothesis. MVP (minimum viable product (see+)). The methodology lean startup (+) What is the first thing we do when we want to solve a scientific problem? Define a hypothesis, which through an experiment and according to its result, we will be able to know if this hypothesis is true or null. 

How do I start validating my new company?

Therefore, the fundamental hypotheses, which must be validated, are as follows: 

1. Who is my customer (+)?
2. Does the problem (+) what have I detected?
3. Is the solution or value proposition (+), satisfies and solves your problem?

If we do not want to put the cart before the horse and we want to establish a solid and strong foundation when creating our company, these are the three initial questions that we must resolve. 

• The million-dollar question: Can my friends, acquaintances, family, mother... answer the hypotheses?
• Million answer: NO! Unless you want to fool yourself and create an enterprise that is not based on solid foundations and validated hypotheses. Friends and family are not objective and will always support me in the idea.

The only people who will be authorised and empowered to validate our hypotheses will be: our real customers. 

HENCE THE FIRST HYPOTHESIS:

HYPOTHESIS A

It will be the only one that they do not answer and we will be the only ones in charge of defining, as segmented as possible, what our customer segment is, and in this way, give an answer to the question:

• Who is my client?

HYPOTHESIS B

Once the customer segment has been identified, we will have to know how serious the problem we have detected is. To do this, we will have to approach the customer and ask about the problem, the level of pain they have and how they are solving it at the moment. 

HYPOTHESIS C

After having validated the two previous ones, we will have to check if the solution we have in mind is the right one for our customer segment, in this case, we will prepare a prototype, fast and cheap, that will allow us to check if it really gives value to the customer and solves the problem they have. Once we have validated hypotheses A, B and C, we will be able to create our business model (+) with the help of the methodology lean startup (+) and with the canvas (+).

Of the 9 blocks to be filled in, the customer segment and the value proposition have already been validated, and now we have to test the different blocks and, through experiments, check whether the hypotheses are valid or not. 

For example: 

• To know if the distribution channel chosen is the right one.
• The choice of communication channels. 
• Test whether the way to charge is through a subscription or a freemium system. 

As we progress in the development of our business model, new ideas or assertions or assumptions will emerge... all of them should be treated as hypotheses to be validated. In the long run, this gives solidity to the project and will help us to get investors, participate in acceleration programmes or win an entrepreneurship award. For each hypothesis, we will always obtain an initial question, an experiment or questionnaire carried out and metrics that we will obtain from the analysis of the market test.

EXAMPLE OF HYPOTHESIS VALIDATION:

Imagine that it has occurred to you, through personal experience, that it is very difficult for your company to find service providers (from lawyers to designers or consultants) who understand your requirements, both in terms of speed, cost and type of services.

WE START VALIDATING

We include who we think are the specific customers, who we think will be early adopters and the problem they have. We do not include the solution for the moment, as we have not yet spoken to anyone. We point to the most risky hypotheses, the most risky of which we think is that companies are not able to easily find such suppliers. To validate whether this is true or not we will interview other companies (at least 10). To consider the hypothesis to be true, we need 6 to mention that it is a real problem. We went out into the real world, did the relevant interviews and found that indeed, 7 of the companies interviewed confirmed that it is a problem.

VALIDATED HYPOTHESIS

Now we can think about how we are going to solve the problem... and we decided that the best way is through a "marketplace" where supply and demand come together. The next hypothesis to be validated is that companies are willing to pay (€10 per month).

• For example: for the service, and we will send an offer to a mailing list we have with companies, hoping that 60% will sign up.

Unfortunately, although they showed interest in solving the problem, they are not willing to pay for it (only 20% have signed up).

INVALID ASSUMPTION

We have to pivot (+), and we thought that even if the companies don't want to pay, maybe the suppliers do... so, we change the client and the problem, and we start experimenting. In this case, the first thing we want to test is whether the suppliers are willing to pay €50 a month to be registered. We tried a different mailing list, and hoped that 50% would sign up to the platform. We have validated that our main customer is willing to pay! Although we are far from having validated the entire model, things are looking good.

VALIDATES FROM THE BEGINNING: CLIENT-PROBLEM-SOLUTION

Launching a new venture is a task not for the faint of heart, as it requires the courage to land our ideas and the common sense not to stick to them... because it is riddled with untested hypotheses. And we often don't start validating them until it's too late... a bad idea. However, it is possible to validate many of our hypotheses without even putting a single "brick" in our product or without creating a single line of code... because many of the hypotheses on which our new company is based are not about the product, but about who the customer is and the problems they have, something that we already talked about in validating your business model is transforming hypotheses into facts and that is the first phase of your business.

The problem is that often, although we are aware of the importance of interacting with the customer and asking him, we tend to stay in the safety of our office or at home and invent methods to avoid facing him... and all because we are terrified of discovering that we are not on the right track, stage fright, fear of failure (...).see TIP Fears (see+))!!

BUT...

It is much better to find out early, when you have time to pivot, that your business model is not on the right track. The epitome of "badness" and the cave complex is surveys... possibly the number 1 way to draw the wrong conclusions from customers. Not because they are bad per se, but because to create a good survey we have to be experts at it, create the questions with the conclusions we want to discover in mind and above all, reach enough people... something complicated at this point. Excellent article on customer validation problem solution (see+).

In this article, the author discusses the importance of validating a startup's business model by validating key assumptions. In particular, it focuses on the validation of hypotheses related to the customer, the problem and the solution. The article begins by explaining that most entrepreneurs have a vision of how to solve a specific problem they have identified. However, This vision is often based on assumptions rather than facts. This is where hypotheses come into play, which need to be tested and validated before moving forward in the business development process.

In the case of customer-related hypotheses, the author points out that it is necessary to clearly define who the potential customers are and what their profile is. It is important to go out to the market to validate this hypothesis, for example by interviewing or surveying potential customers to determine whether there is a real demand for the proposed solution. In terms of the hypotheses related to the problem, the author points out that it is important to have a thorough understanding of the problem one is trying to solve. The problem must be defined in concrete and specific terms, and go to the market to validate that there is indeed a real need and that the problem is important enough to justify the development of a solution.

Regarding the hypotheses related to the solution, the author points out that it is necessary to clearly define what is going to be offered and how the identified problem is going to be solved. It is necessary to create a prototype or minimum viable product (MVP) in order to test and validate the solution with potential customers and see if they are really willing to pay for it. In a nutshell, the article highlights the importance of key assumptions in a startup's business model and how validating these assumptions can help entrepreneurs build solutions that meet real customer needs and succeed in the market. Help tool to validate (see+).

The article talks about a tool called "Validation Board" that is used to validate hypotheses and pivot in a startup. The tool consists of a template that includes four areas: Problem, Solution, Target Audience and Value Proposition. In each of these areas, hypotheses must be defined and ways must be found to validate them. The idea is to make a list of hypotheses for each area and then find ways to test whether these hypotheses are true or not.

Furthermore, the article mentions the importance of constant validation and not waiting for a finished product to start doing so. Instead, early and frequent testing is recommended to ensure that the final product is aligned with what the market wants and needs. In short, the Validation Board is a useful tool for startups to validate their hypotheses and pivot at an early stage of product development.

I share excellent article (see+) How to validate strategic hypotheses?

In this article, the author addresses the question of how to validate the key assumptions on which a company's business model is based, i.e. how to ensure that the business idea is viable and sustainable in the long term. The author begins by explaining that, while it is impossible to completely eliminate the risk associated with starting a business, there are certain techniques and strategies that can help reduce risk and validate the assumptions that are made in the business development process.

One of these techniques is the MVP (minimum viable product), which consists of creating a product or service that allows you to get early feedback from your customers and validate your assumptions about what they want and need. However, the author points out that MVP is not always sufficient, especially when it comes to key hypotheses that are difficult to validate through direct observation of customers. In these cases, the author proposes an approach based on controlled leaps of faith.

This approach involves identifying the key assumptions underlying the business model and designing experiments to validate them. These experiments should be designed in such a way as to obtain relevant and valuable information, but without risking too much the viability of the business in case the assumptions are not validated. The author gives several examples of how such experiments can be designed, such as setting up a website to validate the demand for a product or service before investing in its production, or conducting interviews with potential customers to assess their interest in the product or service offered.

In summary, the article highlights the importance of validating the key assumptions underlying a company's business model and proposes different strategies for doing so. The controlled leaps of faith approach is one of these strategies, and is based on conducting carefully designed experiments to validate key assumptions without risking too much the viability of the company in case the assumptions are not validated.