Como você projeta e executa experimentos eficazes para testar suas suposições?

The article emphasizes the importance of identifying and articulating assumptions in business ideas. It suggests using tools like Lean Canvas or Business Model Canvas to clarify assumptions such as target customer problems, willingness to pay, solution preference, and channel reach. This process is crucial for validating business concepts and mitigating risks.

To design and run effective experiments, start by clearly identifying your assumptions—statements you believe to be true about your product, customer, or market. Prioritize these assumptions based on the risk they pose to your success. Formulate hypotheses around each, specifying what you expect to happen if the assumption is correct. Design experiments that isolate and test these hypotheses, using methods like A/B testing, prototypes, or customer interviews. Ensure you define success criteria and collect measurable data. Run the experiments quickly and iteratively, analyzing the results to validate or refute your assumptions, and adjust your strategy accordingly.

Es muy importante reconocer y sustentar las suposiciones que sustentan la idea de negocio, esto se convierte en los cimientos sobre los cuales se construye la empresa es crucial validarlas de manera rigurosa. sugiero algunos puntos importante para considerarlas: La Viabilidad economica ¿son mis precios competitivos? El dolor del cliente ¿comprendemos realmente el dolor que esta viviendo el cliente? Propuesta de valor ¿Cual es mi diferencial?

To design and run effective experiments to test assumptions: #Define Clear Hypotheses: Identify specific assumptions to test and frame them as measurable hypotheses. #Start Small: Create simple, low-cost experiments (MVPs or prototypes) to validate key assumptions without over-investing. #Choose Metrics: Establish clear success criteria and key performance indicators (KPIs) to measure results. #Run Controlled Tests: Isolate variables and run controlled experiments to avoid bias and get reliable data. #Iterate Based on Data: Analyze results, refine your approach, and run additional tests to continuously validate or adjust assumptions.

Define hipótesis claras sobre aspectos clave del negocio, como: • El problema que resuelve tu producto • La solución propuesta • El mercado objetivo • El modelo de ingresos. Por ejemplo, "Creemos que [nuestro producto/servicio] resolverá [problema específico] para [segmento de mercado] generando ingresos a través de [modelo de ingresos]". Estas hipótesis te guían en la creación de experimentos para validar tu concepto y adaptar tu estrategia según los resultados.

Este proceso de priorizar es claramente fundamental, pero no solamente implica seleccionar las mas criticas y riesgosas sino tambien tener la valentia como emprendedores de cuestionar nuesttras propias convicciones y estar dispuestos a ajustar el rumbo si es necesario, es decir priorizar con absoluta conciencia.

Identifying assumptions is foundational. We hypothesize about customer problems, willingness to pay, solution preference, and channel reach. Tools like Lean Canvas aid in articulating these assumptions, guiding our validation process for a robust business model.

Priorizar las hipótesis o la suposiciones dependerá en gran manera de la cantidad y calidad de información disponible, ahora bien, además de esto debemos identificar esas dimensiones determinantes para nuestro negocio, que desde una perspectiva muy práctica lo referido a los clientes (comportamientos, percepción, tendencias, modos) sobre los proveedores (disponibilidad, opciones, entre otros) y así en la medida que se ponderan las dimensiones críticas.

Develop a detailed plan for how you will conduct the experiment. Consider factors such as sample size, experimental conditions, control groups, and data collection methods.

Es fundamental priorizar las hipótesis para enfocar adecuadamente los recursos y maximizar la eficiencia del proceso. Existen distintos puntos a tener en cuenta a la hora de realizar esta planificación: 1. Evalúa el impacto potencial. Examina el posible impacto de cada hipótesis en relación con los objetivos. 2. Grado de incertidumbre. Considera el nivel de incertidumbre de cada hipótesis. 3. Viabilidad y factibilidad. Analiza la viabilidad de probar cada hipótesis. 4. Riesgos y beneficios. Evalúa los riesgos asociados con cada hipótesis. Debes estar constantemente iterando la validación en función de las nuevas certezas que vayas descubriendo.

Ademas de tener estas herramientas fundamentales para evaluar el progreso de estas suposiciones, es muy importante tener en cuenta que las metricas no son estaticas, y van evolucionando a medida que se va obteniendo mas informacion y se van ajustando las diferentes estrategias. Por lo tanto este seguimmiento a estas mediciones permite una evaluacion continua y sobre todo una acertada toma de decisiones que van dando la direccion hacia el progreso de la empresa.

Minimize the influence of factors other than the independent variable on the dependent variable. This might involve randomizing participants or conditions, standardizing procedures, or using control groups. Collect data: Carry out the experiment according to your plan, ensuring that you collect accurate and reliable data. Use appropriate tools and techniques for data collection, and document your procedures carefully. Analyze the data: Use statistical methods to analyze the data you have collected. Look for patterns, trends, or significant differences between experimental conditions. Consider consulting with a statistician if you are not familiar with statistical analysis techniques.

Clear, measurable outcomes guide decision-making. I set metrics that offer real insight into customer behavior, like conversion rates or engagement levels, rather than vague data points. These numbers give us clarity on whether to double down, pivot, or scrap an idea altogether. Without meaningful metrics, assumptions remain just guesses.

Las métricas deben ser determinadas en atención al propósito del experimento, por tanto la definición de las métricas es un aspecto directamente impactado por el propósito e incluso la claridad del investigador en torno a la prueba.

Defining clear and quantifiable metrics is crucial for measuring the validity of assumptions. Metrics like sign-ups, conversion rate, retention rate, customer satisfaction, and revenue help gauge assumptions' accuracy. Tools like Pirate Metrics or Lean Analytics assist in defining relevant metrics for effective validation.

Experiment design is pivotal. Utilize methods like interviews, surveys, landing pages, prototypes, MVPs, or A/B tests to test assumptions rigorously. Tools like Experiment Canvas or Test Card aid in designing experiments, ensuring reliable and valid results for informed decision-making.

If your hypothesis is not supported, consider alternative explanations or modifications to your original assumption. Use the insights gained from the experiment to refine your understanding and develop new hypotheses for further testing.

Designing experiments entails creating methods to test assumptions effectively: - Isolate variables for precise testing. - Control biases and confounding factors. - Utilize diverse experiments like interviews, surveys, landing pages, prototypes, MVPs, or A/B tests. - Employ tools like Experiment Canvas or Test Card for design and execution.

Designing experiments involves creating methods to test assumptions and gather data effectively. These experiments must isolate variables, control biases, and produce reliable results. Utilize various methods such as interviews, surveys, landing pages, prototypes, MVPs, or A/B tests. Tools like the Experiment Canvas or Test Card aid in experiment design for rigorous validation.

Designing and running effective experiments involves several key steps: clearly defining the assumption or hypothesis, methodically designing the experiment with variables and controls, implementing it while considering biases, analyzing data using statistical tools, and interpreting results to validate assumptions.

Executing experiments is pivotal. Engage potential customers, launch landing pages, distribute prototypes, release MVPs, or set up A/B tests to gather data efficiently. Tools like Experiment Board or Validation Board aid in experiment execution, ensuring accurate measurement of metrics for informed decision-making.

La eficacia como resultado de la ejecución del experimento será en gran medida asociada a la rigurosidad del diseño de este, con esto resaltando que la definición clara del propósito que a su vez debe estar vinculada a la propuesta de valor y las correspondientes métricas. Y todo esto cobrará sentido cuando ejecutemos el experimento a clientes potenciales, y me refiero a estos porque serán los que nos pueden acercar a resultados reales!

Take time to reflect on the experiment and what you have learned from it. Consider how you can apply these insights to future experiments or to inform your decision-making process.

Running experiments involves executing actions to gather data effectively: - Contact potential customers, launch landing pages, distribute prototypes, release MVPs, or set up A/B tests. - Ensure experiments reach target customers and deliver value propositions. - Measure metrics to assess experiment effectiveness. - Utilize tools like Experiment Board or Validation Board for streamlined execution.

Running experiments involves executing actions to gather data efficiently. These experiments should reach target customers, deliver the value proposition, and measure metrics effectively. Actions may include contacting potential customers, launching landing pages, distributing prototypes, releasing MVPs, or setting up A/B tests. Tools like the Experiment Board or Validation Board can aid in running experiments smoothly.

Result analysis is crucial. Utilize statistical methods, data visualization, and metric comparison to determine assumption validation. Identify patterns and trends for informed decision-making. Tools like Learning Card or Pivot or Persevere aid in insightful result analysis, guiding next steps for business development.

Use statistical methods to analyze the data you have collected. Look for patterns, trends, or significant differences between experimental conditions. Consider consulting with a statistician if you are not familiar with statistical analysis techniques.

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Analyzing results involves deriving insights from data: - Utilize statistical methods, visualize data, and compare metrics. - Determine if assumptions are validated or invalidated. - Identify patterns and trends to inform next steps. - Consider using tools like Learning Card or Pivot or Persevere for structured analysis.

El análisis de los resultados no debe ser un proceso formal basado en una metodología o proceso, esto evitará que los datos convertidos en información y que ahora es el insumo principal sean evaluados al ojo o percepción del equipo que realiza la experimentación. En esta etapa debemos combinar el proceso metodológico con la experticia y conocimiento acumulado sobre el proceso o producto objeto de evaluación.

Hay 2 puntos interesantes a tener en cuenta que pueden ayudar a la hora de realizar experimentos. 1. Trabaja en público: Muestra tus resultados en tiempo real y esfuérzate por atraer a un público de fieles seguidores desde el inicio de tu proyecto. Es una pequeña acción que puede tener un gran impacto en el éxito de tu proyecto a corto/medio plazo. 2. Bootstrapping: Trata de experimentar con los mínimos recursos. Esto ayudará a trabajar de forma pragmática y creativa para poder sacar resultados con el menor esfuerzo possible.

Algo que siempre debemos tener en cuenta son los sesgos a la hora de validar una idea de negocio o una suposición. Cuando no somos consientes de los mismos solemos "buscar probar" que lo que estamos pensando si tiene mercado o es correcto. Partiendo de que además de suposiciones son NUESTRAS suposiciones podemos permitirnos abrir la mente y buscar frameworks que limiten el alcance de nuestros sesgos en la investigación.

Keep it very very simple. You don't have the time and resources to spend as a startup. Define what you are trying to do and find out. Determine a way to do it. Do it. Run for 30 - 90 days depending on the project and industry you are in. See what happened. If it worked do more of it. If it didn't work, change what you are doing. Stay true to your project, don't lie to yourself and park the ego at the door.

The experiment might not work for many reasons. It might be because of the design of the experiment itself. It might be because of the acquisition process. It might be because of external factors (es. selling high hells during COVID lockdown). When you draw the conclusions of your experiment, you should think about all the variables. And sometimes make a guess.

With my engineering brain on - it's important to first simplify your problem statements and minimise bias in assumptions, design or results analysis. Avoid reinventing the wheel - have you done your research on understanding current knowledge and resources available including the unconventional results. Ensure you have a trusted network full of diverse perspectives and backgrounds to help identify any blind-spots and challenge your thinking. Ensure you've really understood which problem you're trying to solve for when defining your hypothesis to test for and bake in that you'll need to continue to learn from results and iterate by constantly refining the initial assumptions.