¿Cómo diseña y ejecuta pruebas efectivas de propuesta de valor?

Don't start with the value proposition right away, but start with developing a customer avatar. Based on the customer avatar think about hers/his Jobs-to-be-Done. Understand the ultimate problem that you are trying to solve for them. This means not only to e.g. "help them make something faster" but also think about what this means for them as an individual. You could also think about this as 1) customer discovery, 2) problem hypothesis, 3) solution hypotheses and 4) transformation hypothesis. To have a solid and robust understanding of these 4 areas is crucial. And breaking it down into these junks makes it possible to run tests to evaluate & validate your assumptions. Combine all your insights as the ingredients for the value proposition.

Las hipótesis son creencias o supuestos en torno a la idea, sobre las que no estamos seguros y que requieren ser validadas mediante un experimento. Un error frecuente que he visto en mi experiencia como consultor de innovación es declarar como hipótesis la simple descripción de un problema. El problema no es la hipótesis: se puede hipotetizar sobre la causa o el efecto de dicho problema. Si la causa o el efecto de un problema es conocido o cierto, entonces NO es una hipótesis, porque no requiere comprobación.

Hypothesis is not just something used in science but essential to innovation and business. When you define your hypothesis, you are: -more curious -working to prove or disprove something -have a North Star However, do not take your hypothesis for fact. As a hypothesis is merely an educated guess, you must continue to see how it changes over time!

Definir uma hipótese é a melhor forma de começar qualquer teste ou pesquisa. Porque em vez de começar de uma página em branco, você parte de uma pressuposição para prová-la certa ou errada. Evidentemente, quanto melhor for a sua hipótese e mais baseada em fatos, mais assertivo será o seu trabalho de testagem.

Entender las necesidades es clave para desarrollar un producto efectivo. En Point CRM, nuestra experiencia con PYMEs nos ayudó a generar varias suposiciones. Al establecer nuestras hipótesis, priorizamos la honestidad: lo que sabemos, creemos y admitimos no saber. Datos revelan que el 55% de PYMEs en LATAM carecen de habilidad digital. Nosotros lo atribuimos al alto costo y conocimiento limitado. Pero aún no sabemos cuánto podemos simplificar sus operaciones de venta. Nuestra propuesta va hacia la eficiencia: Point CRM simplifica la gestión de ventas para que puedan brillar en su especialidad. No buscamos ofrecer todas las herramientas posibles a nuestros clientes, sino aquellas que realmente necesitan, de la manera más sencilla posible.

Choosing the right method for value proposition tests is crucial. In early stages, I use interviews to deeply understand the problem and solution fit. They offer insights surveys can't. As I progress, surveys help to gauge broader market reactions, especially for messaging and positioning. When it's time to test market demand, a well-designed landing page is invaluable. It offers real-time feedback on customer interest and behavior. It's important to blend methods. Interviews, surveys, and landing page analytics together give a comprehensive view. The key is adapting your approach based on ongoing insights.

Los experimentos o pruebas o pilotos deben entregar la mayor cantidad de información posible. El objetivo del experimento es aprender. En mi experiencia como consultor en innovación corporativa, algunos errores comunes que he visto son: - Omisión de variables que luego resultan relevantes - No medición de la línea base de las métricas elegidas (antes del experimento). Es importante tener una línea base de comparación para conocer el impacto del experimento. Por ejemplo, si la prueba es la venta promocional de un nuevo sabor de producto en una zona piloto, una medida de línea base relevante es una predicción de venta en escenario "sin piloto" de los demás sabores, para entender cuánto canibalizó el nuevo sabor a los anteriores.

It is always better to assess potential base on what respondents do as opposed to what they say. If you can, prototype different value proposition, even if these are at concept stage and ask people to pre-purchase.

Na minha experiência, uma boa prática é combinar diferentes métodos de teste para obter uma visão mais completa. Por exemplo, use entrevistas para insights qualitativos e páginas de destino para medir o interesse quantitativo. Essa abordagem multifacetada ajuda a validar sua proposta de valor de forma mais robusta e precisa.

I have a structured approach to compose an offering that has a value proposition responding to the unmet customer needs - it consists of the following elements: - employed assets: which can be hardware, data, software - manageable risks / pains - payment model - pricing tactics Then a value proposition is the actual response to the customer deeds to meet the expectation of a customized, seamless, contextualized service or product, that provides: Access to hardware, equipment, data etc. (the asset), in a determined quality (e.g. flexibility or other pain relievers), for a specified time span (e.g. one-time, unlimited --> this determines then the pricing tactic).

Structure your test not just to validate or invalidate your hypotheses but also to uncover deeper insights that could lead to significant improvements or pivots in your value proposition. While designing a test, it is important to have a visionary approach and base it on your hypothesis. It's beneficial to ask questions related to your hypothesis. Ask yourself, + Which hypothesis do I want to test with which persona and why? + Do I include the hypotheses I want to test? + Do I ask unique questions for the hypotheses? + Do I structure the in a way that the outcomes will lead the comprehensive change? + How will I measure and interpret the results to ensure they are actionable and aligned with long-term strategic goals?

In designing your test, consider using A/B testing for elements like web pages or email campaigns. This method allows you to make more data-driven decisions by comparing two versions of a single variable to see which performs better.

As innovation facilitator of 100+ projects, I advise you to EXPERIMENT BY PRETOTYPING, like TESLA… FAKE IT BEFORE YOU MAKE IT 🤗✅. Pretotyping is a technique for testing - and validating ideas quickly and cheap, founded by Savoia. It involves creating a simplified version of a product or service to collect user feedback and understand market demand. It’s a non-working prototype. Pretotyping reduces the risk of failure by assessing market response early in the development process. A famous pretotyping story is how Tesla ‘faked’ their first electrical car, the Roadster, using a so-called ‘glider’ of a Lotus Elise to confront their potential customers with a ‘real car’, leading to 250+ launching customers who paid $ 5000,- in advance.

Ao realizar pesquisas é essencial ter clara definição do público-alvo para garantir a relevância dos dados coletados. A elaboração do questionário deve ser feita com muita atenção, evitando jargões e viés, garantindo a objetividade. Realizar um teste costuma ajudar a identificar problemas antecipadamente.

To design and run effective value proposition tests, first identify your target audience and create clear, testable hypotheses based on your value proposition. Use A/B testing or multivariate testing to compare variations of your value proposition. Collect qualitative and quantitative data through surveys, analytics, or user feedback. Analyze the results to determine which version resonates most with your audience. Continuously iterate based on findings, refining the value proposition to better meet customer needs. Ensure the tests are conducted under consistent conditions to validate the results accurately.

Value Proposition Test Launch: Pilot Precision: Gather a small crew of customers or colleagues for a test run. Iron out any kinks before the grand unveiling. Feedback Fuel: Adjust your test mid-flight based on real-time insights. Course-correct for optimal results. Documentation Detective: Meticulously track test details, observations, and outcomes. Data is your post-flight analysis goldmine. Ethical Excellence: Prioritize customer privacy and respect throughout the process. Trust is your most valuable asset. Remember: Orderly Execution: Stick to your protocol, and collect data systematically. Adaptive Approach: Embrace feedback, and tweak your test for maximum impact. Document Everything: Insights await in the details.

When I want to design and run effective value proposition tests, I love to execute on a small scale, sometimes with no brand, to learn before scaling up. the top reasons why are: 1. Risk Mitigation: reduces the financial and reputational risks. 2. Rapid Feedback Loop: quicker collection and analysis of customer feedback. 3. Cost Efficiency: Smaller tests require less investment. 4. Market Validation: provides concrete evidence of market demand. 5. Customization and Personalization Opportunities: flexibility to explore different customer segments and tailor the value proposition 6. Confidence in Scaling: ensures the business model, product, or service is viable and that there is a market demand. 7. Unbranded Testing for Unbiased Insights

Exécuter votre test avec rigueur est essentiel. Pour l'enrichir, envisagez d'utiliser des méthodes de feedback en temps réel, comme des sondages instantanés ou des outils d'analyse de sentiment, pour ajuster rapidement votre test en fonction des premiers retours. Cela permet une adaptation agile et une optimisation continue du test.

◉ Be ready to hear what you might not want to hear - in my years as a consultant, I've had to steer clients in the right direction based on the data. Encourage open-mindedness, remind stakeholders of the importance of de-risking, and know pitfalls before a post-launch deficit. ◉ Be nimble and flexible - perhaps you hoped to run interviews. However, recruitment challenges have given you 10% of the desired sample and delayed your project for four weeks. Now what? Learn and reflect on the hurdles and problem-solve other experiments that could be run to supplement. Assess their runway to launch. For example, you might need more time to create new discussion guides and stimuli, but you could run 3-question surveys to the target segment.

Enfin, pour l'analyse de vos tests, au-delà de la visualisation des données, envisagez d'intégrer des méthodes d'analyse qualitative pour comprendre le "pourquoi" derrière les comportements des utilisateurs. Des techniques comme l'analyse thématique ou les études de cas peuvent révéler des insights plus profonds sur l'efficacité de votre proposition de valeur.

◉ Visualising your results - there are more creative and meaningful ways to visualize your data, such as tree maps, scatterplots, and infographics. Try to avoid the basic Excel table, which can be cluttered (depending on data), or pie charts since these can be misleading. Pro-tip: consider color theory in graphics with red being alarming, dark blue being authoritative, green being positive or a gain ◉ Adjusting your story - understand who might be analyzing the data. You might be presenting to deep subject experts or senior leadership, so make sure to highlight the most critical aspects to them for decision-making. Always think about the "WIFM - What's In It For Me" for your audience.

Collect and organize the data obtained from the test, paying attention to both quantitative metrics and qualitative feedback. Use statistical analysis techniques to determine the significance of any observed differences between test groups. Look for patterns, trends, and insights that emerge from the data, focusing on how well your value proposition resonates with the target audience.

Para fazer a melhor análise possível de um teste, você precisa ser isento e imparcial, sem ter apego ao objeto do teste. Analise os dados e as informações da forma mais neutra possível e procure encontrar a essência do que o teste mostra. E depois de ter esta análise feita aplique o que você encontrou na direção do seu projeto de inovação. Procure não usar a análise do seu teste para corroborar o que você fez até então. Esteja aberto a mudar.

At the Microsoft Garage Vancouver, after running a test, we focus on thorough data analysis. We start by cleaning the data to remove duplicates, outliers, and errors. Then, we visualize the data using charts or graphs to uncover key patterns and trends. The crucial step is interpreting this data against our initial hypotheses and assumptions. This helps us evaluate the validity and relevance of our value proposition. By doing this, we ensure that the insights gained truly inform our next steps, driving meaningful innovation based on solid evidence.

Consider conducting iterative tests to refine and optimize your value proposition over time. Keep an open mind and be willing to pivot or adjust your approach based on the insights gathered from the tests. Ensure that your test results are communicated effectively to relevant stakeholders, informing decision-making and future strategies. Continuously monitor market trends, competitor actions, and changes in customer behavior to adapt your value proposition accordingly.

Creo que lo mas importante a tener en cuenta, tanto durante el procesos, como luego en el análisis de resultados, es que lo que se busca finalmente es encontrar el mejor camino para resolver el problema base. La hipótesis puede ser aceptada o rechazada, cualquiera de las dos opciones está bien. El punto es que no debe perderse de vista que el objetivo final es resolver el problema base de la mejor forma, y no, simplemente hacer una prueba para encontrar justificación a una idea de la cual se esta "auto-enamorado". El enamoramiento debe ser con el problema, con la pregunta que quiere responderse, pues las respuesta seguramente irán cambiando en el tiempo, pero la pregunta seguirá siendo la misma.

projetar e executar testes de proposta de valor eficazes não é apenas sobre validar uma ideia, mas sim sobre criar um loop contínuo de feedback e inovação que impulsiona o crescimento do negócio de forma sustentável e orientada por dado. Uma startup que eu gosto é a Experiment Engine. Ela fornece uma plataforma que permite às empresas gerenciar e otimizar experimentos em grande escala. Com essa ferramenta, as empresas podem lançar rapidamente testes de hipóteses, coletar dados e aplicar aprendizados, acelerando assim o ciclo de inovação. A plataforma também oferece análises avançadas e recomendações automatizadas, o que facilita a identificação de alterações que produzem os maiores impactos em termos de proposta de valor.

◉ Validation is not a zero-sum effort, you are constantly gaining - even if an experiment or test doesn't go well or you need to rethink your original hypothesis, it's essential to learn all the lessons, be curious with the next opportunity, and take it in stride that you're getting closer ◉ Iterate, Iterate, and Iterate - when scoping research projects, I recommend several sprints and experiments to ensure you can account for the learning curve and focusing that will happen through each round. It is better to have three opportunities than one to gather your insights.

Consider these three additional factors when designing and testing a value proposition: Scalability, assess the scalability of your value proposition early on. It's essential to understand if the value proposition is attractive and if it can be delivered consistently at scale. Market & Environmental changes, keep an eye on market and environmental changes that could impact the relevance and effectiveness of your value proposition. Be ready to adapt. Cost-Benefit Analysis, evaluate the cost of delivering your value proposition against the potential benefits. This will help ensure that your value proposition is desirable, viable and sustainable from a business perspective.