How do you use lean startup methods to validate customer needs?

At the outset, the problem may be based on a hypothesis, gut instinct, or alternatively, it may be based on actual evidence. After defining your problem, good practice would be to conduct a customer problem discovery interviews, to see if "your hypothesis" about the problem actually matches the real world problems experienced by your potential customers. It is only through speaking with your potential customers that you truly discover, or uncover, their "actual" problems.

Even though most processes start with problem identification, I would suggest actually starting with the person. Who are you trying to life better for? From there, discover what their biggest pain-points (problems) are, how they're solving it today, and what's the reason why they remain unsolved. Lastly, ask them how much they're paying currently on the problems. By starting with the person, the opportunity to discover other problems that you may not have thought of arises, and can help you solve a bigger problem for them than your first identified problem. If the biggest pain-point that the user identifies aligns with the problem you were set out to solve, then you get a double-confirmation that you're on the right track!

One trick to find out if a customer really understands what it needs is to recite a technology-agnostic User Story (aloud) to them: "As a Compliance Manager, I want to know which employees are failing a training objective, so I can perform a corrective action..."

Drawing from my experience as a product manager for multidisciplinary products, I've seen the value of lean startup methods firsthand. In one project, our goal was to launch a social networking app. We began by defining the problem, which revolved around users' desire for meaningful connections in the digital age. Through extensive user surveys and persona creation, we pinpointed the pain points and preferences of our target demographic. This clear problem definition served as the cornerstone of our lean approach, allowing us to iterate and refine our solution until it perfectly aligned with our customers' needs and aspirations.

Certainly, a way to express my willingness to assist my clients in improving their work or business is: "I'm here to understand your goals and how I can tailor our software solutions to drive your business forward."

Hypothesis can be missleading as they are difficult to form without introducing a whole host of biases. Alternative that I've used is to consider what a customer is currently doing to try to solve their problem right now... the greater the hassle/pain it is for them the better for you. People will only persevere with difficult processes or solutions if the problem they have really matters to them.

Hypotheses can only turn to "facts" through evidence and feedback from the market. Completing a lean canvas offers a means for you to put to paper, (or get out of your head) what your initial business model assumptions may be - however, it is likely, that most, if not all of your assumptions may change (in varying degrees) as you progress on your journey. Completing a lean canvas is not a "just once" exercise - it is an exercise that can be repeated as you update your canvas with new evidence from the market, as it comes to light.

I recall a project where we aimed to create a cutting-edge language learning platform. Formulating hypotheses was the first step in our journey. We hypothesized that our value proposition of adaptive, AI-powered lessons would revolutionize language education. Visualizing our approach on a lean canvas allowed us to outline assumptions about user engagement and market competition. This method not only helped us identify risks, like content scalability, but also paved the way for a product that fulfilled our customers' unique needs.

Next is to clearly defining our assumptions about what our customers need or want. These could be related to features, user experience, pricing, or any other aspect of our product or service.

Remember that these tools are just ways to capture your assumptions and hypotheses. Just because you write it down does not mean that it is true! Use these tools to organize these assumptions and then go about rapidly proving or dis-proving them. Then update them with your new learnings. This should be an active and iterative process.

In my company Nano4Imaging, we're currently building up a software as medical device, which is a process known to be more a regulatory exercise (design, validation, connecting with notified bodies) than coding. And to get this right in our first go, we strongly rely on lean methods such as prototyping. Mapping the user journey during a medical procedure is best done with a prototype and this helps us also to document and fulfill the functionality of our product for regulatory purposes.

We applied a control in one of our experiences to ensure that we can compare the results as being attributed to the solution.

Prototyping, rather than building a tested product, is a cheaper way to validate hypotheses. If the conceptual version of your product is successful, you'll know you're on the proper trajectory.

Designing experiments is a crucial step in the lean startup methodology. Identifying the most critical and uncertain hypotheses to validate first is key. It's essential to select the best methods and metrics for measuring customer feedback and behavior accurately. Creating a minimum viable product (MVP) allows for early testing and learning, shaping the product based on real user insights. Tools like experiment boards, validation boards, and learning cards aid in planning and documenting experiments, ensuring a systematic and data-driven approach to product development.

Develop a simplified version of our software that addresses the core aspects of our assumptions. The MVP should be quick and cost-effective to build. Its purpose is to test your hypotheses, not to deliver a fully-featured product.

Running experiments with customers is a pivotal stage in the lean startup process. Finding and recruiting participants demands a well-thought-out strategy, often involving targeted outreach, user communities, or partnerships. Ensuring the ethical, reliable, and valid execution of experiments is paramount, requiring informed consent and privacy safeguards. Experimentation methods, such as surveys, interviews, and prototype testing, can provide valuable data. Leveraging analytics tools aids in accurate data collection and analysis, facilitating evidence-based decisions and iterative product development.

Implement analytics and data collection tools in our MVP to gather user feedback and usage data. This data will help us understand how customers are interacting with our product and whether it aligns with their needs.

Step four is about running experiments with customers. Recruit participants effectively and maintain ethical and reliable practices. Record and secure data meticulously. Consider incorporating techniques like A/B testing and user observation to gain deeper insights during your experiments.

Within the lean startup methodology, the analysis of results serves as a crucial phase. To make sense of data, leveraging visualization tools like charts and tables simplifies the process. Comparing results against initial hypotheses and expectations is essential for drawing meaningful conclusions. Identifying patterns, surprises, and insights involves a methodical approach and attention to detail. Communicating findings and recommendations is facilitated through clear reports, dashboards, and presentations, ensuring that the team is well-informed and aligned for the next steps in the product development journey.

Launch our MVP to a small group of users or a targeted audience. Monitor their behavior and gather feedback. Use this information to refine our product continuously. Make changes based on what we learn and continue testing.

Step five is analyzing results for valuable insights. Organize, interpret, and compare data with initial hypotheses. Look for patterns and surprises. Effectively communicate findings to stakeholders using visual aids like charts and dashboards. Consider involving a diverse team for diverse perspectives in the analysis, which can lead to deeper insights

Here, I suggest looking at this whole process as a continuous cycle that never ends. You want to make the cycle as short as possible, and also happen as frequently as possible (just like agile methodology). Insights from experimentation do become stale overtime, and often the decisions that you make in your product also need to be made at pace. You want to avoid research fatigue, and be able to amplify the insights to your team when it matters the most (vs in hindsight)!

The pivot-or-persevere moment marks the culmination of the lean startup process. Evaluating evidence and feedback from experiments requires a meticulous approach. Tools like pivot tables and decision matrices assist in quantifying and presenting data, simplifying the assessment of your existing hypotheses. Deciding whether to pivot, making significant changes, or persevere with minor adjustments depends on a comprehensive analysis of the evidence. Setting up feedback loops ensures that continuous learning informs this pivotal decision, empowering you to navigate the evolving landscape of your product or service with confidence.

It’s always important to remember that competition will come. They will look better, move faster, be cheaper and to a lean start up, that’s deflating. Stay true to your vision and your why, the rest is just noise and short term distraction.