Embracing Continuous Learning for Product-Market Fit
A healthy tension exists between wanting to know everything about product market fit before the launch of something new, and being open to learning post-launch through live market feedback. While not mutually exclusive, I would argue that in the digital age we have the tools, techniques, and development structures to make post-launch live learning about product market fit much more useful in meeting customer needs.
Great Voice of the Customer research is at the heart of any product that resonates, but maybe it is a lesser known fact that putting your product out in the wild is what really helps you refine your target segments, features, and KPIs. And in many industries, like healthcare, those targets will always be moving and morphing. Your product market fit has to adapt too. It is not a one time pre-launch exercise.
The journey to achieving product-market fit is no longer a straight path with pre-planned dependencies for execution, but is instead lean, nimble, and driven my real time data. While traditional waterfall approaches had the benefit of step-by-step guides to product market fit, they often left little room for flexibility or rapid response to change. In a digital first market, we are presented with the opportunity to create continuous feedback loops (passive and active) that are leveraged to adjust product market fit with each point release. Your product market fit should change as fast as you can analyze and leverage Voice of Customer Data to inform your backlog.
Continuous learning isn't just about frequent updates or new features; it's a commitment to understanding your users deeply and iterating based on Voice of the Customer data. This approach allows teams to adapt to changing market needs, pivot quickly, and reduce the risks associated with long development cycles. And unlike the a more monolithic approach to product market fit, which assumes we can predict market needs far in advance, continuous learning recognizes that the market is a moving target. By embracing this mindset, product teams are better equipped to find and maintain a true product-market fit, fostering innovation and driving sustainable growth.
Let's shift our focus from fixed plans to fluid learning and create products that resonate with the ever-evolving needs of our customers.
领英推荐
Related Reading:
The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses by Eric Ries.
The Messy Middle: Finding Your Way Through the Hardest and Most Crucial Part of Any Bold Venture by Scott Belsky.
Product Leader | Experimentation, Business Outcomes, UX
6 个月Nathan, if you haven't, I recommend you to read about The Product Market Fit Engine framed by Rahul Vohra. https://review.firstround.com/how-superhuman-built-an-engine-to-find-product-market-fit/ In a nutshell, the team at superhuman used a simple 4 questions survey: 1. How would you feel if you could no longer use Superhuman?A) Very disappointed B) Somewhat disappointed C) Not disappointed 2. What type of people do you think would most benefit from Superhuman? 3. What is the main benefit you receive from Superhuman? 4. How can we improve Superhuman for you? And from each question, they are able to assess, segment, refine and get closer to PMF. I have used this and found it to always bring valuable insights while fairly cheap to put int place.