Continual iteration is a fundamental principle in agile thinking as well as in startup methodologies such as Customer Development and Lean Startup. Basically, its premise is that a startup will mitigate risk and uncertainty by shortening product and customer learning loops, and adjust its product-market fit accordingly.
A startup achieves product-market fit when it masters the balance of building a solution, or product that acts on a customer’s problem, or vice versa. Both product development and customer development each has its own iterative loop structure. I believe that the two should be reciprocally acting and proceed in parallel towards the goal of product-market fit and that this makes a multiple-loop system. This invokes an ambidextrous challenge to early-stage ventures.
Building on my former post on Disciplined Creativity with Mihály Csíkszentmihályi’s Flow diagram, I would add to the Lean Startup model.
The diagram above shows product-market flow as a result of efforts in parallel iteration between agile product development (at the y-axis) and customer development (at the x-axis). In order to achieve a product-market flow state, that is product-market fit, a balance must be struck between customer development and product development. If a startup is drifting too far along one of the axis without iterating, flow cannot occur.
I believe that iteration beyond the product-market flow zone could be considered pivoting – that is when you change a fundamental part of your business model in regards to products and customers. To successfully iterate between product and customers and achieve product-market fit, you would develop a minimum viable product offering that enables you to learn about your customers needs and wants.
At startup you must pay close attention not only to the iterative tasks within customer development and agile product development separately, but also to the feedback loops in between the two. However, time is limited, and you should be aware of trade-offs in achieving flow in a Lean Startup. This is where continual iteration and validated learning allows for greater risk reduction under extreme uncertainty.
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