After your alpha release hopefully you are getting some feedback that should inform your next steps. At this point there are 2 possible outcomes, either some of your fundamental assumptions are being challenged in which case you might end up with a pivot, or if you are lucky and have done your homework right up to this point, and only some of your more minor assumptions are being challenged in which case you would just go ahead and improve things to prepare for the long-term use of your product.
You do a pivot when you were pretty much completely wrong about a fundamental aspect of your problem, or maybe based on the initial feedback all of a sudden you identify a huge opportunity in a different aspect of the problem statement you are working on. More concretely, a pivot is a change in a specific but important aspect of your strategy to get you closer to your vision.
You should always pivot to a better idea, rather than away from a bad idea. In other words, it must be based on data and rigorous observations, and learning, and not frustration. To put it more bluntly, doing a random pivot is a good way of not getting anywhere, but a carefully calculated and well informed pivot is the foundation of successful product development. In fact, since strategy is just a vehicle to help you get to your vision, it’s very common and expected to have a few pivots along the way.
Now, let’s say you have done any pivots you need, and finally are getting favorable feedback, and ready to move closer to your beta release. In that case you have moved on to the second scenario I mentioned earlier.
Just to remind you, with all learnings, iterations, pivots, leading up to your alpha release and after that you are at a point that, hopefully, you are solving the users’ problem. This might not be the best solution to the users’ problems, but at least you have laid the foundations for rapid improvements As you move on ahead for a long term validation and use of your solution, there are many more aspects that you have to start taking into account beyond your bare minimum pipeline.
For the starter, you have to think about the data you are using and your data collection practices.
Next moving on to your model: