Having identified the assumptions in your product, it is important to prioritize these assumptions. Prioritising them helps you work in an orderly manner. The previous article on Product Assumptions established that whatever is not backed by data to be true is an assumption.
Let’s take a popular product- Piggyvest. Assuming in the role of Piggyvest’s product manager, you identify that there might be a need for a digital means of saving seamlessly. Underline the word might because you are not sure. You also identify that people might want to invest the money they have saved on this digital platform- assumption number two. You also realize that people might want to save in various currencies (dollar, naira)- assumption number three. And then you keep assuming various solutions till you have exhausted most ideas relating to this solution. You can obviously not solve all these assumed problems at once.
This is where prioritizing your assumption comes in.
You need to find the most important need of your target user. After finding it, focus on that need, and provide a solution for that need before bringing other features in. In the case of this product we are analyzing, based on the data you must have gotten from your target user either through research, surveys or interviews, you are able to sense what the biggest problem is. If you can’t immediately figure it out, you can make an assumption based on the data you have. This could be the problem with the highest mention during most of your interviews. Once the most important need is identified, you will then need to work on just that very problem keeping the rest in your product backlog for future reference.
Sometimes there might be more than one major need identified.
You will need to choose the one thing to focus on based on the effort required to bring it to life. It is generally best to focus on one solution to an assumption at a time for maximum productivity. There are various methods used in estimating the effort required to bring a feature to life. One such method is the T-shirt size method. Estimating the effort required to build a feature based on T-shirt sizes (S, M, L, XL…)