Prioritization is a very common challenge faced by product managers of all experience levels. Some people take a very light approach, making decisions based on their own experience and data about the direction of the product. Others take a much more rigorous approach, applying scorecards and measures across different possible metrics.
There’s nothing wrong with either of those approaches, but there’s no “silver bullet” to ensure that your prioritization decisions will be right.
There is an interesting opinion in the article posted on Cleverpm. The author lists three things that he thinks every prioritization system needs to take into account. Here’re the extracts of the article:
So, what is prioritization about?
When thinking about the primary problem that we’re trying to solve with our prioritization efforts, it should be that we’re trying to deliver the most value possible. If this is your goal, then one of the most important factors to consider in your prioritization efforts is the value.
There are different ways to score this with a numeric value. However, basing your decisions solely on market value can often result in deprioritizing technical debt and infrastructure investments.
If you apply customer value as the sole or primary metric, please ensure that you have an entirely separate track of work that allows you to pay down your technical debt and invest in infrastructure improvements.
It’s also important to understand the amount of work that’s going to go into any feature or improvement to our product. It doesn’t matter if the value to the customer is insanely high, if you’ll never actually get through the work to deliver the improvement in time to capitalize on the opportunity.
Assessing the amount of effort required to bring any feature or capability to life is most accurate when it’s done by involving the people who will actually be working on it. They know their abilities better than you do, they know the technology better than you do, they know the existing code better than you do, etc.
Quantitative data is an amazing input into any prioritization process: the more data you can get, the better your decisions are likely to be. However, there’s also an ineffable quality to prioritization that is what makes the whole process more art than science.
There’s always going to be some subjective analysis involved in looking at the data and making decisions about whether or not the direction the data is pointing actually makes sense in the broader scheme of things.
Great product managers know when to insert instinct and experience into the equation and shift or rearrange choices made solely on “objective” data. Without an instinct, without a gut-feel, only the safest choices will be made, and innovation will go out the window.