Discussion
Effective Prioritization for Product Managers
A deep-dive into product prioritization frameworks and their benefits when included in the product management process.
Product management as a profession sits at an interesting crossroad between business development and engineering. A good product manager needs to perform a complex balancing act between engineering initiatives, customer needs, and business priorities. These goals can often be in conflict with one another. It is up to the product manager to navigate these murky waters, making hard decisions and making appropriate trade-offs where necessary.
Along this journey, it helps to have tools and frameworks to help you make informed and consistent decisions about prioritization. Unsurprisingly, many prioritization frameworks have been developed to help product leaders achieve better business outcomes as a result of long-term product planning.
In this article, we will discuss the benefits of prioritization frameworks, give a number of examples of popular frameworks, and discuss how to pick one to use.
What are the benefits?
There are a few clear benefits to choosing a prioritization framework and using it across your organization. For one, choosing a framework helps product managers make apples-to-apples comparisons between different product changes. These frameworks generally operate by analyzing the properties of a proposed feature along two or more competing axes, such as business impact or complexity, and reducing them to numbers that can be combined and compared across proposed changes and even teams to help make the best decisions.
Consistency is of particular importance for large organizations with many product leaders. This helps make product stories crystal clear at the executive level, so difficult trade-offs can be made across teams in a way that doesn’t harm the business.
Some Examples
Value vs. Complexity Matrix: Balancing Effort and Impact
The Value vs. Complexity Matrix is perhaps the simplest prioritization framework. It works by scoring potential features on two dimensions: Value, i.e. value added to the customers or business impact, and Complexity, i.e., effort required to implement the change. The scores along these dimensions are typically combined as the ratio Value / Complexity, and features can be easily prioritized in decreasing order.
The strength of the Value vs. Complexity Matrix is the simplicity with which it removes ambiguity in the decision-making process using heuristics that many teams already intuit. However, this simplicity can make this model more prone to error, as value can be hard to estimate accurately.
ICE Scoring Model: Striking the Right Balance
The ICE Scoring Model is a simple extension of the Value vs. Complexity Matrix. Standing for “Impact, Confidence and Ease,” the ICE Model seeks to improve the accuracy of prioritization by splitting the Value dimension into its constituent parts, Impact and Confidence, while replacing Complexity with its inverse, Ease. In this model, we combine our scores as Impact * Confidence * Ease and prioritize in decreasing order.
The advantage of this model over the Value vs. Complexity Matrix is in how it eases the cognitive load of determining value accurately. Impact and Confidence, when split out as separate dimensions, are easier to assign a score to than when all the complex factors that determine value are combined into a single score.
RICE Scoring Model: Scoring for Success
The RICE Scoring Model is another extension of the previous models. This model adds an additional Reach dimension to the ICE model above, while replacing the Ease dimension with an analogue for Complexity called Effort. In this model, Reach attempts to scale the impact of a product change by explicitly considering how many customers or users will benefit from a product change. That is to say, features that impact a broad-band of customers score higher than features that are specific to only a handful.
To make this more concrete, consider an extremely high effort feature, i.e., something requiring more than a year’s worth of development time. Such investments are easier to prioritize if the development cost can be amortized across your entire user-base, and that is what Reach seeks to capture.
In this model, we combine the dimensions using the formula Reach * Impact * Confidence / Effort and prioritize in decreasing order.
Weighted Scoring Model: A Data-Driven Approach
The Weighted Scoring Model is flexible alternative to the other methods presented earlier. In this model, you define an arbitrary number of positive and negative scoring dimensions with associated weights that scale their importance. In particular, the sign of the dimension is chosen based on whether that dimension implies a positive or negative impact on the business, and the weights are such that the total sum of weights across all dimensions is 1.
The dimensions and weights chosen are specific to your business and product processes. This allows you to tailor the output to meet the needs of your team. This adds a lot of flexibility to the model, and helps keep planning exercises grounded in terms that your team understands. This has the added benefit of likely producing more reliable results. On the other hand, this flexibility comes at the cost of the ease of adoption of this framework, as it is much less turnkey than the others.
In this model, we combine the dimensions using the formula Σ Sign_i * Weight_i * Score_i for each dimension i and prioritize in decreasing order.
What Framework Should I Use?
There is no straightforward answer to this question. Product leaders should evaluate the options available, and pick a framework that suits both the maturity of the team, and the maturity of the product itself. For example, the added complexities of setting up a Weighted Scoring Model might not make sense for an immature product where the nature of the business, and hence the weights, are still being figured out. In this case, it may be sufficient to use the Value vs. Complexity Matrix to get some quick process improvements.
Furthermore, teams can take an iterative approach, adopting something like Value vs. Complexity Matrix at first and running with it until the team is comfortable with the model. Afterwards, additional dimensions can be simply added to the process as a refinement to get the team operating on the ICE or RICE scoring models. As a result, teams can continually add value without getting overwhelmed by complex new processes.
At PlanEngine, we support all the above prioritization frameworks out of the box to make it easy for teams to get up and running with better processes faster. Sign up for early access to PlanEngine to unlock efficiencies in your product planning process.
Conclusion
In this article, we took a look at some different prioritization frameworks, what their benefits are, and how to pick one for your team. Thank you for reading, and I hope you learned something useful for your team.