Navigating Prioritization Frameworks: A Guide for Product Teams
Knowing how to prioritize effectively is a core skill for any product manager. Not only does an effective and well-defined prioritization strategy keep your product on track, but it can protect your product team from last-minute high-priority requests that have not been fully evaluated or integrated into the product roadmap.
Teams should be cautious not to base prioritization choices on persuasive arguments or anecdotal evidence. It is about adopting a structured approach that quantifies business value, effort, and other critical factors. This way, development priorities reflect the strategic vision rather than the sway of impromptu discussions. There are several well-known frameworks available to product teams, each with its own set of strengths. The effectiveness of a given framework can vary depending on the nature of the product, team size, and other dynamics. Ultimately, the choice of framework often boils down to personal or team preference and the strategic goals of the product. For instance, my personal favorite is the RICE scoring model. My brain likes the clarity of its math-based approach and level of complexity.
In this blog, I walk through the RICE scoring model and other common prioritization frameworks, discussing their mechanics, strengths, and limitations, as well as the contexts in which they excel.
RICE Scoring Model
We will start with my favorite. The RICE scoring model is a nuanced, data-driven framework with four main components: reach, impact, confidence, and effort.
Imagine you are evaluating a new feature that automates the creation of financial reports. Reach would measure how many users this feature would affect within a set timeframe, such as the number of affected users per year. Impact would assess how significantly the feature would enhance efficiency. Confidence is the level of certainty about these estimates—how likely is it that the feature will achieve the predicted reach and impact? Finally, the effort is the total work needed from all team members to build the feature. By scoring each component and then calculating a RICE score, you can establish the feature's priority.
Advantages: RICE is effective at removing personal biases, providing a clear, numerical basis for comparing features. Tt works well for teams that like data-driven justifications for their decisions.
Disadvantages: This approach is dependent on the accuracy of the estimates. If the estimates are more educated guesses than data, the RICE score may not be accurate, and the process of gathering the required data can be time-intensive. It is also important to note that the timeframe used in the Reach calculation needs to be consistent across features; otherwise, it will skew the results.
I use RICE when working through a large backlog of features to help pinpoint which features could deliver the most significant ROI. It is effective for driving growth and optimizing product-market fit.
There is a link to a great article about the RICE method on my resources page
MoSCoW Method
The MoSCoW method is a straightforward framework that categorizes tasks into four buckets: must have, should have, could have, and will not have.
Consider a situation where your team has a release deadline to meet. The ‘must-haves’ are non-negotiables for the release to go live—like critical bug fixes or compliance updates. ‘Should haves’ are important but not essential. ‘Could haves’ are your wish list items that are nice to include if time permits. ‘Will not haves’ are things that will not make this release, such as deferred low-impact or high-complexity items. This method helps you quickly sort features into a release plan which includes all the essentials while managing expectations for everything else.
Advantages: This is one of the simplest methods, which makes it accessible and quick to apply. It is effective for managing scope and ensuring that critical features do not get overlooked.
Disadvantages: There is no clear way to prioritize within the MoSCoW categories. Without clear priorities, they are prone to subjective decisions, such as what is a ‘must’ versus a ‘should’. Also, because it does not take into account effort, there is a risk of overcommitting to a set of ‘must-haves’ that are unfeasible.
The MoSCoW method is particularly effective for projects with fixed deadlines and budgets, where delivering a functional product on time is more critical than perfection. This approach works well for Minimum Viable Products (MVPs), where the focus is on core features that deliver the most value to the user. The categories help teams clearly identify and commit to the essential features that define the MVP.
Kano Model
The Kano Model is a framework that prioritizes features based on customer satisfaction, categorizing them into five groups: basic needs, satisfiers, delighters, indifferent, and reverse quality.
Let us use the example of Smartphones. Basic needs are those features that customers expect as a minimum. Excluding these features will cause dissatisfaction, but including them will not necessarily increase satisfaction because they are a baseline requirement. An example of a basic need for Smartphones is the presence of an acceptable camera.
Satisfiers have a linear relationship with customer satisfaction. Keeping with our example, camera quality would be a satisfier, with higher quality cameras result in higher satisfaction and lower quality cameras result in lower satisfaction.
Delighters are features that customers do not expect and which can significantly increase customer satisfaction when done right. However, because customers are not expecting these features, their absence will not cause dissatisfaction. Apple Face ID was a delighter feature. It is important to note, however, that once customers know about a delighter, it may become a satisfier or basic need. Apple customers would certainly be dissatisfied if their newest iPhone lacked Face ID.
Indifferent features do not significantly affect customer satisfaction, whether they are present or not. Reverse quality features increase dissatisfaction when included (or executed poorly) - such as bloatware or poor UX/UI.
When using the Kano Model, you want to categorize features into these groups and then prioritize development based on a combination of the product strategy and the desired impact on customer satisfaction. Basic needs are typically non-negotiable. Satisfiers and delighters are where you can be more strategic. Satisfier features must be competitive and align with customers' desired functionality. Delighters can be employed to differentiate the product, build customer loyalty, and drive word-of-mouth promotion. At the same time, manage indifferent and reverse quality features to avoid wasting resources on unimportant features or inadvertently compromising the product.
Advantages: The Kano Model is great for creating user-centric products, aiming to exceed customer expectations, or carve out a competitive edge.
Disadvantages: The approach requires in-depth user research and what customers want can be subjective. Also, because customer preferences can shift over time, it requires continuous reevaluation.
The Kano model works well for consumer-facing products where user satisfaction is a critical success factor. I use this model most frequently when evaluating product features within the context of competitive analysis to inform decisions about product positioning.
Value vs. Complexity
This simple framework helps teams prioritize tasks by plotting them on a grid based on their business value and implementation complexity.
With this framework, you create a two-dimensional grid. On one axis, you have business value, and on the other, complexity - each feature is plotted on this grid. Quick wins are high-value, low-complexity tasks - they are the low-hanging fruit. Big bets are high-value but also high-complexity; they could pay off but require significant resources. Maybes are low-value, low-complexity—they might be easy to do but do not offer much return. Time sinks are low-value and high-complexity. This visual approach helps teams see which features will give the best value for the least effort.
Interestingly, a similar approach is also a good tool for determining value. Plotting customer satisfaction scores against feature use intensity allows teams to evaluate the value of improving on a feature. Improving features with low satisfaction and high use will provide the most value while features with high satisfaction and low use will offer the least.
Advantages: This approach simple to implement, and the visual nature of the grid helps build quick consensus.
Disadvantages: It can oversimplify the decision-making process and may not account for strategic initiatives that are highly complex but necessary in the long term.
This framework is ideal for small teams that make fast decisions and focus on delivering incremental value with each release.
Weighted Scoring Model
The Weighted Scoring Model evaluates features against a set of business-driven criteria. Features are assigned a score against criteria such as cost, risk, strategic alignment, and customer satisfaction. Each of these criteria is assigned a weight by importance. For instance, if customer satisfaction is the priority, it would be weighted more heavily. The final prioritization score for the feature is the product of the scores and their weights.
Advantages: The model’s flexibility allows it to be tailored to unique goals and ensures a comprehensive evaluation of the impact of each feature.
Disadvantages: It can be overly complex and depends on the correct weighting and scoring. Also, reaching a consensus on the weights can be challenging.
The weighted scoring model works well for complex products where the team needs to balance many factors. I have tried this framework but found it overly complicated.
Other Frameworks
There are many other prioritization frameworks out there. Common ones include the Eisenhower Matrix, which distinguishes urgent and important tasks; Cost of Delay, focusing on the economic impact and timing; Opportunity Scoring, which gauges the potential value of features; and Weighted Shortest Job First, a key component of the Scaled Agile Framework that considers job size and the cost of delay. Personally, I do not frequently utilize these frameworks in my work on small startup teams because the need for speed and adaptability often trumps adopting complex prioritization methods. However, for those looking to implement these methods, there is an abundance of resources online to guide you through the process.
Whichever framework you ultimately select, the most important thing is choosing a method that the team will use consistently. A basic framework the team can manage is more beneficial than a complex one that is not sustainable. Something as simple as using priority scores from tools like Jira, combined with story point estimates, can be a good choice if it is what the team has the capacity for (this is very close to the Value vs. Complexity framework described above, but with less granularity and no grid). Selecting the correct prioritization framework is a critical decision for any product team, and there are lots of options available. The key is to choose a framework that resonates with your objectives and the nature of your work.
