Prioritization in product management is hardly about what metric takes precedence over the other. Of all the roles a product manager plays, one of the most difficult is deciding what to work on next. Why? Because everything feels urgent. Engineers want to fix technical debt, users want improvements, stakeholders have more opinions than practical solutions, and so on. With that kind of pressure and without a proper framework, it is easy to focus on the wrong thing.
And this is where prioritization comes in. In this article, you'll learn how product managers prioritize work, what guides their decisions, and the frameworks they use to decide what does and does not get built.
Table of Contents
Why Prioritization is Difficult
Prioritization can be difficult because product managers must almost always choose between multiple viable options. Between requests centered on optimization and bug fixing, increasing revenue, and reliability concerns from users, stakeholders, the design team, engineers, and sales departments, we can see how easy it is to build the wrong thing.
Each of the requests would be entirely legitimate when considered separately, but determining which is more crucial at any given moment can be challenging. Prioritization decisions are frequently made based on incomplete information, such as not knowing which features users will actually adopt, whether a request represents a real problem or just a loud minority, or whether it will significantly improve a metric. which, in contrast to engineering tasks, are simple: is the bug there or not? Does the code work or not?
The fact that the trade-offs are not immediately apparent is another factor making prioritization challenging. Other worthwhile projects are postponed or abandoned when a product manager gives priority to one. Although these lost chances don't immediately result in failures, their effects can mount up over time and affect long-term results, growth, and product quality.
What Do Product Managers Really Prioritize?
A common misconception is that product managers only prioritize features. PMs don’t just choose between whether to ‘add dark mode’ or ‘add in-app messaging’, they also choose between reducing churn and improving retention. Therefore, what PMs are really prioritizing are:
User Problems and Not Feature Ideas
At face value, user feedback consists of solutions and not problems, like:
Add dark mode
I want to be able to edit a typo after posting on my story
Can you make it possible to export this as a PDF?
A PM won’t just take these requests as they are but will ask why, how, and what.
What pain point is the user experiencing?
How many users are experiencing this?
Why does the user want this?
After asking these questions, the PM doesn’t just prioritize being adding the dark mode feature but rather, evaluates that users requested for that feature because, apart from personal preference, they struggle to use the product for long periods especially at night and that dark mode is one possible solution and there may be others
Business Outcomes, not Tasks
We can look at it this way: a task is something the team does and an outcome is the result the business wants. Some examples of tasks are:
Fixing bugs
Redesigning the onboarding process
Adding dark mode
Increasing activation rate is one example of an outcome. Product managers prioritize outcomes because they are measurable, connect work to business goals, and tasks alone do not guarantee impact. So, when deciding what to prioritize, a PM considers whether it will increase engagement, improve retention, or reduce churn.
Trade-offs, not Preferences
This is where things get complicated because, as a product manager, you occasionally have to make trade-offs between things like speed and quality, features and time, resources and scope, quality and cost, technical debt and new features, and so forth.
What this means is that PMs don’t make preference-based decisions when they have to prioritize, but rather, their decisions are constraint-based. This implies that instead of saying, “I prefer this idea,” they are saying, “If we do this now, we can’t do that.”
What Inputs Drive Product Prioritization?
Before anything is prioritized, PMs gather input from multiple sources like:
User Feedback and Research
These include interviews, usability tests, user reviews, social media comments, direct feature requests, quantitative data from product analytics, and metrics like Net Promoter Score (NPS) or customer satisfaction (CSAT) scores, which offer a human-centered approach that relies heavily on customer needs, behaviors, and pain points.
Product Metrics
Metrics like activation rate, churn, feature adoption, retention, session duration, Daily Active Users (DAU) and Monthly Active Users (MAU), session frequency, and so on, help PMs understand if and where the product is underperforming.
Business Goals
Prioritization has to align with the company’s strategy and vision because every product exists to serve a business objective, and those objectives directly influence what a product manager prioritizes at any point in time. Business objectives have a big impact on product priorities. While a revenue-focused company might invest in monetization features, a growth-oriented company might prioritize onboarding and acquisition. Product managers frequently give core user experience enhancements and dependability top priority when retention is the aim. Because of these changing objectives, prioritization is dynamic and changes as the company does. So, it depends on whether the business is focused on growth, retention, cost optimization, or revenue.
Technical Limits and Demands
Simply put, even if an idea is great, it may not be possible at the moment because, even if they sound simple, they may be quite complex to implement. For example, a product that supports offline mode. Also, many features depend on another being completed. Lastly, there is technical debt, which basically refers to shortcuts taken that make a system hard to change in the future. An example includes poor documentation or design. Technical debt, amongst other things, increases bugs and slows down development.
Prioritization Frameworks
Prioritization frameworks are structured approaches that product managers use to make explainable and rational decisions. They are decision aids that help PMs compare options. Prioritization frameworks eliminate guesswork from decision-making in product management, thereby helping the product manager make informed guesses instead of instinctive ones.
Let us have a look at some of these frameworks, why they exist, why and when to use them.
Why Do Prioritization Frameworks Exist?
Prioritization frameworks are like aids, not formulas that help a product manager make defensible choices. These frameworks exist to enable PMs make informed decisions, and they do that by asking ‘who?’ and ‘how?’.
Who does this help?
How does it help?
How is it going to be built?
How does it benefit/align with the company vision?
So no, frameworks do not replace judgement, they only support it. Therefore, instead of making decisions based on whether they think something will be a good idea, PMs they ask the hows and the whats. Some prioritization frameworks in product management include:
RICE Scoring
Developed by Sean McBride at Intercom, RICE stands for the four elements—reach, impact, confidence, and effort—that are taken into consideration when developing a product idea. This framework provides an answer to the question of how much value will be produced relative to the effort by comparing each element.
Reach: Measures how many users will be affected by the product in a given time period. It is about scale and must always include a timeframe, otherwise the numbers become useless. For example, 5000 users monthly or 35% of weekly active users.
A small improvement for many users can outweigh a large improvement for very few users. This is evident when comparing the reach of two features, like Feature A, which affects 20,000 users monthly, and Feature B, which affects 500 users monthly. It is evident that even though the improvement is small, its reach surpasses that of the big improvement, therefore making it more significant.
Impact: Measures how those customers will be affected by the product. Unlike reach which is about scale, impact is about depth or value. Sean McBride suggests estimating the impact on an individual person. Like, “How much will this project increase the conversion rate when a customer encounters it?” And points out that this can be replaced with other goals, such as “increase adoption” or “maximize delight”. Seeing as impact can be difficult to measure, a scale of 0.25 to 3 is used, where 0.25 is minimal, 0.5 is low, 1 is medium, 2 is high and 3 is massive impact.
Confidence: This measures the level of confidence in executing ideas and is gotten from sources like user feedback and A/B testing and uses a 100% scale, where 50% is ‘low confidence’, 80% is ‘medium confidence’ and 100% is ‘high confidence’. This accounts for uncertainty and risk, and it helps to answer the question, “How sure are we about this idea?” and prevents speculative ideas from ranking too highly.
Effort: Refers to the time it will take the team to execute an idea. Effort is like the investment, and the other elements, reach, confidence and impact, are potential benefits. Effort is usually measured in person-months or person-weeks and puts things like engineering time and design work into consideration. This is the most important element in this framework because two ideas with similar value may differ greatly in effort, and a lower effort increases the RICE score.
The RICE formula is:
RICE Score = (Reach × Impact × Confidence) ÷ Effort
While the RICE scoring method of prioritization is excellent for data-driven teams, it can be too long and difficult to understand.
Kano Model
This categorizes features based on customer needs and how they affect user satisfaction and was developed by Noraki Kano in 1984. It has three parts, namely:
Basic needs: Features that customers expect your product to have, like core reliability and login functionality. Sometimes, users don’t notice them when they exist but when they don’t exist, it leads to dissatisfaction.
Performance needs: Features where better performance increases satisfaction. This means that this features scale satisfaction linearly, that is the better they perform, the happier users are. An example is faster load time.
Delighters: Unexpected features that create excitement. These are unexpected features that make users feel heard and happy like TikTok allowing users post images in comment sections.
One of the cons of the kano model is that delighters eventually become basic expectations.
MoSCoW Method
This framework was developed by Dai Clegg while working at Oracle in 1994 and it categorizes work into:
Must have: Essential for the release and can either make, or mar the product.
Should have: Important but not critical. That is, the success of the product is not dependent on it.
Could have: Nice to have but not important.
Won’t have: These are not relevant to the product.
This framework is very easy to use and understand, and that is one of its biggest advantages and it is particularly useful for release planning, MVP definition, and scope control. But the disadvantage is that without proper categorization, every feature can start to look like a must have and categorizing features as a ‘won’t have’ can also prove difficult.
ICE Framework
The ICE framework is a lighter-weight alternative to RICE, commonly used when data is limited or speed matters more than precision.
ICE evaluates initiatives based on:
Impact: How strongly the initiative is expected to move a key metric.
Confidence: How certain the team is about the expected impact.
Ease: How easy the initiative is to implement relative to others.
Each dimension is typically scored from 1–10 with the total average being the ICE score.
ICE works well when:
Products are early-stage
Teams need quick prioritization
Detailed effort estimates are unavailable
A disadvantage of this framework is that it sacrifices accuracy for speed and it can give biased results.
Impact vs Effort Matrix
This is also known as the value vs effect or value vs effort matrix and is one of the best suited for visual team discussions and quick alignment with stakeholders.
Initiatives are plotted on a 2×2 grid that is based on the value it will yield (impact) and what it will take to achieve it (effort):
- High impact/Low effort → They are top priority and also known as big wins.
High impact/High effort → The features that fall into this quadrant are high yielding features but must be planned carefully, hence, the high effort.
Low impact/Low effort → Features that fall here are known as fill-ins. They are optional or nice to have. They require low effort and yield low impact and should only be worked on when high priority tasks are complete.
Low impact/High effort → Low impact and high value? Nobody wants their team to waste time on such features so they should be avoided at all costs.
The elements that make up this framework are much like that of MoSCoW which contributes to its simplicity. It is also intuitive and can serve as a more flexible and visual substitute for RICE. A disadvantage is that it can be subjective and both impact and effort are estimates.
Other frameworks include:
Weighted Scoring Model: Prioritizes ideas by scoring them across multiple criteria, such as user impact, strategic alignment, effort, risk, and key metrics, with each criterion weighted according to its importance to the business.
Opportunity Scoring: Identifies aspects of the product that are of importance to the users but are not being satisfied. The formula for opportunity scoring is: Importance + (Importance – Satisfaction) = Opportunity
Desirability, Feasibility and Viability Framework: Evaluates ideas based on desirability, feasibility and viability. That is whether users want them, if the team can build them, and finally, if the business can sustain them.
Cost of Delay: This measures the financial impact of postponing a task and is an excellent framework when dealing with time-sensitive products.
Product Tree: Is a visual method that replaces the roots, trunk, branches and leaves with systems, current set of features (core functionality), features/enhancements, and new ideas respectively. This helps teams explore ideas and visualize growth in a structured way.
How to Choose the Right Framework
No prioritization framework works in every situation, so choosing the right one depends on the team’s goal at that point in time. Factors that affect how to choose the right framework include:
The product stage has significant impacts on framework selection. Early-stage products frequently lack accurate data, therefore a lightweight framework such as Desirability-Feasibility-Viability will be more effective for rapid learning. Growth-stage products often have enough users and analytics to provide structured comparisons, making RICE, Weighted Scoring, and Impact vs Effort more useful. Finally, mature goods prioritize optimization, revenue, and risk, where timing is critical. Frameworks such as the Cost of Delay help to assess added value and urgency.
Data availability is also an important factor because frameworks presuppose varying degrees of confidence. Using data-driven frameworks without accurate data can result in false precision. When metrics and past performance are available, scoring frameworks become more useful. When they are not, simpler qualitative models are more reliable and honest.
Stakeholder and team requirements also have to be considered because frameworks are useful for communication as well. They aid persuading stakeholders' decisions, collaboration between teams like design and research is facilitated by user-centered frameworks like the Kano model. Also, teams and stakeholders can rapidly align with frameworks such as the product tree.
Adaptation over rigid use basically means that frameworks should be modified, instead of using them in a rigid manner. They should be used as guidelines for thinking not a direct path to certainty. That being said, frameworks are often adjusted, simplified, or combined to fit the product stage, available data, and team context.
Common Prioritization Mistakes
Prioritization can still go wrong even with excellent frameworks and data. Such mistakes are common, but they can have a big impact on long-term results, team trust, and product quality. Some of these mistakes include:
Prioritizing the loudest speaker is a common mistake in product prioritization which happens more than you’d think. Some stakeholders or team members, for example, speak loudly and frequently that they unintentionally dominate the roadmap even when their ideas or requests don’t align with the user interests or company goals. It is necessary to distinguish between the person making the request and its actual value in order to prioritize tasks effectively.
Confusing urgency with importance is another very common mistake in prioritization because sometimes, everything looks like a must have, but not everything that looks urgent is important. Urgent tasks come with deadlines or short-term pressure—for example, an upcoming demo presentation—but important tasks yield long-term value like reducing technical debt.
Relying on instinct alone and not fact will most likely not get you the desired results as a product manager. If you make a decision just because it feels like a good idea, without validating it through research or grounding it in a prioritization framework, the effort required to fix the resulting mistakes can be significant, often costing more time, resources, and trust.
Conclusion
Finding the ideal framework or making error-free choices are not the goals of prioritization. It is about making conscious trade-offs with limited time, data, and resources—and being able to explain why those trade-offs were necessary. Prioritization frameworks are used as tools to eliminate bias, reveal assumptions, and encourage better decision-making in the face of uncertainty rather than as rigid rules.
Finally, because effective prioritization is a constant process, priorities must be revisited often, assumptions must be validated early, and decisions must be documented.
As products evolve, user needs change, and company goals shift, priorities must be examined and adjusted. Product managers who constantly prioritize clarity, proof, and intent are considerably more likely to create solutions that add genuine value for both users and businesses.