Over the years in product management, I've come to recognize the true significance of one skill — prioritization.
It's an art that product managers must master to ensure a product's success: guiding the product development cycle, and determining what features make it to the users, and when. Considering diverse customer needs, fierce competition, and limited resources, effective prioritization is the key to unlocking true value.
In my search for effective prioritization methods, several frameworks have proven to be useful. Each of them offers unique perspectives and approaches to prioritizing features and tasks. These frameworks, however, shine in different scenarios, and understanding when to use which framework is just as important as understanding the frameworks themselves. Let's figure this out together.
The RICE Scoring Model
The name of this model is from an acronym for Reach, Impact, Confidence, and Effort. Therefore, the core of the RICE scoring model consists of four components:
-
Reach: This factor refers to the number of users or customers impacted by a task or feature within a given period. Reach is an important determinant of a task's value as it quantifies the breadth of its potential impact.
-
Impact: it assesses the degree of change a task or feature will have on an individual user's experience or the business. The larger the positive change a task can bring, the higher its impact score.
-
Confidence: this is a measure of certainty about your estimates. It's natural to have varying degrees of confidence about different tasks, especially when dealing with assumptions or forecasts. The higher your confidence in the reach, impact, and effort estimates, the higher the confidence score.
-
Effort: Effort estimates the total amount of work a task will require from all team members to complete. This typically includes design, development, testing, and other associated costs. Tasks that require less effort get a higher score.
The RICE scoring model is particularly useful in situations that demand a data-driven, balanced approach to decision-making. It considers both the potential benefits of a task (Reach and Impact) and the resources required to execute it (Effort), while keeping the certainty of estimates in check (Confidence). This view allows product managers to avoid the common pitfall of prioritizing tasks based on intuition or bias.
Using the RICE scoring model effectively involves regularly updating the scores as new information emerges and reassessing the priorities accordingly. It encourages transparency and open discussion, as all team members can understand the reasoning behind prioritization decisions.
The ICE Scoring Model
This is a simplified version of the RICE framework for making easier decisions. Here, ICE is an acronym for Impact, Confidence, and Ease. Again, this method allows product managers to assess and rank tasks by considering their overall impact and ease of implementation.
Here's what each component of the ICE Scoring Model represents:
This is a slightly changed variation of the previous model. Again, we’re assessing several factors.
-
Impact: this refers to the potential effect of a task or feature on your product, business, or users.
-
Confidence: like before, this is all about how sure you are about your impact and ease estimates.
-
Effort: this measures how easy or difficult it is to implement the task.
Using the ICE Scoring Model is fairly straightforward. You give each task a score from 1 to 10 for each component — impact, confidence, and ease. Then, you calculate the ICE score by finding the average of these three scores. Tasks are then ranked based on their ICE score, and then you put those with higher score first in you to-do list.
The ICE Scoring Model comes into its own when you're grappling with tasks where the reach isn't a primary concern or is hard to estimate. It helps you zero in on the tasks that can make the biggest impact and are relatively easy to implement, enabling a focused approach towards resource allocation.
The MoSCoW Method
This method comes from the world of software development and project management, but it has since found its home in the toolkits of product managers. Yet again, it is an acronym that represents four categories of tasks: Must have, Should have, Could have, and Won't have.
-
Must have: these are the non-negotiables, the essential elements without which the product simply cannot function or launch.
-
Should have: tasks falling under this category are important but not critical for the initial launch or operation. If neglected, “Should have” tasks might cause inconvenience or impair some functionalities, but the product remains usable.
-
Could have: these tasks are nice-to-have features or elements. They enhance the user experience or add value but aren't fundamental to the product's basic operation. They are often first candidates for scope cuts if time or resources become tight.
-
Won't have: the final category is often overlooked, yet crucial. It outlines the tasks or features that will not be implemented in the current phase of the project. Identifying “Won't have” items provides clarity, sets expectations right, and ensures you can focus on more important tasks.
The MoSCoW method is beneficial in situations where decisions are driven by expertise and intuition, in scenarios with limited data but a strong internal perception of significance.
The Kano Model
Understanding and predicting customer satisfaction is beyond important in product management. Here, the Kano Model can help you. Named after its creator, Noriaki Kano, it provides a structured way to categorize features based on their potential impact on customer satisfaction. It includes five classifications: “Must-be”, “One-Dimensional”, “Attractive”, “Indifferent”, and “Reverse”.
-
Must-be Features: these are basic expectations that customers have, even if they don't expressly state them. They are so fundamental to the product that their absence would cause great dissatisfaction. However, just having these features doesn't lead to increased satisfaction since customers view these as a given.
-
One-Dimensional Features: these are the features that linearly impact customer satisfaction. The better these features perform, the higher the satisfaction, and vice versa. Customers often explicitly express their needs for such features.
-
Attractive Features: these are the lovely extras, the features customers didn't know they wanted until they saw them. They can significantly enhance customer satisfaction, but their absence doesn't cause frustration since customers aren't expecting them.
-
Indifferent Features: these are features towards which customers are neutral. Their presence or absence doesn't significantly impact customer satisfaction. Identifying such features is crucial to avoid investing resources in areas that won't bring you substantial returns.
-
Reverse Features: these are features that can lead to dissatisfaction when present. Although, different customer segments may react oppositely to the same feature, making it attractive for some and a reverse feature for others.
Applying the Kano Model involves continuous customer feedback and market research. As customer preferences and market trends evolve, so too will the categorization of features within the model.