What are the benefits and challenges of using a scoring model for backlog prioritization?

Product managers have various methods to score backlog items. - Kano: Assesses features based on customer satisfaction and functionality, identifying basic, performance, and delight features. - RICE: Prioritizes based on Reach, Impact, Confidence, and Effort, ensuring a data-driven approach. - MoSCoW: Categorizes features into Must-haves, Should-haves, Could-haves, and Won't-haves, helping to focus on critical needs. - Value vs Effort: Balances feature benefits against the effort required, optimizing for maximum value with minimal resources. Each method has its strengths, depending on project goals and constraints.

The main benefit to using a scoring model is ensuring the product being delivered is actually engineered to achieve the business goals. Eg., "Building the right product..."

The main prioritization models are: Kano, RICE, MoSCoW, and Value vs Effort. The benefits of each model are: - Kano: Helps identify features that will most enhance customer satisfaction, distinguishing between basic, performance, and delight features. - RICE: Provides a quantitative approach to prioritization, ensuring decisions are based on Reach, Impact, Confidence, and Effort. - MoSCoW: Clarifies priorities by categorizing features into Must-haves, Should-haves, Could-haves, and Won't-haves, focusing on critical needs first. - Value vs Effort: Optimizes resource allocation by balancing the benefits of features against the effort required to implement them, ensuring maximum value with minimal resources.

It's crucial to listen to all customer feedback while avoiding undue emphasis on the most vocal users. Customers can be broadly categorized into four segments, each offering diverse perspectives that should be balanced against your organization's goals. Additionally, roadmap prioritization varies significantly between bootstrapped startups and well-funded enterprises. Startups must strategically allocate development resources, prioritizing features that cater to their highest-paying customers to fuel growth-driven product efforts. This approach ensures that investments align closely with business objectives and customer value creation.

The first thing to remember when using scoring models for backlog items is that we are making estimates. Product development is inherently complex, and engineers aren't solving the same problems repeatedly. Consequently, predicting the time required for a backlog item can be challenging. Another issue we encounter is Parkinson's Law which states that work expands to fill the time allotted for its completion: if engineers complete a task in one day and we then expect them to finish another task with the same score in the same amount of time, they may start inflating scores to create a buffer. This practice leads to less accurate scores and results in the team taking longer to complete future tasks even if it can be done faster.

The term backlog and backlog item should be defined here - it's very challenging to score individual PBIs, especially at the Sprint backlog level. Typically, a product feature is what is adding value to a product and is score-able, against business KPIs/OKRs. Yet, it can take numerous PBIs to develop a feature and not all will/should make the cut. These are harder to align to organizational goals and instead affect moreso other tradeoffs, such as usability.

The main prioritization models are: Kano, RICE, MoSCoW, and Value vs Effort. Each has challenges as well as benefits. The challenges are: - Kano: Requires extensive customer research and can be complex to accurately classify features into the right categories. - RICE: Can be time-consuming to gather accurate data for Reach, Impact, Confidence, and Effort, and subjective estimations can lead to biased prioritization. - MoSCoW: May result in disagreements among stakeholders about what constitutes a Must-have versus a Should-have, and can oversimplify prioritization. - Value vs Effort: Balancing value and effort can be subjective and may lead to underestimating the effort or overestimating the value of features.

Prior to listing the examples, I'd emphasize the importance of creating a scoring model that measures the backlog item's alignment to the business and product objectives (i.e., potential to contribute to market segment growth, easy to use, etc.).