Relative estimation, often referred to as “story point estimation,” is a technique used in Agile methodologies to estimate the effort or size of work items, such as user stories or tasks, in relation to one another. Instead of assigning absolute time-based values (e.g., hours or days) to each item, team members use a relative scale to compare the items’ sizes or complexity.
The primary unit of measurement in relative estimation is the “story point.” A story point is a relative measure that reflects the effort, complexity, and scope of a work item compared to other items in the backlog. This approach is inherently less precise than traditional time-based estimation but provides valuable insights into the relative sizes of work items.
Relative estimation offers several advantages in Agile development:
Simplicity: It simplifies the estimation process by focusing on relative comparisons rather than precise time estimates.
Flexibility: Relative estimation accommodates uncertainty and allows teams to adapt to changing project requirements and complexities.
Collaboration: It encourages collaboration among team members during the estimation process, fostering shared understanding.
Consistency: Over time, teams develop a consistent understanding of their estimation scale, improving the accuracy of future estimates.
Focus on Value: Relative estimation shifts the focus from exact time estimates to delivering value to the customer.
Principles of Relative Estimation
Relative estimation is guided by several key principles:
1. Comparison
Team members compare the size and complexity of work items to one another. They don’t provide absolute estimates but rather assess the relative effort required to complete each item.
2. Consensus
Estimation is a collaborative process that involves team members discussing and reaching a consensus on the size of each work item. This consensus-building fosters shared understanding.
3. Relative Scale
Teams use a relative scale for estimation, which can be any scale that works for the team, such as a Fibonacci sequence (1, 2, 3, 5, 8, 13, etc.) or t-shirt sizes (XS, S, M, L, XL). The scale should reflect the team’s judgment of relative effort.
4. Historical Data
Teams leverage historical data and their collective experience to guide their estimations. Past work items serve as reference points for estimating new items.
Methods for Implementing Relative Estimation
Several methods can be employed to implement relative estimation effectively:
1. Planning Poker
Planning Poker is a popular technique for relative estimation. Team members gather to discuss and estimate work items. Each member uses a deck of cards or a digital tool with the chosen estimation scale. They reveal their estimates simultaneously, facilitating discussion until a consensus is reached.
2. Bucket System
In the Bucket System, work items are categorized into predefined buckets or size ranges. For example, one bucket might represent small items, another medium, and so on. Team members assign items to buckets based on their relative size.
3. Affinity Mapping
Affinity mapping involves grouping work items into clusters based on their perceived relative size. Team members physically or digitally organize cards or sticky notes representing items into groups of similar size. This method encourages discussions and consensus-building.
Real-World Examples of Relative Estimation
Let’s explore a few real-world examples to illustrate how relative estimation is applied in Agile teams:
1. Software Development
In Agile software development, development teams use relative estimation to estimate user stories or backlog items during sprint planning. Team members assign story points to each item based on their judgment of relative effort and complexity. This estimation process helps the team plan and commit to a manageable amount of work for the sprint.
2. Marketing Campaigns
Marketing teams adopting Agile methodologies can use relative estimation to estimate the effort required for various marketing tasks or campaigns. For instance, they might estimate the relative effort to create social media content, design graphics, or analyze campaign data.
3. Product Management
Product managers and product development teams use relative estimation to prioritize features on a product roadmap. By estimating the relative size of each feature, they can make informed decisions about which features to tackle next and how to allocate development resources.
4. Research and Development
In research and development projects, teams can use relative estimation to estimate the complexity of different research tasks or experiments. This helps in planning research activities and allocating resources effectively.
Conclusion
Relative estimation, also known as story point estimation, is a valuable technique in Agile project planning. By focusing on the relative size and complexity of work items rather than exact time estimates, teams can streamline their estimation processes, foster collaboration, and adapt to changing project requirements. While it may not provide precision, relative estimation offers flexibility and agility, making it a practical approach for Agile teams striving to deliver value efficiently.
Key Highlights:
Definition of Relative Estimation:
Relative estimation, used in Agile methodologies, involves comparing the effort or size of work items relative to each other rather than assigning absolute time-based values.
Unit of Measurement:
The primary unit of measurement is the “story point,” which reflects the relative effort, complexity, and scope of a work item compared to others in the backlog.
Advantages:
Advantages include simplicity, flexibility, collaboration, consistency, and a focus on delivering value to the customer.
Principles:
Guiding principles include comparison, consensus, relative scale, and leveraging historical data for estimation.
Methods for Implementation:
Effective methods for implementing relative estimation include Planning Poker, the Bucket System, and Affinity Mapping.
Real-World Examples:
Examples of its application include Agile software development, marketing campaigns, product management, and research and development projects.
Conclusion:
Relative estimation, while not precise, offers practical benefits for Agile teams by streamlining estimation processes, fostering collaboration, and enabling adaptability to changing project requirements.
Related Framework
Description
When to Apply
Planning Poker
Planning Poker is a consensus-based technique used in agile project management for estimating the relative size of user stories or tasks. Team members assign story points or complexity values to user stories based on their understanding of the requirements and complexity involved. It encourages collaboration, reduces bias, and leverages the collective wisdom of the team to arrive at more accurate estimates.
When estimating the effort required for user stories or tasks in agile projects, fostering collaboration and consensus among team members, and leveraging the expertise of the team to arrive at more accurate estimates.
Fibonacci Sequence
The Fibonacci Sequence is a sequence of numbers where each number is the sum of the two preceding ones, typically starting with 0 and 1. In agile estimation, the Fibonacci sequence (e.g., 0, 1, 2, 3, 5, 8, 13, …) is often used to assign story points to user stories. The sequence reflects the uncertainty and variability inherent in estimating effort, with larger numbers indicating greater complexity or uncertainty.
When assigning relative sizes or complexity values to user stories or tasks in agile projects, providing a scale that accommodates uncertainty and variability in effort estimation, and facilitating comparison and prioritization of user stories based on their relative sizes.
T-shirt Sizing
T-shirt sizing is a high-level estimation technique where user stories or tasks are categorized into small, medium, large, or extra-large sizes based on their complexity, effort, or size relative to each other. It provides a quick and intuitive way to estimate the size or effort required for user stories without getting into detailed analysis. T-shirt sizing helps teams to prioritize and plan work based on the relative sizes of user stories.
When conducting high-level estimations for user stories or tasks in agile projects, prioritizing and planning work based on relative sizes or effort required, and providing a quick and intuitive way to estimate without detailed analysis.
Dot Voting
Dot voting is a collaborative decision-making technique where team members use dot stickers or marks to vote on different options or proposals. In relative estimation, dot voting can be used to build consensus on the relative size or complexity of user stories or tasks. Each team member places dots on a board or card representing user stories to indicate their perception of the effort involved. The voting results help the team converge on a collective estimate.
When seeking consensus on the relative size or complexity of user stories or tasks, leveraging the collective wisdom of the team to arrive at estimates, and facilitating discussions and alignment among team members.
Bucket System
The bucket system is an estimation technique where user stories or tasks are categorized into predefined buckets or categories based on their size or complexity. For example, buckets may be labeled as small, medium, large, or extra-large. Team members assign user stories to the appropriate bucket based on their relative size or complexity, without assigning specific numerical values. The bucket system provides a simple and intuitive way to estimate effort.
When conducting high-level estimations for user stories or tasks in agile projects, categorizing user stories based on their relative sizes or complexity, and providing a quick and intuitive way to estimate without the need for precise numerical values.
Affinity Estimation
Affinity estimation is a collaborative technique where user stories or tasks are grouped into clusters or affinity groups based on their perceived similarity or complexity. Team members discuss and compare user stories to identify patterns and similarities, then group them into clusters representing different levels of effort or complexity. Affinity estimation helps teams to identify outliers, clarify requirements, and arrive at consensus on effort estimates.
When estimating the effort required for user stories or tasks in agile projects, identifying patterns and similarities among user stories, and facilitating discussions and alignment among team members to arrive at consensus estimates.
Magic Estimation
Magic estimation is an estimation technique where team members independently estimate the size or effort required for user stories or tasks, then reveal their estimates simultaneously. Any significant differences in estimates are discussed, and team members may revise their estimates based on the discussion. Magic estimation encourages independent thinking while leveraging collective knowledge to arrive at consensus estimates.
When seeking independent estimates from team members, fostering discussions and alignment on effort estimates, and leveraging collective knowledge to arrive at consensus estimates.
Comparative Estimation
Comparative estimation is an approach where user stories or tasks are compared to reference stories or benchmarks to determine their relative size or effort. Team members may use historical data, reference stories, or benchmark tasks to gauge the complexity or effort required for new user stories. Comparative estimation helps teams to leverage past experience and existing knowledge to estimate new work items more accurately.
When leveraging historical data or reference stories to estimate the effort required for new user stories or tasks, providing a basis for comparison and benchmarking, and improving the accuracy of effort estimates by leveraging past experience.
Planning Game
The planning game is an agile practice where team members collaboratively estimate the size or effort required for user stories during sprint planning sessions. Team members discuss each user story, share their insights, and collectively assign story points or relative sizes based on their understanding and expertise. The planning game fosters collaboration, knowledge sharing, and consensus-building among team members.
When conducting sprint planning sessions in agile projects, collaboratively estimating the size or effort required for user stories, fostering discussions and knowledge sharing among team members, and arriving at consensus estimates for sprint backlog items.
Wideband Delphi
Wideband Delphi is a structured estimation technique where experts independently estimate the size or effort required for user stories or tasks, then share and discuss their estimates iteratively until consensus is reached. The process involves multiple rounds of estimation and feedback, with the aim of converging towards a consensus estimate through iterative refinement and discussion. Wideband Delphi leverages the collective expertise of the team while mitigating bias and groupthink.
When seeking estimates from subject matter experts (SMEs), mitigating bias and groupthink in estimation, and arriving at consensus estimates through iterative refinement and discussion among experts.
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Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
