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1.Creating Affinity Groups[Original Blog]
One of the key steps in the affinity diagram technique is creating affinity groups. Affinity groups are clusters of related ideas, issues, or insights that emerge from the brainstorming session. They help to organize and categorize the large amount of data generated by the participants. Creating affinity groups is not a straightforward process, as it requires careful analysis, collaboration, and creativity. In this section, we will explore some of the best practices and tips for creating effective affinity groups. We will also look at some examples of how affinity groups can be used for user experience analysis.
Here are some of the points to consider when creating affinity groups:
1. Use a bottom-up approach. Affinity groups should be created from the bottom up, meaning that they should be based on the data and not on preconceived notions or assumptions. The goal is to discover the underlying patterns and themes that emerge from the data, not to force the data into existing categories or frameworks. This way, the affinity groups will reflect the true voice of the users and their needs, problems, and preferences.
2. Use a silent sorting method. A common way to create affinity groups is to use a silent sorting method, where the participants sort the data cards (or sticky notes) into groups without verbal communication. This allows each participant to focus on their own intuition and logic, without being influenced or distracted by others. The silent sorting method also prevents groupthink, where the participants conform to the dominant opinion or perspective of the group, rather than expressing their own views or insights.
3. Use descriptive labels. Once the affinity groups are formed, they should be labeled with descriptive phrases that capture the essence of the group. The labels should be concise, clear, and meaningful, and should avoid using jargon, acronyms, or vague terms. The labels should also be written on the same type of cards or notes as the data, and placed on top of the group. The labels will help to communicate the main idea or theme of the group, and will also serve as a reference point for further analysis or discussion.
4. Use subgroups and supergroups. Affinity groups can be further refined by creating subgroups and supergroups. Subgroups are smaller clusters of data within a larger group, that share a more specific or nuanced relationship. Supergroups are larger clusters of data that span across multiple groups, that share a more general or overarching relationship. Subgroups and supergroups can help to create a more detailed and comprehensive picture of the data, and to reveal more subtle or complex connections and insights.
5. Use examples and evidence. Affinity groups should be supported by examples and evidence from the data, such as quotes, observations, feedback, or metrics. Examples and evidence can help to validate and illustrate the affinity groups, and to provide more context and depth to the analysis. Examples and evidence can also help to identify gaps, contradictions, or outliers in the data, and to generate new questions or hypotheses for further exploration.
An example of how affinity groups can be used for user experience analysis is shown below. The data cards are based on a user research project for a mobile app that helps users to find and book hotels. The affinity groups are labeled with descriptive phrases, and some of them have subgroups and supergroups. The examples and evidence are shown in parentheses.
- User goals and motivations
- Find the best deal (e.g., "I always compare prices before booking", "I look for discounts and offers")
- Find the best location (e.g., "I prefer hotels near the city center", "I check the map and the distance to the attractions")
- Find the best quality (e.g., "I read the reviews and ratings", "I look for photos and videos of the rooms")
- User pain points and frustrations
- Lack of information (e.g., "Some hotels don't have enough details", "I wish I could see more photos and videos")
- Lack of trust (e.g., "Some reviews are fake or biased", "I don't trust the ratings and stars")
- Lack of control (e.g., "Some hotels have hidden fees or charges", "I don't like the cancellation policy")
- User preferences and expectations
- Ease of use (e.g., "The app should be simple and intuitive", "I don't want to fill in too many forms or details")
- Personalization (e.g., "The app should remember my preferences and history", "I want to see recommendations based on my profile and interests")
- Security (e.g., "The app should protect my personal and payment information", "I want to see a confirmation and a receipt after booking")
- user behavior and patterns
- Planning ahead (e.g., "I usually book hotels a few weeks or months in advance", "I like to have everything organized and ready")
- Searching and browsing (e.g., "I use filters and sorting options to narrow down my choices", "I open multiple tabs and compare different hotels")
- Booking and paying (e.g.
Creating Affinity Groups - Affinity Diagram: How to Use This Technique for User Experience Analysis