- How did this exercise help you build empathy with prospective users?
After the contextual interview, We processing the interpretation session, with extracting the useful content which would represent the perspective of future users, and write down crucial details specifically about users’ needs on post- it card, and then continue our interpretation session. The affinity diagram could shows in one place the common issues, themes, and scope of the customer problems and needs. It could acts as the voice of the customer and the issues it reveals become the basis for user requirements. which outcomes could perfectly help us build empathy with future customers.
- How did the clustering of information help you to understand user needs?
Clustering info helps us to categorize different ideas, skim useless and repeated needs, synthesis useful and meaningful content, Group common themes, thus, allowing us summarize authentic users’ needs, and use it to stimulate design thinking.
With the use of Affinity diagram, which could provides us with a structure to spread out all the affinity notes, to quickly scan and regroup the labels, and to pair up to complete the affinity building quickly. Moreover, different color labels could help us orient to the data, they draw you in or encourage you to skim a section, they could also give us an overview of the key issues so that we can move from section to section easily without getting disoriented.
- What was difficult or challenging with the technique? How would you do it better next time?
1. Too many labels in one section make scanning difficult and represent too many concepts to think about at once.
—If there is repetitive similar contents contained in one interview, then we don’t need to record it again.
Moreover, we should create new distinctions by moving notes, if we still can’t create a new group, then leave it wherever it is. The labels are what we care about, not the individual data points
2.The description on the label isn’t very clear, sometimes because of the style of handwriting, sometimes we can’t recall specific details when we encounter with complex categorize issue.
—we need a neatly and logically handwriting style, plus, record details on label, not only with the key words. Because we need to categorize notes by underlying implementations, we should avoid pre-conceived in order to interpret and reflect data precisely, not to judge which group it belonging to by keywords.
3. Higher levels labels hide the theme suggested by the lower levels labels and do not represent the customer data well. we always write the blue label with too much abstract, but a good label captures what its group is saying in detail. Bad labels would inadvertently encourage the team to skip the issues in that section.
—let the data tell the story, don’t assume our pre-defined perceptions are the best way to organize the findings. If we recognize the notes are too incoherent, then never hesitate to create a new group, make sure the label talks about the work.