WEEK 4 - Scenarios & Storyboards

Date Created:          September 24th, 2018

Date Last Updated: September 27th, 2018

Concepting

Rose, Thorn, Bud

After doing user research and synthesis of what we have found. We decided to focus our design on how to group people sharing similar diet habit / preference / desire. We inspected some grouping methods widely used in existing products. The Rose, Thorn, Bud method was used to help better compare them and informed us what we can improve.

 

The audit of design precedents indicated that there are 3 major grouping methods: questionnaire (with input box), multiple choice and multiple choice with matching degree as a result. By using rose, thorn, bud, we realized that there needs to be a balance between collecting information in a playful way and collecting information that is not as fun to enter, but useful for the matches.

Collecting information in a playful way is to intrigue and appeal to users. However, the quality of information still matters for it would be used to match users sharing similar relationship to food.

I organized  the Rose-Thorn-Bud mapping after the discussion

Affinity Diagrams

I have always loved affinity diagrams for it helps us cluster and bundle ideas and facts. In this project we used affinity clustering during brainstorming during which we got some fun ideas as well as some wild ideas.

Affinity Clustering.png
Affinity diagram
(Click to enlarge it)

User Scenarios

 

We have come up with 5 key scenarios.

Our 5 scenarios

Scenario 1: It is 8 p.m.. Amy just got back from work. She is tired, hungry, yet don't know what to eat for dinner. So she pulls out her phone and see what other people, who share similar relationship to food with her, are eating.

Scenario 2: It is a busy day. Amy is thinking about working out or going to a healthy lunch or activity but has no idea where to go. She opens the app and looks at her matches, and finds a girl favorited her favorite restaurants. She is so happy to find that the girl just posted the food from one of the restaurant it looks good! Amy comments her posts below asking her more details about the restauant. It is a good restaurant indeed! The food is good-tasting while low-calory. It is even not far from her house since the girl mathces her is not far from her.

Scenario 3: Amy decides to lose weight and this time she means it! She starts to look for a more healthy diet. She used to eat a no-meat-and-no-carb diet to lose weight but she gains weight back very soon. She knows that no-meat-and-no-carb diet would do harm to her body but she doesn't know what else she could do to lose weight in a more healthy way. This time, she got the app! She starts to look for those who she matches and see what they do along with their losses and wins. She learns a lot for their posts and gets inspired!

Scenario 4: Adam is a vegan. He has to eat some supplements to obtain sufficient nutrition. He just got promotion recently and moves intoa new city. Here comes the problem: where he should go to get good-quality supplements and vegan food at lower prices? Is there any good vegan restaurant around here? He pulls out his phone and sees is any of his matches living in this city. He finds one! Josh, who matches with him highly, lives in this city! He checks Josh's posts and comments seeking for good places and he is happy now!

​Scenario 5:Amy finds one of her matches just posts a coconut oil and it looks very healthy. So she comments below to ask for more details.

Storyboarding

We picked three key scenarios and storyboard them. I did the first senario where Amy is looking for inspiration for her dinner.

My storyboard: Amy doesn't know what to eat for dinner

Reflection:

  • Storyboard should show enough details  of user experiences explaining how the product fits into each persona's life and environment, and how it helps them achieve their goals. It should include why they are using the product ( the context and motivation), how they use it and what goal they can achieve. 

  • I think next time I should use affinity diagram in analyzing data collected from user research before empathy mapping as well. Because helps us a lot to organize and group the data.

Anything worth doing is worth doing well

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