PROBLEM SOLUTION OVERVIEW

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1 [Design] Katie Hendricks [Development] Venkat Rao [Documentation] Tyler Zuver [Manager] Alisa So [User Testing] Derek Tseng [URL] PROBLEM SOLUTION OVERVIEW Sometimes, people will see an image, a pattern, or a style that they really like while going about their daily lives. These people may like what they saw so much that they will want to own something in that same style. Unfortunately, there is currently no easy way for people to find items with a specific style that they are looking for. Our proposed solution to this problem is a mobile searching application that utilizes crowd sourcing to find its results. The way the application will work is that the person using it will somehow document the image, pattern, or style that they like with their phone and upload it to the application s server along with a description of the item that they want to have in that style. The application will then use a crowd-sourced AI such as Mechanical Turk to allow other people to try and find items that are identical or similar in style to what the requester has submitted. STANDARD TASK ANALYSIS QUESTIONS [Who is going to use the system?] The results from our contextual inquiry (CI) show that our main customers will consist of smart phone users who care about how they are dressed and shop for clothes for themselves. Most of the people we interviewed had their own personal style and favored certain stores, brands, or types of clothing. Some of our target customers like Jack will lead busy lives and won t have time to go out and shop very often. Jack said that he likes shopping online because it is fast and he is able to look for bargains and deals easily. Most of our Mechanical Turk users will likely be people who want to help others find what they are looking for, whether it is for a reward or because they work for certain clothing stores. [What tasks do they now perform?] From our CIs, we have gathered that many of our target customers currently look for clothes that match their style online or in stores. Customers like Kristin use websites filtering features to help narrow down the many options presented to them. If given a description of an item by someone else, most target customers currently try to find similar items in specific clothing stores or websites that they know about. [What tasks are desired?] Our target customers also brought up several additional tasks that they thought would be helpful in their shopping experience. A few of our customers stated that it is very diffi-

2 cult to find a desired clothing item that someone else is wearing. They wished for the ability to efficiently find an item without knowing where it is from. Kristin also said that she would like to be able to find items quickly and in one location, without having to browse multiple stores and investing a lot of time. A few of our customers also mentioned that they want to be able to buy the cheapest version of a desired item. Instead of going to different stores to find the best deal, Jack would appreciate a way to sort similar clothes by lowest price. [How are the tasks learned?] Some of our CI customers learned tasks by routinely engaging in the same activity. By regularly shopping, Angelica learned how to filter for certain stores and brands that git her style. Some customers try to learn tasks by trial and error. Several customers like Jack and Angelica learned that purchasing clothes online can be difficult by experiencing size/fit issues. However, they have learned to get around this fact by shopping online at stores they have already visited. [Where are the tasks performed?] In general, the tasks that our target customers perform occur at home or in clothing stores. Many of them browse and filter through clothes online, saying that using a computer is faster and more convenient. Several of them like Jack, Micky, and Kay prefer browsing and making purchases in stores. Though some of them found items they liked while they were on the go, they rarely looked for the item at that moment. [What s the relationship between customer & data?] There is a lot of data available to customers, but there isn t an easy way to effectively sort and search through it to find a desired style or clothing item. The Internet has countless websites filled with designs, patterns, and styles that customers like. The item a customer is looking for may be displayed on a store website somewhere on the Internet, but search engines alone make it difficult to retrieve matches for a style described by a combination of images, filtering conditions, and written and verbal descriptions. This illustrates a gap between the customer and data that prevent customers from obtaining their desired results. [What other tools does the customer have?] Several of our customers utilized store websites and real-world clothing stores to accomplish some of their tasks. For example, Angelica and Kirsten both liked to browse online websites to help them find items they liked. Furthermore, almost all the customers used a search engine in our contextual inquiries when browsing/searching. [How do users communicate with each other? ] A few of our customers mentioned that they like to shop with their friends or family members. They like to give and receive advice about what clothes look good. Furthermore, when they are on the hunt for a certain item, it is helpful to have an additional pair of eyes. [How often are the tasks performed?] From our CI, it is evident that task frequency is often varied. Identifying one s style is an action that is performed once in a while, but tasks such as buying clothes are likely to happen more often. Individuals, like Kristin, end up spending a lot of time browsing for clothes online, while others, such as Micky, like to make quick, infrequent purchases in a store. Mechanical Turks, who could likely be store employees, would perform tasks based the arrival of new merchandise, which might be weekly or even daily.

3 [What are the time constraints on the tasks?] Tasks like selecting an outfit to fit your style often have a time constraint involved. Someone may need to find clothing for a specific date or occasion. It also may be the case that certain styles may only be available for a limited time or certain season. In regards to shopping at physical stores, purchases are constrained by the operating hours of the establishment. Many people, like Jack and Kay, seek out discount deals and these are usually offered for a specific period of time. [What happens when things go wrong?] Sometimes people are not able to find clothes that match exactly what they are looking for. To avoid this, people will shop at familiar locations, which was seen with Kay and Kristin. This causes stores to lose out on potential customers who might have made a purchase if they were aware of the inventory that matched their needs. Clothing companies also want to makes clothes that are in demand, but may not have the data necessary to do so. When this data is not available, decisions on design may be less successful. REVISED TASKS 1. Finding a piece of clothing similar to a style seen (moderate, high importance, high frequency) Our previous moderate task was documenting style preferences but from our task analysis we discovered that documenting a style was just a sub-step to the customers actual goal of finding an item. Locating specific fashion items of interest is a primary task because all CI participants were able to describe the type of designs and clothing they like, however, couldn t always locate and purchase them. We observed Kay finding fashion inspiration in a clothing catalog and Mickey described his style influence from hiphop. These individuals identified items they liked, but even after searching several stores couldn t find what they were looking for. Kay, Jack and Kristin were all very busy and described how they often gave up searching for items because they didn t have time to spend browsing. 2. Filter results to match individual preferences (simple, low importance, low frequency) Our CI participants were all able to describe the brand names, stores, textures, colors, etc. that they prefer. This indicates that there is a use for filtering shopping results to include the most relevant items. Our task analysis confirmed that filtering is still an important task for our customers. Although filtering is a task that currently exists, are app will streamline the process for our target audience. 3. Provide suggestions from image/ description (complex, high importance, high frequency) We believe that human review, or Mechanical Turk, is the best way to suggest items similar to user submissions. Angelica mentioned that she was often able to guess what store an item came from based on the design/style because stores usually maintain a certain style. Our task analysis revealed that store employees are one group who could provide suggestions by comparing submissions to their merchandise to promote their brand. Providing suggestions is an extremely complex task because it requires a great deal of human effort and highly important because it functions as the back end of the system.

4 Mechanical Turk website view Mechanical Turk website view (for store employees)

5 While browsing the internet on a touchscreen, outline to save items Verbally describe an item and see it visualized on screen

6 Search by keyword Writing a request Using an existing item to find another

7 Basic home page Home page with recent searches Select from multiple ways to input (file, picture, QR code, recording) Saved searches Submit screen Search

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