User Analysis
We have identified three demographics who would likely benefit using a mobile device to look up products while shopping: technological innovators, power users, and casual users. In this section, we describe these demographics. We discuss their problems and explain how our application may benefit them. Finally, to substantiate our claims, we located three research subjects and interviewed them about how a mobile product identification software will assist them in their lives. Our results are intriguing and suggest that our proposed mobile phone application can have a significant impact.
Technological Innovators
Overview: A compelling demographic are technological innovators and researchers who are likely to be early adopters of technology. People in this demographic typically use sites like Amazon.com and Newegg.com to find products and read their reviews. These users have grown accustomed to quick access to reviews and product information while shopping. In other words, they desire efficiency. Consequently, we believe this demographic will find a mobile lookup application useful since they want to replicate the online experience while in stores.
User Study: We located one research subject who was currently looking to purchase a laptop. We asked him how he makes purchasing decisions. He responded that he typically uses a variety of metrics such as price, appearance, functionality, and reviews. In order to find reputable reviews while at stores, he uses web-services such as Amazon to type in queries for products he is on the verge of purchasing. Despite its slow speed, he prefers to use a text-based query input method. He argued that picture-based product lookup applications from Amazon or Google do not work well because he found them difficult to use. He often wanted to specify multiple objects. In other words, he found the user interface inadequate for this needs because he was unable to easily specify the products he wanted to lookup.
Proficient Users
Overview: Another type of user are power users of technology, but not necessarily innovators. They adopt technology into their lives, but do not necessarily
User Study: Subject B, female, stated that because furniture is also a relatively large purchase, she would have previously gotten an idea of what price ranges to expect by Googling products from home. When told that one of the three selected products was not initially found in their online searches, she said she would talk to the salespeople and later go home, look it up and come back. When asked if she ever looked up pricing and reviews from her phone while at the store, she said that she would consider doing it if she was alone, but not if she had already engaged a salesperson as she felt doing so would be rude.
Causal Users
Overview: The final demographic we consider are users who only use a casual amount of technology in their life. While they rarely use technology, when they do they need it to work reliably and minimize mistakes. Indeed, these users need safety.
User Study: Subject C, male, stated that to determine which coat to purchase, they would first want to try it on and then subsequently look up reviews for determining how durable it is. Their chosen method for doing so was to "awkwardly type in the product name into Amazon."
Summary: These results suggest that looking up products on mobile devices is useful, but that it is currently slow and awkward. We believe that for certain types of products, image-based product lookups can be made more appealing and efficient in three ways: 1) by providing the user with an interface in which it is easy to specify what product is interesting (thereby helping computer vision algorithms), 2) by allowing the user to specify multiple products of interest (both helping computer vision algorithms, and providing interfaces for efficient comparison shopping), and 3) by providing search results that include price and links to reviews.
Task Analysis
Inspired by our experiences after interviewing subjects, we identified three primary tasks for a mobile phone application: quickly locating reviews for a product, efficiently comparing products, and clearly showing results to the user. In this section, we discuss these tasks for each user demographic.
How do users lookup products?
Current systems either have no method for specifying the object (instead, they assume it is centered), or they have (obtuse) interfaces for drawing a rectangular bounding box. Due to the sensitivity of state-of-the-art computer vision algorithms, poorly placed localizations lead to significantly incorrect identification results. Both cause losses in efficiency which compel users to use typed queries, or to not use their mobile device at all. Indeed, we need more efficient methods for specifying objects of interest in a photo.
How do users compare products?
Current systems do not support looking up multiple items in a single image, causing the user to have to repeatedly do the same task for multiple products. What is a visible, learnable and efficient interface for allowing users to specify multiple products in the same image?
How do users interpret results?
Current systems (e.g. Amazon Remembers, Google Goggles) will open a product page or search engine results page. Neither method would scale to multiple lookups. Even for individual lookups, it isnot clear that these are the best methods for conveying what a user is likely to be interested in for both prices and reviews. What is the best way to convey results to the user?