Rheme Development (myself, MJ Sun, and Rick Pflasterer) sought to create an iPhone application that would augment the efficiency of a shopper’s experience in a grocery setting. Aisle-x-Aisle allows a user to create a shopping list, search for nearby stores, and then locate the list items within the store via a specified route that will speed up the process of finding and gathering items for checkout.

 

We aim to solve the problem of finding obscure items, locating them (possibly in an unfamiliar store) and optimizing routes through the store to create an efficient experience. The application has the ability to specify which items are in what stores. Selecting a store with all of the items in the list is the ideal outcome.

 

Our solution is the Aisle x Aisle application (AxA). A smartphone user typically has their phone on them and adding another functional shopping assistant that suggests optimal locations for the items proves, through our research, to be a tool that people are interested in using.

The full project report can be read in PDF form, giving a full description of each stage of the project, including user research, creating personas, iteration of designs, completing heuristic evalutations, and doing user testing.

 

We started off by doing user research about shoppers, through surveys and interviews. AxA targets an audience that uses a smartphone and does their own shopping.  We studied college shoppers who represent enough of a varied field to exemplify numerous traits within the smartphone technological field. We found that 93% of the users in our survey grocery shop once per week or less. This fact appeals to our notion that planning before shopping is important thus yielding a single trip if not less.

 

With our users in mind, we brainstormed and developed scenarios and personas to develop user tasks they might want to do, with the app, listed below: 

 

A medium fidelity, interactive version is available online, rendered in Axure. A smartphone prototype is also available. 

The grocery list is made from a Grocery database “backend” to help categorize and populate results. A learning algorithm might be implemented here, so that the database has the potential to be updated and augmented by search optimization - similar to that of a search engine. When users add items, the “backend” guesses what the user is likely to type, and allows the user to tap the suggestion instead of typing the entire item name. Users can also type out and enter custom items that are not in the database. Users can categorize their list (based off of predetermined broad grocery categories) and order the categories in the way they want, or they can turn off categories and customize the order of the entire list. This allows flexibility for users, since different people use and organize lists differently (as found in our data). When items are checked off on the list, they slide down to the bottom of the list.  A “Delete Checked Items” appears at the bottom of the unchecked items, but above the checked items, allowing users to easily delete the items they have already gotten.

Some items are bought on a regular basis. We wanted to make a featured that gave the user the ability to select these items without having to enter them into the search each time they went to edit their shopping lists. Items are added to the list by clicking on the star icons. From the favorites screen, users can click on the plus icon to add the favorite item to the shopping list so they don’t have to type and search for commonly bought items every time they plan a new trip.

A store search screen appears when users click on the “Aisle by Aisle” tab if they have not yet selected a store. We identified two ways people might search for stores. The first is based on the amount of desired items that one store might have. The second factor is store distance. “Pie Pins” were the solution to the former - These pins, similar to ones that you might find on a typical Google / iPhone map, have a pie chart that demonstrates the amount of items that the store holds. The Pie Pin, when clicked, gives information on what store you’re looking at, where it is, and which items are missing from that store. Users can then select that store to view the store map. Users can search for stores by using their current location, or by searching by address, using a common iPhone map interface.  Our search displays the stores in a map view showing distance from the user’s location and approximate driving time. This gives the user the option to weigh distance vs importance of item selection.

 

The store map shows the user’s selected store, and plots out the location of the user’s grocery list items using colored dots. It also overlays a suggested route through the store. Users can zoom in and out of the map by pinching. The dots are colored by clusters. A group of items are in a cluster if they are close to each other. When a cluster is tapped, a pop up will show a list of all the items in the cluster, allowing the user to easily check off items they have gotten. Once an item is checked off, the corresponding dot turns grey. A bigger, more prominent blue dot representing the user tracks the user’s movement through the store in real time. Users can also click on “Routes” to change the route optimization, finding the shortest distance, going to produce first, or getting refrigerated foods last.

 

The Tutorial will be displayed on first use, and can be reset in iphone settings if necessary to view again. And, presumably, help and documentation about the app will be presented on the app's download website.