Location-Based Shopping App (iPhone, Android)
ShopNearby was a location-based shopping app helping shoppers discover the closest or lowest price stores for products near you. ShopNearby partnered with retailers such as Best Buy, Sears, Kmart, and several others to aggregate inventory and price in local stores. The problem ShopNearby solved was having to download multiple apps, visit multiple websites, call multiple stores, and/or waste time driving from store-to-store when shoppers wanted to buy specific products from local stores. I was the founder, product manager, UX designer/researcher, and UI designer. The team consisted of me, two back-end engineers, two native iOS and Android engineers, and one QA engineer.
- Launched native iOS app to the App Store
- Provided inventory and price for products in over 3,000 stores nationwide
- Developed and released retailer APIs
- Co-architected the technology infrastructure
- Developed and launched universal shopping cart across retailers and local stores
Tools, Technologies, and Products
- Google Drive
- Axure RP 8
- GoToMeeting, Join.me
- AWS: EC2, Lambda, Elastic Beanstalk, Mobile Hub, S3, ELB, CloudWatch, SNS, SES
- Node.JS, MongoDB, Objective-C, Java, Angular.JS, Express.JS
- Elastic Search, Kibana, Logstash
- Google Suite
User Research: Moderated On-Site and Unmoderated Remote Usability Tests
UserTesting.com was the primary tool I leveraged in order to gain feedback on user flows, usability issues, and concept-design feedback. The screeners were vital in finding participants we determined were most likely to become brand evangelists for the app.
User Research: Ethnographic Field Studies
To understand the thought-process, mindset, and decision-making process of shoppers, I accompanied users while shopping in physical stores. The biggest insight from this research was the decision-making process of everyday products vs. one-of-a-kind purchases. Based on previous research, shoppers indicated price being the most important factor when purchasing everyday products, but they ended up buying the same items at stores more expensive. For example, one person purchased dog food that was over 30% more expensive at a local grocery store compared to the major retailers location in the shopping plaza across the street. The reason was because it would take more time having to go to the other store and because she associated the major retailers location as a store where she would spend more money than she preferred. As a result, she decided to pay more in the local grocery store and be satisfied with her decision.
User Research: Desirability Testing
During the visual design process, I created several design patterns to evoke certain responses from end-users. The goal was to understand what feelings, thoughts, or emotions they associated with each pattern. Since shopping for everyday products was associated as a non-pleasurable, but necessary activity, it was vital to design the app in a way to invokes positive emotions. The result of the qualitative desirability test using lots of white with green throughout the app. Having lots of white space helped people focus on the most important elements in the page.
The app had two different end users: shoppers and partners. Once partners integrate their data feed with our back-end, the process became automated. With shoppers, they shopped based on different needs in different contexts. As a result, it was vital to understand the forces that drive them to show and understand the
Create Task Flows
To understand shoppers decision-making process when shopping, it was imperative to understand how they expect and use current solutions. As a result, I created numerous task flows to map out their process. By understanding it, the team and I can design and develop solutions to solve for problems and improve the overall user experience.
Design Low-Fidelity Wireframes
Low-fidelity wireframes were developed to test our hypothesis on the expected user flows for accomplishing specific tasks in usability tests throughout the project lifecycle.
Design High-Fidelity Wireframes
Natively designed high-fidelity wireframes were handed off to each native engineer to develop. As a result of numerous sessions on usability tests, rework was minimized and the iOS app was launched as planned.