Born at the intersection of convenience and personalization, Style Out is a fashion app designed and prototyped to inspire creativity and sartorial confidence. Leveraging AR and machine learning technology, the platform is designed to providing style suggestions for individuals overwhelmed with decision fatigue. Style Out creates looks from both the user’s wardrobe and online retailers.
Services
Prototyping
Copywriting
Marketing Research
App Service Design
User Testing & Research
UI/UX Strategy & Design
Challenge
The proliferation of technology and e-commerce have revolutionized traditional methods of shopping for clothing. Customers are no longer only visiting brick & mortar retailers for their daily outfits but shopping online and through social media platforms like Instagram. Style Out was designed to mitigate the decision fatigue & timeliness associated with selecting looks on a daily basis. The challenge was to develop an experiential platform accurately addressing consumer pain points in their styling journey.
“Not all people have the time to invest in buying and styling their clothes.”
Approach
1. Research
The first phase of Style Out development was market & user research. Over 200 users from a wide array of demographics, backgrounds and interests were interviewed to understand customer thoughts and experiences with their personal styling. The goal of the research phase was to gather enough insights to visualize an accurate and clear user-experience map of targeted users, along with understanding Style Out’s “jobs to be done.”
“Digging deeper than visual aesthetics through extensive user-experience research and strategic frameworks.”
Based on user and market research, a competitive landscape map was designed to compare others in the market currently seeking to address similar pain points with personal styling both physically and on applications. The main areas that were emphasized were time-efficiency and intrusiveness of app notifications.
2. Experience Mapping
Upon a better understanding of aggregated user and market research, a shopping experience map was created to clarify specific areas of focus for Style Out’s value proposition.
The typical user’s experience begins with waking up and looking in their closet, debating what to wear. The pain points that required attention were frustrations with the timeliness associated with styling existing looks or the realization that perhaps some shopping was necessary to complete a look in their closet. The map also highlighted that even when customers visited a retail store for shopping, there were still vexations associated with issues finding a correct size or feeling as if time was wasted when not finding what they were looking for; ultimately leading to either settling or not buying anything at all. These experiences surfaced the need for a platform that could efficiently and accurately provide stylistic consulting services based on personal preferences.
Solution
Style Out’s features were rooted in human-centered design. An app with features that alleviated dissatisfied emotions and sentiments throughout one’s daily dressing routines was the quintessential market opportunity.
“A personalized styling consultant providing looks from both your closet and online via “Style Out” push notifications.”
A customer journey and service blueprint map were designed to provide a holistic lens of the app’s entire interaction flow along with the backend processes required to fulfill these technological features.
Style Out’s unique point of difference is its ability to allow user’s to upload existing clothing in their wardrobe within the app using the AR closet-scan technology. This feature enables users to not only repurpose existing items in their closet but also allows Style Out’s machine learning algorithm to study their user’s preferences. Through questionnaires, Style Out continually improves to provide accurate “Style Outs.” While other fashion styling and retail applications insistently bombard your phone with promotional notifications, Style Out only sends a morning and evening notification: a morning look suggestion and an evening notification asking about opinions on the look. The amount of notifications received are within the user’s discretion.
