Dappr

That period of time in the morning before leaving for work never feels quite long enough. Whether it's the weight of the impending work day or the menial personal tasks (going to the gym, preparing meals, showering, getting dressed), waking up always seems too early and work seems to come too soon. I saw an opportunity to simplify the mornings, in turn, allowing more time to focus on…whatever else!

I wanted to create a product that could intelligently organize mens' wardrobes and assemble outfits for them on a daily basis to give them back some time in their morning.

The Research

The first thing I did was explore what was on the market currently for similar products to understand what was available and ensure I could create some differentiators. I assembled a feature inventory to expose where my product could potentially fill in gaps, on top of focusing on my primary goal of time-saving. From there I needed to validate my assumptions around my potential users' interests. I gathered five participants trying to match up to my prospective demographic and ran some initial user interviews. I then distilled all the feedback into a single persona.

I collected a few key takeaways from these activities, all of which seemed to offer an opportunity to save the user time. Based on the participants interviewed, I began to work through initial concepts starting first with storyboarding and user journeys, then to high-level sketches. The two major points I wanted to focus on solving for this MVP would be:

• Suggesting outfits that intelligently account for users environment (weather, activities, dress code)

• Help the users simplify or reduce their wardrobe

I knew I wanted to get the concepts in front of users as soon as possible to begin to gather usability feedback so I quickly moved to paper prototypes. For the prototypes, I approached a handful of new participants and had them go through a couple main task flows within the product. My goals were qualitative feedback around features' usefulness.

The Execution

I wanted to limit the scope to a simple MVP that would begin to solve for my initial problem focuses. I would build out a, "feature dream-list,” but the first iteration would only focus on a few core features. I settled on creating a native iOS app because I knew I would need local storage and preferred the user didn't have to be connected to wi-fi. The main features for this version were:

Suggestion (main) interface

Similar to Tinder, the user would open up the app and land directly on an outfit suggestion for that day. They could easily swipe between other available suggestions and once they were satisfied, make their selection.

For the MVP, the user would not be able to swap individual items, only entire outfits. I did this not only to limit functionality and keep scope in check but also to remove part of the inherent original problem (too many choices).

Wardrobe management system

User would input articles of clothing that they own based on matching default options offered by the app (eg. blue button down; dark denim jeans; taupe boots). This served two functions, assembling the clothing into a wardrobe database while also showing the user when they have multiple instances of similar articles.

As the user uploaded more pieces of clothing, the app would know exactly which pieces would coordinate.

Preferences page (activities, dress code, and weather)

One primary influence for outfit selection was weather. While this MVP would not initially automatically pull weather data, the user would have the option to go into their preferences page to select what their day essentially looked like and the app would only suggest outfits relevant to those parameters.

The MVP