Wear
The problem
When it comes to apparel, there are adventurous and conservative shoppers. Adventurous shoppers spend hours researching, browsing, experimenting, and trying on different styles of fashion, and have fun doing it. Conservative shoppers just want to occasionally purchase slightly different hues of the clothes they have worn for years, and the idea of shopping fills them with dread. They like to stick with the familiar because they often cannot visualize how different styles of clothes can look. The problem is often not that these shoppers are unwilling to wear different apparel, but that they are unwilling to put in the search costs. The unwillingness to experiment constrains the growth the $225bn apparel market and contributes to a fashionably duller world and opens up an opportunity for us.
Wear – The solution
The team proposes an augmented judgment system that uses a shopper’s non-apparel preferences to predict apparel preferences that they may not be aware of. For example, if [Wear] knows that a shopper likes James Bond movies, enjoys wine tastings, drives a Mercedes, spends 45 minutes every morning for personal grooming, and prefers to eat at Grace, it would predict the type of apparel the shopper would like through a model that connects non-apparel preferences to apparel tastes. [Wear] would then remove apparel styles already owned by the shopper from the output to form a set of recommendations of new styles the shopper may like. This model will use augmented perception techniques to understand different parts of the user’s environment that provide insight into their preferences. The algorithm will form user’s profile by sourcing user preferences from three key sources: 1) user’s social media accounts such as their twitter posts and who they follow, facebook event posts, instagram likes, yelp posts etc., 2) current retailer accounts to track their purchase history for apparel as well as apparel purchases like Macy’s, Ticketmaster, eventbrite etc., and 3) direct self data input by users, such as color choices of their current wardrobe, size/fit preferences, and category of recommendations needed (t-shirts, jeans, etc.). The result would be distinct from that produced for a shopper who likes Mission Impossible movies, enjoys dive bars, rides a Harley Davidson, spends 5 minutes on personal grooming, and prefers to eat at Smoque BBQ. The algorithm will generate apparel recommendations that fit the needs and preferences of shoppers who may not understand their own preferences, leading to higher consumer willingness to purchase new apparel and higher industry sales.
Typical user profile
Our target user market is 18-35 year olds (millennials) who are tech savvy and fashion conscious. We are targeting all income brackets and both men and women. In total, we expect our target addressable market to be 75 million users.
Use cases
Common use cases that we expect are: users shopping for themselves or for a gift for someone else either for a special event or as part of their routine shopping, for example to replenish one’s closet before a new year at school. The platform would allow the user to make choices on what they intend to do, and what they are looking for (like, a particular type of clothing item), and how far out of their comfort zone do they want to go. We define it as filling the gaps in the closet or extending the closet by being adventurous.
Business model
We plan to launch our platform first with big department stores like Nordstrom, Macy’s. The reason being that 1. [large department stores] already have lot of user data which gives [Wear] a good starting point, 2. they are investing in boosting their digital sales and 3). They are facing intensified competition from various international brands. We plan to then leverage the success and connect our platform with international brands. The users will get the recommendation based on algorithm and also links to the department stores where that particular item could be bought from. If a sale is activated through our platform then [Wear] gets 3% of the GMV.
In a steady state we expect to generate annual revenue of $190M. This is assuming we penetrate 10% of the $63 billion US clothing market which is serviced through e-commerce channel. The average price of an item is $19, showing that all income brackets are appropriate to target. The average per capita spend on clothing is $978 per year, but our target market spends nearly twice that, at $1,708 per year. Although, our revenue estimate uses all apparel e-commerce as our baseline, but our target customers spend more than average, so this estimate is conservative.
The demonstration
In order to demonstrate the potential success of our platform, we would compare not just the analytical power of our model but also the process that the user currently goes through to purchase new items for his/ her wardrobe. Therefore, for our test users we will have them get recommendations from three sources: select a piece of clothing they want to add to their wardrobe themselves, have a sales agent at a store such as Nordstrom give them a recommendation, and have our product give them a recommendation. For each of the three scenarios, we will test the inputs to the user, and how the user reacts to each of them. For inputs to the user, we will judge what inputs and data sources are used by each of the methods, and what information is actually provided to the user and in what form. We think that part of the reason we are differentiated is our process of asking about the user’s personality outside of their wardrobe, which other sources often may not. On the output side, we will judge the user’s reaction in terms of what they thought about the efficiency and convenience of the process, whether they end up purchasing the recommended apparel, and whether they valued the experience enough to return/ recommend it.
The funding
We are looking to raise $750k at this stage. These funds will be used for building the product MVP, acquiring 500k users and one big department store as customer by Q4 2018.
Sources:
https://www.statista.com/topics/965/apparel-market-in-the-us/