One of Quinn’s main objectives is to get users to consistently come back to our app and browse clothing, not only in cases when they need immediate help finding an new outfit, but because they enjoy their recommendation feed.
One milestone that would closely correlate to “users have successful search results and tailor content feed” is the # of outfits saved. More specifically, the ration of # of outfits saves compared to the number of outfits browsed in search/feed. We can also look at how often the user actually goes to revisit these saved items and how many conversions we get to the purchasing links for items in the saved outfits.
Anothe key result we are looking to acheive is hitting a critical mass of users that would get big clothing brands to notice. For example, if we got 2% of Adidas target customers on our app, then we could approach Adidas and potentially get them to pay for ad slots in our search results and feed recommendations. Since this is our main method of monetization, it’s really important to actually focus on growing the platform’s user base.
