Visual Fashion Data Analysis

In this post I’ll demonstrate how we turn app gestures made by users into data points that help us understand trends. Data points are projected from an app called GLANSE that aggregates clothes on sale from 1,500 brands. The data is extremely useful because it compares a lot of brands against each other through geo-tags.
The data below shows last week’s items swiped through by our users broken into brands. The color green means that a transaction has occurred.
Even though some brands are more swiped through than others, like JCrew Factory, tahari actually had more purchases (as indicated by dark green).
This graph helps us understand & segment our users. The top 3 viewed retailers are {Anthropologie, Asos, & J.Crew Factory}. The top retailers purchased from are {Tahari, Asos & Urban Outfitters}. Asos is definitely a common factor here, which holds a lower price point and is favorited by a median of age of 24. Urban Outfitters targets early 20’s & Tahari is on the higher end.

​The following graph demonstrates the number of items swiped through geographically in the top 7 countries over a time interval. This clearly indicates that top engaged users are concentrated in the US & then Canada. While we only focus on US users, we might want to keep an eye on other regions like Mexico, India and Russia — that have a growing number of swipes.

The graph below shows the categories liked over a year. The search for dresses peaked before Christmas & then after January, because of the excess discounted inventory that brands wanted to clear after overbuying for the holidays season. Then again in April users shopped the Summer collection & in July because of the sales season (Semi-annual sale).

​The Graph below demonstrates the number of times users clicked on “Buy” according to color throughout 2 quarters. You can see that there’s an increase in black, brown, and a decrease in beige, white, red, purple & pink. Lighter/ colorful colors were more popular in Q2 because that’s when users shopped for the summer. In Q3 some users start shopping for fall so that explains the slight increase in darks & decrease in colorful & lighter colors.

​To collaborate on historical data points & work with us on forecasting data visit us at GLANSE or email me. We can help you get detailed insights into how your brand compares to others per age group, geography, and many other parameters. We can even tell you which specific items are the most popular.