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 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.

Last year we have launched Glanse - an iPhone app that simplifies shopping on sale. 
Here's how: 
1- We aggregate women's clothes on sale from 1,500 brands (Anywhere from low end to backend)
2- Ask users for some criteria such as sizes & budget
3- Show them one item at a time, if users swipe right it will add it to their wish list, if users swipe left it will dismiss the item 

4- We send you a summary of the best items on your wish list as a digest to your email, you can shop straight from your inbox without going back to the app.
What do brands get?
-We generate reports based on users' swipes, such as:

The goal of the report is to help brands:
1- Relevancy to customers and brand discovery
2- Learn about customers/ potential customers. The issue with traditional web browsing, or looking at a single brand, is that it limits brand to monotonous data. We show what other items your customer is looking at or buying.
3- On a product level, we show you the ratio of likes vs. dislikes for each item. 
Our vision is to provide brands with bottom-up data analysis on each user  that will act as an ID with a taste graph, which will help brands with accurate retargeting for eCommerce advertising.
Imagine having access to everything you need to know about a customer's taste and shopping behavior including  sizes, spending behavior and purchase locations. Imagine how accurate your ads would be, promoted items won't be commercial anymore, because there will be less effort put into convincing, since you're already ahead of the game with the info you have about each user.
If you're in the space & have some thoughts or want to use our platform please feel free to email me at evelyn[at]Glanse[dot]co