Amaze, The ‘Tinder For Fashion’, Brings Fashion Data To A New Level


Amaze, the first mobile app built by a startup on Zalando’s new technology platform and its APIs, has been available in Germany since June 2015. (German announcement by Zalando) Now the app is available in Europe.

As has been said here before, Amaze is a great showcase for why an online fashion retailer like Zalando would want to go the API platform route. Amaze uses the swipe gesture made popular by the dating app Tinder to do several things at once, as co-founder Robert Lacher told the audience on stage at our K5 conference earlier this year1:

  1. Interface: This interaction mode allows for full screen pictures. Useful for small screens and fashion. It also allows for small sessions. The time you wait in line at the supermarket e.g. Exactly where a lot of mobile usage happens.
  2. Discovery: It as a very deliberate discovery mode.
  3. Data: Every interaction is an intentional evaluation of what one sees. (o-1: negative or positive) The data generated can be used to better serve the users and create cohorts of users for clustering for other features.

(3) means that with every swipe, every user interaction, the data gets richer and Amaze can better understand the user. This is very important. This way Amaze can learn very fast what the preferences of its users are.2 There is a positive feedback loop between (2) and (3). The better the data the more likely a given user likes what they are shown and the more likely they like the app in general and keep using it (and last not least keep buying stuff). And the cycle continues.

Amaze is right now working solely with Zalando. This essentially means that every item one can see on Amaze can be bought on Zalando and only on Zalando through Amaze. From one perspective Amaze is an alternative user interface for Zalando. From a revenue perspective it is the modern -mobile app- aquivalent to traditional affiliate partners.

One reason to confine the service to one retailer according to Lacher is that this way Amaze can offer one click shopping no matter what item and how many items people buy.

Amaze is working together with fashion bloggers for the content. There is a separate app for bloggers who upload pictures to Amaze. Amaze does revenue sharing with their bloggers.

It is a fascinating network approach.

But let’s get back to the data and swipes for one moment. It can not be overstated how valueable those numerous tiny data points are.

Here is Janel Torkington, editor for Appszoom, on Tinder-like apps:

The card-based UI updates the classic way in which we’ve always interacted with physical cards. When you think about it, cards are nothing more than bite-size presentations of concrete information. They’re the natural evolution of the newsfeed, which is useful for reading stories but not for making decisions.

The problem with newsfeeds is one of information overload. When scrolling through an endless list of options, it’s impossible to reach the end.


We can call it small data. Imagine if every time you made a yes or no decision on Tinder, the app learned what kind of profiles you tended to like, and it showed you profiles based on this information in the future.
“With swipes on Tinder, the act of navigating through content is merged with inputting an action on that content,” says Rad. That means that every time a user browses profiles, it generates personal behavioral data.
At the time of publication of this article, Tinder gets over 800 million swipes per day.

At the time of publication of this article, Amaze is at 8.283.895 swipes so far.

What kind of insights ‘small data’ makes possible in aggregate has recently been demonstrated by Foursquare. Foursquare, which lets users check-in to places, can track foot traffic data this way. It was able to predict accurately the first weekend sales of the new iPhone models this year based on previous data even before that launch weekend.

Imagine what kind of market insights a fairly successful Amaze will be able to produce in a year or two.

  1. You can find a German summarization of his talk here. (Google Translate
  2. It is like Amazon’s tracking of its customers browsing the Amazon shop, only far better suited for fashion because Amaze gets a thumps down or up for every item it shows the users. 


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