Tinder and Automated Analytics

Earlier this week as I was logging entry number 3000 into my Tinder statistics I found myself going a bit up in levels of abstraction. The type of work I’m doing with the Tinder statistics is very detailed and looks into specific workings of the algorithms and systems that Tinder uses in order to make the app work. One of the recent developments is a little piece of legislation called the Global Data Protection Regulation (GDPR for short) that gives people the right to request the data that companies like Tinder and Facebook have collected on them. Thanks to this, a gentleman was able to request all the data Tinder had on him, and as a side-effect we figured out what data they track on every user.

The classical personal information such as name, email, age, and age bracket selected is not very interesting, what is interesting is that they track among others:

  • Your swipes (Right and left)
  • How many people who swipe left and right on you
  • Matches
  • Messages sent and received
  • Profile completeness

The reason why I found this interesting, is that if we start to reverse engineeer how Tinder works, those tracked data are the perfect tool for it. Odds are that Tinder also stores other “match related” things such as time spent looking at a profile before swiping, how many of the people you match with you actually have a conversation with, we know they track how often you get reported, and so on. This becomes important later.

If we think of Tinder and other apps as a bar, we all know what a bar has to do in order to do well (sportsbars and cigarbars excluded), and that is get a lot of hot girls in the door. If your bar has hot girls, the men will follow and buy them drinks. The girls are attracted to “how cool the bar is” the guys are attracted to “how hot the girls who think the bar is cool” are. From this perspective, we can thus outline the 3 major success criteria for Tinder:

A) Keep the girls happy

B) Maximize your user base

C) Keep the men around

Just like a bars revenue is based on a mixture of cover charges and drink sales, Tinder’s revenue is based on advertising, subscriptions to Plus and Gold, in addition to sales of consumables such as superlikes and boost. In the night club analogy, sending a girl a super-like is the equivalent of sending a girl a drink, a boost is equivalent to the club promoter shaking your hand and taking you to your reserved table with bottle service.

However, the most important man in the bar is the doorman, you see, his job is to:

A) Maintain a good mix of men and women

B) Keep the creeps out

C) Get rid of any troublemakers.

A bar without a solid doorman rapidly becomes a very unpleasant place to be. In the same way, Tinder has to walk a fine line between maintaining their female user-base, maximizing their user-base in general and maximizing the revenue from subscriptions and consumables (which I assume are mostly bought by men).  How does Tinder do this?

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