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Analysis - Demo

Example of insights from a Tinder data extract.

Solid moosh

Moosh score

Note: score based on matches/months, matches/likes and conversations/first messages, all minmax normalized and averaged linearly, so it evolves with new users joining


Photogenic-thirstiness matrix

Note: as high volumes of likes reduces proportion of matches, the terms 'hot' and 'photogenic' are fallacious, but was funny to name them like that

In the most attractive 58% ...

Match per like over distribution

... and the pickiest 33%

Like per swipe over distribution

More than 3k matches!

Number of matches, cumulated

Note: timeframes since extract date

Spot the relationships

Usage, app open over years

Heavy pipeline

Funnel from swipes to convos

Note: convo carried on if >4 messages and >10 minutes between first and last message

Summer 2017 was gooood

Some records

Most swipes in a day


28 Apr 15

Most matches in a day


9 Aug 17

Longest chat (# messages sent)


26 Mar 16 - 13 Apr 16

Longest message (# characters)


21 Mar 20

Longest time off the app (# days)


22 May 16 - 2 Jun 16

Note: day considered off the app if 0 swipe recorded

Saturday fever

Messages sent by time of week, UTC

Note: time is in UTC, and making a converter was a pain in the neck, so a little convertion effort is needed if you don't live on Greenwich longitude

English english english

Language split, percentage of messages

Smooth texter

Words most used in conversations



Most used emojis, total count

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