Sign - in experiments – Learning Launch       Challenge   I am the owner of a mobile application which is a free application and has features which   can work without login and also some features which require login . Even though a lot of   features in the App can work without login ( sign - in ) but we still wanted to get users logged   in into our App . My challenge was to get maximum number of users signed in into the App   but without impacting the App ’s play store ratings . After getting users signed in , there ’s a lot   product management can do by tracking the users behaviors and interests . We had about a   period of 3 months to come up with the best optimal of way of driving sign - ins . The intent   was to present user with the Sign in screen on App launch itself but which design and which   behavior would work the best was still unknown .       Selection   Since we were doing sign in on launch for the first time and also we had to keep the App   ratings also intact , learning launch via experiments became an obvious choice for us . The   learning launch is an experiment meant to quickly test and prototype .       Application   We came up with different variants of the Sign in screen – mandatory sign - in , optional sign- in , optional sign - in with a more subtle cancel option and variants of all these with and   without social sign in options . Over a period of 3 months , we rolled out each experiment for   about a week to funnel the path users are taking till they eventually sign in and to see how   many sign in success and failures are there . Also , which type of sign in options users are   using the most – corporate login , Google login or Facebook login .      Each of these experiments were run in exclusion to get clear idea about which variant   results in maximum success . App rating was another factor . We had to make sure that when   that experiment variant was live , then there was no negative impact on the App ratings .        Insight   The above exercise help us quickly launch one variant into the market , track how its   progressing and on getting those results , iterating over the next variant . Each variant helped   us learn something different about users behaviours . Social sign in options were a win but   the most interesting result that came out of the experiment was that enforcing mandatory   sign in to the users was quite annoying for them , whereas optional sign in worked and that   too the optional sign in with more subtle option was the winner .     Approach   Given that our team has already built this set up of running experiments quickly and   analyzing the results achieved by the same and quickly iterating over the next one , the   learning launch tool is appropriate for us for coming with best possible solution for a given   problem . We are now thinking of applying the same approach to solve the challenge of 

 driving subscription of services via our mobile application and which App entry points or   triggers can help us with getting maximum subscriptions . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Sign - in experiments – Learning Launch       Challenge   I am the owner of a mobile application which is a free application and has features which   can work without login and also some features which require login . Even though a lot of   features in the App can work without login ( sign - in ) but we still wanted to get users logged   in into our App . My challenge was to get maximum number of users signed in into the App   but without impacting the App ’s play store ratings . After getting users signed in , there ’s a lot   product management can do by tracking the users behaviors and interests . We had about a   period of 3 months to come up with the best optimal of way of driving sign - ins . The intent   was to present user with the Sign in screen on App launch itself but which design and which   behavior would work the best was still unknown .       Selection   Since we were doing sign in on launch for the first time and also we had to keep the App   ratings also intact , learning launch via experiments became an obvious choice for us . The   learning launch is an experiment meant to quickly test and prototype .       Application   We came up with different variants of the Sign in screen – mandatory sign - in , optional sign- in , optional sign - in with a more subtle cancel option and variants of all these with and   without social sign in options . Over a period of 3 months , we rolled out each experiment for   about a week to funnel the path users are taking till they eventually sign in and to see how   many sign in success and failures are there . Also , which type of sign in options users are   using the most – corporate login , Google login or Facebook login .      Each of these experiments were run in exclusion to get clear idea about which variant   results in maximum success . App rating was another factor . We had to make sure that when   that experiment variant was live , then there was no negative impact on the App ratings .        Insight   The above exercise help us quickly launch one variant into the market , track how its   progressing and on getting those results , iterating over the next variant . Each variant helped   us learn something different about users behaviours . Social sign in options were a win but   the most interesting result that came out of the experiment was that enforcing mandatory   sign in to the users was quite annoying for them , whereas optional sign in worked and that   too the optional sign in with more subtle option was the winner .     Approach   Given that our team has already built this set up of running experiments quickly and   analyzing the results achieved by the same and quickly iterating over the next one , the   learning launch tool is appropriate for us for coming with best possible solution for a given   problem . We are now thinking of applying the same approach to solve the challenge of 

 driving subscription of services via our mobile application and which App entry points or   triggers can help us with getting maximum subscriptions .
### Output: 