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.


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 .