Challenge  I am a product manager of a tech SaaS company, am responsible for laying the roadmap of the  product in the right direction which gives value to the app owners (our customers) and helping  app owners get insights. We built software that plugs into any mobile app to collect data to  answer questions like: Why are users uninstalling the app? , Why are users exhibiting a  negative behaviour? (deleting their feed, abandoning cart without purchasing after adding it,  discarding ). The challenge is that the data collected automatically is not sufficient at times for  app owners to validate their assumptions in answering 'why' though it identified the problem on  where people are exhibiting a negative behaviour and how many are doing so.(Eg: People who  add an item to the cart and don't make a purchase - 'Where' - After adding to the cart? - 'How  many' - 'x' number - 'Why?' - Unknown). The challenge was on me to design a solution that  helps app owners get closer to the 'Why' in which performance data(Network,CPU usage,  RAM), Usability(Taps, DoubleTaps, Fling, Swipe) and behaviour(Add To Cart, Not Making A  Purchase) is not helping to find an answer to validate assumptions.    Selection  We have already gone through several design thinking activities/tools, so we are ready to try the  learning launch tool to test the idea and prototype quickly.    Application  We thought the user feedback interspersed with the data we collect would solve the problem in  answering the 'why' question and I've started working on contextual mobile app survey design  that asks the user for feedback right at the time he exhibits a negative behaviour.    For our first learning launch, we gathered the assumptions on the type of audience and  preferences for a social app that allowed users and sought permission to talk to their users who  are using the app for more than 30 days where I called 100 of them. Guess what? It blew my  mind. The social app owners assume their audience to be urban - college going kids - aged 21  to 24 - who use the app to seek entertainment when they are bored while the reality was the  users where tier 2,3 users who are daily workers in the rural population who use them every day  at work who installed the app via sharing. I was amazed that they don't use youtube to consume  video content or install the app from the official play store which helped the social app  understand how tier 2,3 users consume content from the research. I said 'WOW' the potential  for user feedback is enormous and we quickly shipped our first version of surveys after ensuring  that putting all the data together makes sense as we are a startup and discarding on the  whiteboard is less costly involving only my time while 10X costly to ship it out.    Then for second learning launch we identified another app owner who runs one of Indias largest  fitness company (like Starva app / Runtastic app), and while talking with him during our  customer research, we found out his burning problem was to understand why people who are  recording an exercise activity on their app are discarding it. Example: User starts cycling and  recording in the app after he completes cycling he is deleting the metrics recorded like - the  number of Kms they have cycled, duration of cycling, cadences, heartbeat.

Here we plugged in our contextual mobile surveys where they could launch a question asking.  "What was the reason to discard the activity ?"  - was it by mistake, inaccurate data recorded,  GPS not tracking activity, selected wrong activity mode or they did not do it like this  https://www.youtube.com/watch?v=V5BQnifIwA0    It worked well in scale launched to 1000+ users, and we got the data in a day which helped  them fix a severe GPS issue in the app which recorded the map different from user expectation.    Approach  After selecting learning launch as the appropriate tool, we wanted to iterate quickly on our  assumptions.    #1 User data would be helpful when interspersed with the data we collect   Validated: true   With the social app we worked with and to identify pain points like few users didn't have the  context, It took a week of time and much effort and is not scalable. A lot of manual effort to be  put and chances of human interpretation and error are high as well with an increase in the  volume of users.    #2 Asking a survey at the right time while using the app would have good response rates to  validate quantitatively as well  Validated: true  With the fitness apps when they tried calling first a few users didn't even remember they have  deleted a recording in the previous week so with the surveys we have built, they were asked at  the moment of performing the action which captured sentiments well and is scalable. It had a  response rate of more than 50% which is mainly good data.  Learnings:   Few users closed the app as they got a survey? How can it be less obtrusive?  Few users were asked to enter text after the survey where the response rate was low? How do  we improve this?    With feedback from iterations, we were quickly able to ship and improve the product getting  near to solving the problem of 'Why' app users exhibit negative behaviour. It gave immense  value to the customer solving his problem and gave us the questions we need to focus on to  make it better.

