Reflection : Learning Launch 

 Challenge : 

 How do we drive the adoption and use of self - service on digital channels like Apps and Bots to reduce   calls to our contact centre and still ensure that customer satisfaction measured by a high NPS ( Net   Promoter Score ) is attained ? 

 My team and I faced that question as we looked to increase the penetration and usage of digital   channels to self - serve to reduce the number of inbound calls to the call centre . 

 As a team , we have been introducing new channels over the past few years and despite the growth in   these channels , we needed to increase the usage and ability of the customers to resolve their issues by   themselves without having to call or contact a live agent by introducing a chat bot . 

 The use of chat bots within our market was not that common and very few corporates had adopted this   as a means to serve customers . We therefore needed to do something different to get customers   engaged and to use the chat bot to resolve the issues especially for simple and repetitive queries . 

 The current penetration stood at 40 % , we needed to get this to 60 % within a period of one year . We felt   this was a high target but we knew we could do it- if we asked the right questions and got the right   solution together with our customers . 

 Selection : 

 Having reviewed the different tools , we chose to go with the Learning Launch , as the purpose was to   learn and then improve through running experiments and gathering insights and feedback from real   customers .   The learning launch gave us an opportunity to test , learn and improve on the chat bot   service so we could scale it up to drive engagement and usage with the customers . 

 Application : 

 We started the process by first developing a working prototype accessible to customers on an   application they already use to minimize any barriers to them testing the chat bot service . 

 As the team was already working in agile , we were able to develop this within a couple of sprints and   ensured the integrations to internal systems were working well . 

 For the first phase of the learning launch , we engaged a number of customers using our online platforms   and informed them we were testing the chat bot service and needed them to give us feedback and   suggestions on services and features they wanted . We ran this for a period of 3 weeks and collected the   feedback both online and within the bot . 

 After reviewing the feedback from the users and suggestions on what we could do to improve the   service , we spent some more time in development and worked on the quick wins we identified from the   insights and road mapped the items that were more complex and required more time to integrate . 

 We then took an updated prototype to the customers and observed usage of some of the new features   as well as received additional feedback and this helped firm up the preferred customer journey and   method to engage with the customers using the chat bot before the full launch with a more aggressive   Go to Market campaign . 

 Insight : 

 From the learning launch model , we learnt the need to let go of some of the things we thought would   work and was relevant to us but was not that relevant to the customer . We also got additional insights   on the importance of personalization where the customers want the bot to greet them using their own   names and to have additional features that are not necessarily linked to self - service . This insight helped   us introduce brainteasers and riddles to the bot and increased the relatability with especially the   younger users who are the majority of the customers using digital platforms . 

 These insights together with the others picked during the learning launch phases really helped us launch   a chat bot that stood out from the other corporates and enabled us to achieve the desired growth in   penetration . 

 Reviewing the suggestions and feedback continuously from the customers and acting on it is necessary   for the long - term success of any product . 

 Approach : 

 From the experience we had as a team and a better understanding of Design Thinking through   additional training , we would still use the Learning Launch tool as this enables us to test a hypothesis ,   experiment and pick learnings that will create more value . 

 We would however refine the questions we were asking to get deeper and better insights that would   enable us develop more innovative features that will drive repeat use by the customers and higher   engagement as we meet the business objective to increase self - service usage . 


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: Reflection : Learning Launch 

 Challenge : 

 How do we drive the adoption and use of self - service on digital channels like Apps and Bots to reduce   calls to our contact centre and still ensure that customer satisfaction measured by a high NPS ( Net   Promoter Score ) is attained ? 

 My team and I faced that question as we looked to increase the penetration and usage of digital   channels to self - serve to reduce the number of inbound calls to the call centre . 

 As a team , we have been introducing new channels over the past few years and despite the growth in   these channels , we needed to increase the usage and ability of the customers to resolve their issues by   themselves without having to call or contact a live agent by introducing a chat bot . 

 The use of chat bots within our market was not that common and very few corporates had adopted this   as a means to serve customers . We therefore needed to do something different to get customers   engaged and to use the chat bot to resolve the issues especially for simple and repetitive queries . 

 The current penetration stood at 40 % , we needed to get this to 60 % within a period of one year . We felt   this was a high target but we knew we could do it- if we asked the right questions and got the right   solution together with our customers . 

 Selection : 

 Having reviewed the different tools , we chose to go with the Learning Launch , as the purpose was to   learn and then improve through running experiments and gathering insights and feedback from real   customers .   The learning launch gave us an opportunity to test , learn and improve on the chat bot   service so we could scale it up to drive engagement and usage with the customers . 

 Application : 

 We started the process by first developing a working prototype accessible to customers on an   application they already use to minimize any barriers to them testing the chat bot service . 

 As the team was already working in agile , we were able to develop this within a couple of sprints and   ensured the integrations to internal systems were working well . 

 For the first phase of the learning launch , we engaged a number of customers using our online platforms   and informed them we were testing the chat bot service and needed them to give us feedback and   suggestions on services and features they wanted . We ran this for a period of 3 weeks and collected the   feedback both online and within the bot . 

 After reviewing the feedback from the users and suggestions on what we could do to improve the   service , we spent some more time in development and worked on the quick wins we identified from the   insights and road mapped the items that were more complex and required more time to integrate . 

 We then took an updated prototype to the customers and observed usage of some of the new features   as well as received additional feedback and this helped firm up the preferred customer journey and   method to engage with the customers using the chat bot before the full launch with a more aggressive   Go to Market campaign . 

 Insight : 

 From the learning launch model , we learnt the need to let go of some of the things we thought would   work and was relevant to us but was not that relevant to the customer . We also got additional insights   on the importance of personalization where the customers want the bot to greet them using their own   names and to have additional features that are not necessarily linked to self - service . This insight helped   us introduce brainteasers and riddles to the bot and increased the relatability with especially the   younger users who are the majority of the customers using digital platforms . 

 These insights together with the others picked during the learning launch phases really helped us launch   a chat bot that stood out from the other corporates and enabled us to achieve the desired growth in   penetration . 

 Reviewing the suggestions and feedback continuously from the customers and acting on it is necessary   for the long - term success of any product . 

 Approach : 

 From the experience we had as a team and a better understanding of Design Thinking through   additional training , we would still use the Learning Launch tool as this enables us to test a hypothesis ,   experiment and pick learnings that will create more value . 

 We would however refine the questions we were asking to get deeper and better insights that would   enable us develop more innovative features that will drive repeat use by the customers and higher   engagement as we meet the business objective to increase self - service usage .
### Output: 