Reflection – Learning Launch 

 Challenge 

 I work in the digital solutions department of my organization / bank . We focus on delivering digital   services to our customers in a time efficient and cost - effective manner . The customer can still avail all   of the services that a bank would offer such as paying bills , transferring money , checking balance etc   but through the digital channel – the mobile banking app . 

 I was working on one of the candidate processes , for digitization , in the legacy system . Looking at the   current data and volumes , we could tell that a number of customers were reached out to avail this   ‘ confirmation service ’ through either phone or SMS . The hypothesis was , that if the same confirmation   is sought through the digital channel , it can improve customer response rate and at the same time   decrease the load on call centres . 

 Selection 

 Although we had the data and volumes for the legacy approach , but this was not a guarantee that the   service would appeal to the customers on the digital app as much and bring the benefits and revenue   that we expected . 

 Therefore , we had to test the hypothesis , in the real market place , in a cheap and fast way – learning   launch was just the right tool ! As suggested in the lecture by Kevin Born in the design thinking course ,   the learning launch tool would allow us to gather the right quantity and quality of data from a set of   carefully selected customers , which we can then analyse of confirming and disconfirming facts and   decide to either pivot or shelf this idea . 

 Application 

 We quickly shaped the idea into a basic design through visualization tools and drafted a UI . We came   up with a basic prototype with least complexity . 

 We then selected a set of customers to test our hypothesis . The customers had to be very carefully   selected in order to get the right quantity and quality of data . The group was around 20 customers .   Diversity was the key . We had customers from different age groups and varied skills at using apps and   digital services . 

 During the test exercise , the customers had to go through the prototype as if they were using the   service . They would then rate the service , as well as take a survey , to rate the different features and   options for the same service . 

 The data that was then collected was analysed by the cross functional team and decision makers from   across different departments to exclude any bias and to evaluate the confirming and disconfirming   facts to determine the best way forward . 

 Insight 

 The analysis of the data gathered through learning launch showed us that that idea was more likely to   succeed . Majority of the customers felt it was more convenient as well as safe to rely on the digital   version of the service . There were a lot of confirming facts – ease of use , time efficiency , safety and   security . 

 However , there were a few suggestions and areas for improvement in the way the information was   presented to the user/ user interface . These should be addressed and accommodated for a successful   final service / product . 

 Approach 

 As the results from the learning launch were promising , the next step would be to accommodate the   customers feedback and suggestions into the next prototype in a quick and cost - effective way , and   run it past a set of customers to evaluate it – learning launch phase 2 . This will help us shape and   deliver a successful final product . 


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 

 I work in the digital solutions department of my organization / bank . We focus on delivering digital   services to our customers in a time efficient and cost - effective manner . The customer can still avail all   of the services that a bank would offer such as paying bills , transferring money , checking balance etc   but through the digital channel – the mobile banking app . 

 I was working on one of the candidate processes , for digitization , in the legacy system . Looking at the   current data and volumes , we could tell that a number of customers were reached out to avail this   ‘ confirmation service ’ through either phone or SMS . The hypothesis was , that if the same confirmation   is sought through the digital channel , it can improve customer response rate and at the same time   decrease the load on call centres . 

 Selection 

 Although we had the data and volumes for the legacy approach , but this was not a guarantee that the   service would appeal to the customers on the digital app as much and bring the benefits and revenue   that we expected . 

 Therefore , we had to test the hypothesis , in the real market place , in a cheap and fast way – learning   launch was just the right tool ! As suggested in the lecture by Kevin Born in the design thinking course ,   the learning launch tool would allow us to gather the right quantity and quality of data from a set of   carefully selected customers , which we can then analyse of confirming and disconfirming facts and   decide to either pivot or shelf this idea . 

 Application 

 We quickly shaped the idea into a basic design through visualization tools and drafted a UI . We came   up with a basic prototype with least complexity . 

 We then selected a set of customers to test our hypothesis . The customers had to be very carefully   selected in order to get the right quantity and quality of data . The group was around 20 customers .   Diversity was the key . We had customers from different age groups and varied skills at using apps and   digital services . 

 During the test exercise , the customers had to go through the prototype as if they were using the   service . They would then rate the service , as well as take a survey , to rate the different features and   options for the same service . 

 The data that was then collected was analysed by the cross functional team and decision makers from   across different departments to exclude any bias and to evaluate the confirming and disconfirming   facts to determine the best way forward . 

 Insight 

 The analysis of the data gathered through learning launch showed us that that idea was more likely to   succeed . Majority of the customers felt it was more convenient as well as safe to rely on the digital   version of the service . There were a lot of confirming facts – ease of use , time efficiency , safety and   security . 

 However , there were a few suggestions and areas for improvement in the way the information was   presented to the user/ user interface . These should be addressed and accommodated for a successful   final service / product . 

 Approach 

 As the results from the learning launch were promising , the next step would be to accommodate the   customers feedback and suggestions into the next prototype in a quick and cost - effective way , and   run it past a set of customers to evaluate it – learning launch phase 2 . This will help us shape and   deliver a successful final product .
### Output: 