Reflection – Learning Launch 

 Challenge     After several months of creating a customer experience strategy , a small cross - functional team was   assigned the task of designing the initial engagement experience for this strategy .    Once an understanding of the customer ’s needs had been documented , the group set about generating   ideas over several ideation sessions .    The challenge that confronted the group was which of the ideas would be most effective in engaging the   target customer segment .      Selection     To deal with the challenge of identifying which ideas would be most effective , the Design Thinking tool I   leveraged was the Learning Launch Tool . As Matthias Behrens enthusiastically discusses in his lecture 18 video , this   tool encourages fast and cheap experimentation of ideas to determine if they will in fact meet the business   need , which in our case was effectively engaging our target customer segment .     Application     To mitigate confirmation bias , the first task I had the group complete was prioritising the ideas based on   the popular vote of the group . Also by selecting the top 3 ideas , it assisted in narrowing our initial focus .   For the next step , we broke off into pairs and allocated each pair one idea . Each pair designed a hypothesis   for their given idea . Each hypothesis outlined the customer need , what we could do to meet that need and   what might be the result of meeting that need .   The testing of hypotheses was executed via an organisational test and learn function with a small customer   segment . We also involved a behavioural specialist and other SMEs to provide input and advice .       Insight     One of the best insights we gained from utilising this tool was in relation to ideas that people originally   thought were ‘ gold ’ back in the ideation sessions .    When these ideas were tested the with customer segment , it became obvious that they did n’t care about   certain gimmicks or certain tactics we thought would get the desired result . This feedback lead to a   revision of the hypotheses which are being retested . It was fantastic to see the power this tool had in   changing opinions of members of the working group – not to mention the valuable customer insights it   produced !     Approach     In a situation where a new service or product is being designed , I will use the learning launch tool every   time to determine if the ‘ idea ’ has merit and is worth proceeding with . It provides results that you could   never come up with by making assumptions or by guessing . It produces results that are scientific and   factual which can not be disputed because they come from your customers in the real world . 


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     After several months of creating a customer experience strategy , a small cross - functional team was   assigned the task of designing the initial engagement experience for this strategy .    Once an understanding of the customer ’s needs had been documented , the group set about generating   ideas over several ideation sessions .    The challenge that confronted the group was which of the ideas would be most effective in engaging the   target customer segment .      Selection     To deal with the challenge of identifying which ideas would be most effective , the Design Thinking tool I   leveraged was the Learning Launch Tool . As Matthias Behrens enthusiastically discusses in his lecture 18 video , this   tool encourages fast and cheap experimentation of ideas to determine if they will in fact meet the business   need , which in our case was effectively engaging our target customer segment .     Application     To mitigate confirmation bias , the first task I had the group complete was prioritising the ideas based on   the popular vote of the group . Also by selecting the top 3 ideas , it assisted in narrowing our initial focus .   For the next step , we broke off into pairs and allocated each pair one idea . Each pair designed a hypothesis   for their given idea . Each hypothesis outlined the customer need , what we could do to meet that need and   what might be the result of meeting that need .   The testing of hypotheses was executed via an organisational test and learn function with a small customer   segment . We also involved a behavioural specialist and other SMEs to provide input and advice .       Insight     One of the best insights we gained from utilising this tool was in relation to ideas that people originally   thought were ‘ gold ’ back in the ideation sessions .    When these ideas were tested the with customer segment , it became obvious that they did n’t care about   certain gimmicks or certain tactics we thought would get the desired result . This feedback lead to a   revision of the hypotheses which are being retested . It was fantastic to see the power this tool had in   changing opinions of members of the working group – not to mention the valuable customer insights it   produced !     Approach     In a situation where a new service or product is being designed , I will use the learning launch tool every   time to determine if the ‘ idea ’ has merit and is worth proceeding with . It provides results that you could   never come up with by making assumptions or by guessing . It produces results that are scientific and   factual which can not be disputed because they come from your customers in the real world .
### Output: 