Example Reflection – Learning Launch Tool 

 Challenge & Selection The Learning Launch Tool is a powerful tool that I used together with my team to design and   implement a web - based customer awareness tool in the context of life insurance business . Life   insurance – and especially the disability insurance segment – is a difficult field in the sense that   customers often do not self - drivenly want to deal with this topics . Furthermore , the target   customer groups vary from professional beginners over young - married to mid - agers .   Consequently , we did not know exactly what kind of approach we should use to pick up the   customer needs at the right place . The range of hypothesis and assumptions was large and the   option space was broad and heterogeneous . 

 To tackle these challenges we decided to choose an iterative prototyping approach which uses the   Learning Launch techniques of validating ideas and assumptions to hit customer needs . 

 Application First of all , we set up an interdisciplinary team where we put together business experts like sales   experts , product managers , marketing experts as well as IT experts . We also tried to vary the ages   and years of experiences of the team members to garantuee a variety of hypothesis and reduce   the risk of confirmation biases within the group . 

 Our ideation phase to create hypothesis statements was based basically on design thinking tools :   We defined so - called „ personas “ - i.e. archetypes representing the most important customer   groups - and sketched the „ as - is “ customer journey to stimulate the creative process taking into   account the broad range of customers and their pain points . For stating our key assumptions on   the customer needs , we derived from our „ as is “ journey the „ should be “ customer journey   identifying the gaps and , therefore , the potential value creation . As we wanted to implement a   customer awareness tool we focused on the first phase of the customer journey . 

 To prioritize our key assumptions , we consulted market research results on customer needs and   our own t - NPS results . „ t - NPS “ stands for customer touchpoint - based net promoter scores we   evaluated over time . The quantity of data was of a significant order of magnitude . The NPS score   allowed us to focus on the most painful customer touchpoints . We also take into account some   financial aspects in the prioritization process by conducting a business leakage analysis to identify   the most rewarding measures . Some of our key assumptions were for example „ The customer does not like to share personal information on his health and constitution with an anonymous interview   partner . “ or „ Purchasing a disability insurance product relies on trust . “ . 

 To test our key assumptions we designed an iterative prototyping approach with prototype   releases on a regular basis . Every iteration consisted of a certain set of features as known from the   common agile approaches e.g. scrum sprints . For example , our first prototype was a simple „ click   dummy “ raising some basic features for the awareness tool . After every iteration cycle we   conducted customer acceptance tests and interviews to confirm or disconfirm our feature   implementations representing our assumptions . Depending on the results we a ) further developed and refined our feature set and/or b ) pivoted our scope by changing the feature set and/or c )   dismissed some features . 

 Finally after several iteration cycles , we created a simple , but insightful and customer - centric click- 

 by - click awareness tool which is still online on our web page . 

 Insight & Approach From my point of view , the strength of the Learning Launch Toolset is based on the following two   aspects : Firstly , we were forced to continuously questioning our hypotheses by means of „ hard   facts “ ( quantitative data , customer feedback , etc . ) and a clear and transparent process . Secondly ,   this approach teached us to discard tons of ideas . Especially in the field of unknown customer   behaviors , this is a really strong approach to create reliable ideas and to mitigate the risk of failure . 

 But of course , we faced some challenges : Providing suitable data and un - biased statistics is   demanding ! Since ( quantitative and qualitative ) data is key for identifying the „ must be trues “ , you   have to spend a lot of effort in supply these data . For example , the evaluation of the NPS scores is   costly since they depend on customer interviews – each of them taking at least a couple of   minutes . Next time , I would propably try to reduce the number of customer groups in advance to   reduce the uncertainty of our assumptions . 


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: Example Reflection – Learning Launch Tool 

 Challenge & Selection The Learning Launch Tool is a powerful tool that I used together with my team to design and   implement a web - based customer awareness tool in the context of life insurance business . Life   insurance – and especially the disability insurance segment – is a difficult field in the sense that   customers often do not self - drivenly want to deal with this topics . Furthermore , the target   customer groups vary from professional beginners over young - married to mid - agers .   Consequently , we did not know exactly what kind of approach we should use to pick up the   customer needs at the right place . The range of hypothesis and assumptions was large and the   option space was broad and heterogeneous . 

 To tackle these challenges we decided to choose an iterative prototyping approach which uses the   Learning Launch techniques of validating ideas and assumptions to hit customer needs . 

 Application First of all , we set up an interdisciplinary team where we put together business experts like sales   experts , product managers , marketing experts as well as IT experts . We also tried to vary the ages   and years of experiences of the team members to garantuee a variety of hypothesis and reduce   the risk of confirmation biases within the group . 

 Our ideation phase to create hypothesis statements was based basically on design thinking tools :   We defined so - called „ personas “ - i.e. archetypes representing the most important customer   groups - and sketched the „ as - is “ customer journey to stimulate the creative process taking into   account the broad range of customers and their pain points . For stating our key assumptions on   the customer needs , we derived from our „ as is “ journey the „ should be “ customer journey   identifying the gaps and , therefore , the potential value creation . As we wanted to implement a   customer awareness tool we focused on the first phase of the customer journey . 

 To prioritize our key assumptions , we consulted market research results on customer needs and   our own t - NPS results . „ t - NPS “ stands for customer touchpoint - based net promoter scores we   evaluated over time . The quantity of data was of a significant order of magnitude . The NPS score   allowed us to focus on the most painful customer touchpoints . We also take into account some   financial aspects in the prioritization process by conducting a business leakage analysis to identify   the most rewarding measures . Some of our key assumptions were for example „ The customer does not like to share personal information on his health and constitution with an anonymous interview   partner . “ or „ Purchasing a disability insurance product relies on trust . “ . 

 To test our key assumptions we designed an iterative prototyping approach with prototype   releases on a regular basis . Every iteration consisted of a certain set of features as known from the   common agile approaches e.g. scrum sprints . For example , our first prototype was a simple „ click   dummy “ raising some basic features for the awareness tool . After every iteration cycle we   conducted customer acceptance tests and interviews to confirm or disconfirm our feature   implementations representing our assumptions . Depending on the results we a ) further developed and refined our feature set and/or b ) pivoted our scope by changing the feature set and/or c )   dismissed some features . 

 Finally after several iteration cycles , we created a simple , but insightful and customer - centric click- 

 by - click awareness tool which is still online on our web page . 

 Insight & Approach From my point of view , the strength of the Learning Launch Toolset is based on the following two   aspects : Firstly , we were forced to continuously questioning our hypotheses by means of „ hard   facts “ ( quantitative data , customer feedback , etc . ) and a clear and transparent process . Secondly ,   this approach teached us to discard tons of ideas . Especially in the field of unknown customer   behaviors , this is a really strong approach to create reliable ideas and to mitigate the risk of failure . 

 But of course , we faced some challenges : Providing suitable data and un - biased statistics is   demanding ! Since ( quantitative and qualitative ) data is key for identifying the „ must be trues “ , you   have to spend a lot of effort in supply these data . For example , the evaluation of the NPS scores is   costly since they depend on customer interviews – each of them taking at least a couple of   minutes . Next time , I would propably try to reduce the number of customer groups in advance to   reduce the uncertainty of our assumptions .
### Output: 