Example reflection – Learning Launch 

 Challenge 

 I am member of a commercial team for a Telecom operator . One of our subsidiaries in an Asian market   faced the following challenge about their tariff portfolio : they already had S- , M- and L - versions of a   specific tariff with a good number of customers sitting on the L - version of the product . However , within   customers of that top product there was a significant share using even much more data and voice calls   than what the package included , making them pay more than the original tariff promised .   Facing this   situation , teams were undecided how to proceed , one group wanted to maintain the status quo ,   another to introduce an XL - version of the tariff which would offer ( much ) more value for ( moderately )   more money . 

 Selection 

 We needed to trial and chose the Learning Launch tool to tackle the issue for various reasons : we felt we   had an idea how to solve the dilemma but there was a lot of money involved and we needed to make   sure we got this right . What ’s more , we had sceptics within our own group that needed to be convinced ,   they also provided a good shield against confirmation bias , and we knew the Learning Launch Tool   profited from having sceptics in the project group . 

 Application 

 Our hypothesis was that the overpaying L - version customers would migrate to an XL - version of the tariff   if we designed it right . Our key assumption was they were looking out for a bigger package anyway , and   that would increase loyalty and tenure with us , which would overcompensate the short - term revenue   dip . We knew this was a “ must be true ” assumption . We designed a paper version of the XL tariff with   much more data and call allowances for a moderately higher price . We selected app . 100 overpaying L- customers that we picked randomly , conducted interviews , and asked them about preferences and   choices between the L and the XL versions . We made sure to include the sceptics prominently in the   testing process to uncover potential disconfirming facts . 

 Insight and approach 

 In general , our key hypothesis was confirmed . The majority of that particular customer group favored   the XL version ; some were suggesting this should have been done way back . They would happily pay   more for receiving more value . They also rated their satisfaction with the potential XL version higher   indicating a higher tenure and reduced churn . 

 We also found some disconfirming facts that helped us to apply the solution in a smarter way for the   rollout . Overpaying customers who still liked the L - version better than the XL - version had a certain social 

 profile where they preferred flexibility to affordability and we were able so learn that subtle pattern and   were able to exclude those customers from active communication . 

 As a result , the XL version was launched successfully and a large share of the formerly overpaying L- customers migrated to it plus it attracted also high end customers from the competition that we were   not able to attract before at all . 

 The beauty of the Learning Launch tool was that it eventually combined the inputs and beliefs of the   proponents and the sceptics into a best - case solution . The main hypothesis of the proponents was   confirmed and created lots of value for us and the customers , and there was also truth in the sceptics   view which was instrumental to come up with a differentiated communication strategy for that 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: Example reflection – Learning Launch 

 Challenge 

 I am member of a commercial team for a Telecom operator . One of our subsidiaries in an Asian market   faced the following challenge about their tariff portfolio : they already had S- , M- and L - versions of a   specific tariff with a good number of customers sitting on the L - version of the product . However , within   customers of that top product there was a significant share using even much more data and voice calls   than what the package included , making them pay more than the original tariff promised .   Facing this   situation , teams were undecided how to proceed , one group wanted to maintain the status quo ,   another to introduce an XL - version of the tariff which would offer ( much ) more value for ( moderately )   more money . 

 Selection 

 We needed to trial and chose the Learning Launch tool to tackle the issue for various reasons : we felt we   had an idea how to solve the dilemma but there was a lot of money involved and we needed to make   sure we got this right . What ’s more , we had sceptics within our own group that needed to be convinced ,   they also provided a good shield against confirmation bias , and we knew the Learning Launch Tool   profited from having sceptics in the project group . 

 Application 

 Our hypothesis was that the overpaying L - version customers would migrate to an XL - version of the tariff   if we designed it right . Our key assumption was they were looking out for a bigger package anyway , and   that would increase loyalty and tenure with us , which would overcompensate the short - term revenue   dip . We knew this was a “ must be true ” assumption . We designed a paper version of the XL tariff with   much more data and call allowances for a moderately higher price . We selected app . 100 overpaying L- customers that we picked randomly , conducted interviews , and asked them about preferences and   choices between the L and the XL versions . We made sure to include the sceptics prominently in the   testing process to uncover potential disconfirming facts . 

 Insight and approach 

 In general , our key hypothesis was confirmed . The majority of that particular customer group favored   the XL version ; some were suggesting this should have been done way back . They would happily pay   more for receiving more value . They also rated their satisfaction with the potential XL version higher   indicating a higher tenure and reduced churn . 

 We also found some disconfirming facts that helped us to apply the solution in a smarter way for the   rollout . Overpaying customers who still liked the L - version better than the XL - version had a certain social 

 profile where they preferred flexibility to affordability and we were able so learn that subtle pattern and   were able to exclude those customers from active communication . 

 As a result , the XL version was launched successfully and a large share of the formerly overpaying L- customers migrated to it plus it attracted also high end customers from the competition that we were   not able to attract before at all . 

 The beauty of the Learning Launch tool was that it eventually combined the inputs and beliefs of the   proponents and the sceptics into a best - case solution . The main hypothesis of the proponents was   confirmed and created lots of value for us and the customers , and there was also truth in the sceptics   view which was instrumental to come up with a differentiated communication strategy for that product .
### Output: 