Reflection – Learning Launch 

 Challenge      In my country , youths constitute approximately 45 % of our overall population . My employer , a   telecommunications company is challenged with the demography of its subscriber Base . A minute   fraction of their subscribers is below Age thirty , with the rest being above . Within our local   telecommunications industry , people under 30 consumed the highest volume of data for internet   connectivity . This reality stirs up a quest to attract young subscribers to our network . We are needed to   develop a product in 12 weeks . One that will appeal to the youth and make them prefer us to our   competitors ; consequently , growing a sustainable customer base , increasing our revenue , and delivering   on annual KPIs .     Selection     Our team have had weeks of interaction with potential subscribers within our target age range . They   developed personas from the diverse characters they interacted with . Based on these personas , we   tailored a product that is expected to suit their interests . We have decided to use the Learning launch   tool since it enables us to run fast cheap experiments to confirm or disconfirm our assumptions .   Experimental Learning Launch enables us to search for the truth . The truth enables us to identify void in   our assumptions . We can use truths to pivot , which means to change our idea , amend it , and keep going   until we develop at a finished result .       Application      Our initial learning launch focused on a sample group of 30 young people . We provided free weekly   subscription of an experimental data offer that also enabled their lifestyles . The offer was valid for seven   days ( 7days ) only . They were able to earn coupons per volume of data consumed within the one week .   The accumulated coupons could be used to earn game , concert and/or movie tickets , meals , and drinks .   We noticed that about 39 % of the sample group voluntarily opted to extend their subscription to   continue using our offers . We compared the expected outcome to our assumptions and figured there   was more margins to cover .     After keenly analyzing all data from the first Launch , our next iteration focused on personas that were   not initially considered . We recruited a group of 33 young people to test our new experimental offers .   We again provided free week subscription for data offers that also enabled their lifestyles . In addition to   accumulating coupons that could be used to earn game , concert and/or movie tickets , meals , and   drinks , the coupons were also being used to earn discounts on purchases made at partner stores . We   noticed that 61 % of the sample group voluntarily opted to extend their subscription . 

 Insight      Using the Learning Launch tool , our team was able to roll out and test the offers fast and cheap . In Our   initial iteration we did not assume that youth are low income earners . The learning launch made us   identify element that we skipped or ignored when we were developing the prototype . We were able to   use the useful information from the first learning launch to modify the design of the second learning   launch . Based on the performance of the second learning launch , we were convinced to rolled out the   offer on a larger scale to attract young subscriber .      Approach     The dynamics of customer behavior changes over time . In future product designs , we would use the   Learning Launch tool when designing a product . This enables the design thinking process to connect   directly with stakeholders to co - create the product by experimenting the different stage of production   and gathering relevant truths to improve subsequent launches . Provided that our assumptions will not   always be correct , our team will be able to use analyses of behaviors , traits , and patterns from   experiments to create personas that would correctly represent our target group .   Today , through learning launch and our customer - centricity , my employer has earned a youthful   subscriber base with hopes of significant growth in revenue from data 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      In my country , youths constitute approximately 45 % of our overall population . My employer , a   telecommunications company is challenged with the demography of its subscriber Base . A minute   fraction of their subscribers is below Age thirty , with the rest being above . Within our local   telecommunications industry , people under 30 consumed the highest volume of data for internet   connectivity . This reality stirs up a quest to attract young subscribers to our network . We are needed to   develop a product in 12 weeks . One that will appeal to the youth and make them prefer us to our   competitors ; consequently , growing a sustainable customer base , increasing our revenue , and delivering   on annual KPIs .     Selection     Our team have had weeks of interaction with potential subscribers within our target age range . They   developed personas from the diverse characters they interacted with . Based on these personas , we   tailored a product that is expected to suit their interests . We have decided to use the Learning launch   tool since it enables us to run fast cheap experiments to confirm or disconfirm our assumptions .   Experimental Learning Launch enables us to search for the truth . The truth enables us to identify void in   our assumptions . We can use truths to pivot , which means to change our idea , amend it , and keep going   until we develop at a finished result .       Application      Our initial learning launch focused on a sample group of 30 young people . We provided free weekly   subscription of an experimental data offer that also enabled their lifestyles . The offer was valid for seven   days ( 7days ) only . They were able to earn coupons per volume of data consumed within the one week .   The accumulated coupons could be used to earn game , concert and/or movie tickets , meals , and drinks .   We noticed that about 39 % of the sample group voluntarily opted to extend their subscription to   continue using our offers . We compared the expected outcome to our assumptions and figured there   was more margins to cover .     After keenly analyzing all data from the first Launch , our next iteration focused on personas that were   not initially considered . We recruited a group of 33 young people to test our new experimental offers .   We again provided free week subscription for data offers that also enabled their lifestyles . In addition to   accumulating coupons that could be used to earn game , concert and/or movie tickets , meals , and   drinks , the coupons were also being used to earn discounts on purchases made at partner stores . We   noticed that 61 % of the sample group voluntarily opted to extend their subscription . 

 Insight      Using the Learning Launch tool , our team was able to roll out and test the offers fast and cheap . In Our   initial iteration we did not assume that youth are low income earners . The learning launch made us   identify element that we skipped or ignored when we were developing the prototype . We were able to   use the useful information from the first learning launch to modify the design of the second learning   launch . Based on the performance of the second learning launch , we were convinced to rolled out the   offer on a larger scale to attract young subscriber .      Approach     The dynamics of customer behavior changes over time . In future product designs , we would use the   Learning Launch tool when designing a product . This enables the design thinking process to connect   directly with stakeholders to co - create the product by experimenting the different stage of production   and gathering relevant truths to improve subsequent launches . Provided that our assumptions will not   always be correct , our team will be able to use analyses of behaviors , traits , and patterns from   experiments to create personas that would correctly represent our target group .   Today , through learning launch and our customer - centricity , my employer has earned a youthful   subscriber base with hopes of significant growth in revenue from data usage .
### Output: 