Challenge and selection :      I work in a company that provides financial solutions with a diversified portfolio of   customers . Our challenge was to reduce the percentage of calls of customers who did   not know about their transaction status or they are receiving a generic error and do not   know how to proceed or continue their operation .   Based on that , we decided to use the learning launch tool to test the idea of exchange all   the messages and transacción status for others more clear to the final user . We believe   that possibly improve the situation , by reducing the percentage of calls .       Application     With the idea of reducing the percentage of calls , we took one of the most used   transactions and we changed the messages that were received by the customers   ( including the informative messages , status of transactions , warnings , and errors that   prevent continuing with the normal operation flow ) .   We set some parameters to find the flows we wanted to test more easily and we   contacted a group of clients with different needs ( customers operating with corporative   and individual home banking with ages between 35 and 70 ) .   We asked them to operate normally and naturally with the selected functionality ( The   customer has to log in in the home banking , operated , and taking actions based on the   message they received on the screen .       Insight and approach     By applying the learning launch tool , the team realized that a percentage of customers   did not call the call center because they could be identified and understood the problem   and they have been able to solve by themselves .    The other percent had to call anyway because they needed some actions from the bank   to solve the problem . But here we could detect that the time of response of the call   center team decrease considerably because they already know the problem informed by   the customer and they knew how to fix it .    Besides , we realized based on the second group that there is a huge percent of messages   that requires some actions from the bank to continue the operation , and to decrease even   more the percentage of calls , we should take those problems , analyze them and take   some new actions .    We think that use the learning launch was the right tool to use to learn more about this   problem since we could implement an intermediate solution to solve the most important   inconvenience and , we could learn about other 's “ obstacles ” to have even less percent of   calls . 


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: Challenge and selection :      I work in a company that provides financial solutions with a diversified portfolio of   customers . Our challenge was to reduce the percentage of calls of customers who did   not know about their transaction status or they are receiving a generic error and do not   know how to proceed or continue their operation .   Based on that , we decided to use the learning launch tool to test the idea of exchange all   the messages and transacción status for others more clear to the final user . We believe   that possibly improve the situation , by reducing the percentage of calls .       Application     With the idea of reducing the percentage of calls , we took one of the most used   transactions and we changed the messages that were received by the customers   ( including the informative messages , status of transactions , warnings , and errors that   prevent continuing with the normal operation flow ) .   We set some parameters to find the flows we wanted to test more easily and we   contacted a group of clients with different needs ( customers operating with corporative   and individual home banking with ages between 35 and 70 ) .   We asked them to operate normally and naturally with the selected functionality ( The   customer has to log in in the home banking , operated , and taking actions based on the   message they received on the screen .       Insight and approach     By applying the learning launch tool , the team realized that a percentage of customers   did not call the call center because they could be identified and understood the problem   and they have been able to solve by themselves .    The other percent had to call anyway because they needed some actions from the bank   to solve the problem . But here we could detect that the time of response of the call   center team decrease considerably because they already know the problem informed by   the customer and they knew how to fix it .    Besides , we realized based on the second group that there is a huge percent of messages   that requires some actions from the bank to continue the operation , and to decrease even   more the percentage of calls , we should take those problems , analyze them and take   some new actions .    We think that use the learning launch was the right tool to use to learn more about this   problem since we could implement an intermediate solution to solve the most important   inconvenience and , we could learn about other 's “ obstacles ” to have even less percent of   calls .
### Output: 