Challenge     I lead the product management team for an online marketplace that sells used cars . We connect   potential car buyers with car dealerships . Our site features thousands of cars . We allow buyers   to search for cars , apply relevant filters , browse car information ( photos , car history etc ) and   enquire on specific cars . We define success by the number of enquiries submitted by our   buyers .      Our daily challenge is to find new ways of getting car buyers to submit enquiries on cars . More   specifically , we came up with an idea which we needed to test . It was based on the following   hypothesis : Car buyers would be willing to provide their contact details ( and enquire on a car ) in   exchange for a free car history report​.      Selection     The most appropriate tool to test our hypothesis appeared to be a ​“learning launch”​. We knew   that building the full solution would turn out to be costly ( reports are provided by a third party   and our team would have had to build technical integrations ) .      We decided to find the quickest and cheapest way to test our hypothesis . And if it turned out to   be true , we would then dedicate engineering and product resources to building the proper   solution .     Application      In order to test our hypothesis quickly and cheaply , we ran an A / B test on our website . We   created a very “ light ” version of the new feature ( report download ) and only showed it to a   portion of our users ( group A ) . Other users ( group B ) remained on the current experience and   were not exposed to the report download feature .      We ran the test for 3 weeks and compared results between group A and B. We agreed that our   success metric should be “ enquiry conversion ” ( % of users who end up submitting an enquiry on   a car ) . Group A enquiry conversion turned out to be significantly higher than that of group B   ( +20 % ) !     This proved that our hypothesis was correct . From that point on , we knew that providing free   reports to car buyers would lead to more car enquiries . It also provided us with data to build a   business case and justify investing engineering and product resources to build the end solution .      It ’s worth noting that our approach involved building a very light version of the feature . We opted   for a manual process : when a user would request a report , an email would be sent to our   customer service team who would purchase the report and email it back to them . Now that the 

 test has been successful , we can invest engineering resources to build a proper solution , having   our system automatically querying the supplier ’s systems .     Approach     I believe the tool we selected ( learning launch ) was the right one . We started with a hypothesis ,   ran a quick test and got data to validate our hypothesis .      That being said , there are a few things we could have done differently . During the test , we   compared two versions of the product : A ( with the report download feature ) and B ( without the   report download feature ) . Besides the new feature , A also included several usability tweaks .   When the first set of results came through , we were unable to determine if the increase in   enquiries was solely due to the report feature .      We had to go back to the drawing board and re - run the test with the two versions ( A and B )   identical from a usability perspective . This allowed us to have absolute confidence that the   increase in enquiries was due to people wanting to download reports . 


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     I lead the product management team for an online marketplace that sells used cars . We connect   potential car buyers with car dealerships . Our site features thousands of cars . We allow buyers   to search for cars , apply relevant filters , browse car information ( photos , car history etc ) and   enquire on specific cars . We define success by the number of enquiries submitted by our   buyers .      Our daily challenge is to find new ways of getting car buyers to submit enquiries on cars . More   specifically , we came up with an idea which we needed to test . It was based on the following   hypothesis : Car buyers would be willing to provide their contact details ( and enquire on a car ) in   exchange for a free car history report​.      Selection     The most appropriate tool to test our hypothesis appeared to be a ​“learning launch”​. We knew   that building the full solution would turn out to be costly ( reports are provided by a third party   and our team would have had to build technical integrations ) .      We decided to find the quickest and cheapest way to test our hypothesis . And if it turned out to   be true , we would then dedicate engineering and product resources to building the proper   solution .     Application      In order to test our hypothesis quickly and cheaply , we ran an A / B test on our website . We   created a very “ light ” version of the new feature ( report download ) and only showed it to a   portion of our users ( group A ) . Other users ( group B ) remained on the current experience and   were not exposed to the report download feature .      We ran the test for 3 weeks and compared results between group A and B. We agreed that our   success metric should be “ enquiry conversion ” ( % of users who end up submitting an enquiry on   a car ) . Group A enquiry conversion turned out to be significantly higher than that of group B   ( +20 % ) !     This proved that our hypothesis was correct . From that point on , we knew that providing free   reports to car buyers would lead to more car enquiries . It also provided us with data to build a   business case and justify investing engineering and product resources to build the end solution .      It ’s worth noting that our approach involved building a very light version of the feature . We opted   for a manual process : when a user would request a report , an email would be sent to our   customer service team who would purchase the report and email it back to them . Now that the 

 test has been successful , we can invest engineering resources to build a proper solution , having   our system automatically querying the supplier ’s systems .     Approach     I believe the tool we selected ( learning launch ) was the right one . We started with a hypothesis ,   ran a quick test and got data to validate our hypothesis .      That being said , there are a few things we could have done differently . During the test , we   compared two versions of the product : A ( with the report download feature ) and B ( without the   report download feature ) . Besides the new feature , A also included several usability tweaks .   When the first set of results came through , we were unable to determine if the increase in   enquiries was solely due to the report feature .      We had to go back to the drawing board and re - run the test with the two versions ( A and B )   identical from a usability perspective . This allowed us to have absolute confidence that the   increase in enquiries was due to people wanting to download reports .
### Output: 