Challenge   I work as a consultant in banking and my client was preparing the launch of a new pure - digital   bank for young professionals . The core of the product would provide an app to open and use   current & savings accounts , and debit & credit cards .     As they were preparing the market entry , they wanted to put aside some budget to incentivize   new customers to actively use their new account & card . For example , they could do it by   providing “ cashback ” on transactions ( e.g. you get 20 $ back in your account if you use your card   at least 5 times in a week ) or by “ promoting deals ” with third party merchants ( e.g. you get 20 %   off a burger at a popular chain if you pay for the burger with your new credit card ) .   In the case of “ promotion deals ” , merchants would fund part of the deal with the bank , making   this option cheaper for the bank than the “ cashback ” option . Let ’s assume , for example , that with   20 $ the bank could either incentivize the customer with cashback ( i.e. I give you 20 $ if you   transact with your credit card this week ) or give a 30 $ equivalent promotion deal ( i.e. you get 30 $   discount off your restaurant bill if you pay by card – with 20 $ funded by the bank and 10 $ funded   by the restaurant )     Their challenge was the following : how do we invest our 20 $ budget on incentives ? Specifically ,   how can we know if it is better to provide the customer with 20 $ cashback or 30 $ discount at a   popular restaurant ?       Selection   I decided to propose to my client to use the Learning Launch tool to solve this challenge . Instead   of deciding internally based on bank ’s biases and any public data available ( which was very   limited on this topic ) , I thought that , as the video lesson described , learning launch could be use   to test customer preferences directly with the bank ’s customers and let them decide directly .   In addition , as the bank was planning to gain customers organically and progressively , the   learning launch tool was relatively low risk , as the bank was able to focus the test on a limited   customer sample ( e.g. first 200 customers )     Application   We therefore included a feature in the new bank app that would allow the following : a new   customer , after getting their new account and card , would receive smartphone notifications   providing them with the following message : “ Enjoy our new customer promotion – open to see   your reward ! ” .   Then , when opening the link , customers would be prompted to a choice between two offers : they   could choose between the 20 $ cashback promotion , or the 30 $ restaurant discount promotion .   In this way , the bank was ( i ) sure that they would provide customers with their preferred choice ,   and ( ii ) able to get a better understanding of customers preferences around incentives     Insights   This solution provided the bank with great insights : 

    They understood that majority of customers would choose the 20 $ cashback option 

    They were able to segment customers based on their choice ( not all customers like that   restaurant chain , but if the discount was on another product maybe they would pick it   instead of cashback ) 

    After adding additional “ promotional deals ” options to customers ( e.g. with electronics   stores , e - commerce websites , retailers ) , they found out that some merchants are valued by   customers more than others 

 Approach   This approach allowed bank to understand the value of learning launch . They gave options to   their first customers when they launched the new app and , based on the feedback gathered , they   were able to adjust and complete their offering .   For example , they started adding additional promotional deal with more merchants and remove   deals that customers did not like .     Also , they also used the approach a bit differently on a small number of customers . They started   increasing the 30 $ restaurant discount gradually until they reached the level where customers   chose that instead of the 20 $ cashback . In the example , they found out that with a 40 $ discount   most customers would choose the discount and not the cashback anymore .   In this way , they were able to go back to the merchant with real data and use that to negotiate a   new deal were the restaurant would fund a larger part of the promotion . 


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 work as a consultant in banking and my client was preparing the launch of a new pure - digital   bank for young professionals . The core of the product would provide an app to open and use   current & savings accounts , and debit & credit cards .     As they were preparing the market entry , they wanted to put aside some budget to incentivize   new customers to actively use their new account & card . For example , they could do it by   providing “ cashback ” on transactions ( e.g. you get 20 $ back in your account if you use your card   at least 5 times in a week ) or by “ promoting deals ” with third party merchants ( e.g. you get 20 %   off a burger at a popular chain if you pay for the burger with your new credit card ) .   In the case of “ promotion deals ” , merchants would fund part of the deal with the bank , making   this option cheaper for the bank than the “ cashback ” option . Let ’s assume , for example , that with   20 $ the bank could either incentivize the customer with cashback ( i.e. I give you 20 $ if you   transact with your credit card this week ) or give a 30 $ equivalent promotion deal ( i.e. you get 30 $   discount off your restaurant bill if you pay by card – with 20 $ funded by the bank and 10 $ funded   by the restaurant )     Their challenge was the following : how do we invest our 20 $ budget on incentives ? Specifically ,   how can we know if it is better to provide the customer with 20 $ cashback or 30 $ discount at a   popular restaurant ?       Selection   I decided to propose to my client to use the Learning Launch tool to solve this challenge . Instead   of deciding internally based on bank ’s biases and any public data available ( which was very   limited on this topic ) , I thought that , as the video lesson described , learning launch could be use   to test customer preferences directly with the bank ’s customers and let them decide directly .   In addition , as the bank was planning to gain customers organically and progressively , the   learning launch tool was relatively low risk , as the bank was able to focus the test on a limited   customer sample ( e.g. first 200 customers )     Application   We therefore included a feature in the new bank app that would allow the following : a new   customer , after getting their new account and card , would receive smartphone notifications   providing them with the following message : “ Enjoy our new customer promotion – open to see   your reward ! ” .   Then , when opening the link , customers would be prompted to a choice between two offers : they   could choose between the 20 $ cashback promotion , or the 30 $ restaurant discount promotion .   In this way , the bank was ( i ) sure that they would provide customers with their preferred choice ,   and ( ii ) able to get a better understanding of customers preferences around incentives     Insights   This solution provided the bank with great insights : 

    They understood that majority of customers would choose the 20 $ cashback option 

    They were able to segment customers based on their choice ( not all customers like that   restaurant chain , but if the discount was on another product maybe they would pick it   instead of cashback ) 

    After adding additional “ promotional deals ” options to customers ( e.g. with electronics   stores , e - commerce websites , retailers ) , they found out that some merchants are valued by   customers more than others 

 Approach   This approach allowed bank to understand the value of learning launch . They gave options to   their first customers when they launched the new app and , based on the feedback gathered , they   were able to adjust and complete their offering .   For example , they started adding additional promotional deal with more merchants and remove   deals that customers did not like .     Also , they also used the approach a bit differently on a small number of customers . They started   increasing the 30 $ restaurant discount gradually until they reached the level where customers   chose that instead of the 20 $ cashback . In the example , they found out that with a 40 $ discount   most customers would choose the discount and not the cashback anymore .   In this way , they were able to go back to the merchant with real data and use that to negotiate a   new deal were the restaurant would fund a larger part of the promotion .
### Output: 