CHALLENGE 

 I am CEO of a intercity passenger transportation busline in Mexico . Historically , our industry has   not engaged in price - competitive dynamics , so busline tickets are sold typically at a fixed prices   with few discounts available for the elderly and students .   We had the hypothesis that offering our   passengers access to lower price tickets would créate value for the customers and drive demand   and sales up , as well as increase bus occupation rates . Creating a dynamic pricing system was not   easy and would demand a substantial amount of investment and time , so we decided to take a   design thinking approach . 

 SELECTION 

 We chose the Learning Launch tool , since we wanted to test a hypothesis , we had a bunch of   assumptions ( e.g. , customers would be willing to buy round - trip instead of one - way if a discount   was offered , or people would be inclined to plan their trips earlier if tickets were sold cheaper for   future travel dates instead of paying full - price tickets buying the same date ) . We had some data   from similar industries such as the airline industry but no data for our industry , so we wanted to   test the hypothesis and assumptions fast and cheap before engaging in any system development . 

 APPLICATION 

 As professor Essen explained in his Learning Launch tool video , we followed a similar approach to   testing our hypothesis .   We formulated the hypothesis ( i.e. , dynamic pricing will increase number   of passengers in a route ( occupation ) and yield higher revenue ( average price ticket will decrease   but overall total revenue will be higher ) . 

 The assumptions were : there are different type of price - sensitive customers that will take   advantage of discounts for buying earlier or buying round - trip tickets . Other assumption was that   cannibalization for current prices would be offset by the additional passengers gained . 

 We selected 10 different routes to serve as pilots to target different type of customers ( leisure ,   work - related , college students , etc ) . We created a baseline using historic data from previous   months to compare occupation , revenues and average price ticket sales . 

 Finally we also incorporated some in - depth interviews to customers in those routes to gain some   additional insights and to corroborate or contrast with the hard data obtained from the pilots . 

 INSIGHT & APPROACH 

 We were able to gain many insights on what our customers valued and expected . 

 1.-We learned that the customers valued the discounts and they were willing to plan ahead in   order to get the discount . We also discover interesting insights from the in - depth interviews , for   example , we discover there was also a potential improvement in brand value since they felt our   company was the first one in the market to care for them offering these lower price alternatives . 

 2.-We also learned that heavy - users valued the discounts the most , so that created a whole set of   new hypothesis on how to maximize the value for these customers , including the potential 

 creation of a loyalty program and other new initiatives to test specifically with these type of   customers . 

 3.-We also gained insights into optimal levels of discount based on anticipation to reduce   cannibalization and achieve income growth through incremental passengers . It required a lot of   iteration and constantly testing and adjusting prices to capture different responses from our   customers . 

 4.-One of the things we would do differently next time is reduce the number of pilots . It was   probably too much information and complexity to handle , so it was hard and time consuming to   analyze and did not add any additional insights to the learning process . 


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 am CEO of a intercity passenger transportation busline in Mexico . Historically , our industry has   not engaged in price - competitive dynamics , so busline tickets are sold typically at a fixed prices   with few discounts available for the elderly and students .   We had the hypothesis that offering our   passengers access to lower price tickets would créate value for the customers and drive demand   and sales up , as well as increase bus occupation rates . Creating a dynamic pricing system was not   easy and would demand a substantial amount of investment and time , so we decided to take a   design thinking approach . 

 SELECTION 

 We chose the Learning Launch tool , since we wanted to test a hypothesis , we had a bunch of   assumptions ( e.g. , customers would be willing to buy round - trip instead of one - way if a discount   was offered , or people would be inclined to plan their trips earlier if tickets were sold cheaper for   future travel dates instead of paying full - price tickets buying the same date ) . We had some data   from similar industries such as the airline industry but no data for our industry , so we wanted to   test the hypothesis and assumptions fast and cheap before engaging in any system development . 

 APPLICATION 

 As professor Essen explained in his Learning Launch tool video , we followed a similar approach to   testing our hypothesis .   We formulated the hypothesis ( i.e. , dynamic pricing will increase number   of passengers in a route ( occupation ) and yield higher revenue ( average price ticket will decrease   but overall total revenue will be higher ) . 

 The assumptions were : there are different type of price - sensitive customers that will take   advantage of discounts for buying earlier or buying round - trip tickets . Other assumption was that   cannibalization for current prices would be offset by the additional passengers gained . 

 We selected 10 different routes to serve as pilots to target different type of customers ( leisure ,   work - related , college students , etc ) . We created a baseline using historic data from previous   months to compare occupation , revenues and average price ticket sales . 

 Finally we also incorporated some in - depth interviews to customers in those routes to gain some   additional insights and to corroborate or contrast with the hard data obtained from the pilots . 

 INSIGHT & APPROACH 

 We were able to gain many insights on what our customers valued and expected . 

 1.-We learned that the customers valued the discounts and they were willing to plan ahead in   order to get the discount . We also discover interesting insights from the in - depth interviews , for   example , we discover there was also a potential improvement in brand value since they felt our   company was the first one in the market to care for them offering these lower price alternatives . 

 2.-We also learned that heavy - users valued the discounts the most , so that created a whole set of   new hypothesis on how to maximize the value for these customers , including the potential 

 creation of a loyalty program and other new initiatives to test specifically with these type of   customers . 

 3.-We also gained insights into optimal levels of discount based on anticipation to reduce   cannibalization and achieve income growth through incremental passengers . It required a lot of   iteration and constantly testing and adjusting prices to capture different responses from our   customers . 

 4.-One of the things we would do differently next time is reduce the number of pilots . It was   probably too much information and complexity to handle , so it was hard and time consuming to   analyze and did not add any additional insights to the learning process .
### Output: 