Applying “ Learning Launch ”     Challenge    We wanted to test a news business model ( subscription ) and a direct to consumer approach   for a well known beverage company .    There were several hypothesis concerning the specific customer needs we had to validate   and there were several doubts concerning the pricing ( what these specific customers would   be willing to pay ) and different customer segments .    We wanted to test a beverage subscription service that “ learns ” over time from consumption   preferences , habits and favorite ingredients . The concrete customer need we identified is the   logistical nightmare of shopping beverages and serving all different needs within the family   ( taste and nutrition and occasions ) . 

 Selection    Based on the huge number of different hypothesis we decided to run small experiments   based on the learning launch method . 

 1 . Idea 

 2 . Specific customer . 

 3 . Specific need . 

 4 . Solution / idea that meets the customer need . 

 5 . Better than current alternative    = Hypothesis 

 Application , approach   We first created a multi disciplinary team from different functions , a small and diverse team +   a skeptic ( to avoid confirmation bias = find evidence that it is a good idea ) .   We then elaborated key assumptions based on “ What do I know ? ” , “ What do I not know ? ” ,   “ what do I need to know ? ” and “ how do we learn what we need to know ? ” .    Before creating a prototype we then talked to potential customers to validate ( or not ) some of   our key hypothesis ( customer need , problem - solution fit , price ) concerning value creation   and talked as well to logistic provider to validate execution .    As some of them were validated we only pivoted / changed some aspects of our first solution   approach .     Insight    As we quickly got real life feedback from clients we “ failed ” cheap and fast with some of our   main hypothesis .    People thought it would be a gain to get personalised beverages delivered to their doorstep .   Pricing was too high ( which was basically a problem of logistic costs ) and yes , they were OK   with sharing important data for personalisation such as Apple Health and Fitbit data as well   as geolocation data to track their daily commute etc . to offer drinks on the go at certain cafes   etc . 


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: Applying “ Learning Launch ”     Challenge    We wanted to test a news business model ( subscription ) and a direct to consumer approach   for a well known beverage company .    There were several hypothesis concerning the specific customer needs we had to validate   and there were several doubts concerning the pricing ( what these specific customers would   be willing to pay ) and different customer segments .    We wanted to test a beverage subscription service that “ learns ” over time from consumption   preferences , habits and favorite ingredients . The concrete customer need we identified is the   logistical nightmare of shopping beverages and serving all different needs within the family   ( taste and nutrition and occasions ) . 

 Selection    Based on the huge number of different hypothesis we decided to run small experiments   based on the learning launch method . 

 1 . Idea 

 2 . Specific customer . 

 3 . Specific need . 

 4 . Solution / idea that meets the customer need . 

 5 . Better than current alternative    = Hypothesis 

 Application , approach   We first created a multi disciplinary team from different functions , a small and diverse team +   a skeptic ( to avoid confirmation bias = find evidence that it is a good idea ) .   We then elaborated key assumptions based on “ What do I know ? ” , “ What do I not know ? ” ,   “ what do I need to know ? ” and “ how do we learn what we need to know ? ” .    Before creating a prototype we then talked to potential customers to validate ( or not ) some of   our key hypothesis ( customer need , problem - solution fit , price ) concerning value creation   and talked as well to logistic provider to validate execution .    As some of them were validated we only pivoted / changed some aspects of our first solution   approach .     Insight    As we quickly got real life feedback from clients we “ failed ” cheap and fast with some of our   main hypothesis .    People thought it would be a gain to get personalised beverages delivered to their doorstep .   Pricing was too high ( which was basically a problem of logistic costs ) and yes , they were OK   with sharing important data for personalisation such as Apple Health and Fitbit data as well   as geolocation data to track their daily commute etc . to offer drinks on the go at certain cafes   etc .
### Output: 