Reflection on Design Thinking 

 David Perez 

 June 7 , 2020 

 The challenge :   In order to persuade prospects for a warehouse membership purchase , Sam ’s Club uses   various incentives .   The company has found that many consumers will move from the consideration   state to the purchase state faster if incentives are offered .   While the purchase price would remain the   same at $ 45 , additional incentives after purchase help hundreds of thousands of households annually   commit to an annual membership .   However , no incentives have universal appeal .   Equally important is   the fact that warehouse clubs sell products at a price that covers the overhead of club operations ;   organizational profits are derived from membership fees .   Costly incentives essentially defer any   membership income to year two members up on renewal .   The challenge is to develop incentive   programs with a lower cost and in a new manner that do not require half a year to get measurements   on effectiveness . 

 The tool I have implemented is Learning Launch .   It ’s important to note that this is not a hypothetical or   theoretical test .   An actual learning launch was launched a few weeks ago .   This tool was chosen   because it is an approach our team has not tried before .   In the past , we have executed large - scale   national campaigns making assumptions provided from our team .   The appeal is in the concept of “ not   knowing what we do n’t know . ” 

 Instead of a large , national , multi - million dollar ad campaign , we have identified a test market and a   random audience and just launched a new program for the sake of learning .   We are testing five vastly   different offers with two different messaging themes .   For application , since we do n’t know what would   be most appealing , we are pushing a variety of incentives and campaigns and starting off by giving each   of them equal funding in the test market . 

 The insights have thus far been fascinating , but we are still so early on in the findings that we do n’t   know what we do n’t know yet .   By launching multiple tiny programs with a very different incentive , we   are measuring real - time responses and social media shares .   Within the week , we will have enough data   to determine which ad messaging has been most effective in the test market , and will invest only in the   most productive 50 % of the offers and messages .   We will repeat this process until we determine which   strategy or strategies are most effective . 

 We do not know what we will do different next time as we do n’t have the data back to measure what is   a success this time .   The next iteration will just reflect what we learned as we are considering an   unsuccessful ad element as not a failure , but a learning toward optimizing what is more of a success . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: David Perez
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: Reflection on Design Thinking 

 David Perez 

 June 7 , 2020 

 The challenge :   In order to persuade prospects for a warehouse membership purchase , Sam ’s Club uses   various incentives .   The company has found that many consumers will move from the consideration   state to the purchase state faster if incentives are offered .   While the purchase price would remain the   same at $ 45 , additional incentives after purchase help hundreds of thousands of households annually   commit to an annual membership .   However , no incentives have universal appeal .   Equally important is   the fact that warehouse clubs sell products at a price that covers the overhead of club operations ;   organizational profits are derived from membership fees .   Costly incentives essentially defer any   membership income to year two members up on renewal .   The challenge is to develop incentive   programs with a lower cost and in a new manner that do not require half a year to get measurements   on effectiveness . 

 The tool I have implemented is Learning Launch .   It ’s important to note that this is not a hypothetical or   theoretical test .   An actual learning launch was launched a few weeks ago .   This tool was chosen   because it is an approach our team has not tried before .   In the past , we have executed large - scale   national campaigns making assumptions provided from our team .   The appeal is in the concept of “ not   knowing what we do n’t know . ” 

 Instead of a large , national , multi - million dollar ad campaign , we have identified a test market and a   random audience and just launched a new program for the sake of learning .   We are testing five vastly   different offers with two different messaging themes .   For application , since we do n’t know what would   be most appealing , we are pushing a variety of incentives and campaigns and starting off by giving each   of them equal funding in the test market . 

 The insights have thus far been fascinating , but we are still so early on in the findings that we do n’t   know what we do n’t know yet .   By launching multiple tiny programs with a very different incentive , we   are measuring real - time responses and social media shares .   Within the week , we will have enough data   to determine which ad messaging has been most effective in the test market , and will invest only in the   most productive 50 % of the offers and messages .   We will repeat this process until we determine which   strategy or strategies are most effective . 

 We do not know what we will do different next time as we do n’t have the data back to measure what is   a success this time .   The next iteration will just reflect what we learned as we are considering an   unsuccessful ad element as not a failure , but a learning toward optimizing what is more of a success .
### Output: 