Reflections Learning Launch ( Module 4 ) 

 Challenge 

 I lead a team of performance marketers in our organization , and our organization is present in 10   countries across Southeast Asia . Our scope involves media plan , improving workflow , being on top of ad   tech and platforms , executing and optimizing campaigns to deliver client ’s goal . The challenge is how to   execute more experiments properly in order gain more learnings and scale that would benefit both   client and us . The idea is for us to create an experiment framework to push out regionally . 

 Selection 

 Our team has gone through identifying the opportunities and need , to brainstorming a concept so we   are on to the next stage of learning launch . This will help us test different ideas , gain learnings and   insights and shape the experiment framework . 

 Application 

 We categorized experiments into four , and focused on the most cost effective , least constraints and   valuable category as it ’s the heart of the company , which is data category . We nominated accounts , and   shortlisted to three accounts , based on assumptions , budget , timeline and value to client . We had   scheduled meetings between different departments , from data analyst , account management , and   performance marketers get everyone ’s input and practice the discipline of incorporating experiment   process in all departments as it ’s something beyond performance department . 

 For the first account of our learning launch , we had debates and discussions on the structure , which   hypothesis should be prioritized , how we ’ll execute and determine the success metrics which took four   meetings to finish and got the result in a week . The result of the experiment was inconclusive , it was   neither a success nor a failure . It did give us some insight , therefore we proceeded with retesting it with   a few changes on the variables compared to previous . We noticed we got more synergy , and the team   as able to work faster , but still had meaningful discussions . Hence , we also tried the framework on the   other two accounts we shortlisted . 

 Insight 

 The learning launch tool gave our team a more structured approach . The first discussions we felt lost , as   each had different opinions and expertise , but we were able to design “ launches ” or our initial   experiment framework . The results of our launches were a mix of success and failures , as it was more   about the learning and then proceeding with the next step , whether it was to continue , eliminate or   adjust . We are still in the initial stages to determine the right framework , but it has given more clarity on   the direction of our data experiments and improved the collaboration between departments . 

 Approach 

 The learning launch tool is proven to move the needle in pointing us to the right direction of our data   framework . We will continue using this tool at this stage as it has given us immediate insights and   progress . We tested this for our data experiments approach , and will be using this as well for other   categories to gain more learnings , which will help the campaign performance , scale and , hence help our   clients . 


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: Reflections Learning Launch ( Module 4 ) 

 Challenge 

 I lead a team of performance marketers in our organization , and our organization is present in 10   countries across Southeast Asia . Our scope involves media plan , improving workflow , being on top of ad   tech and platforms , executing and optimizing campaigns to deliver client ’s goal . The challenge is how to   execute more experiments properly in order gain more learnings and scale that would benefit both   client and us . The idea is for us to create an experiment framework to push out regionally . 

 Selection 

 Our team has gone through identifying the opportunities and need , to brainstorming a concept so we   are on to the next stage of learning launch . This will help us test different ideas , gain learnings and   insights and shape the experiment framework . 

 Application 

 We categorized experiments into four , and focused on the most cost effective , least constraints and   valuable category as it ’s the heart of the company , which is data category . We nominated accounts , and   shortlisted to three accounts , based on assumptions , budget , timeline and value to client . We had   scheduled meetings between different departments , from data analyst , account management , and   performance marketers get everyone ’s input and practice the discipline of incorporating experiment   process in all departments as it ’s something beyond performance department . 

 For the first account of our learning launch , we had debates and discussions on the structure , which   hypothesis should be prioritized , how we ’ll execute and determine the success metrics which took four   meetings to finish and got the result in a week . The result of the experiment was inconclusive , it was   neither a success nor a failure . It did give us some insight , therefore we proceeded with retesting it with   a few changes on the variables compared to previous . We noticed we got more synergy , and the team   as able to work faster , but still had meaningful discussions . Hence , we also tried the framework on the   other two accounts we shortlisted . 

 Insight 

 The learning launch tool gave our team a more structured approach . The first discussions we felt lost , as   each had different opinions and expertise , but we were able to design “ launches ” or our initial   experiment framework . The results of our launches were a mix of success and failures , as it was more   about the learning and then proceeding with the next step , whether it was to continue , eliminate or   adjust . We are still in the initial stages to determine the right framework , but it has given more clarity on   the direction of our data experiments and improved the collaboration between departments . 

 Approach 

 The learning launch tool is proven to move the needle in pointing us to the right direction of our data   framework . We will continue using this tool at this stage as it has given us immediate insights and   progress . We tested this for our data experiments approach , and will be using this as well for other   categories to gain more learnings , which will help the campaign performance , scale and , hence help our   clients .
### Output: 