Learning Launch to Build an Early Warning System 

 Challenge : I am a member of my organization ’s Transformation team . Our challenge is to build a   mechanism which will help one of our biggest clients to proactively identify future dissatisfied customers   ( the end users ) . Currently there is no such mechanism available with the client to capture the same .   Implementation of a robust mechanism will help the client to improve customer satisfaction and protect   revenue . In terms of customer base , the project scope includes all their customers in the USA region , in   terms of data accessibility and usability , the project scope includes only the customers ’ change request   emails that they send to the client . 

 I want to mention one very important point here , doing a customer survey to identify dissatisfied   customers is not an option as the client wants to do it without the customers / end - users realizing that   they are being assessed so that client can take proactive actions . 

 Selection : We have formed a small team of 6 people who are all familiar with Design Thinking tools and   activities . Our team is a combination of innovation , analytics , transformation expert from my   organization and Subject matter expert , account manager from our client organization . We want to   build a mechanism / tool to identify dissatisfied customers using their change request emails database .   We are now ready to try learning launch . A learning launch is an experiment / a series of experiments   conducted quickly and inexpensively . 

 Application : For our first learning launch we focused on gathering our thoughts on how the data to be   used . We made a list of criterion basis which the emails will be classified between good / bad / neutral . We   also decided on how the mechanism is going to work at a high level ( as depicted in the below image ) . 

 Instead of building and testing a full - blown tool , we created a macro - based tool on excel . We used a   sample of 300 emails to test if our assumption that dissatisfied customers can be identified from the 

 tones of their emails is correct . We took the learnings from our first learning launch and moved ahead   with the second learning launch . In second learning launch we built a tool called ‘ Early Warning System ’   using advanced NLP , N - Gram charts & sentiment analytics . This tool analyzes unstructured text from   emails , gauge customer sentiment , flag negative emails quickly & accurately , thus enabling concerned   teams to take prompt remedial action . 

 Insight : By using learning launch tool , we quickly tested our assumption and concept . We spent very   minimal money to test if our hypothesis was right . We gathered our learnings and built the final tool .   The client was supremely delighted to see the results . The tool has been deployed and currently it is   running with an accuracy of 65 % . As time passes the accuracy will keep on increasing . 

 Approach : Learning launch is a great tool to be used especially during prototyping phase . It lets you test   key customer , execution and defensibility assumptions . If the hypothesis is incorrect , it helps you to   learn fast and modify / change your hypothesis or gives you a confirmation to scrap the idea . Although   the tool has been deployed , we are still gathering insights to improve the tool ’s accuracy . We will be   using these insights in our next learning launch activities . 


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: Learning Launch to Build an Early Warning System 

 Challenge : I am a member of my organization ’s Transformation team . Our challenge is to build a   mechanism which will help one of our biggest clients to proactively identify future dissatisfied customers   ( the end users ) . Currently there is no such mechanism available with the client to capture the same .   Implementation of a robust mechanism will help the client to improve customer satisfaction and protect   revenue . In terms of customer base , the project scope includes all their customers in the USA region , in   terms of data accessibility and usability , the project scope includes only the customers ’ change request   emails that they send to the client . 

 I want to mention one very important point here , doing a customer survey to identify dissatisfied   customers is not an option as the client wants to do it without the customers / end - users realizing that   they are being assessed so that client can take proactive actions . 

 Selection : We have formed a small team of 6 people who are all familiar with Design Thinking tools and   activities . Our team is a combination of innovation , analytics , transformation expert from my   organization and Subject matter expert , account manager from our client organization . We want to   build a mechanism / tool to identify dissatisfied customers using their change request emails database .   We are now ready to try learning launch . A learning launch is an experiment / a series of experiments   conducted quickly and inexpensively . 

 Application : For our first learning launch we focused on gathering our thoughts on how the data to be   used . We made a list of criterion basis which the emails will be classified between good / bad / neutral . We   also decided on how the mechanism is going to work at a high level ( as depicted in the below image ) . 

 Instead of building and testing a full - blown tool , we created a macro - based tool on excel . We used a   sample of 300 emails to test if our assumption that dissatisfied customers can be identified from the 

 tones of their emails is correct . We took the learnings from our first learning launch and moved ahead   with the second learning launch . In second learning launch we built a tool called ‘ Early Warning System ’   using advanced NLP , N - Gram charts & sentiment analytics . This tool analyzes unstructured text from   emails , gauge customer sentiment , flag negative emails quickly & accurately , thus enabling concerned   teams to take prompt remedial action . 

 Insight : By using learning launch tool , we quickly tested our assumption and concept . We spent very   minimal money to test if our hypothesis was right . We gathered our learnings and built the final tool .   The client was supremely delighted to see the results . The tool has been deployed and currently it is   running with an accuracy of 65 % . As time passes the accuracy will keep on increasing . 

 Approach : Learning launch is a great tool to be used especially during prototyping phase . It lets you test   key customer , execution and defensibility assumptions . If the hypothesis is incorrect , it helps you to   learn fast and modify / change your hypothesis or gives you a confirmation to scrap the idea . Although   the tool has been deployed , we are still gathering insights to improve the tool ’s accuracy . We will be   using these insights in our next learning launch activities .
### Output: 