Coursera : Design Thinking for Innovation : 

 Learning Launch Tool 

 Challenge : I 'm part of a Performance Management team and I work towards   analysing Customer Satisfaction ( CSAT ) trends in the Call Centre space . The   challenge I ’m faced with is that CSAT for a voice function ( approx . 250 heads ) is   much lower than the goal and needs improvement . We need to understand   what is bringing down the function CSAT performance and take corrective   measures to increase the overall CSAT for the function so they meet the goal . 

 Selection : I 've decided to use the Learning Launch tool ; the key here is to do   experiments that are cheap and fast . I pick a team of 3 individuals viz . a   process lead from another function , a specialist from the function and a   member from the Customer experience team . The team I ’ve selected is diverse   and small as my cognitive blindness will be different from the others and vice   versa . Also , I felt that there is a need to have a sceptic within the team to   protect ourselves from confirmation bias . 

 Application : We begin by coming together and brainstorm on the reasons why   we are facing the specified challenge with each individual getting his / her   freedom of speech . Parallelly we have the MIS team gather customer data .   Post the brainstorm sessions and the data we gathered , we make a list of   assumptions that would help us experiment . We would deliberate if   assumptions are not likely to be true , we would change the idea or Trash can   and move on further . We select 2 teams ( 25 heads ) as our population who   would be part of the experiment . We analysed that majority of the customer   dissatisfaction calls were when calls were cold transferred ( directly transfer a   call to another specialist without any knowledge transfer between the   specialists ) to another function . It was decided that the 25 specialist that were   selected would be trained to connect and introduce the customer to the   specialist of the other function with appropriate knowledge transfer between   them . 

 Insight : We learnt that there was an increase in customer satisfaction scores   for the analyst that were part of the experiment . The human touch made the   difference when the specialist connected and introduced the customer to his   colleague . The CSAT scores had seen an improvement of 10 % to the baseline   for those specialists . This also positively impacted the overall morale of the   team . 

 Approach : Since the experiment was successful on the 25 specialists , the idea   is to roll it out to the entire function . We decide to have the training in batches   of 25 instead of the entire function so that there is no impact to service levels .   The roll out of the new process will be completed within a period of 2 weeks .   With the improvement of CSAT scores seen within the experiment , we would   estimate that the overall function would begin meeting its CSAT goal 3 months   from the time every specialist is onboarded with the new process . The   assumptions gathered in the brainstorming sessions which were not likely to   be true were tabled for possible latter use . 


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: Coursera : Design Thinking for Innovation : 

 Learning Launch Tool 

 Challenge : I 'm part of a Performance Management team and I work towards   analysing Customer Satisfaction ( CSAT ) trends in the Call Centre space . The   challenge I ’m faced with is that CSAT for a voice function ( approx . 250 heads ) is   much lower than the goal and needs improvement . We need to understand   what is bringing down the function CSAT performance and take corrective   measures to increase the overall CSAT for the function so they meet the goal . 

 Selection : I 've decided to use the Learning Launch tool ; the key here is to do   experiments that are cheap and fast . I pick a team of 3 individuals viz . a   process lead from another function , a specialist from the function and a   member from the Customer experience team . The team I ’ve selected is diverse   and small as my cognitive blindness will be different from the others and vice   versa . Also , I felt that there is a need to have a sceptic within the team to   protect ourselves from confirmation bias . 

 Application : We begin by coming together and brainstorm on the reasons why   we are facing the specified challenge with each individual getting his / her   freedom of speech . Parallelly we have the MIS team gather customer data .   Post the brainstorm sessions and the data we gathered , we make a list of   assumptions that would help us experiment . We would deliberate if   assumptions are not likely to be true , we would change the idea or Trash can   and move on further . We select 2 teams ( 25 heads ) as our population who   would be part of the experiment . We analysed that majority of the customer   dissatisfaction calls were when calls were cold transferred ( directly transfer a   call to another specialist without any knowledge transfer between the   specialists ) to another function . It was decided that the 25 specialist that were   selected would be trained to connect and introduce the customer to the   specialist of the other function with appropriate knowledge transfer between   them . 

 Insight : We learnt that there was an increase in customer satisfaction scores   for the analyst that were part of the experiment . The human touch made the   difference when the specialist connected and introduced the customer to his   colleague . The CSAT scores had seen an improvement of 10 % to the baseline   for those specialists . This also positively impacted the overall morale of the   team . 

 Approach : Since the experiment was successful on the 25 specialists , the idea   is to roll it out to the entire function . We decide to have the training in batches   of 25 instead of the entire function so that there is no impact to service levels .   The roll out of the new process will be completed within a period of 2 weeks .   With the improvement of CSAT scores seen within the experiment , we would   estimate that the overall function would begin meeting its CSAT goal 3 months   from the time every specialist is onboarded with the new process . The   assumptions gathered in the brainstorming sessions which were not likely to   be true were tabled for possible latter use .
### Output: 