Example Reflection – Learning Launch 

 1 . Challenge : 

 We worked with one of the utility projects , the biggest challenge for this client was to 

 address number of calls they got regarding the billing ( Electricity bills ) , the queries ranged 

 from simple due date of bill payment and modes of payment or even asking the breakdown 

 of cost . The client ’s customer support team was overworked , and it was not a good sign for 

 client to get so many ( 3000 per day ) calls with queries which basically meant customer has 

 some issues with their bill . 

 2 . Selection : 

 We were working with clients from past 2 years and knew the technical landscape of client 

 well and their business model . Since we had idea of what client challenge was , we adopted 

 learning launch so that we can ideate and come up with prototype in short span of time . 

 Also we had initial design thinking sessions with both client and their customers . 

 3 . Application : 

 Based on our understanding from client and customer discussions majority of the queries 

 customer support teams got were there to be found on Bill , which basically pointed that 

 existing bill format was the culprit . To validate this assumption one more round of design 

 thinking session was held only with clients customers and we learnt from each one of them 

 in detail to confirm our assumption . Not so surprisingly most of the customers pointed 

 towards cumbersome billing format and not having the required information ex Bill Payment 

 date highlighted or breakdown of billing provided .. Existing bill format was being used from 

 2 decades and not been revisited . So the idea was to redesign the bill and create a 

 prototype . 

 4 . Insight : 

 With inputs from customer we wanted to test our assumptions , hence we started working 

 on protype of new bill and within 1 week we were ready with prototype . The new bill had 

 most common customer support queries highlighted so that its clear by glancing at bill what 

 is what . Also we removed 30 % of unwanted information from billing which added no value 

 but was just put there from 2 decades . We even changed the Font and Size of font on the bill 

 and instead of light brown background complete white background was used for billing . So 

 customers had clear white background with the information they wanted to know 

 highlighted on bill . 

 5 . Approach : 

 Since we were working with client from few years and knew at surface what the issue is , we 

 took learning launch as tool . This is iterative process to verify the customer queries even 

 after roll - out of new bill and to ensure the goal of < 100 calls per day is achieved . 


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: Example Reflection – Learning Launch 

 1 . Challenge : 

 We worked with one of the utility projects , the biggest challenge for this client was to 

 address number of calls they got regarding the billing ( Electricity bills ) , the queries ranged 

 from simple due date of bill payment and modes of payment or even asking the breakdown 

 of cost . The client ’s customer support team was overworked , and it was not a good sign for 

 client to get so many ( 3000 per day ) calls with queries which basically meant customer has 

 some issues with their bill . 

 2 . Selection : 

 We were working with clients from past 2 years and knew the technical landscape of client 

 well and their business model . Since we had idea of what client challenge was , we adopted 

 learning launch so that we can ideate and come up with prototype in short span of time . 

 Also we had initial design thinking sessions with both client and their customers . 

 3 . Application : 

 Based on our understanding from client and customer discussions majority of the queries 

 customer support teams got were there to be found on Bill , which basically pointed that 

 existing bill format was the culprit . To validate this assumption one more round of design 

 thinking session was held only with clients customers and we learnt from each one of them 

 in detail to confirm our assumption . Not so surprisingly most of the customers pointed 

 towards cumbersome billing format and not having the required information ex Bill Payment 

 date highlighted or breakdown of billing provided .. Existing bill format was being used from 

 2 decades and not been revisited . So the idea was to redesign the bill and create a 

 prototype . 

 4 . Insight : 

 With inputs from customer we wanted to test our assumptions , hence we started working 

 on protype of new bill and within 1 week we were ready with prototype . The new bill had 

 most common customer support queries highlighted so that its clear by glancing at bill what 

 is what . Also we removed 30 % of unwanted information from billing which added no value 

 but was just put there from 2 decades . We even changed the Font and Size of font on the bill 

 and instead of light brown background complete white background was used for billing . So 

 customers had clear white background with the information they wanted to know 

 highlighted on bill . 

 5 . Approach : 

 Since we were working with client from few years and knew at surface what the issue is , we 

 took learning launch as tool . This is iterative process to verify the customer queries even 

 after roll - out of new bill and to ensure the goal of < 100 calls per day is achieved .
### Output: 