Learning Launch tool 

 Challenge 

 We were designing the telco offerings to launch for Indonesia market . We had to decide the   product ( including which pain points we take up ) along with pricing to increase the number   of transactions at a low acquisition cost . Product in Telco means the base plan that customer   subscribes ( Data in GB , Voice in Mins , SMS in count along with pricing and validity of the   package ) 

 We had conducted interviews to understand the key pain points in the market and designed   a solution . Before we invested more money into marketing the new ideas and developing full   blown solutions , we wanted to be sure that the solution we had ideated would result in   better outcomes ( ie . Increased transactions and increased transactions at low customer   acquisition cost ) 

 Selection of the Tool 

 We selected learning launch technique because of the following reasons . 

 • We had done a business plan with quite a key assumption on payback period , demand , 

 penetration , and revenue . But as presented in the lectures , these were guesses . 

 • We did not have past data to support our assumptions across multiple ideas .   • Since we had multiple ideas and key assumptions for which we did not have data to 

 support , we went for learning launches tool . 

 Also , for the solution that we designed to solve the customer pain point , customer execution   and defensibility assumptions , the main one where our confidence level was low was around   customer assumptions . So , we took up the customer assumptions to validate that using   learning launches . 

 Application 

 We listed down the key assumptions on our solution on why our solution will solve the   problem better than the current alternative options available for the customer . They key   assumptions were- 

 a ) Enabling the customer to buy online would increase the gross adds owing 

 convenience and COVID-19 - related behavioural change ; They are more comfortable   buying in branded websites . 

 b ) Enabling the Customers to buy from ecommerce stores ( such as amazon ) would 

 increase the number of transactions as they are more comfortable because they have   done that multiple times in the past . 

 c ) Offering customers with extremely high data packages and unlimited plans will result 

 in more transactions Customers data usage has increased owing to CoVID-19 . 

 Once we designed our key assumptions , we designed tests by creating a website and ads   online and tested for increase in conversion rate as the success metric to pick the ideas to   execute . 

 Insights and learnings 

 Insight : Reflection on the design thinking process 

 • The key insight I got from this exercise was , this provided us with unquestionable data 

 to support our business plan . 

 • Usually , when we are faced with uncertain circumstances , the idea that wins and is 

 eventually launched is the idea approved by the senior most member in the group .   But in this case , the idea that was approved was supported by data . 

 Insight : Reflections related to the challenge 

 • It was clear that out of the 3 ideas , idea # 1 was not a good idea , whereas ideas # 2 and 

 # 3 worked well . 

 Learnings 

 One thing we could have done better is to test these assumptions for a segment . In the   current form , although , it was unsegmented , we could get a baseline in terms of the success   of the initiative , but we could n’t get many insights into why because the reasons are   segmented . 


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 tool 

 Challenge 

 We were designing the telco offerings to launch for Indonesia market . We had to decide the   product ( including which pain points we take up ) along with pricing to increase the number   of transactions at a low acquisition cost . Product in Telco means the base plan that customer   subscribes ( Data in GB , Voice in Mins , SMS in count along with pricing and validity of the   package ) 

 We had conducted interviews to understand the key pain points in the market and designed   a solution . Before we invested more money into marketing the new ideas and developing full   blown solutions , we wanted to be sure that the solution we had ideated would result in   better outcomes ( ie . Increased transactions and increased transactions at low customer   acquisition cost ) 

 Selection of the Tool 

 We selected learning launch technique because of the following reasons . 

 • We had done a business plan with quite a key assumption on payback period , demand , 

 penetration , and revenue . But as presented in the lectures , these were guesses . 

 • We did not have past data to support our assumptions across multiple ideas .   • Since we had multiple ideas and key assumptions for which we did not have data to 

 support , we went for learning launches tool . 

 Also , for the solution that we designed to solve the customer pain point , customer execution   and defensibility assumptions , the main one where our confidence level was low was around   customer assumptions . So , we took up the customer assumptions to validate that using   learning launches . 

 Application 

 We listed down the key assumptions on our solution on why our solution will solve the   problem better than the current alternative options available for the customer . They key   assumptions were- 

 a ) Enabling the customer to buy online would increase the gross adds owing 

 convenience and COVID-19 - related behavioural change ; They are more comfortable   buying in branded websites . 

 b ) Enabling the Customers to buy from ecommerce stores ( such as amazon ) would 

 increase the number of transactions as they are more comfortable because they have   done that multiple times in the past . 

 c ) Offering customers with extremely high data packages and unlimited plans will result 

 in more transactions Customers data usage has increased owing to CoVID-19 . 

 Once we designed our key assumptions , we designed tests by creating a website and ads   online and tested for increase in conversion rate as the success metric to pick the ideas to   execute . 

 Insights and learnings 

 Insight : Reflection on the design thinking process 

 • The key insight I got from this exercise was , this provided us with unquestionable data 

 to support our business plan . 

 • Usually , when we are faced with uncertain circumstances , the idea that wins and is 

 eventually launched is the idea approved by the senior most member in the group .   But in this case , the idea that was approved was supported by data . 

 Insight : Reflections related to the challenge 

 • It was clear that out of the 3 ideas , idea # 1 was not a good idea , whereas ideas # 2 and 

 # 3 worked well . 

 Learnings 

 One thing we could have done better is to test these assumptions for a segment . In the   current form , although , it was unsegmented , we could get a baseline in terms of the success   of the initiative , but we could n’t get many insights into why because the reasons are   segmented .
### Output: 