LEARNING LAUNCH Of PROPOSED SERVICE OFFERING BLUEPRINT 

 CHALLENGE 

 I recently took over a role to incubate new service offerings in my organization . We , as an   organization currently do not have any similar offering that can be used as a foundation to evolve or   create what we want . Being a globally recognized thought leader in this technical domain ( service   management ) , I have been entrusted with the responsibility to come up with the new service line   that is apt for the Next Generation of business services . I had already ideating and creating the   service blueprint before taking the Design Thinking sessions . So , the key challenges that I wanted to   address was : 

 •   What we conceptualized , would it really sell with our existing or potential customers ( will   they really appreciate what we are bringing forth for them ) ? 

 •   If they do , would our execution blueprint really work ? 

 SELECTION 

 I wanted to : 

 •   Test our idea , our blueprint 

 •   Identify what works and what does not 

 •   If fail , fail fast → Learn →Refine → Mature → Scale 

 Thus , I was determined to conduct a series of small experiments with our selected customers , with   each experiment meant to validate a hypothesis based on one of the core components of our new   service offering . Hence , I needed to test the key assumptions that went into the creation of the   blueprint , and thus the service offering blueprint itself . Inspired by the video on IBM Story , I decided   to use ‘ Learning Launch ’ as the tool to conduct the experiments and eventually refine our blueprint . 

 APPLICATION 

 Our proposed service offering constituted of a number of elements , each of which were required to   be tested & validated independently as well as the way they perform in an integrated fashion . So , I   created hypothesis that we needed to validate for each of them . I worked with my delivery   organization to identify the customers ( 10 customers were identified based on their interest and   agreement from varied industry domains ) to whom we can take our service offering blueprint and   collaboratively refine the same . Since , I wanted to experiment and see what works for us and what   does not in minimal time , I grouped the offering elements and related hypothesis . We assigned a   priority ( based on criticality and urgency for readiness of the element for launch ) to each service   element that steered the way we grouped them . We experimented this set with one customer and   another with the another one . The purpose was to : 

 •   Maximise the elements that are being experimented in parallel 

 •   Learn and refine what does not work before experimenting with the next customer   ( initiating the next stage of Learning Launch ) 

 •   Minimize financial exposure and risk 

 Below figure depicts the approach we took to run our hypothesis testing in parallel . 

 The way we applied the Learning Launch tool is diagrammatically depicted below 

 As represented above , I created a hypothesis for each service element which drove the set of   assumptions that I wanted to validate . We tagged each offering elements and thus the hypothesis to   be tested to a customer and the sequence in which the other customers would be approached for   experimenting the hypothesis . Our goal was to experiment each service element with at least 5   customers and then decide if there was a need to take the experimentation to other customers . This   fundamentally formed the next stage of our Learning Launch . We designed the way in which the   experiment would be performed . E.g. for a service element , we did the following 5 key activities   collaboratively with our customer SMEs and resources : 

 •   Refined the customer ’s policy & process in a joint workshop 

 •   Mapping the value chain for that element / hypothesis being tested 

 •   Identified when , where and how the value was created ( if not then why → fed into   refinement of our proposition ) ; Also steered the way we represented and called out the   value , benefits and outcomes 

 •   Identified the challenges that affected the execution and the way we managed them → fed   into refinement of our execution strategy 

 •   Identified the dependencies ( in some cases , we realized this as part of challenges ) → fed   into refinement of our execution strategy with better articulated dependencies 

 To execute the experiments , we grouped our SMEs based on their experience and expertise . I   ensured that our goal is clearly understood by our team so that we all worked towards our common   goal . 

 Data from the experiment was used to refine our overall proposition – commitment towards value   creation / benefits , execution strategy and the dependencies on as well as expectations from various   stakeholders . This also attributed to refinement of our go to market strategy that was meant to scale   our service offering . 

 INSIGHT 

 As described in the previous section the insights gained from the experiments has helped us to   refine our overall service proposition . But in addition to those it changed the way we were   approaching our customers with our service offerings . 3 key ways our overall proposition changed   were : 

 •   We realized that our Service offering can be industry domain contextualised easily ; some   elements were domain contextualized during the experimentation phase 

 •   We recognized that a number of smaller or sub - service offerings can be created providing a   quick win as well as a significant potential financial return for our organization ; This even led   to creation of a number of service packages 

 •   We identified new ways in which the value was being created and more importantly the way   our customers appreciated the value . 

 APPROACH 

 I would prefer to continue with my approach as ‘ Learning Launch ’ gave us compelling benefits for   the service elements that we have tested . So far , we have been able to experiment 12 core   components of our Service Offering blueprint based on the priority that we had assigned to them . We   are continuing with the experimentation of other elements and intent to complete that by end of this   quarter . Results from the previous Learning Launch initiatives has convinced us that this is the best   approach for completing the testing of our complete offering . Additionally , it has triggered an   interest in the organization for testing the current relevance and feasibility of some of our other   ideas too . One thing that I would preferably want to change for experimenting the remaining service   offering blueprint would be to have more customers from each industry domain as it would help me   in having a greater confidence on my domain contextualized offerings . 


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 Of PROPOSED SERVICE OFFERING BLUEPRINT 

 CHALLENGE 

 I recently took over a role to incubate new service offerings in my organization . We , as an   organization currently do not have any similar offering that can be used as a foundation to evolve or   create what we want . Being a globally recognized thought leader in this technical domain ( service   management ) , I have been entrusted with the responsibility to come up with the new service line   that is apt for the Next Generation of business services . I had already ideating and creating the   service blueprint before taking the Design Thinking sessions . So , the key challenges that I wanted to   address was : 

 •   What we conceptualized , would it really sell with our existing or potential customers ( will   they really appreciate what we are bringing forth for them ) ? 

 •   If they do , would our execution blueprint really work ? 

 SELECTION 

 I wanted to : 

 •   Test our idea , our blueprint 

 •   Identify what works and what does not 

 •   If fail , fail fast → Learn →Refine → Mature → Scale 

 Thus , I was determined to conduct a series of small experiments with our selected customers , with   each experiment meant to validate a hypothesis based on one of the core components of our new   service offering . Hence , I needed to test the key assumptions that went into the creation of the   blueprint , and thus the service offering blueprint itself . Inspired by the video on IBM Story , I decided   to use ‘ Learning Launch ’ as the tool to conduct the experiments and eventually refine our blueprint . 

 APPLICATION 

 Our proposed service offering constituted of a number of elements , each of which were required to   be tested & validated independently as well as the way they perform in an integrated fashion . So , I   created hypothesis that we needed to validate for each of them . I worked with my delivery   organization to identify the customers ( 10 customers were identified based on their interest and   agreement from varied industry domains ) to whom we can take our service offering blueprint and   collaboratively refine the same . Since , I wanted to experiment and see what works for us and what   does not in minimal time , I grouped the offering elements and related hypothesis . We assigned a   priority ( based on criticality and urgency for readiness of the element for launch ) to each service   element that steered the way we grouped them . We experimented this set with one customer and   another with the another one . The purpose was to : 

 •   Maximise the elements that are being experimented in parallel 

 •   Learn and refine what does not work before experimenting with the next customer   ( initiating the next stage of Learning Launch ) 

 •   Minimize financial exposure and risk 

 Below figure depicts the approach we took to run our hypothesis testing in parallel . 

 The way we applied the Learning Launch tool is diagrammatically depicted below 

 As represented above , I created a hypothesis for each service element which drove the set of   assumptions that I wanted to validate . We tagged each offering elements and thus the hypothesis to   be tested to a customer and the sequence in which the other customers would be approached for   experimenting the hypothesis . Our goal was to experiment each service element with at least 5   customers and then decide if there was a need to take the experimentation to other customers . This   fundamentally formed the next stage of our Learning Launch . We designed the way in which the   experiment would be performed . E.g. for a service element , we did the following 5 key activities   collaboratively with our customer SMEs and resources : 

 •   Refined the customer ’s policy & process in a joint workshop 

 •   Mapping the value chain for that element / hypothesis being tested 

 •   Identified when , where and how the value was created ( if not then why → fed into   refinement of our proposition ) ; Also steered the way we represented and called out the   value , benefits and outcomes 

 •   Identified the challenges that affected the execution and the way we managed them → fed   into refinement of our execution strategy 

 •   Identified the dependencies ( in some cases , we realized this as part of challenges ) → fed   into refinement of our execution strategy with better articulated dependencies 

 To execute the experiments , we grouped our SMEs based on their experience and expertise . I   ensured that our goal is clearly understood by our team so that we all worked towards our common   goal . 

 Data from the experiment was used to refine our overall proposition – commitment towards value   creation / benefits , execution strategy and the dependencies on as well as expectations from various   stakeholders . This also attributed to refinement of our go to market strategy that was meant to scale   our service offering . 

 INSIGHT 

 As described in the previous section the insights gained from the experiments has helped us to   refine our overall service proposition . But in addition to those it changed the way we were   approaching our customers with our service offerings . 3 key ways our overall proposition changed   were : 

 •   We realized that our Service offering can be industry domain contextualised easily ; some   elements were domain contextualized during the experimentation phase 

 •   We recognized that a number of smaller or sub - service offerings can be created providing a   quick win as well as a significant potential financial return for our organization ; This even led   to creation of a number of service packages 

 •   We identified new ways in which the value was being created and more importantly the way   our customers appreciated the value . 

 APPROACH 

 I would prefer to continue with my approach as ‘ Learning Launch ’ gave us compelling benefits for   the service elements that we have tested . So far , we have been able to experiment 12 core   components of our Service Offering blueprint based on the priority that we had assigned to them . We   are continuing with the experimentation of other elements and intent to complete that by end of this   quarter . Results from the previous Learning Launch initiatives has convinced us that this is the best   approach for completing the testing of our complete offering . Additionally , it has triggered an   interest in the organization for testing the current relevance and feasibility of some of our other   ideas too . One thing that I would preferably want to change for experimenting the remaining service   offering blueprint would be to have more customers from each industry domain as it would help me   in having a greater confidence on my domain contextualized offerings .
### Output: 