Example Reflection – Launching the learning tool 

 Challenge : Ideation and experimentation of hypothesis was quite an interesting experience for me ,   whilst leading and working on a digital transformation program for a marquee client within the Telco   sector . 

 To remain competitive in the rapid - paced digital world and prevent erosion of their existing customer   base due to tariff wars , our client partnered with us on their journey towards digital transformation . 

 Through a rapid solutions environment and running a series of hackathons by mapping the customer   journeys and applied innovation , we generated disruptive and bold ideas on building multiple variations   of a self - serve digital platform to provide their customers with a frictionless and enhanced experience . 

 Since our ideas were based on an inside - out approach , it was imperative for us to test our hypothesis   through experimentation and ensure that we neutralized our confirmation bias through a pragmatic   approach . 

 As a logical starting point , we put together a multi - function crack team with a diverse range of skills   comprising of sales manager , digital marketeer , management consultant , solution architect , sector   domain consultant , full - stack digital engineer and an alliance partner who would help us with our cognitive   biases . 

 Selection : We followed a tradition approach of building a set of minimal viable protypes MVPs with   different variations and simulations that would help the client envision their future business model 

 Our experimental learning launch of the MVP was intended to search if our hypothesis of a self - serve   digital platform would help the client to that could allow the client to surmount the challenge of silent   churn and build in customer loyalty . 

 Application : As part of our experimentation , we launched the platform at one of their subsidiaries that   had a smaller base of their overall customer set ; but was fast emerging as a hotspot of a tariff war with a   lot of new entrants offering lucrative plans for data and voice . 

 This tested our assumption of offering the flexibility to customers to choose their plans based on their   needs as well as consume them as per their choices , as opposed to providing fixed rental plans that was   leading to our client ’s competitors stealing away their business . 

 The MVP was soft launched in the market as promotional platform and targeting existing and new sets of   clients through a digital format of self - service mode . This mode of launch was intended to test the digital   appetite of the customer base and their ability to change from the traditional mode of visiting retail   outlets – and in turn provide our customer with a competitive advantage . We also captured feedback   from clients on their experience , aspirations , needs and requirements in order to tailor plans in the future . 

 Insights : Through experimentation of our MVP in the soft - launch mode , our team identified a series of   useful insights that we had built into the MVP as well as from the data captured from the clients through   the forms . Our actual results were a mixed bag comprising of a boost in sales from the student population 

 and office going younger generation who were digitally more - savvy as opposed to older generation who   were accustomed to the traditional format of recharging or buying new plans from the retail outlets . The   other interesting insight was our client was able to attract new customers through a faster go - to - market   approach and had reduced rate of silent churn during the soft - launch phase . There were a series of new   learnings , approaches , tweaks and design changes that we derived from the soft - launch phase and would   need to be suitably incorporated into the design phase using the “ So What ” approach to eliminate further   confirming biases from this experiment . 

 Approach : The learning soft launch proved to be extremely insightful and impactful for us to derive key   learnings and make the necessary tweaks in our build plans . Results could have been encouraging and we   could have made a bigger impact through experimentation if we had adopted a logical phased approach   and further including options from the skeptic such as digital kiosks , e - sims - as opposed to going with a   ‘ one size fits ’ digital approach . 

 Deriving learnings from here , we could have worked through strongly with our alliance partner as a key   skeptic within the group on building these product features as well as on disconfirming facts with our   client stakeholders on the next hypothesis before commissioning the full roll out of the platform . 


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 – Launching the learning tool 

 Challenge : Ideation and experimentation of hypothesis was quite an interesting experience for me ,   whilst leading and working on a digital transformation program for a marquee client within the Telco   sector . 

 To remain competitive in the rapid - paced digital world and prevent erosion of their existing customer   base due to tariff wars , our client partnered with us on their journey towards digital transformation . 

 Through a rapid solutions environment and running a series of hackathons by mapping the customer   journeys and applied innovation , we generated disruptive and bold ideas on building multiple variations   of a self - serve digital platform to provide their customers with a frictionless and enhanced experience . 

 Since our ideas were based on an inside - out approach , it was imperative for us to test our hypothesis   through experimentation and ensure that we neutralized our confirmation bias through a pragmatic   approach . 

 As a logical starting point , we put together a multi - function crack team with a diverse range of skills   comprising of sales manager , digital marketeer , management consultant , solution architect , sector   domain consultant , full - stack digital engineer and an alliance partner who would help us with our cognitive   biases . 

 Selection : We followed a tradition approach of building a set of minimal viable protypes MVPs with   different variations and simulations that would help the client envision their future business model 

 Our experimental learning launch of the MVP was intended to search if our hypothesis of a self - serve   digital platform would help the client to that could allow the client to surmount the challenge of silent   churn and build in customer loyalty . 

 Application : As part of our experimentation , we launched the platform at one of their subsidiaries that   had a smaller base of their overall customer set ; but was fast emerging as a hotspot of a tariff war with a   lot of new entrants offering lucrative plans for data and voice . 

 This tested our assumption of offering the flexibility to customers to choose their plans based on their   needs as well as consume them as per their choices , as opposed to providing fixed rental plans that was   leading to our client ’s competitors stealing away their business . 

 The MVP was soft launched in the market as promotional platform and targeting existing and new sets of   clients through a digital format of self - service mode . This mode of launch was intended to test the digital   appetite of the customer base and their ability to change from the traditional mode of visiting retail   outlets – and in turn provide our customer with a competitive advantage . We also captured feedback   from clients on their experience , aspirations , needs and requirements in order to tailor plans in the future . 

 Insights : Through experimentation of our MVP in the soft - launch mode , our team identified a series of   useful insights that we had built into the MVP as well as from the data captured from the clients through   the forms . Our actual results were a mixed bag comprising of a boost in sales from the student population 

 and office going younger generation who were digitally more - savvy as opposed to older generation who   were accustomed to the traditional format of recharging or buying new plans from the retail outlets . The   other interesting insight was our client was able to attract new customers through a faster go - to - market   approach and had reduced rate of silent churn during the soft - launch phase . There were a series of new   learnings , approaches , tweaks and design changes that we derived from the soft - launch phase and would   need to be suitably incorporated into the design phase using the “ So What ” approach to eliminate further   confirming biases from this experiment . 

 Approach : The learning soft launch proved to be extremely insightful and impactful for us to derive key   learnings and make the necessary tweaks in our build plans . Results could have been encouraging and we   could have made a bigger impact through experimentation if we had adopted a logical phased approach   and further including options from the skeptic such as digital kiosks , e - sims - as opposed to going with a   ‘ one size fits ’ digital approach . 

 Deriving learnings from here , we could have worked through strongly with our alliance partner as a key   skeptic within the group on building these product features as well as on disconfirming facts with our   client stakeholders on the next hypothesis before commissioning the full roll out of the platform .
### Output: 