Example Reflection – Learning Launch 

 Challenge 

 This reflection is based on a case from my experience with a service organization . The company   offers maintenance and repair services of industrial equipment . When service engineers are on site   they often find equipment potentially mature for replacement ( end of operational life ) . 

 With the project my team and I wanted to set up a lead generation process - implemented in   company 's CRM - to target modernization opportunities for the above category of assets 

 Leads would come in from service engineers via a mobile app . One important requirement was to   design a process with little overhead for service engineers and technical sales who would engage the   end - customer . However the registered information had to be relevant to enable a meaningful   engagement . Services and sales organizations were involved in the project and they were my internal   customer . 

 Selection 

 We could not kick off a full project involving a large number of resources without having the process   right , and a demonstrated upside . 

 The team , me included , did not have much experience with Design Thinking , however we worked   agile and we planned for several refinements . We had few interviews with our customer - a relatively   small subset of the service engineers and tech sales - to define the process , understand the   workflow , the required data set , what we needed to report and measure , lead times , and hit / win   rates , etc . Our hypothesis was that a proactive and facts based engagement with the end customer   would lead to higher hit / win rate , above historical average . 

 We then wanted to focus on learning and measuring these rates through a live process , therefore we   run a learning launch . 

 Application 

 After the process was defined , we briefed and planned the learning launch with the selected   engineers and sales reps . 

 We used a progressive approach . We first simulated the data pushed by service engineers into the   CRM , we created mockup leads and forwarded them to the sales reps to receive feedback on data   quality . Thereafter we verified that service engineers could indeed and with low overhead ( 5 - 10 min )   provide such data . There were two important outcomes from this step : 

    Taking pictures of the equipment was agreed to be a quick way to provide much of the required   information ( rating labels , serial number , visual inspection , type of environment , etc ) . 

    Often a challenge for a service engineer was to figure out end - customer relevant contact   information , such as service manager , operation manager , facility manager , etc . so that tech   sales could engage with the right person at the end - customer . This could be solved by enhancing   the information in the " back office " 

 We then set on the learning launch as such , which was planned to last two months . The chosen   service engineers were enabled to push data of candidate assets by using the mobile app . The lead   was received in the CRM as an email ( at this stage of learning launch ) . It was then examined and   enhanced in the back office by my team by looking up databases of known assets and customers . If   the lead was qualified , it was then forward in the CRM to a sales rep who tried to engage with the   end - customer . 

 Insights and approach 

 By the end of the learning launch we got more than 200 leads from candidate assets . We learnt the   percentage of qualified leads and of those representing actual business opportunities with their   value . We gather additional requirements for the improvements of the front - end app and the   automation achievable in the CRM platform to enhance and qualify leads , thus reducing transaction   cost . 

 We also discovered hidden expectations f.e . the need for a fair incentive scheme for service   engineers ( sales reps have naturally one ) who were creating incremental value for the company . We   had a stronger case , as we proved the hypothesis ; we better understood the implications and the   cost for implementing the process in production , as well as the incremental value for the company .   As I write the lead generation process has been running for a couple of years with significant business   upside . 

 In conclusion , this Design Thinking course made me aware of the importance of the " learning   launch ” , as a powerful tool for minimizing risk of “ failing late and expensive ” .   The scope of a learning   launch can be adapted , by selecting internal customers - in an early phase to learn quickly and cheap   – vs. external end - customers to learn from real life feedback . I would say that learning launch is a   robust end - to - end testing approach , which ought to be used in any new product or service   introductions . 


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 

 Challenge 

 This reflection is based on a case from my experience with a service organization . The company   offers maintenance and repair services of industrial equipment . When service engineers are on site   they often find equipment potentially mature for replacement ( end of operational life ) . 

 With the project my team and I wanted to set up a lead generation process - implemented in   company 's CRM - to target modernization opportunities for the above category of assets 

 Leads would come in from service engineers via a mobile app . One important requirement was to   design a process with little overhead for service engineers and technical sales who would engage the   end - customer . However the registered information had to be relevant to enable a meaningful   engagement . Services and sales organizations were involved in the project and they were my internal   customer . 

 Selection 

 We could not kick off a full project involving a large number of resources without having the process   right , and a demonstrated upside . 

 The team , me included , did not have much experience with Design Thinking , however we worked   agile and we planned for several refinements . We had few interviews with our customer - a relatively   small subset of the service engineers and tech sales - to define the process , understand the   workflow , the required data set , what we needed to report and measure , lead times , and hit / win   rates , etc . Our hypothesis was that a proactive and facts based engagement with the end customer   would lead to higher hit / win rate , above historical average . 

 We then wanted to focus on learning and measuring these rates through a live process , therefore we   run a learning launch . 

 Application 

 After the process was defined , we briefed and planned the learning launch with the selected   engineers and sales reps . 

 We used a progressive approach . We first simulated the data pushed by service engineers into the   CRM , we created mockup leads and forwarded them to the sales reps to receive feedback on data   quality . Thereafter we verified that service engineers could indeed and with low overhead ( 5 - 10 min )   provide such data . There were two important outcomes from this step : 

    Taking pictures of the equipment was agreed to be a quick way to provide much of the required   information ( rating labels , serial number , visual inspection , type of environment , etc ) . 

    Often a challenge for a service engineer was to figure out end - customer relevant contact   information , such as service manager , operation manager , facility manager , etc . so that tech   sales could engage with the right person at the end - customer . This could be solved by enhancing   the information in the " back office " 

 We then set on the learning launch as such , which was planned to last two months . The chosen   service engineers were enabled to push data of candidate assets by using the mobile app . The lead   was received in the CRM as an email ( at this stage of learning launch ) . It was then examined and   enhanced in the back office by my team by looking up databases of known assets and customers . If   the lead was qualified , it was then forward in the CRM to a sales rep who tried to engage with the   end - customer . 

 Insights and approach 

 By the end of the learning launch we got more than 200 leads from candidate assets . We learnt the   percentage of qualified leads and of those representing actual business opportunities with their   value . We gather additional requirements for the improvements of the front - end app and the   automation achievable in the CRM platform to enhance and qualify leads , thus reducing transaction   cost . 

 We also discovered hidden expectations f.e . the need for a fair incentive scheme for service   engineers ( sales reps have naturally one ) who were creating incremental value for the company . We   had a stronger case , as we proved the hypothesis ; we better understood the implications and the   cost for implementing the process in production , as well as the incremental value for the company .   As I write the lead generation process has been running for a couple of years with significant business   upside . 

 In conclusion , this Design Thinking course made me aware of the importance of the " learning   launch ” , as a powerful tool for minimizing risk of “ failing late and expensive ” .   The scope of a learning   launch can be adapted , by selecting internal customers - in an early phase to learn quickly and cheap   – vs. external end - customers to learn from real life feedback . I would say that learning launch is a   robust end - to - end testing approach , which ought to be used in any new product or service   introductions .
### Output: 