Reflection Beescoop Logistics ( beta programme ) – Learning Launch Tool 

 Challenge 

 For a small logistics company called Beescoop who ’s operation is dependent on freelance drivers   with the target audience being individuals requiring delivery of goods and/or relocating from one   place to another we had a challenge of matching drivers to customers . Not being in control of the   driver ’s actions or personal schedules meant one driver could be assigned a job while personally   having a different job which led to delays and dissatisfied customers . Because a job was already taken   on - delay measures for reassigning drivers were not put in place as well as means of communication   throughout our platform . 

 With communication being between the client and the driver , personally we had no records to look   back on and therefore were unaware of the stress our customers were facing and how we could help   them . This also meant we had no data to perform thorough research on to improve our customer   service and meet new customer demands as well as come up with innovative ideas . 

 Selection 

 We chose to use the Learning Launch Tool . With the learning launch tool , we can test out new features   and tools with our customers and get feedback from them so we can decide on whether or not it is   worth the effort to scale or not . In - order to accomplish this , we made use of the ethnographic tool as   well . We are dealing with customers who are going through different experiences and with different   needs and requirements . Before we can test any feature , we must learn what it is our customers are   really experiencing . 

 We also made use of the Mind Mapping tool , our customers are providing use with data , we need to   gather that data , find patterns and insights from it so we can decide which features to put in on   Learning Launch sprints ( small releases to test features ) . 

 By making use of the Learning Launch Tool , we can roll out small features ( i.e. messaging feature ,   community , etc . ) and get results on how they make use of it and whether we can integrate into our   next release for all the customers . For example , during one of the beta programme , we added an inbox   feature where a customer can directly contact a driver within the platform . This beta test ran for two   weeks , and all our customers and drivers were making full use of it and therefore we have scaled and   customers are making full use of it . 

 Application 

 We made use of Learning Launch by initiating a beta programme and sent invitations to our clients for   them to sign up . Here , they can give out suggestions as to what it is , they would like changed , get likes   and reaction from other customers as well as communicate or comment on the requested changes .   This provides us with a base to initiate our “ What is ” research and a community for our customers to   engage . 

 By making use of Mind mapping , we can extract the patterns on the data gathered from the beta   programme and feedback from customers ( such as the request for drivers to be changed during delay ) .   From then , we generate the possible best solution ( like drivers near me or available driver list select )   to incorporate the requests brought forward while in the same time creating criteria customers were   not aware of before ( innovate ) . 

 Once we have decided on the design criteria , we then come up with possible design solutions . From   the solutions gathered , we categories them in our “ What Wow Zone ” to perfectly identify those that   align with our current capabilities , business model and address what customers want ( in the case of   driver delay , we decided to allow a user to select a new available driver , driver near me is our of   current budget as it requires mapping and contracts ) . 

 Solutions which satisfy the conditions are then categorized as our “ wow solutions ” and are prioritized   and are put in line for the next beta release for customers to try out and give feedback . 

 Insight & Approach 

 By initiating the beta programme and thus making full use of the Learning Launch Tool , I have noticed   that there are features which I personally might think are great and fantastic only to find out they are   not as great for the customers ( confirmation bias ) . 

 Our customers can now , on their own find problems and provide solutions . For example , when some   are giving suggestion , some are pointing our errors while others reply to correct other customers . 

 By combining this with Mind Mapping , we are looking to making the beta programme community   portal public to registered users . That we ’ll allow all our customers to look up solutions , help other   customers and provide feedback . We have noted that customers requesting collection / delivery are in   a hurry and we have therefore decided to integrate the portal into the App itself for fast easy access . 

 To enhance our use of the Learning Launch tool , we place to do a pre - beta release . Here we do not   release an actual feature but rather a simulation of the feature , where users will see how the feature   will work . This help reduces the strain on the development as well as the number of small features to   release and at the same time save costs on resources . 


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: Reflection Beescoop Logistics ( beta programme ) – Learning Launch Tool 

 Challenge 

 For a small logistics company called Beescoop who ’s operation is dependent on freelance drivers   with the target audience being individuals requiring delivery of goods and/or relocating from one   place to another we had a challenge of matching drivers to customers . Not being in control of the   driver ’s actions or personal schedules meant one driver could be assigned a job while personally   having a different job which led to delays and dissatisfied customers . Because a job was already taken   on - delay measures for reassigning drivers were not put in place as well as means of communication   throughout our platform . 

 With communication being between the client and the driver , personally we had no records to look   back on and therefore were unaware of the stress our customers were facing and how we could help   them . This also meant we had no data to perform thorough research on to improve our customer   service and meet new customer demands as well as come up with innovative ideas . 

 Selection 

 We chose to use the Learning Launch Tool . With the learning launch tool , we can test out new features   and tools with our customers and get feedback from them so we can decide on whether or not it is   worth the effort to scale or not . In - order to accomplish this , we made use of the ethnographic tool as   well . We are dealing with customers who are going through different experiences and with different   needs and requirements . Before we can test any feature , we must learn what it is our customers are   really experiencing . 

 We also made use of the Mind Mapping tool , our customers are providing use with data , we need to   gather that data , find patterns and insights from it so we can decide which features to put in on   Learning Launch sprints ( small releases to test features ) . 

 By making use of the Learning Launch Tool , we can roll out small features ( i.e. messaging feature ,   community , etc . ) and get results on how they make use of it and whether we can integrate into our   next release for all the customers . For example , during one of the beta programme , we added an inbox   feature where a customer can directly contact a driver within the platform . This beta test ran for two   weeks , and all our customers and drivers were making full use of it and therefore we have scaled and   customers are making full use of it . 

 Application 

 We made use of Learning Launch by initiating a beta programme and sent invitations to our clients for   them to sign up . Here , they can give out suggestions as to what it is , they would like changed , get likes   and reaction from other customers as well as communicate or comment on the requested changes .   This provides us with a base to initiate our “ What is ” research and a community for our customers to   engage . 

 By making use of Mind mapping , we can extract the patterns on the data gathered from the beta   programme and feedback from customers ( such as the request for drivers to be changed during delay ) .   From then , we generate the possible best solution ( like drivers near me or available driver list select )   to incorporate the requests brought forward while in the same time creating criteria customers were   not aware of before ( innovate ) . 

 Once we have decided on the design criteria , we then come up with possible design solutions . From   the solutions gathered , we categories them in our “ What Wow Zone ” to perfectly identify those that   align with our current capabilities , business model and address what customers want ( in the case of   driver delay , we decided to allow a user to select a new available driver , driver near me is our of   current budget as it requires mapping and contracts ) . 

 Solutions which satisfy the conditions are then categorized as our “ wow solutions ” and are prioritized   and are put in line for the next beta release for customers to try out and give feedback . 

 Insight & Approach 

 By initiating the beta programme and thus making full use of the Learning Launch Tool , I have noticed   that there are features which I personally might think are great and fantastic only to find out they are   not as great for the customers ( confirmation bias ) . 

 Our customers can now , on their own find problems and provide solutions . For example , when some   are giving suggestion , some are pointing our errors while others reply to correct other customers . 

 By combining this with Mind Mapping , we are looking to making the beta programme community   portal public to registered users . That we ’ll allow all our customers to look up solutions , help other   customers and provide feedback . We have noted that customers requesting collection / delivery are in   a hurry and we have therefore decided to integrate the portal into the App itself for fast easy access . 

 To enhance our use of the Learning Launch tool , we place to do a pre - beta release . Here we do not   release an actual feature but rather a simulation of the feature , where users will see how the feature   will work . This help reduces the strain on the development as well as the number of small features to   release and at the same time save costs on resources .
### Output: 