In - Store Merchandising Placement – Learning Launch 

 Challenge   I work in a Consumer Electronics company that manufactures and sells Audio equipment   ( such as speakers , headphones , earphones , etc ) . The company sells its products via   different consumer electronics stores , all around the world ( e.g. BestBuy in the US , Fnac   in France , MediaMarkt in Germany , etc ) .   We have been designing our latest Visual   Merchandising elements for the stores , i.e. the way we will be displaying our products in   those stores . That includes the furniture , the placement , the signage , the demonstrations ,   etc . Our challenge was to identify the best solution from the many available , within a   given budget limit , to make the best business impact ( i.e. maximum sales ) . The solution   would be deployed in more than 1000 stores globally and thus it needed to be well   thought and with minimal risk , as after deployment changes would not be easy at this   large scale . 

 Selection   We have been internally working with agile methodology in different parts of the   organization and in this case we decided to use the   learning launch tool . We wanted to   create a control learning launch as an experiment to quickly test the ideas and prototypes ,   before rolling solutions out the market widely . 

 Application   We decided to use 6 stores as trial locations ( 2 in US , 2 in France , 2 in Germany ) . And   we created 2 different visual merchandising concepts that we then trialed in each of those   stores ( i.e. each concept was tried in 3 stores , all in different countries ) . One of the   concepts was more visual ( i.e. large imagery next to the products ) , with photos of how the   products are used ( e.g. in a living room environment , or while traveling ) . The other   concept was more focused on the product specifics ( e.g. materials used , technical   specifications , etc ) . The prototypes had sensors so we could collect data on how many   people reached the display , how many people tried / demo the products , and of course how   many people made a purchase . We run this trial for 2 months , to collect sufficient data . 

 Insight   Apart from the quantitative data , we also collected data by observation ( i.e. looking what   people were doing in the store ) but also talking to the sales personnel about their   observations . We learned that talking more about the lifestyle and context of the product ,   i.e. how it is used and how it adds value to the customer at home or traveling , was   definitely much more inspiring and intriguing for the customers to approach and interact   with the product displays ( vs. the technical specification alternative approach ) . We are   now in the phase of rolling out the concept globally . 

 Approach   While the learning launch tool gave us strong insight on the correct concept to use , next   time we should like to take it into more “ steps ” , in a more iterative approach . While we   answered the top level question , we also have some other smaller questions unanswered .   Next time we would like to make a similar setup with 6 stores , but before launching try a   more refined concept to e.g. 10 - 12 stores , for nailing down more details , before going   global . 


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: In - Store Merchandising Placement – Learning Launch 

 Challenge   I work in a Consumer Electronics company that manufactures and sells Audio equipment   ( such as speakers , headphones , earphones , etc ) . The company sells its products via   different consumer electronics stores , all around the world ( e.g. BestBuy in the US , Fnac   in France , MediaMarkt in Germany , etc ) .   We have been designing our latest Visual   Merchandising elements for the stores , i.e. the way we will be displaying our products in   those stores . That includes the furniture , the placement , the signage , the demonstrations ,   etc . Our challenge was to identify the best solution from the many available , within a   given budget limit , to make the best business impact ( i.e. maximum sales ) . The solution   would be deployed in more than 1000 stores globally and thus it needed to be well   thought and with minimal risk , as after deployment changes would not be easy at this   large scale . 

 Selection   We have been internally working with agile methodology in different parts of the   organization and in this case we decided to use the   learning launch tool . We wanted to   create a control learning launch as an experiment to quickly test the ideas and prototypes ,   before rolling solutions out the market widely . 

 Application   We decided to use 6 stores as trial locations ( 2 in US , 2 in France , 2 in Germany ) . And   we created 2 different visual merchandising concepts that we then trialed in each of those   stores ( i.e. each concept was tried in 3 stores , all in different countries ) . One of the   concepts was more visual ( i.e. large imagery next to the products ) , with photos of how the   products are used ( e.g. in a living room environment , or while traveling ) . The other   concept was more focused on the product specifics ( e.g. materials used , technical   specifications , etc ) . The prototypes had sensors so we could collect data on how many   people reached the display , how many people tried / demo the products , and of course how   many people made a purchase . We run this trial for 2 months , to collect sufficient data . 

 Insight   Apart from the quantitative data , we also collected data by observation ( i.e. looking what   people were doing in the store ) but also talking to the sales personnel about their   observations . We learned that talking more about the lifestyle and context of the product ,   i.e. how it is used and how it adds value to the customer at home or traveling , was   definitely much more inspiring and intriguing for the customers to approach and interact   with the product displays ( vs. the technical specification alternative approach ) . We are   now in the phase of rolling out the concept globally . 

 Approach   While the learning launch tool gave us strong insight on the correct concept to use , next   time we should like to take it into more “ steps ” , in a more iterative approach . While we   answered the top level question , we also have some other smaller questions unanswered .   Next time we would like to make a similar setup with 6 stores , but before launching try a   more refined concept to e.g. 10 - 12 stores , for nailing down more details , before going   global .
### Output: 