Design Thinking Tool Assignment : Learning Launch 

 Challenge 

 As a technology design consultancy , we were engaged by a large retail client with a request to “ design   an app ” for their store locations that would trigger an alert on a shopper ’s phone when that shopper   was near clothing items the retailer wanted to promote . This retailer had good intentions and a clear   understanding of the ultimate business goals they sought to achieve ; however , they had not fully   investigated other technology options that might prove less expensive , more effective , less risk to their   IT infrastructure , and faster to implement .   For this reason , we worked with them on further refining the   scope to establish the shared criteria of a great solution , and then used these criteria to design a suite of   five compelling concepts , including their beloved app . The problem , however , was that the concept   everyone loved the most was a ‘ digital mannequin ’ , not the app that the SVP ’s had tasked their teams   with delivering . 

 Selection 

 While we had the marketing and store - ops teams on board the new mannequin concept , we all   understood that it would take a fast test with compelling metrics and anecdotes to justify the change in   direction . This led us to the use of a tool called a ‘ Learning Launch . ’ 

 A Learning Launch is a real - world experiment that is conducted quickly and inexpensively to challenge   our assumptions and to inform our final product design .   The purpose of this tool is not to evaluate the   project concept as a finished implementation , but to challenge our assumptions and learn any new   confirming or disconfirming data .   The launch needs to be constrained in time and sample size so   changes can be made quickly and inexpensively . It is also vital to track specific , measurable metrics and   behavioral data . 

 Application 

 The “ Digital Mannequin ” design called for double - stacked , flat panel TVs which could show video of a   life - size model wearing clothing items placed on tables and racks near the displays . We knew from prior   work with the retailer that traditional mannequins created a 10 - 15 % increase in sales for the displayed   items and that 20 - 25 % of passersby would glance at the mannequin . 

 The assumptions we wanted to question were : 

 1 ) The motion of a digital mannequin would increase attention given to the items on display .   2 ) The increase in attention would be significantly greater to that of a static traditional mannequin .   3 ) The expected increase in attention would lead to increased sales of the items on display .   4 ) The increase in sales would be significantly greater to that of a static traditional mannequin . 

 These assumptions would be proven or disproven with two metrics : 

 ( 1 ) Gaze Captures : A gaze measurement device from Intel would be mounted to a traditional mannequin   as well as to the digital mannequin . The technology notices every time a human looks at the device and   measures how long the visual engagement lasts . 

 ( 2 ) Unit of Sales : The sales numbers for the clothing items on display would be counted and compared to   each other . 

 The mannequins were installed , and the test was run for 7 days to accommodate the different shopper   patterns that occur throughout a week .   The gaze capture data and the sales receipts were plotted , and   our results were delivered . 

 Insight 

 While I am not allowed to reveal the exact results of the Learning Launch , I am able to say that the sales   increases were significantly greater with the digital mannequin than it was with the traditional   mannequin . The number of gaze captures was almost double for the digital mannequin and correlated   with the unit sales numbers through the week .   The client team was empowered to return to their   management armed with very persuasive numbers as well as positive customer intercept interviews   that we conducted as another aspect of the Learning Launch . 

 Alternative Approach 

 In retrospect , the tool was exactly what was needed for this project as we had limited time and budget   to unhook the team from their focus on a mobile application . While I would not change the Learning   Launch tool as described above , I would change the scoping process by immediately setting up an SVP- level meeting with the retailer to meet Jeanne Liedtka ’s goals of aligning around a common definition of   the problem and the criteria of a great solution .   If the alignment had been done earlier , we could have   avoided a lot of tension and internal frustration in choosing the most appropriate answer to the   challenge . 

 Thank you for reviewing my assignment . 

 Daniel 


EMAIL: 
ID_NUM: 
NAME_STUDENT: Daniel
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: Design Thinking Tool Assignment : Learning Launch 

 Challenge 

 As a technology design consultancy , we were engaged by a large retail client with a request to “ design   an app ” for their store locations that would trigger an alert on a shopper ’s phone when that shopper   was near clothing items the retailer wanted to promote . This retailer had good intentions and a clear   understanding of the ultimate business goals they sought to achieve ; however , they had not fully   investigated other technology options that might prove less expensive , more effective , less risk to their   IT infrastructure , and faster to implement .   For this reason , we worked with them on further refining the   scope to establish the shared criteria of a great solution , and then used these criteria to design a suite of   five compelling concepts , including their beloved app . The problem , however , was that the concept   everyone loved the most was a ‘ digital mannequin ’ , not the app that the SVP ’s had tasked their teams   with delivering . 

 Selection 

 While we had the marketing and store - ops teams on board the new mannequin concept , we all   understood that it would take a fast test with compelling metrics and anecdotes to justify the change in   direction . This led us to the use of a tool called a ‘ Learning Launch . ’ 

 A Learning Launch is a real - world experiment that is conducted quickly and inexpensively to challenge   our assumptions and to inform our final product design .   The purpose of this tool is not to evaluate the   project concept as a finished implementation , but to challenge our assumptions and learn any new   confirming or disconfirming data .   The launch needs to be constrained in time and sample size so   changes can be made quickly and inexpensively . It is also vital to track specific , measurable metrics and   behavioral data . 

 Application 

 The “ Digital Mannequin ” design called for double - stacked , flat panel TVs which could show video of a   life - size model wearing clothing items placed on tables and racks near the displays . We knew from prior   work with the retailer that traditional mannequins created a 10 - 15 % increase in sales for the displayed   items and that 20 - 25 % of passersby would glance at the mannequin . 

 The assumptions we wanted to question were : 

 1 ) The motion of a digital mannequin would increase attention given to the items on display .   2 ) The increase in attention would be significantly greater to that of a static traditional mannequin .   3 ) The expected increase in attention would lead to increased sales of the items on display .   4 ) The increase in sales would be significantly greater to that of a static traditional mannequin . 

 These assumptions would be proven or disproven with two metrics : 

 ( 1 ) Gaze Captures : A gaze measurement device from Intel would be mounted to a traditional mannequin   as well as to the digital mannequin . The technology notices every time a human looks at the device and   measures how long the visual engagement lasts . 

 ( 2 ) Unit of Sales : The sales numbers for the clothing items on display would be counted and compared to   each other . 

 The mannequins were installed , and the test was run for 7 days to accommodate the different shopper   patterns that occur throughout a week .   The gaze capture data and the sales receipts were plotted , and   our results were delivered . 

 Insight 

 While I am not allowed to reveal the exact results of the Learning Launch , I am able to say that the sales   increases were significantly greater with the digital mannequin than it was with the traditional   mannequin . The number of gaze captures was almost double for the digital mannequin and correlated   with the unit sales numbers through the week .   The client team was empowered to return to their   management armed with very persuasive numbers as well as positive customer intercept interviews   that we conducted as another aspect of the Learning Launch . 

 Alternative Approach 

 In retrospect , the tool was exactly what was needed for this project as we had limited time and budget   to unhook the team from their focus on a mobile application . While I would not change the Learning   Launch tool as described above , I would change the scoping process by immediately setting up an SVP- level meeting with the retailer to meet Jeanne Liedtka ’s goals of aligning around a common definition of   the problem and the criteria of a great solution .   If the alignment had been done earlier , we could have   avoided a lot of tension and internal frustration in choosing the most appropriate answer to the   challenge . 

 Thank you for reviewing my assignment . 

 Daniel
### Output: 