Challenge : 

 To design and develop an idea for decreasing the rate of return owing to sizing issues for online clothing   retailing platforms . To meet the requirements , learning launch tool was implemented . 

 Hypothesis : 

 To provide the right fit clothing for consumers looking for online shopping . 

 Assumption : 

 Value creation 

 Customer perspective Data 

 1 ) Digital penetration is high ; therefore , market has high potential for growth 

 o   Men does less online shopping but the market is increasing due to the increasing   awareness and customer satisfaction . More trendy sites are coming up catering to the   latest fashions trends which make it attractive for buyers these days 

 o   Women buyers are more frequent . Also , high sales per transaction 

 o   Children wear also holds substantial portfolio – buyers are mothers 

 2 ) Return rates are high which indicates customers dissatisfaction due to absence of trial mechanism 

 o In the men 's segment the return rate is low , but sizing returns is the major issue   o In the women ’s segment the return rate is high and sizing issue is the major issue , also 

 color is not same as the image , or fit is different , etc . 

 o In the Children ’s segment the return rate owning to sizing is not a major issue , only 

 quality matters for the parent buyers 

 3 ) Sizing charts are not user friendly to provide right fit clothing 

 o Indian market does not have their own sizing chart and lends it from European , US or 

 other such markets which does not always fit the Indian body shape 

 o Alternate feasible methods are not available for digital market to cater sizing needs   o Current online market uses sizing chart and data analytics to give the best fit using user ’s 

 past buying trends , since sizing chart is at default : the analytics is at default 

 Key assumption derived : Working on men ’s collection will be easier to start with and enter the market .   Eventually women ’s collection range can be taken up which is mire varied and rigorous 

 Co - creation : 

 Key stakeholders 

 •   Active consumers of online garment shopping 

 •   Consumers who are willing to buy but do not choose to buy due to various reasons like   skepticisms on quality issues etc . 

 •   Active sellers selling on these platforms 

 •   Impact on sellers due to returns 

 Key assumption derived : To attain fit similar to what one gets from tailor - made , unique for each   individual . 

 Ideation : 

 1 . Send tailors    2 . Self - tailoring video instructions and ask user ’s input   3 . Ask user to send sample garments   4 . Use innovative technology to get user ’s measurement using user ’s pictures . 

 Proceeding with option 4 

 Key assumptions : for option 4 

 •   Is designing a manual algorithm which will take measurement with low standard deviation   possible ? 

 •   What are the requirements to translate that to the digital model ? Does the infrastructure exist or   can be created ? 

 •   If the above two assumptions are met with , take stakeholders feedback in terms of ease of   implementation , scalability , privacy and various other factors . 

 Design testing : 

 •   Algorithm built in tandem with users , tailors , keeping in mind various body parameters 

 •   Digital model built to translate algorithm into reality by capturing picture using smartphones 

 •   Stakeholders feedback taken on various parameters on ease of use , privacy etc . 

 Results 

 Designing manual algorithm : Yes , possible 

 Translation into digital model ; Sufficient for launch 

 Stakeholder ’s feedback : Negative 

 Therefore , work needs to be done on technological infrastructure to counter the negative feedbacks . 

 Approach : The same tool can be used next time since in a short span of time the tool helped to gain key   insight across all variables involved . It also helped to identify the key areas to work upon , bringing one   step closer to the solution . 

 Thank you ! 


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: Challenge : 

 To design and develop an idea for decreasing the rate of return owing to sizing issues for online clothing   retailing platforms . To meet the requirements , learning launch tool was implemented . 

 Hypothesis : 

 To provide the right fit clothing for consumers looking for online shopping . 

 Assumption : 

 Value creation 

 Customer perspective Data 

 1 ) Digital penetration is high ; therefore , market has high potential for growth 

 o   Men does less online shopping but the market is increasing due to the increasing   awareness and customer satisfaction . More trendy sites are coming up catering to the   latest fashions trends which make it attractive for buyers these days 

 o   Women buyers are more frequent . Also , high sales per transaction 

 o   Children wear also holds substantial portfolio – buyers are mothers 

 2 ) Return rates are high which indicates customers dissatisfaction due to absence of trial mechanism 

 o In the men 's segment the return rate is low , but sizing returns is the major issue   o In the women ’s segment the return rate is high and sizing issue is the major issue , also 

 color is not same as the image , or fit is different , etc . 

 o In the Children ’s segment the return rate owning to sizing is not a major issue , only 

 quality matters for the parent buyers 

 3 ) Sizing charts are not user friendly to provide right fit clothing 

 o Indian market does not have their own sizing chart and lends it from European , US or 

 other such markets which does not always fit the Indian body shape 

 o Alternate feasible methods are not available for digital market to cater sizing needs   o Current online market uses sizing chart and data analytics to give the best fit using user ’s 

 past buying trends , since sizing chart is at default : the analytics is at default 

 Key assumption derived : Working on men ’s collection will be easier to start with and enter the market .   Eventually women ’s collection range can be taken up which is mire varied and rigorous 

 Co - creation : 

 Key stakeholders 

 •   Active consumers of online garment shopping 

 •   Consumers who are willing to buy but do not choose to buy due to various reasons like   skepticisms on quality issues etc . 

 •   Active sellers selling on these platforms 

 •   Impact on sellers due to returns 

 Key assumption derived : To attain fit similar to what one gets from tailor - made , unique for each   individual . 

 Ideation : 

 1 . Send tailors    2 . Self - tailoring video instructions and ask user ’s input   3 . Ask user to send sample garments   4 . Use innovative technology to get user ’s measurement using user ’s pictures . 

 Proceeding with option 4 

 Key assumptions : for option 4 

 •   Is designing a manual algorithm which will take measurement with low standard deviation   possible ? 

 •   What are the requirements to translate that to the digital model ? Does the infrastructure exist or   can be created ? 

 •   If the above two assumptions are met with , take stakeholders feedback in terms of ease of   implementation , scalability , privacy and various other factors . 

 Design testing : 

 •   Algorithm built in tandem with users , tailors , keeping in mind various body parameters 

 •   Digital model built to translate algorithm into reality by capturing picture using smartphones 

 •   Stakeholders feedback taken on various parameters on ease of use , privacy etc . 

 Results 

 Designing manual algorithm : Yes , possible 

 Translation into digital model ; Sufficient for launch 

 Stakeholder ’s feedback : Negative 

 Therefore , work needs to be done on technological infrastructure to counter the negative feedbacks . 

 Approach : The same tool can be used next time since in a short span of time the tool helped to gain key   insight across all variables involved . It also helped to identify the key areas to work upon , bringing one   step closer to the solution . 

 Thank you !
### Output: 