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!


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 !