Improving The Online Fashion Retail Store Experience
A case study on one of the challenges faced by both retailers and consumers
Background
The impact of returns to both retailers and consumers
It has become universal especially for most fashion labels to fulfil sales through omni-channel retailing. But alongside the surge of online shopping comes a much less desired activity for both retailers and consumers: RETURNS.
Discovery
Research findings
Return of online purchase are common regardless of age
What are the main reasons for online shopping returns?
What can retailers do to reduce the rate of online shopping returns?
Interview findings
Returns are growing the environmental and labour issues which makes return management process unsustainable.
Most consumers are encouraged to buy stuff online when the price is cheaper or discounted.
Consumers are getting frustrated that they couldn’t get the perfect fit same as with the model.
Some consumers don’t even want to be bothered returning the items when they bought it cheap, as they don’t have time to mail them back or go to the store. These items will end up being thrown if not donated.
Conclusions:
Returns made online are terrible for the environment.
Transport medium to heavy trucks produces carbon footprint.
Many of the returns end up in landfills
More returns means, more stress on the employees who then process them.
Many companies are discouraging returns because of its effects.
Buying online is like taking a leap of faith, hoping that the dress for example will look as good on you as on the model, or that shoes is as comfy as it claims to be.
There are companies that offer free returns, sometimes even for 30 days, but free returns isn’t the only motivator for consumers to make a repeat purchase online.
Define
Problem statement
“How can we provide solution on the growing cost caused by purchase returns, at the same time avoid customer frustration when their expectations aren’t met?”
User personas
Ideate
Brainstorming and assumptions
Research shows that 35% of consumers say an app or technology that helps them determine the correct size before they purchase would also help reduce their returns rate.
According to an MIT research about the use of avatars in Computer Science, “Avatars are a selective projections of a us onto a virtual representation and can support broadening engagement and participation”.
Offer a fun and convenient experience for the consumers to increase their engagement.
The common reason behind consumer returns - wrong size or clothing doesn’t fit perfectly.
Adding a feature that can create consumer’s virtual body by the use of a single photo or setting a few measurements.
Design principles followed
User-focused - Keep the users on top of the mind. The user goals goals and frustrations are defined using Personas. User interviews were conducted.
Consistency - The design should reuse internal and external components and behaviours, maintain consistency between the whole website which will help absorbing information and readability easy.
Discoverability - Recognition is better than memorisation
Learnability - Make sure that the context of the components used convey its exact message. If it’s a back arrow, meaning it goes back to the previous page.
Error prevention - For forms, use formatted fields, or use predefined fields such as drop-downs. Use appropriate user defaults. Like for example the height and weight unit format.
Single-column layout - For forms, when user scans the form in Z pattern, it slows down the comprehension, especially on mobile devices. Labels are also top-aligned so users can scan the form faster.
Less is more - Only ask what is required
Virtual Fitting for H&M App
Why H&M?
They have affordable, wide-range and trendy fashion products.
They care for the environment and they have a vision towards sustainability.
They don’t have online return form.
Paper sketches & wireframes
User flow on mobile
Deliver
Mock-up screens
Prototype of the H&M Try-on Avatar feature
Test, iterate and implement
Test the idea to validate all assumptions/solutions and gather user feedback. Assess the results and reflect on the learning. Iterate quickly and test again.