Myntra — Solving the returns problem
In this blog, we will focus on how Myntra is taking product (mobile app) based initiatives to reduce order returns. Further we will recommend additional product features to solve this issue.
What are Returns?
Product returns can be classified into two types:-
Type 1: User receives product and then requests return after receiving.
Type 2: The product is not delivered to the user for various reasons — like the user cancels before delivery, refuses delivery, etc.
Why is it an important problem for the fashion industry?
~30% of orders are returned on Fashion eCommerce websites.
~2/3 rd of the cost price is spent on reverse logistics.
This is an important problem for the industry as it eats into the profits through supply chain costs.
Why do returns happen?
1. Unmet expectations — Your order doesn’t meet your expectation of colour, size, etc.
2. Impulse purchase — Purchases in a hurry which you regret later.
3. Wardrobing — Purchasing for that one special event and returning after that.
4. Risk hedging purchase — Purchasing multiple sizes/colours of the same item or similar items, and returning the ones that don’t meet your expectations.
5. It doesn’t feel right — The material is great, and the colour looks perfect, but it just doesn’t feel the same on you.
Myntra’s current initiatives to reduce the returns
1. Preventive Measure —
Myntra tracks a metric called order return rate per user i.e. number of orders returned per number of orders placed by a user. Based on this metric, they generate a mean value and rate each user on that scale. If the order returns rate is significantly higher than the calculated mean then the user is notified and penalised based on the extent of deviation from the mean.
These penalties can be —
a. Revocation of Myntra Insider benefits — Myntra Insider is a customer loyalty programme. Users can become a member only when they have shopped for ₹7,000 (net of returns) and made at least 5 orders in the last 12 months on Myntra. Users with higher order return rate than the mean might lose out on a few benefits gained through this loyalty programme.
b. User might be charged a non-refundable convenience fee when they place their next order.
2. Customer Profiles—
Myntra introduced a feature called Profiles. Through profiles, Myntra records details of the user like height, weight, body type, and clothing preferences in order to provide accurate recommendations and a % match score to help the user make better decisions. This in turn will reduce the probability of returns.
3. Early Stage AI Tools —
Myntra has been developing AI tools to help the user to select apparel and fashion accessories. This tool is listed under “Xplore” CTA. These tools are:
a. My Fashion GPT — This tool recommends fashion choices based on text prompts.
b. My Stylist — This feature recommends complete matching outfits by processing the image provided by user or based on past orders.
c. Studio — A news feed like page displaying short videos, photos posts featuring influencers. Below these media posts are the options to purchase the items displayed in them.
What else Myntra can do? (Our recommendations)
User Stories:-
• As a user I want the colour of the product that I received to match with the colour displayed on my screen.
• As a user I want to know how the clothes I wear will look on me.
• As a user I want the clothes that I am purchasing should fit me well.
Recommended features
1. Device level colour adjustment
What is this feature? — Adjust the display colours to a standard colour profile according to device model.
Disable device filters like blue light filters when entering the app, like YouTube does while playing HDR videos.
Why this feature? — Device displays have varying colour profiles depending on the maker of the product. This could mean that colours appear slightly different on different devices — a little extra yellows, or a little extra reds. Some users also prefer using blue light filters, which could make the image displayed look more yellow than usual.
Pain point addressed — Colour seen on the device may not match the colour of the product received.
Benefits — Customers will start trusting the colours which could help “Risk hedging purchases” where the customer buys multiple shades of the same item.
2. Virtual Trial Rooms
What is this feature? — Offer augmented reality (AR) trials for products, similar to what Lenskart does.
Why this feature? — Having an option to “try on” the clothes will allow users to have much more realistic expectations from the actual product. With this feature, buyers will know exactly how the clothes look on them and will allow them to make a more informed choice.
Going one step further, we can also have recommendations of similar apparel and matching accessories, when user is in the trial room, for their convenience.
Pain point addressed — Users are skeptical about how the clothes will look on them.
3. Myntra standard size
What is this feature? — A Myntra standard size which would be derived from the absolute measurement of clothes (chest, length, etc) will be used in place of the size indicator like S, M, L, XL, etc. provided by the manufacturing company.
Why this feature? — User can choose a suitable size of apparel once and stick to it across brands. This also creates a switching cost as Myntra size won’t be carried over to other apparel stores.
Pain point addressed — Every time the user switches brands, they need to go through the size chart as sizes are different for different brands.
Benefit — A loyal customer does not need to think twice as they can select their preferred Myntra standard size which will be the same fit as per their previous purchases. This will improve the purchasing experience of the user.
Written by — Akhil Powar, Vaishnavi Pendke, R Vivek Narayanan