Myntra Fit Assist

Designing for the infamous size & fit problem in e-commerce

  • Different brands have different size standards. It's difficult to go through the size chart every time.

  • The reason why I prefer offline shopping over online is that I can’t get the perfect size or touch and feel of it while shopping online.

Returns across fashion e-commerce is related to size and fit issues
Top article types have high returns due to size and fit issues.
of total returns at Myntra is due to size and fit issues.
Product Objective

Help the users look good by assisting them to make an informed decision on size and fit for the product being ordered on Myntra and thereby also reducing returns due to size and fit issues, increasing conversion and positively impacting the user experience.

Design Problem

Assist the users in identifying and choosing the perfect apparel size. Provide different touch points to collect, analyse and use the data that support size recommendations and thereby increasing the buying confidence.

Qualitative & Quantitative Research

We have done in-depth research sessions with the identified set of users to understand their behaviour while they buy online. Also quantitative data about the return reasons, previous order history, order exchange data, etc. gave us a better way forward towards the right solution.
Here are some of the major insights from the research

Secondary Research

Some of the existing solutions were explored during the initial research phase. A brief categorisation of third party solutions are given here.

Major Challenges

Ideation & Brainstorming

The idea is to build a recommendation engine that learns about the users, understand each product sizes and provide exact size & fit recommendation for the customers. We brainstormed about various use cases which includes; single size profile, multiple profile management, standardising size scale across brands, touch points to add additional size data, finding the right real-estate for recommendation, etc.

Stakeholders from product, engineering and design came together to brainstorm on the problem statement. I got different perspectives of the problem during these sessions and was able to iterate on my designs quickly.

The Tech Behind The Scenes

The project was heavily focused on data science and engineering solutions to determine and predict size & fit. User information like order history, gender, age, etc. and product information like measurements, attributes, material, etc. were fed into the model.

A Glance Through The Iterations...
Size Details 1
Size Details 2
Size Details 3
Size Tagging 1
Size Tagging 2
Size Tagging 3

The Final Solution

How Do Myntra Recommend Sizes

Personalised Size Recommendation

Personalised Size Recommendation (PSR) feature on Myntra accurately predicts the best fit size for a customer. The recommendation comes just below the size selector on any product page and it prompts user to select the correct size.

Multiple Size Profiles In A Single Account

For the customer who orders for multiple people, we introduced size profiling and managing system. For single profile users we were able to recommend the sizes without any further user inputs. For multi-profile users we have nudges on "order confirmation" and "my orders" page to tag the product to related profile.

Size Prompt animation

How Do User Create Profiles & Add Details

Profile tagging

Myntra Standared Scale Based Recommendation

We decided to standardise the sizes and create Myntra standard scale. The newly introduced scale will reduce the inconsistency of sizes between different brands, article types, material and other dependent attributes. Size charts of all the article types have been optimised based on the newly introduced scale.

Size Chart
Size Recommendation On PDP
Results So Far (Sept'17 exit)
Total article type covered
# of unique profiles created
% of relevant items getting tagged
% Revenue contribution
130 bps
Size & fit return reduction fro Men's ATs
What’s Next? Little Bit About v2.0

From the learning of v1.0 of Myntra fit assist we took a step ahead. MFA v2.0 will cater to different user segments(logged out & new users), explore 3rd party services, span to more article types, increase adoption with incentivisation, improve browsing results, etc.