Ending the struggle of finding the perfect jeans with a tailored flow on the AJIO app

Adding a different viewpoint to the existing app to increase better user experience & tailored recommendations.

This is a partnered project with my design buddy Shaik Naveed. We worked on this as a quick passion project.

Many fashion brands have arrived with different kinds of clothing, but the one apparel that tops the list is Jeans. Finding the perfect fitting jeans has been a problem for many people, including me.

Jeans are my comfort clothing, but I am a lazy shopper. I always get bored of trying on dresses or even go to a physical store, so I was forced to go online & then I faced a few problems.

We studied the world market of jeans, to understand the importance of finding the perfect fitting pair of jeans.

A small data snippet sketch of the global jeans market

After understanding the market to validate the importance of this problem, we made some user interviews with our friends, colleagues & family members to understand their problems. These were the 3 main problems (found with internet user insights too)

Now since we found out the problem (that many of us have universally), we finalised the problem statement.

The challenge lies in navigating through different fits, styles, sizes, and fabrics to find the right pair of jeans, all while minimizing user dissatisfaction, returns, and bounce rates. The goal is to enhance customer retention, create a happy shopping experience, and establish a loyal customer base.

So let’s name the user Rohan for instance. Our task was to answer these questions for a good experience of an online customer of AJIO, like Rohan.

1. In what ways can we leverage existing data from users and user-friendly design to minimize Rohan’s frustration and errors when selecting jeans sizes on a mobile app, ultimately reducing returns and improving customer satisfaction?
2. How might we employ personalized recommendations and interactive features in the mobile app to assist users in discovering their ideal jeans size based on individual preferences and body measurements?

Our goal was to generate a broad solution encompassing aspects like conversion rates, retention, and reducing bounce-offs, all stemming from one small & focused flow analysis, just for ordering a jean.

We made a quick flow of all the ways Rohan would think, before choosing his perfect jean, which also serves as the user flow for the solution we created.

People may think that this flow would ring a bell. It is already present in the Myntra app, but here are the flaws in the flow, and this is how we made it better:

1. The whole feature, which was meant to help with a person’s size primarily, is hidden inside the profile page. How often do we use our profile page while browsing dresses? 1% of the time, maybe to see what we ordered previously.
   Instead, we brought the feature outside, to the apparel browsing page, to help users gain knowledge on the apparel fits, also finish the task easier with higher precision.
2. Questions they ask are just time-consuming and not very useful for the dress selection process. For example, informing the app about acne and oily skin or mentioning dandruff may not contribute much to selecting the right dress.
   Instead, sticking to a simple 3–5 questions will help with streamlining the process, making it simpler, and focusing on the precision of fits.
3. Myntra focuses on collecting user details, while the main issue lies in selecting the perfect-fitting dress (in our case, jeans).
   We focused on the same concept and initiated the flow from the apparel browsing screen to enhance the efficiency of the process, also educating users about the apparel along the way.

Here are the screens of the flow.

Case 1

Our user, Rohan knows his size, and he would either want to find his fit quickly, or wants to explore more of the products with his size availability.

or, if Rohan doesn’t know his size, and chooses to get help, then he would have 3 options —

1. To choose from a brand that he already owns, and use that size to get relevant fits.
2. To choose one of his previous orders that fits him perfectly, and get recommendations relevantly.
3. To measure himself with the guide given, and get accurate recommendations.

Case 2.1

Choosing from a previously owned brand.

Case 2.2

Choosing from a previous order.

Case 2.3

Choosing to measuring with a tape.

For better accessibility & visibility, with all the user insights that were gathered, we made minor changes to the AJIO platform screens.

This is our take on the problem with the hypothesis & user insights. After user testing with the same cohort, they found the prototype to be more accessible with better font sizing and visibility. They feel that this may help them receive better recommendations and that the questionnaire steps were not tiresome.

Businesses would have,
increased customer satisfaction, better reviews, better conversion rates, lesser cart abandonment & lesser returns & redelivery.

Users would
feel more easier to shop online, need not worry about jean availability for their body types, need not get confused with hefty unsuitable options, and have a happy ending to their shopping expedition 😊

Another important insight was,
initially, users who said they would order & return if the jean doesn’t fit, found this questionnaire more useful, accurate & time saving.