Case Studies - Product Design

Helping Flipkart Users Buy Beauty Online – A Case Study

A better understanding of user journeys through research helped Flipkart increase conversions for the beauty category

Beauty, as a category, has tremendous potential to drive repeat purchases. Presently, 4.5% of the overall target segment shops online for make-up and personal care products. This article describes how a study of current and potential customers led to higher conversion for Flipkart.

User Research Objectives

  • Understand beauty shopping behaviour in both the offline and online world for beauty products
  • Understand buying behaviour in terms of context, reasons to shop, expectations, issues and workarounds for buying beauty products offline as well as online
  • Identify key growth levers and gaps to scale personal care and makeup
  • Identify best practices across the competitive landscape

Research Design

The study was conducted in two stages.

Stage 1: Intercepts

A team consisting of a researcher, designer, and product manager visited malls in Bangalore. The aim was to understand key triggers behind buying beauty products offline. We visited stalls of multiple brands typically manned by sales representatives, who help users understand and shortlist products. We shadowed these users and tried to understand the interaction between them and the sales representatives.

Stage 2: In-Depth Interviews

In Stage 2, we conducted one-on-one interviews to understand the motivation for beauty and personal care shopping. This included both online and offline experiences, issues, friction, expectations and pain points. We also included a shop-along task that helped us identify test cases on a transaction by transaction basis, from start to finish.

This stage was conducted across four cities shortlisted for their distinct cultures: Bangalore, Kolkata, Pune and Jaipur. We believed that these would help us draw parallels for the whole country. We met sixteen participants from varied demographics and backgrounds.

High-level insights

1. The buying journey is progressive not linear

The typical user journey

Users are still not entirely comfortable shopping for this category online and their purchase journey involves a lot of back and forth, till it progresses to making a transaction.

2. Significant assistance is required for buying beauty products online

Users relied on Youtube and Instagram for makeup reviews of brands and products. They often browsed through their Instagram feeds looking for beauty tutorials.

3. Brands are extremely important and users are loyal to certain brands

Users usually stick to their brands and do not like to experiment while buying products online. Trials are important for this category.

4. Offers, discounts, rating, reviews are key decision drivers 

Offers and discounts are a key driver for the decision to buy online vs. offline. Ratings and reviews are checked to confirm authenticity of the product.


Based on the research, we conducted a few A/B tests, as well as conversion hacks with the help of the product team. Some of the changes were:

1. Moved colour swatch closer to product image

We moved the colour swatch to the first fold just below the images. This made it easier for the users to explore various swatches and compare it with the image.

Before and After: Colour swatch moved closer to product image

2. Reduced size of offer section and moved it up

We reduced the size of the offer section and moved it up. This effectively reduced the product page length. The overall interaction with different widgets on page increased.

3. Moved details section closer to price details

Before and After: Moving the offer section up increased overall interaction with widgets

We moved the details section up on the product page to bring it closer to the price details. Usage of details widget increased by 20%.

Before and After: Moving the details section closer to price details increased use of details widget by 20%

4. Introduced a new recommendation widget

We introduced a new recommendation widget that acted as a brand/colour based filter. The recommendation widget conversion increased by >3% (units/clicks visits).

These experiments led to a significant increase in conversion. Based on this feedback, we have rolled out the experience to all our users and the overall conversion has jumped. Future steps will include making UX, as well as UI changes for the category as a whole – this will be part of a more holistic category specific experience.

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