Enhancing Flipkart’s Beauty Category-UX Research Case Study
The following article expands on the research design and uber level insights that were discovered during the research we conducted for Beauty Category. Further, it also mentions a few of the conversion hacks we tested on the product page to increase conversion.
Beauty, as a category, has a tremendous potential to drive repeat purchases. Presently, 4.5% of the overall target segment shops online for make-up and personal care products. We at Flipkart want to enhance the experience for this target segment as well as make it user-friendly for the newer customers.
This led to the decision of conducting user research to understand our current users as well as potential users.
User Research Objectives:
- Understand beauty shopping behavior in both the offline and online world for beauty products
- Understand buying behavior 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:
Due to the expanse of the study, we decided to conduct it in two stages.
Stage 1: Intercepts
For stage 1- a team consisting of a researcher, designer, and product manager visited malls in Bangalore. The aim was to understand the key triggers of buying beauty products offline. We visited stalls of multiple brands which are handled 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.
In the malls, we shadowed the users and tried to understand their interaction with the sales representatives.
Stage 2: 1:1 In-Depth Interview
In stage 2, we conducted 1:1 interview to dive deeper into understanding the motivation to shop beauty and personal care online and offline, issues/frictions experienced, 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.
For this part, we visited 4 cities viz. Bangalore, Kolkata, Pune, & Jaipur. The decision for these cities was made taking into consideration their distinct cultures. These help us draw parallels for the whole country as it covered all the different parts. We met 16 participants from varied demographics and backgrounds.
Uber level insights:
- The buying journey is not linear, but it is progressive:
As the users are still not comfortable buying this category online, their purchase journey has a lot of back and forth till it progresses to making a transaction.
2. Heavy assistance is required especially when buying beauty products online:
Users relied on Youtube and Instagram for makeup reviews of certain brands/products and platforms. Users were often found to browse through the Instagram feed looking for beauty tutorials.
3. Brands are extremely important and users are loyal to certain brands:
Users usually stick to their brands as they have experience of using it. Further, they do not prefer experimenting while buying products online as trials are quite important for this category.
4. Offers & Discounts, and Ratings & Reviews are the key decision drivers for online shopping
Offers & Discounts are sought after as it is one of the key drivers of users to buy online vs. offline. Furthermore, ratings & reviews are sought to confirm the authenticity of the product.
Impact:
Based on the research, we conducted a few ABs as conversion hacks with the help of the product team. Some of the changes were:
- We moved color swatch to the first fold just below images:
This made it easier for the users to explore varous swatches and compare it with the image
2. We reduced the size of the offer section and moved it up
This effectively reduced the product page length
3. We moved details section up on the product page
This brought the details section closer to the price details
4. We introduced a new recommendation widget
This acted as a brand/colour based filter
These experiments led to an increase in conversion significantly. Based on this feedback, we’ve rolled out the experience to all our users and has shown a significant jump in terms of conversions.
The 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. Furthermore, a few constructs emerged from this study and we’ve brainstormed on a few solutions which are currently being tested.
