Designathon Project

UX Case Study: An Attempt to Impact Meesho’s Conversion Rate

Read in detail about the process and research behind the attempt to improve the user experience with the search and filtering on the Meesho app.

11 min readJun 23, 2023

Imagine you have a big event coming up and you’re looking for the perfect red cotton saree. You have a clear picture in mind — elegant, comfortable, and within your budget. You turn to your trusted app Meesho, type “saree” in the search bar, and hit enter. And BAM! You’re greeted with an overwhelming number of options. You try filtering to your needs and it just keeps getting more complicated. Sounds stressful, right?

This is the question we asked ourselves, what can we as designers do to make this process a little less daunting? I and my team set out on this journey to evaluate and improve the user experience of the ‘search & filter flow’ on the MEESHO app so that users could shop comfortably and Meesho’s business would see a positive conversion rate.

Overview

I had the opportunity to participate in the UXM Designathon, a design hackathon which was organised by the amazing UXM community.

In this case study, I will take you through the intense and exhilarating experience of our team’s nonstop work over a 48-hour period, as we strive for the best possible outcome to evaluate and improve a product flow. The intensity of the hackathon and the pressure to deliver successful solutions made it one of the most memorable 48 hours of my life!

🤟🏻Meet the team

A good team and good team management are crucial for a great end result, and thanks to my amazing team, apart from working hard together we also managed to have an incredible time together.

Although everyone was predominantly involved in each step, we still divided a set of tasks among us to effectively finish the project. Each of us lead a part in the project, I lead the secondary research and documentation throughout the project. All of us worked on ideation and solutions together!

! Quick Note ! I spent some extra time on the project individually after the hackathon ended, for about two weeks. I took it upon myself to create the UI screens from scratch since I wasn’t in charge of that during the hackathon!

Problem Statement

Evaluate the Product Search Flow of Meesho, which includes searching for products by applying filters and sorting, for users to zero down what they are looking for and be able to make the decision quickly and redesign the experience to make the product experience better and further impact conversion rates.

The flow

Home → Search for a product → Product display page → Apply sorting → Apply filters → Product display page (after filters applied)

Existing flow:

Existing vs Redesigned Screens

Here’s a quick overview of the solutions:

🕵🏻‍♀️Detailed Design Process

We’ve seen the solutions and the redesigned screens, but now let’s get a deep dive into the design process and research behind achieving them.

The Product, the Targeted Users, and the Impact on Business

What does Meesho do? Meesho is India’s largest marketplace for resellers, who sell products online. Meesho essentially connects manufacturers to resellers and helps them grow their online business. Clothing, accessories, furniture, culinary utensils, and cosmetics producers are among the suppliers who list their products on Meesho.
Who are the targeted users? Meesho users are mostly online shoppers between the ages of 18-45 who seek out for more affordable options on products.
How do we aim to impact business? The aim is to analyse and enhance a flow within the app with the objective of improving Meesho’s conversion metrics. By enhancing the shopping experience for users, our goal is to minimize drop-offs and facilitate successful conversions, thereby positively impacting their business.

Evaluating the Existing Flow to Identify Problems

The team worked together to evaluate each and every interaction within each screen to identify every possible gap. In the evaluation, we took into account the 10 Heuristics principles and the Gestalt’s laws, while also identifying issues based on our intuition.

Here are the top Problems Identified :

But why do this? Although it is crucial to avoid biases since we are not the users, it is important for us, as designers, to empathize with users and envision potential usability issues they might encounter. By doing a an evaluation and creating a set of assumptions, we can create a foundation to better understand the user’s perspective. These assumptions serve as a starting point, allowing us to effectively address potential problems and improve product design.

Ideating Solutions and Creating Hypothesis

Now that we have a set of possible problems before us, we then start with ideating solutions. However, it’s crucial to acknowledge that we are not sure if these problems are real and if the solution to these problems would effectively address these problems. In order to validate these assumptions, we create hypothesis statements that will be tested and validated through both secondary and primary research.

Here are the set of hypothesis statements that we formulated:

Search Screen:

→ Our hypothesis is to provide a recently viewed items list because our assumption is that users may not recall the product they visited before they left the app. This feature will help them revisit the previous item they were searching for.
→ Our hypothesis is to provide the search history with images because our assumption is that users may recognize the product with images they visited before they left the app better than just text.
→ Our hypothesis is to provide separate action buttons for “CLICK IMAGE” and “SELECT IMAGE” because our assumption is that users may find it easier to understand the action.

Product Display Screen:

→ Our hypothesis is to reduce visual clutter by removing anything that’s not benefiting the decision-making because our assumption is that this will impact the user’s decision-making and reduce confusion.
→ Our hypothesis is to provide indication of status change when a filter is applied and also showing how many filters are applied because our assumption is that providing visibility of system status will help the users be aware and make changes if needed.
→ Our hypothesis is that by giving better prominence to the product name and highlighting the offers, users will not experience confusion with the visual hierarchy of text associated with each product. It is assumed that this feature will enhance users’ ability to identify products more easily, while the increased prominence of offers will attract them to use the app more frequently.

Applying Filters Screen:

→ Our hypothesis is to provide color images alongside the color names because our assumption is that it will will improve users’ ability to recognize and understand the colors, reducing the difficulty they currently face with only textual representation.
→ Our hypothesis is to provide a price slider selection with manual input options because our assumption is that the current use of price ranges in chips can be confusing, and allowing users to manually input prices will improve efficiency and ease of use.
→ Our hypothesis is to display the exact number of applied filters and indicate their status change in a visually enhanced manner because our assumption is that this will effectively communicate the applied filters, indicate the status change when a filter is applied, and enhance the visibility of the system status.
→ Our hypothesis is to reduce the filter options available for product selection to only the most important ones because our assumption is that having too many filter options can overwhelm users and lead to cognitive overload, making it difficult for them to navigate and make efficient decisions. Simplifying the filter options will streamline the selection process, allowing users to focus on their specific preferences and find products more quickly, ultimately reducing decision-making time.

Primary and Secondary Research to Validate Our Hypotheses

During the process of evaluating the screens and formulating hypothesis statements, it became necessary to validate them through secondary research. To accomplish this, we conducted extensive research on articles, research papers, websites, and blogs related to the Meesho app and the overall flow of sorting, filtering, and searching. Our goal was to gain insights into the statistical and behavioral data of Meesho’s users, as well as understand the design principles and reasons behind the chosen flows. Additionally, we sought to identify factors that contribute to higher user retention in e-commerce apps and determine how search and categorization can be optimized to create a user-centric experience.

Through the various articles and blogs we explored, we gained valuable insights that guided us throughout the research process. Conducting competitor analysis allowed us to understand industry standards and identify potential optimizations that could be incorporated into Meesho while disregarding less effective approaches.

As the lead researcher, I ensured that we gathered as much relevant data as possible within our time constraints.

Some of the major insights we obtained were as follows:

Source

Insights: This source provided us with a comprehensive understanding of the Meesho product, including relevant data such as global rank, country rank, bounce rate, and total visits.

Global Rank: 1078 Country Rank: 85 Traffic has decreased by 10.81% compared to last month. Meesho’s audience comprises 61.31% male and 38.69% female users. The largest age group of visitors falls within the 18–24-year-old range.

Source

Insights: Meesho’s official website explained the operational structure of the platform, highlighting the relationships between suppliers, Meesho, resellers, and customers.

Suppliers → Meesho → Customers Suppliers → Meesho → Resellers → Customers

Source

This article focused on best practices for filtering and sorting on mobile platforms.

Key Insights:

A well-designed navigational structure, thoughtful filtering, and sorting mechanisms are crucial for a seamless user experience in finding desired items.
Slide-over Onscreen Filtering: Overlaying the filter view onto the search results provides contextual information about the displayed items.
Search Result Filtering: The effectiveness of search result filtering depends on user input, the number of verticals, and total data, necessitating the selection of the most suitable approach.
Sorting: Sorting functionality, unlike filtering, modifies the display order without restricting the results. Popular sorting options include price, quality, and alphabetical order.
Beyond Filtering: Important choices, such as product categories, should be addressed separately from general filtering options. This approach caters to the majority of users and applies irrespective of their specific search requirements.
By synthesizing the gathered information, we gained a comprehensive understanding of Meesho’s product, enabling us to optimize its functionalities and create a user-centric experience.

Learning from Competitors

During our research, we focused on studying Meesho’s direct competitors, namely Myntra, Nykaa Fashion, and H&M, as they target a similar market segment. These competitors were selected because they are highly relevant in the market and exhibit high engagement and conversion rates. Our data analysis revealed that Myntra has the highest number of downloads, followed by Nykaa Fashion and H&M. Moreover, we observed that these apps share a similar approach when it comes to the search, filtering, and sorting functionalities, mirroring Meesho’s format.

By analyzing these aspects of the competitor apps, we aimed to gather valuable insights that could help us enhance the flows within Meesho’s platform and ultimately boost business.

Here are the Insights:

Primary Research

After acquiring the foundational knowledge about the app, its flows, and the overall user behavior through secondary research, we proceeded to engage directly with individuals to gather firsthand information about their experiences with online shopping apps. To ensure a diverse range of perspectives, we conducted interviews with individuals who aligned well with our target audience. Our aim was to include participants who were active users of Meesho but whom we did not have prior personal connections with. By directly interacting with these individuals, we aimed to gain valuable insights that would further enrich our understanding of user preferences and behaviors in the context of online shopping.

Due to these constraints and being bound by time, we only managed to find 3 candidates to participate in this interview.

Based on the insights gathered from our three interviews, we identified recurring patterns that shed light on user behaviors and preferences. The key findings are as follows:
→ Users tend to rely on the search bar instead of utilizing the filter and sorting options. For instance, when looking for specific items like cotton crop tops, they prefer directly typing their requirements in the search box rather than applying filters on the dedicated page.
→ Users have difficulty locating and accessing the offers section within the search results. Instead, they prefer to look for offer banners on the home screen. However, this approach often leads to challenges in finding the exact items they are interested in and determining if they are under any discounts.
→ Users are largely unaware of the existence and significance of categories related to “M-Assured” or next-day delivery within Meesho’s platform. These categories have not been prominently featured in the search results, resulting in limited visibility and user awareness.
→ Users found the process of applying and removing filters cumbersome, requiring considerable effort to navigate through each filter section. Interestingly, some users were unaware of the “clear all” filter option provided at the bottom of the page.
→ The filter section proved overwhelming and confusing for many users, leading to frustration. Additionally, the slow loading time associated with applying filters discouraged their usage.
→ Users expressed difficulty in recalling their previous searches or viewed items, indicating a desire for a feature that allows them to access their search history or view previously explored categories and products.
These insights provide valuable guidance for improving the user experience and addressing issues such as search functionality, visibility of offers, category prominence, filter usability, and search history recall.

Through our extensive research and user interviews, we successfully validated the majority of our hypotheses. Additionally, we gained new insights that further enhanced our understanding of user preferences and behaviors. Armed with this valuable information, we are now ready to put our ideas into action and embark on the process of redesigning the screens.