Meesho is an Indian-origin social commerce platform founded in December 2015. It enables small businesses and individuals to start their online stores via social channels such as WhatsApp, Facebook, Instagram etc.

Meesho is headquartered in Bengaluru, India and was one of the three Indian companies to be selected for Y Combinator in 2016. It was also a part of the first batch of Google Launchpad — Solve for India program. In June 2019, Meesho became India’s first startup to receive investment from Facebook.

Problem Statement

Meesho’s resellers are faced with increased difficulty about not knowing the products to sell/pitch to their customers confidently to make a sale when they are on the Meesho app.

Our solution should deliver a way to make them feel confident about discovering, deciding, pitching a product to their customers with confidence.

Project Scope

This case study seeks to propose a suitable and actionable solution limited only to improving catalogue & product discovery for Resellers on Meesho app.

Target Users

Users in Tier 2,3,4 cities, who always wanted to do something on their own, particularly women who are home-makers.

My Role

Strategy, Problem Scoping, Competitor Analysis, User Interviews & Surveys, Persona, Empathy Mapping, Customer Journey, Card Sorting, User Flow, Information Architecture, Wireframes, Visual Identity & Design, Usability Testing

Timeline

3 days

Process

The DDA (Double Diamond Approach) process

1. Discovering the problem

Assumptions

It is assumed that the Reseller (hereby referred to as ‘user’) is aware and has used Meesho app in some capacity or any of the app belonging to the same domain.

Research about similar companies

I also looked into the methods applied by for some other popular Resellers platforms like GlowRoad, Shop101, Mall91, ResellMe, Bulbul applied techniques to improve their product discovery and generate impulse buying amongst Resellers.

Primary Research

Qualitative Research
It is used to gain an understanding of underlying reasons, opinions, and motivations. It provides insights into the problem to develop ideas for potential quantitative research. It is also used to uncover trends in thought and opinions, and dive deeper into the problem.

A series of personal interviews were conducted to understand user goals and pain points and to unearth any unexplored areas.

9 users were interviewed, each having varying Reselling experience across Meesho or any other apps in the same category. The idea of the questionnaire is to keep it open-ended and try to gather as many details as possible around Reselling service. The questionnaire is provided below:

9 users were interviewed, each having varying Reselling experience across Meesho or any other apps in the same category.

1. How long have you been using Meesho?

2. Why do you use Meesho?

3. Have you tried out other apps or platforms?

4. If yes, how are they different?

5. Why do you use Meesho instead of other platforms? 

6. How frequent/often do you use the app, say in a week?

7. When do you usually use the app?

8. What do you like about the app?

9. What do you dislike about the app?

10. What do you usually do after you launch the app? Do you mind showing me how do you usually use the app?

11. What kind of products do you usually look for? Did you face any difficulty?

12. When is the last time you purchased a product on the app?

13. Do you research on the price of the product before buying it?

14. Is there anything that frustrates you when using Meesho?

15. What are some of the things that Meesho can improve on?

16. Describe Meesho using 3 words.

Key insights from User Interviews

1. I found out that most of the users were in a highly confused state as to which catalogue/products to share to their customers.
2. Resellers disclosed that it was easy for them to close deals for products which were latest, new in the market.
3. Reseller’s customers craved for new products from them in terms of the latest trends in the markets- which in turn led the Resellers to themselves put in efforts to research and figure out from other external sources about which products were trending.
4. Majority of the Resellers wanted some information about what all products/catalogues other Resellers are selling. They are feeling left out in their self taken micro-entrepreneurial path with no guide or helping hand in sight.
5. Each of the people I interviewed is critical about high product prices at the seller level, thereby leaving them with a very small margin to earn a sustainable profit in the long run.
6. Resellers were also found to be highly critical on the actual delivery timings too. Some products were delivered to their customers almost a month late- leaving their customers in a frustrated state of mind with high cancellation probability.

Play Store Reviews

I also went on to the Google Play Store to take a look at the user reviews and find out about the issues they faced when using the app. These are the screen captures of their feedbacks:

Few Play store reviews of Meesho App organised by nearest related categories

Key insights from Google Play Store Reviews

Findings from several Google Play Store reviews re-iterates the key findings found during my user-interview phase- those being-

1. …high prices…costly…
2. …late delivery…
3. new category suggestion… trending… more sub-categories like product colour etc.
4. sorting option

Quantitative Research

While doing this, I purposefully held off sending Surveys consisting of questionnaires.

As the target group for Meesho were mostly belonging from Tier 2, & 4 cities, they weren’t habituated to clicking on an online link to answer that many questions. Instead, they will be very comfortable to answer those if someone else asked them those questions over on-call/in-person (preferably in their native mother tongue).

Due to my time constraint, I chose to go for the App Review analytics route to do my quantitative research which will give a statistical data-backed view of the problem faced by reviews written by actual Meesho users. It’ll quantify the problem by way of generating numerical data that can be transformed into usable statistics.

It is also widely used to quantify attitudes, opinions, behaviours, and other defined variables- and generalising results from a larger sample population.

App Reviews AnalysisUses:
* Detecting or prioritizing problems
* Monitoring performanceCost: Low
Type of Method: Behavioral (what users do)
Context of Use: Live

Custom ‘phrase’ analysis for keywords showcasing low sentiment, leading to low Play Store ratings.

2. b. Secondary/Desk Research

A study by ClickTale revealed that shoppers spent more time attempting to find their product on e-commerce websites that were not optimized for product discovery, but left because they were unable to find it. On the contrary, e-commerce websites with strong user insight make the customer stick around to make a purchase despite spending less time on the site.

The study said small retailers tracked a longer time on site (ToS) (around 200 seconds) than large retailers (around 70 seconds). But, the bounce rate for small retailers was higher at 34 percent in comparison to that for large retailers (9.4%).

200 seconds- average Time on Site (TOS) spent by an user to find their desired products for sites which are poorly optimised.
34 percent- average Bounce Rate for sites poorly optimsed leading to users abandoning their purchases midway.

Quicker and efficient product discovery also becomes a positive customer experience, leading to retention. Due to substandard customer experience, customers are abandoning their purchases midway from small retailers.

Due to substandard customer experience, customers are abandoning their purchases midway from small retailers.

Persona Creation

Based on hearing to different stories and comments, I came up with a User Persona, I created the profiles, their mission statement, end goals and frustrations to better understand the user.

Persona

Empathy Mapping

The empathy map was created to gain a deeper insight into the customer’s thinking, feelings, worries, and aspirations.

Empathy Map for Rima Das

Customer Journey Map

Next we develop a customer journey map for Rima to map out her experience over time, identifying relevant touchpoints. This is a timeline of the activities the persona undertake with Meesho.

Customer Journey Map of Rima

2. Defining the problem

After gathering data and understanding the requirements, a set of problems that are needed to consider while designing was formulated. This was done using the HMW (How might we) method in which each pain point is converted to a “How might we…” question. Further condensing & optimising these above HMW questions, we get-

1. How might we help users identify products to be shared amongst their customers?
2. How might we help the users act on insights about the products sold by other resellers?
3. How might we ensure resellers identify, share quality, reliable products that also fits in into their customer’s budget?
4. How might we ensure clear visibility of shared products being in stock?
5. How might we ensure dispatch and delivery dates being clearly communicated to the user and their customers?

3. Developing a solution- Ideation and Brainstorming

At this point, we are half-way through the double diamond and have decided on the problems to try and solve for.

Creative Matrix- Idea generation

In this step, we begin to open our mind and start ideating — generating a list of whacky, amazing, creative, and innovative ideas.

Creative Matrix- to find out possible solutions to solve the problem statements

4. Delivering- finalising the solution

In this last step, I went for convergent thinking again, focusing on what we can actually deliver and which solutions will solve the users’ needs.

Choosing a solution

We now decide on a better idea from the Creative Matrix framework. The green tick mark on the creative matrix is what I’ll be proceeding with for prototyping, testing & analysing.

The green tick mark on the creative matrix is what I’ll be proceeding with for prototyping, testing & analysing.

Impact Vs Difficulty Matrix

Now that we’ve explored Desirability in an unconstrained way, I now start converging on the right solution by plotting the selected ideas from the creative matrix framework on the impact/difficulty matrix and then deciding on which ones to pursue…

Difficulty Vs Impact Matrix

As seen from the above matrix, for the scope of this case study, due to time constraint, we will be focusing on solutions falling under Easy wins and High ROI quadrant. Saying which, very difficult and low impact problems aren’t necessarily a write off — these are luxuries that some users may be willing to pay a premium for.

…very difficult and low impact problems aren’t necessarily a write off — these are luxuries that some users may be willing to pay a premium for.

Solutions

1. Product Card

Initial Sketches
I started off by sketching the various solutions possible for product card to build up buying intent and improve discoverability.

Rough sketches for Product Card

Proposed Final Product Card Design

After sketching out several iterations, I arrived at this as seen from the image below. Following things are now added-

i) Product card with no. of items sold
ii) Product card with share nos. by resellers
iii) Product card with ‘Express shipping’ label
iv) Default/Regular card

Note: All other existing attributes such as Deal of the Day, Next day dispatch, etc. to remain same and weren’t touched upon due to time constraint.

Proposed final design for product card

2. Homescreen

As primary screen that a user sees every time that a user opens Meesho app, homescreen hence proves to be the perfect starting point as this is the screen where a Meesho Reseller begins his journey.

Initial Sketches
I started off by sketching the various solutions possible for homescreen layout to build up buying intent and improve discoverability of products.

Rough sketches for Homescreen layout

Proposed Final Homescreen Design

After sketching out several iterations, I arrived at this as seen from the image below. Following things are now added-

i) Realtime signal for other resellers
ii) Product card with share nos. by resellers
iii) Product card with ‘Express shipping’ label
iv) Default/Regular card

Proposed final version for Homescreen screen

3. Collection, Categories and sub-categories visibility

Proposed Final Design
An app’s navigation can have a significant impact on product discoverability, leading to user constantly scrolling on an app but at the end exiting the app.

Proposed final version for Categories, Collections and Sub-category/collections screens

What’s Next?

The next step is to test out the proposed designs with it’s targeted user base and gain valuable feedback and validate the design decisions made.

After the feedbacks are incorporated and addressed, will be handed off to the development team for its actual implementation where I will be consistently in touch with various teams to see that the actual product that comes out is according to what was conceptualised.

Future scope of improvement

Due to limited time constraint, had to scissor out solving for various use cases. These too have the potential to greatly improve product discoverability. Some of these are:

1. ̶’̶M̶e̶e̶s̶h̶o̶ ̶C̶e̶r̶t̶i̶f̶i̶e̶d̶/̶Q̶u̶a̶l̶i̶t̶y̶ ̶c̶h̶e̶c̶k̶e̶d̶’̶ ̶t̶a̶g̶ ̶f̶o̶r̶ ̶g̶r̶e̶a̶t̶l̶y̶ ̶i̶m̶p̶r̶o̶v̶i̶n̶g̶ ̶p̶r̶o̶d̶u̶c̶t̶ ̶q̶u̶a̶l̶i̶t̶y̶ ̶a̶n̶d̶ ̶i̶m̶p̶r̶o̶v̶i̶n̶g̶ ̶d̶i̶s̶c̶o̶v̶e̶r̶a̶b̶i̶l̶i̶t̶y̶ ̶o̶f̶ ̶r̶e̶l̶i̶a̶b̶l̶e̶ ̶p̶r̶o̶d̶u̶c̶t̶s̶ ̶s̶o̶u̶g̶h̶t̶ ̶b̶y̶ ̶m̶a̶n̶y̶ ̶r̶e̶s̶e̶l̶l̶e̶r̶s̶ ̶f̶o̶r̶ ̶p̶r̶o̶d̶u̶c̶t̶s̶,̶ ̶e̶s̶p̶e̶c̶i̶a̶l̶l̶y̶ ̶o̶f̶ ̶a̶ ̶h̶i̶g̶h̶e̶r̶ ̶a̶m̶o̶u̶n̶t̶.̶
   (reason described here).
2. Improving Deal of the Day countdown visuals- Resellers have repeatedly mentioned the timer being missed out while they were scrolling. ‘Deal of the Day’ seemed to be a part of neither the card above nor the card it was actually meant for. Improving this can significantly improve product discoverability and buying intent.
3. Switching from ‘For You’ to ‘Collections’ seem the same for many of the Resellers.

Thank you for reading!