Data is used as a bedrock for 3 broad principles for problem solving across Meesho:
Principle 1 - Being Problem first
Being Problem first is a core value and the essence of problem solving at Meesho. Asking Why repeatedly until we get to the root of the problem is a habit inculcated to ensure we never jump to solutions or end up solving for low impact problem statements. Constructing Data backed hypotheses help us arrive at problems which have to be solved on priority and then move the needle with solutions.
Principle 2 - First principles thinking
First principles thinking is the process of eliminating all assumptions and breaking the problem statement down to its most basic constituents. As an e-commerce platform where majority of our users belong to Tier 2+ cities and a large chunk of users who sign up on Meesho are new to E-commerce, we use first principles thinking to build a seamless shopping experience for a user base for whom no one has solved for in the past.
Principle 3 - Iterative experimentation
Iterative experimentation, popularised by the The Lean Startup by Eric Reis drives all implementation cycles at Meesho. We strongly believe in Speed over Perfection - experimenting and failing fast helps us ship faster. This involves defining clear hypothesis, running A/B tests and using data to validate growth in metrics that drive business.
Here are a couple of examples of how we have solved business problems in the past:
Example 1 - The conundrum of Supply growth
One of the approaches to grow selection on the platform could have been to check which categories and products are working on other E-commerce apps and simply sourcing them. However, that is not the Meesho way. Our focus has always been to grow selection on the app with constant focus on fulfilling user needs and wants.
So, how do we find out what products our users want? The answer lies in data.
We regularly go over our users’ searches amounting to millions of terms per day and then bring in the products they have been searching for. By being problem first, we have identified new categories and products that our users specifically wanted but were unable to find on Meesho.
Through this process, we have launched multiple new categories which might not have been prioritized if we simply looked at other players; examples like Sports & Fitness, Office Supplies and Food & Beverages. Taking our passion for putting user needs at the forefront and obsessing over data, we have also personalised our non-search real estates for every user basis their prior purchase data so users can easily find exactly what they’re looking for.
Example 2 - Pricing Improvement Levers
Meesho’s core value proposition to consumers is that we sell products at the lowest price possible. We keep looking for levers to reduce pricing of products on a continual basis.
Product price data showed us that the largest cost bucket for most purchases is shipping cost(cost incurred to deliver products from suppliers to users). Shipping costs vary across different shipping zones and a large set of users were paying higher shipping than they should have because of standard shipping costs for all users.
Leveraging first principles thinking and data backed analysis, we figured out that the best way to solve for this was:
- To make users pay for the shipping they actually incur
- To bring supply hubs closer to demand to decrease cost per shipment and pricing across the platform
This project utilised iterative experimentation and A/B tests to solution for the right way to communicate this value proposition to users and maximise platform conversion after implementation of different shipping costs for different users.
We have numerous such examples where data has helped us uncover invaluable insights and also led to disproving pre-conceived notions and assumptions about our users. We strongly believe that our customers constantly tell us what they want through their shopping behaviour and these insights can be seen loud and clear when data is viewed without inherent biases.
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Credits:
- Written by Matthew Dilip (Twitter: @matt_dilip), Edited by Vaibhav Chowdhury
- Design: Shoumita Dhar
