Hi there! My name is Rohit Mehta. I am a Product/UX Designer from India. Currently, a senior product designer with Ola Cabs.
While my personal portfolio site with case studies is long overdue, this is a quick make-shift page which summarizes some of my selected projects. I have listed them below. This page is unlisted. Please do not share it with anyone else :)
I joined Ola cabs in January 2017 as a Senior Product Designer. I was tasked to lead Ola Money and Payments design primarily. I worked on a few projects outside payments. Following are selected project prototypes and mockups.
Case Study#1: Debit card payments
a.k.a Debit Card Fraud Prevention
Payments made via debit card were deferred payments. Which means, Ola would have to trust the user to make the payments after the ride ends or before he takes his next ride. While most users honored and made the payments immediately after the ride ended or before the next booking, there were few users who used this to de-fraud the company. Users with intention to defraud used this deferred payment to take the ride and never comeback.
Ola has its own wallet, Ola Money. Maximum rides are either Ola Money or Cash. Credit and Debit cards were not even close to the staggering numbers of Ola Money wallet or Cash payments. But one decision by India’s Reserve Bank of India was set to change this.
Reserve Bank of India a.k.a RBI mandated all the digital wallet (Ola Money, Paytm, Freecharge, Oxigen etc.) users be KYC verified. Users would have to do a mandatory KYC (Know your customer. Details which helped us and RBI identify the user using the app). Just like you do when you open a bank account. This meant, the wallet apps would have to go the extra mile and ask users to be KYC verified, else their wallet would cease to exist. They wouldn’t be able to use the app to make their cab ride payments anymore. This meant, either we would experience a sharp dip in ride, or increased cash transactions. But, once a cashless user, always a cashless user. Ola would have to open up Debit and Credit card payments to most of its users, which meant the defaulter numbers would sky rocket.
With this backstory, we had to look forward for a robust fraud capture mechanism and prevent it from happening.
This whole product construct went to Product Spec to Design and back to drawing room 3 times. We had to identify various connected sections and provide an experience with minimum friction. The whole idea kept refining till we reached a minimum friction solution which would cater to all users. There had to be a mechanism to identify high risk users and take them to a different flow and no-risk (trusted) users to a different flow. Some of ideas which were bounced off.
- Ask the user to make an authorization to charge the debit
- Ask the user to make the payment within stipulated time (15 mins) after the ride starts.
- Get the user to make the payment when he is riding, maximum 5 minutes before the ride ends
- Convert the payment mode to cash if the user fails to make the payment
While some of the solutions appealed, we were most concerned about ruining the experience of a trusted rider. He didn’t do anything wrong to get that kind of experience.
After a lot of deliberation, delay, we finally arrived at a solution which would provide unchanged experience for trusted user, while get the untrusted user to pay upfront before his ride ends.
Case Study#2: Drop location on PWA (Progressive web app/Ola Lite)
Ola’s Progressive Web App was featured in Google I/O 2017.
On the progressive web app, a decision was made not to show maps by default to avoid heavy data usage. User would enter their pickup and drop location and could select them from suggestions. This resulted in inaccurate pickup locations as the location would be off by some distance. Even though users had an option to pick on map, it was deep inside and not discoverable.
Drivers cancelled the ride after reaching the pickup point as there was a gap between actual pickup point and the one entered. On the Android/iOS app, it was easier because the map was always loaded and users had a visual feedback on where they were. This was absent in the progressive web app.
About 17% rides were cancelled by the drivers ‘after’ reaching the pickup point.
We realised that the only way to fix this was to give users a visual representation of their pickup location before they confirm their ride. Tweaking their pickup location would give them greater control and also communicate to the driver-partner a more certain pickup location.
Even though we wanted to show the user their location on map, we didn’t want to add that data load to all the users. This problem was limited to cases where location was not accurate. Besides, PWA came with a promise of being light, fast and easy on data.
We wanted to give users a chance to edit the location before confirming the ride. A snapshot of user’s location on confirmation screen where they can edit the location on a map.
This would solve 3 key problems —
- Users could now see their location before confirming the ride
- Loading the map was now need driven. Only those users who saw a difference in their actual vs detected location would edit and tweak on a map
- Ola PWA would still be light on data for 92% of the users.
I did multiple iterations to figure out the interaction. Approach 1 was finalized and is now live.
Case Study#3 KYC Verification: Ola Money
RBI declared it was compulsory for all digital wallet users to complete KYC verification before 31st December 2017. This deadline was then pushed back to 28th February 2018. Following is a run down of how this project was thought about, designed, executed in initial 1 month and then 3 days.
Reserve bank of India had mandated all Prepaid Payment Instruments (PPI) ask users to complete KYC. For transactions as low as Rs. 10,000. Failing to which, the users won’t be able to add money to wallets and pay for services. Ola Money is not only a wallet for cab ride payments but also for Recharging prepaid phones, bill payments for mobile bills, utility bills such as electricity and water, DTH recharges, metro travel card recharges etc. Ola Money can also be used to pay for local services eg: at Petrol Pumps, Tea/Coffee shops and local stores.
We wanted to inform users to complete their KYC verification before 31st December 2017. Maintain the urgency while keeping the experience frictionless.
We had a head start on this. While this was announced in October 2017, the rules and regulations kept changing. The flow had to adapt to the changing rules.
Basic KYC — The most basic form of KYC verification. Valid for 1 year. Max limit of Rs. 10,000 per month. Transfers to Bank and Individuals not allowed.
E-KYC — Aadhar OTP based verification. Valid for 1 year. Max limit of Rs. 1,00,000 per month, Rs. 2,00,000 per year. Transfers to Bank and Individuals allowed.
Full KYC — Submit proof of Address and Proof of Identity documents and manually verify the documents to get verified for lifetime. No limit whatsoever on transactions. Full access.
Full KYC was the ultimate goal. E-KYC was the most preferred option as the user would get full access to the wallet features without any friction. Basic KYC was the easiest. We designed it to be the easiest, go to option. I personally felt that this whole feature was made too complex by adding these various pieces. A user doesn’t care what Basic, e-KYC and Full KYC would mean. He just wants to get done with it so that the wallet is not blocked.
Explorations: High fidelity wireframes
We envisaged it as a quick, web view built, structure where the user selects the type of KYC. Inputs details. Proceeds to completion.
Urgency and Quick turn around
While explorations continued and multiple solutions were being worked upon, sense of urgency started to grip. Time was running out and construct had started looking stable.
Final version shipped was a much simpler and lighter variant. A simple landing page where user selects the ID and enters data. Full KYC was dropped for now and E-KYC was to be handled by an authorized service provider (Fino).
LoanGaaga is an upcoming stealth mode startup aimed at improving NPA and Loan management workflows. I worked with the founders to build a bare minimum basic prototype to test out the idea with various Banks to initiate discussions towards funding of the product. Embedding the prototype below.
Scandid is an e-commerce aggregator app in India. At its core is price comparison from various e-commerce sites (Amazon, Flipkart, Snapdeal, Infibeam, TataCliq, Myntra, Jabong, Ajio, etc.), Daily deals and coupons. I worked with Scandid for 4 years. I was one of the founding team member. Over the period of 4 years, the product went through a lot of transformations and changed consistently over the years. I worked with scandid’s websites, Android, iOS apps and partner websites (Payback, American Express shopping, etc).
Below is a small snapshot of their iOS app which I redesigned, just before I left.
Scandid Website Product page redesign
Product Page — Mobile site
Some explored concepts
Information Architecture: Navigation design (2014)
More on Scandid later :)
Concepts — Part of various interview processes.
I interviewed and got from 2 companies over last 3 years (apart from Ola). TableHero (2015 & 2016) and HelpShift (2017). As part of their interview process I was tasked with two distinct assignments. Embedding concept prototypes below.
Casestudy#1: Rethinking Mobile Banking
In 2015, I interviewed with TableHero for the first time. This assignment got me through. However, due to various constraints, I had to drop the offer. We got talking again in 2016. This time, TableHero had to put the position on hold after 2 months of discussion. I took Ola’s offer after that.
(Detailed explanation on design decisions and more ideas.)
I interviewed with Helpshift in April 2017 (4 months after joining Ola). My task was to create an app for monitoring Pandas.