Google Pay — Designing Product with Data Analytics

Arguably, one of the best product design in recent times is GPay (previously Tez). Google Pay has 67 million monthly active users in India, 300 million transactions processed in the last year worth $110 billion. They have 36% market share in UPI payments.

But we are here to talk about the how!

GPay Android App — Homepage

When Tez initially launched, it knew in order to gain market share in India you need to reach more people (distribution moat), make it viral, make it one of a kind (something new and quirky), reward the users who use the platform more, ensure word of mouth happens and people boost about the product.

Steps taken:

1. A whole new approach to UI elements and design standards were followed.
2. Rewarding users via scratch cards (first in digital, I believe)
3. Bluetooth based payments (big Send <-> Receive button)
4. Merchant rewards
5. Referral programs to invite your friends

And over a period of time almost all the steps taken has worked positively for them.

We will discuss only 2 use cases where I figured out data analytics played a big role in future product designing of GPay.

1. Removing prominency of Bluetooth based Send <-> Receive feature. I know I haven’t used it much. I m also guessing others also didnt use it as much as QR scan feature, Transfer via Phone Number/UPI ID/Bank A/c feature. This is where data analytics comes into picture to decide the future roadmap or enhancements of your product.
2. Showing 3 users from your phonebook who are not using GPay as part of people. From the referral (invite your friends) campaign they got viral. But now that buzz is gone. If you look at the picture above, the 11 people that is shown, here is the classification of them. First 8 are those with whom I have had recent transactions. Next 3 are those from my phonebook who are not using GPay, that too in order of my Favorite contacts first, then frequently contacted (phone/sms), then the rest(that’s pure data crunching to arrive at relevancy).

For designing a great product, it has to be a mix of intuition and data backing that intuition.

— Someone!