I Tracked My Google Pay Rewards For 20 Months And Here’s What I Found Out

PART I: THE HOOK

If Google Pay were an employee, it would have a storied career only 2 years into employment. Launched in 2017 as Tez, the payments app now boasts of 65 million active users making transactions worth $110 billion annually. With the digital payments market in India projected to double to $135 billion in 2023 and Google Pay emerging as the second most used UPI-based app, Google Pay’s numbers, and its future, look quite robust.

Google Pay is another usual payments app but in India it rides on the Government’s revolutionary UPI system. Credit: Google Pay logo, pay.google.com.

Like any good payment app, Google Pay uses the tried-and-tested reward system to hook new users into using it. The rewards system is presented as scratch cards, which the user unlocks upon sending money either to people (P2P) or businesses (P2B.) There’s a subversive dopamine hit that gets deployed every time a scratch card gets unlocked — let alone scratched — to reveal whether the user is rewarded or not. It’s a clever, effective UX decision that makes using Google Pay that much more fun, and addicting, to use.

I was 3 months late to the Google Pay party — joining in January 2018 — but was quickly suckered into the scratch card system. While the ease of a UPI-based transaction is delivered across payment apps, I distrusted BHIM due to its Government links, and had a very rational hatred for PayTM and its digital wallet system that once screwed me over while paying a 400-rupee mobile bill.

Over the last year and a half I noticed a declining trend in getting rewards. It felt like in 2018, I was getting maybe 5–10 rewards a month and that had slipped drastically in 2019. Many work friends echoed this sentiment, saying it had been months since they’d got a cash-back for their transactions in 2019. Of course, having racked up nearly 800 rupees in rewards, it was possible that I simply did not fit the bill of a customer to be rewarded anymore. But, before I decided to go about flinging accusations — and warning other users of what would be their eventual fate — I needed proof.

So I decided to do a little data analysis on the 230 transactions I made on Google Pay since January 2018.

Let the deep dive begin.

PART II: THE METHODOLOGY

While I began the arduous process of logging my Google Pay data on Excel — manually! — I wrote down the basic answers I wanted from this project. I also resigned myself to the idea that even if I was right; I was unlikely to switch from Google Pay to another app, because:

a) I trust Google more than any other app out there (that’s brand image for you)

b) I enjoy the UI greatly.

c) I don’t like having multiple apps that do the same work

My primary reason for using Google Pay is the sheer convenience and trust in their security apparatus. The scratch cards are an added, effective and attachment-inducing bonus.

While logging data I asked new questions with the wealth of information I uncovered. The app registers all the rewards given to a user, all transactions made, to/by whom they were made, and when they were made. By matching my rewards log to the all transactions page, I was able to correctly determine which transactions were rewarded, and which were not.

Another key aspect of this cross-referencing helped reduce the errors when I got multiple rewards on the same day despite there being no transactions. While scratch cards are unlocked after a transaction, a user has the option to scratch them at a later day and get the reward amount credited many days after the initial transaction. I had a habit of collecting many scratch cards and then checking them all for rewards many days later.

Another important part of the rewards system that it has certain rules governing it:

a) Only P2P transactions above Rs 150 unlocks a scratch card

b) Once given a scratch card, transactions on the same day after that will not be eligible for the same kind of scratch card (The user won’t unlock another P2P scratch card but can unlock a P2B scratch card)

c) The user is only allowed 3 scratch cards per week.

I attempted to remove transactions that break these rules — but admittedly may not have done the best job since I did most of it visually on an Excel sheet.

Finally, to check how often I get rewarded for P2P transactions, I have added money received as a transaction since that also makes me eligible for a scratch card. I did, however keep track of how many payments I received Vs how many I made, as well as their total amounts, so as to provide a complete breakdown.

For now, the questions I want answered:

a) Was there a declining trend in rewards, or was that my imagination?

b) What activity did I get the most monetary rewards from: P2P, Uber Bills Or Vodafone Bills? (These made up the bulk of my transactions)

c) What activity gave me the most chance of a reward (ignoring the amount actually rewarded per transaction)?

PART III: THE FINDINGS

a) Did The Rewards Go Down?

After logging all 230 transactions from 24th January 2018 to 12th Oct 2019, the answer to whether the rewards have decreased, is a little bit complicated. To answer that, I’ve rustled up a few charts. A quick look at how I’ve named the timeline, which is consistent across all other line charts used in the analysis:

Table 1: I have broken up the time period into quarters (3 month periods) in a calendar year.

And now to the chart itself —

Fig 1: While the rewards line shows a decrease, it doesn’t seem as drastic as I imagined.

To address a discrepancy before we understand the data: I haven’t included October 2019’s data in this particular chart since it would be the only month in Q4 2019. As of the day I started this project, I have made 5 transactions and received 0 rewards for it. This data ends up in some other charts, so it’s important I put it down in the beginning for transparency sake.

From the blue line on Figure 1, it’s obvious the rewards have come down, but also not as drastically as what I’d imagined. For some reason, I held a very strong belief that I was getting 5–10 rewards per month, when in actuality, the maximum rewards I got — 13 — was in Q4 2018. There’s a reason why there’s such a spike in rewards in that quarter by the way, and here’s a breakdown of the rewards I got that quarter.

Fig 2: Uber transactions accounted for 86% of my rewards in Q4 2018: a staggeringly high number.

In Q4 2018, I got 12 rewards from Uber transactions! Compare this to the total number of Uber Rewards in my entire analysis period —

Fig 3: Rewards from Uber transactions in Q4 2018 accounted for 67% of all my rewards from Uber transactions.

So Q4 2018 is responsible for more than half of all my rewards from Uber! What happened in Q4 2018? We’ll answer that question in detail in the next section, but the takeaway for now is that the 13 rewards spike in Q4 2018 is not the norm. Let’s also look at the breakdown of the second-highest rewards period (8) in Q1 2018 —

Fig 4: A breakup of all the rewards I got in Q1 2018. Uber transactions accounted for nearly 40% of those rewards.

Aside from the obvious Uber domination, I also want to point out that 25% of my rewards in Q1 2018 came from referrals. Referring people to Google Pay is actually a foolproof way to getting rewarded. Instead of the luck-of-the-draw algorithm with other scratch cards, referring someone (or being referred) will most definitely reward you. I joined by referral — which rewarded me 51 rupees — and then referred someone else in turn — which rewarded in me another 51 rupees. Later in the year, I referred another friend, which earned me 81 rupees (Google upped the ante,) for a total of 182 rupees in referral rewards.

The trouble is, it’s hard to find people to refer the app to, and including it in my rewards analysis throws off the trend to see whether rewards are actually on a downtrend. If I remove the referral rewards from Q1 2018, and the one I got later, from Q3 2018, the chart ends up looking something like this —

Fig 5: A revamped attempt at the earlier timeline chart of rewards and transactions but having removed referral rewards.

Which now — ignoring the big spike in Q4 2018 — doesn’t look like a huge downtrend, except for Q2 2019 (2 rewards) which is probably the three-month period when I noticed and started to complain. Perhaps Google eavesdropped on my complaining because I was back to 5 rewards in Q3 2019.

So, for all intents and purposes, the number of rewards I’ve gotten every quarter since I started using the app has actually stayed fairly stable.

Why then, did I feel that my rewards had started coming down, especially as I entered the New Year?

To answer that, let’s look at the climbing red line of total transactions. With a reward based system it’s easy think that:

The More You Transact = The More You Get Rewarded.

From the above charts, it’s entirely untrue. Google Pay probably has a cap on the number of rewards I get, irrespective of the number of transactions I make. Even as my transactions climbed in later quarters, I still only got between 4–6 rewards (again ignoring the 13 reward spike.) In fact looking at the percentage of rewards per transaction it looks like this:

Fig 6: Once you look at the percentage of rewards I’ve been getting, the slump is fairly obvious.

Which tells us that my rewards percentage has come down. Not even looking at Q4 2018’s ginormous spike, the rewards percentage in all three quarters in 2019 are beneath their comparative quarters in 2018, which further leads me to believe that my rewards are capped between 4–6 rewards every quarter, no matter how much I end up transacting.

(I should point out here that I have emailed Google asking if they have an official policy to cap rewards per user. They have not responded yet.)

So to answer my first question: Have My Rewards Gone Down?

The answer: in absolute terms: not really. In percentage terms (since my total transactions have exploded since I started using the app): yes, definitely!

b) The Vodafone — Uber Duopoly

As I was slaving away logging the data, two names — Vodafone & Uber — began to jump out at me. Here’s a breakdown of all of my reward transactions:

Fig 7: A breakup of all the rewards I got from Jan 2018 to October 2019 showing which transactions triggered rewards. Uber at 41% followed by Vodafone at 25%.

Of the 44 rewards I got, 66% were from Uber & Vodafone Bills, 18% from P2P transactions and 16% from ‘Other’ transactions (which includes referrals, and other businesses I transacted with two or more times: Zomato, Ola, Swiggy & Amazon)

Now from previous findings we know the Uber number is rather skewed:

Fig 3: Rewards from Uber transactions in Q4 2018 accounted for 67% of all my rewards from Uber transactions.

In fact, adding the figures we saw from looking at Q1 2018’s rewards breakdown, we get a more interesting figure:

Fig 8: Q1 & Q4 2018 account for 83% of all my Uber rewards. The remaining 3 rewards are spread out over the next one and a half years.

Two quarters (Q1 & Q4 2018) account for 83% of my Uber rewards, and one quarter (Q4 2018) accounts for 67% of my Uber Rewards.

So what happened in Q4 2018?

The answer isn’t all that dramatic: from October to December, 2018, Uber and Google Pay ran a tie-up which rewarded a user every time they paid for an Uber with Google Pay. The offer lasted till December 31, 2018 or when the user finished 10 rides — whichever came first — and rewarded a flat Rs 15 per ride, irrespective of how much the ride actually cost.

(Fun side note: one time Uber deducted 1 rupee off me — I’m not entirely sure why because I never got it back — and Google Pay gave me 15 rupees for it, giving an off-the-charts 15x return on that transaction.)

Let’s delve a bit further into Uber and Vodafone and try to answer a few more questions —

a) Which transaction gave me a higher chance of a reward?

b) Between the two, where did I spend more money?

c) And which transaction afforded me more reward money?

We’ll start by seeing how often I was rewarded for an Uber and Vodafone transaction:

Fig 9 and 10: I have a 19% chance of being rewarded off an Uber transaction and a 55% chance of being rewarded off a Vodafone transaction.

In the analysis period I ended up making Uber transactions that were 5x the number of Vodafone transactions — easily explained by the fact that I pay a monthly phone bill but take Ubers everywhere, all the time.

It is surprising to note however that I’ve actually been rewarded more times (55%) for paying my Vodafone bill versus not. I was also harbouring the impression that I get more rewards off Uber — probably because of those 10 straight rewarded transactions in Q4 2018 — but that’s been put to rest (19% chance of reward with Uber, 55% with Vodafone.)

In fact the chances of getting a reward from Uber is actually even worse than what the charts say because of the skewing of results. Taking another look at the data: of the three transactions that did not occur in Q1 & Q4, 2018, two of them occurred in Q2 2018 and one of them occurred in Q1 2019. To put that in perspective —

Fig 11: Aside from the big spike in Q4 2018, Uber transactions have actually rewarded me very poorly.

I have gone 3 entire quarters — 9 months! — without a reward from Uber, despite the 27 rides I took in that time period. Compare this to Vodafone —

Fig 12: Vodafone transactions have remained very consistent with their rewards.

Vodafone did not reward me at all in Q1 2019 (Jan-March) but otherwise gave me a reward fairly regularly. In case you’re wondering how I did 4 transactions in Q2 2018 and Q2 2019 (when one quarter = three months = three bills,) it’s because in Q2 2018 I also paid for someone’s prepaid pack and in Q2 2019, I paid my bill for the previous month, late — thus pushing it into the next quarter’s transaction list.

So even though numerically, Uber has given me more rewards (18) compared to Vodafone (11,) I should actually only entertain hopes of triggering a reward from Vodafone, (50% of the time anyway) and nothing much from Uber, unless they have another tie-up with Google Pay.

While Vodafone’s steadfastness in rewarding me is appreciated, it’s also noticeable that my rewards in 2019 (2) are below those in 2018 (9) are therefore on a downtrend, which is rather saddening.

I also want to illustrate a breakdown of the total rewards and their actual monetary value —

Fig 13: I earned the most money from Uber transactions (Rs 311) followed by Referrals (Rs 182) followed by Vodafone transactions (Rs 181.) My total rewards in the period stood at: Rs 844.

Uber bills have contributed the most to my rewards monetarily (Rs 311) followed by referrals (Rs 182) and Vodafone (Rs 181).

On the transactions side, I spent Rs 75,163 and received Rs 26,153, totalling Rs 1,01,316 worth of transactions on Google Pay between January 2018 and October 2019. In the same period I amassed a total of Rs 844 from rewards, amounting to about 1% of my transactions. Here’s a breakup of where I did the most transactions and (excluding P2P,) spent the most money.

Fig 14: Aside from P2P transactions, I have spent money on all other transactions. Their monetary value counts as direct expenditure. P2P transactions accounts for both money I spent and received so does not count as total expenditure.

Putting them side-by-side with the reward break-up chart, we see that:

Fig 13 and 14: A side-by-side comparison of monetary breakup of both rewards and transactions.

a) Uber is responsible for the second highest expenditure (24%) and is the highest contributor to the rewards (37%) — but also most of those rewards came in one quarter (Q4 2018)

b) Vodafone contributed the least in my expenditure (9%) and was the third-highest contributor to the rewards (21%) — getting pipped by referrals by literally one rupee.

c) My expenditure in the ‘Others’ category (14%) is higher than my Vodafone expenditure (9%) but makes up only 6% of my rewards compared to Vodafone. (21%.)

d) The P2P transactions make up more than half of my transactions (53%) but only contribute 14% — the second-lowest — towards the rewards.

So to answer the questions I raised in the beginning of this part:

a) Which transaction gave me a higher chance of a reward? — Vodafone (55%)

b) Between the two, where did I spend more money? — Uber (24% to 9%)

c) And which transaction afforded me more reward money? — Uber (37%)

But let’s delve into those interesting P2P transactions a bit more!

c) Those Pesky P2P Transactions!

Perhaps the biggest strength of any UPI-based payment app is that it facilitates paying your friends for all those split bills, shared cabs and bets you’ve lost, in literally a few seconds. In my analysis I’ve counted both paid and received transactions in my total number of P2P entries, since I get rewarded for both. It emerged as the second-most popular transaction on Google Pay for me even numerically —

Fig 15: P2P transactions were the second-highest in my total transactions list, coming in second (37%) to Uber (42%).

Of those 85 P2P transactions I made, only 8 of them were rewarded —

Fig 16: 8 out of 85 P2P transactions were rewarded, making it a very unreliable transaction to earn rewards on.

That’s less than a 10% chance of getting rewarded from a P2P transaction, compared to Uber (19%) and Vodafone (55%.) Considering P2P transactions make up 53% of my transactions and 27% of my total expenditure, this is not a welcome sign in the slightest. Let’s take a look at the reward trends for P2P transactions every quarter —

Fig 17: Despite my skyrocketing P2P transactions, the rewards have simply not kept pace.

From the charts above, I went two quarters (6 months) with no rewards from P2P transactions, and three quarters (9 months) with one reward each. It’s a little dismal considering the number of P2P transactions I did — and the whopping Rs 27,114 I spent and Rs 26,153 I received — that I can’t help but feel slightly cheated with the lack of rewards. For some comfort, the reward trends are not in a downward slope, but then again there are hardly any rewards that it’s not much to see a trend in the first place.

I made back about 0.2% in rewards from P2P transactions compared to what I spent and received. In comparison, I made back 1% of my Uber spend and 2% of my Vodafone expenditure. Not numbers, as you can see, that one could write home about.

PART IV: THE CONCLUSION

To cut a long story short, I was both right and wrong with my assumptions — which is the best kind of answer from any analysis, really. The chance of a reward did go down, but absolute rewards didn’t really change all that much. To refresh —

Fig 5 &6: A side-by-side view of absolute rewards earned versus the chance of a reward earned.

From the two trend graphs, the absolute blitzkrieg of rewards in Q4 2018, followed by the reversion to the norm in Q1 2019 — and then continuation of the norm — led to the feeling that my rewards were in a downtrend in 2019, even though the number of my rewards hadn’t really changed. My exponential increase in transactions — and the lack of parallel increase in rewards — helped cement the idea.

What I’ve Learnt

For an optimum transaction-reward ratio, I should probably cap my expenditure at 10–18 transactions every quarter so that I can go back to expecting a reward every 3 transactions. If Google Pay insists on capping the numerator (rewards) between 4–6, then I shall also insist on capping the denominator (total transactions) between 10–18, so that I retain a healthy 35% chance of a reward.

On the other hand, while it’s unlikely that I’ll reduce my transactions a great deal (bills to pay, taxis to go places,) I can reduce my expectations, especially from paying off an Uber bill or sending / receiving money. Even while paying a Vodafone bill, I should only expect to be rewarded 50% of the time per quarter, (which is a time period spanning 3 months) and which has also been on a significant downtrend.

What Google Can Learn

Far be it from me to give Google Almighty advice on how to keep users engaged in a product, but I can’t help but feel that rewarding users for more transactions, instead of limiting it, will keep them engaged with the app. My analysis has told me in fact to spend less (which one should anyway) because the rewards simply aren’t keeping up with the transactions. If, instead I was rewarded for spending more money, and more frequently, I’d probably see the correlation and end up increasing my usage.

But Also…

The objective of a reward scheme is to get new users hooked to the app — which it did — and the ease and functionality of the app is what retains users — which it also did. As I said in the beginning, I don’t intend to switch apps based on these results, but I’m also quite interested to know how the rewards trends looks like for other Google Pay users, as well as other platform — PayTM — users. Are they being more aggressive with their rewards? Do they also focus on getting new users hooked and do they also cap the rewards? Alas, I’ll have to steal a friend’s phone to find out.