Top-Up Screen A/B Testing
Company
PayPay
Industry
Fintech
Timeline
2025/2 - 2025/6
Responsibility
End-to-end design process
Prototyping
Overview
Every PayPay top-up comes with a processing fee. At millions of transactions a month, that adds up fast.
I noticed users kept topping up in small amounts, around ¥5,000 multiple times a month, instead of loading a larger amount once. I dug into past research and data to understand why, formed a hypothesis on how to encourage larger top-ups and reduce frequency, and ran a one-month A/B test to validate it.
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0
%↑
Top-up amount
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%↓
Cost of top-up
Start with the problem
We found that users tend to top up small amounts (around ¥5,000) multiple times rather than adding a larger amount at once.
Each top-up comes with a processing fee, and with PayPay's large transaction volume, these fees quickly add up to tens of millions of yen each month.
Understanding user behavior
To better understand why users top up in small amounts, I reviewed past research and user data, and identified 3 key patterns:
Users are hesitant to keep a large balance in PayPay due to security concerns: this came up repeatedly in user interviews, suggesting that the barrier isn't just habitual, it's emotional.
The current amount input experience creates friction: usage data showed drop-offs at the input step, indicating that the UI itself was discouraging larger entries.
Many users lack visibility into their monthly PayPay spending: without a clear reference point, users default to topping up small amounts reactively as their balance runs low, rather than planning ahead.
Choosing where to start
Security concerns would need policy or comms work, not something an A/B test could easily validate. Input friction pointed to a larger redesign, with more dependencies and a longer timeline. Spending visibility was different. It was a clear before-and-after change we could ship fast and measure with a clean A/B test.
So we started there. Our core hypothesis: if users could see their last month's spending at the moment of top-up, they'd have a concrete reference point and be more likely to load a larger, more intentional amount.
Beyond the spending insight itself, we were also curious whether layout would influence behavior. We decided to test 3 variants, each presenting the information differently.
Let's begin with the top-up screen
Most users focused on the blue highlighted areas, so the variants were designed around those elements.
Ideation
Variant A
But the problem is, it adds an extra step, users have to leave the top-up flow, check their spending, then navigate back.
Next, we needed to make it easier for users to enter that amount.
Variant B
However, it didn’t work well visually, as it made the layout look off and pushed the other quick amount buttons.
Variant C
But it made bottom bar too large and could potentially cover content at the bottom.
This gave us three testing cases to move forward with.
Just when things seemed to be going smoothly, an issue came up in variant A
Revisit Variant A
After second thought: this felt too aggressive, some users may prefer to enter the amount manually, but when they tapped the placeholder, we automatically filled in a value, which could lead to a poor experience over time.
This made the design less intrusive and maintained the input field as the primary focus in the information hierarchy.
While we’re making it easier for users to input the amount, what if they tap it by accident?
If they didn’t mean to top up that amount, they’d have to hit the delete key multiple times, which could lead to frustration.
Introducing Error Correction Button
This reduces friction for users who enter an amount manually and need to correct it quickly.
Before the rollout, it's time to look into details and edge cases.
Checking with Risk and Compliance
Since this feature encourages users to hold a larger balance, I checked with the risk and compliance team early on to confirm we wouldn't be pushing users past any regulatory balance limits, or creating exposure we hadn't accounted for.
They confirmed the existing thresholds had enough headroom for the behavior change we were expecting, so we moved forward without additional restrictions.
When should we show the recommended amount?
If users see the recommendation in the middle of the month, chances are they’ve already topped up a small amount. At that point, they’re less likely to follow our suggested amount.
To validate this, I reached out to a data team to check the average top-up frequency. The data showed that most users top up about every two weeks, meaning they visit the top-up page roughly twice a month.
So instead of displaying the suggested amount throughout the month, I decided to:
Show it at the beginning of the month, when they’re more likely to make a larger top-up
Limit it to bi-weekly display — shown once every two weeks, and then hide it until the next month. This helps prevent fatigue and ensures the suggestion remains relevant.
What About One-Off Large Spendings?
During QA, I noticed a few users had big spikes in spending, for example, investment-related payments or rare purchases. These aren’t regular behaviors and could throw off the recommended amount.
To keep things accurate, we excluded certain categories from the calculation if they’re clearly not part of regular monthly expenses.
Round the Amount Up — for Tech and Business Reasons
Lastly, I looked at the actual numbers. Users’ monthly spending often ends in awkward figures, like 14,563. Recommending that exact amount didn’t feel helpful or practical.
From both a technical and business point of view, I decided to round up to the nearest thousand. So if a user spent 14,563 last month, they’d see a suggested top-up of 15,000. It’s cleaner, easier to act on, and nudges the user slightly toward a higher amount.
Down to 2 variants
We initially explored 3 variants. Testing all three simultaneously would have required a significantly larger sample size and extended the timeline, which didn't align with our goal of iterating quickly. So we moved forward with Variant A and Variant B.
These two represented the most distinct interaction patterns. The contrast between them would give us the clearest signal about what actually drives behavior change.
Variant A and Variant B were each rolled out to 10% of users, while the remaining users saw the standard top-up screen as the control group.
Variant A Demo
Variant B Demo
Results
Variant A won.
Top-up amount per user: +7%
Top-up frequency: -22% (from 4.5 to 3.5 times/month), reducing per-transaction processing costs at scale
Variant B showed no significant change
Trade-off We Watched
One thing we kept in mind: users told us they avoid keeping a large balance because of security concerns. Our solution was nudging them to do the opposite.
Before rollout, I checked with the risk team to confirm the existing balance limits and fraud monitoring already covered this shift in behavior. We also tracked support tickets related to account security after launch, no spike. Still, this is something we'd keep an eye on if top-up amounts keep climbing.
Future Planning
With the results in, we're planning to roll out to all users and run another round of testing, this time focusing on the input experience.
Rolling out to all users also meant rethinking how we calculate last month's spending. Computing it live for every user wasn't realistic at our scale, so I worked with engineering on a batch calculation that updates once a day, cached and ready before users open the top-up screen.
Our hypothesis is that reducing friction in the amount input could nudge users toward larger top-ups.
Reflection
Our core hypothesis was confirmed: showing users their last month's spending at the moment of top-up does shift behavior. But the result came with an important nuance. Variant A, which surfaced the information through the input field, drove a 7% increase in top-up amount. Variant B, the banner with an explicit CTA, showed no significant change.
The same information, presented differently, produced completely different outcomes. Users entering the top-up flow already have a number in mind. A banner at that point feels like an interruption. An input field suggestion feels like a natural reference.
This points to our next hypothesis: if we surface spending context before users enter the top-up flow, when they haven't yet anchored to a number, the nudge may be more effective. We're planning to test this as the next intervention point.
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