Hardi Jain · AI and SaaS product manager

B2C · STORY 01

When the funnel lied

SRT optimization through bot detection

A platform serving more than 10 million users saw its Success Rate of Transactions fall from 65% to 60%.

01 · THE SIGNAL

What changed?

A platform serving more than 10 million users saw its Success Rate of Transactions fall from 65% to 60%.

AWS monitoring flagged the deviation once it passed two percentage points.

02 · INVESTIGATION

Before changing the product, I had to find the pattern.

I segmented the decline across merchant level, industry segment and transaction source to identify where the SRT movement was concentrated.

03 · THE FIRST HYPOTHESIS

The obvious explanation wasn’t the answer.

My initial hypothesis was that NGO merchants were driving the decline, because publicly shared payment links could attract users with low purchase intent.

The data disproved my first hypothesis. That is part of the story, not something to hide: hypothesis, evidence, revision.

04 · THE ANOMALY

Then the transaction data changed the direction of the investigation.

Transaction-level logs revealed approximately 500 transactions initiated through a single email ID within a five-minute window, originating from outside India during non-standard business hours.

The metric wasn’t telling the whole story.

05 · FOLLOW THE TRANSACTION

Where were these transactions coming from?

I worked with Backend Engineering to trace the abnormal transactions back to their origin.

The investigation confirmed that automated scripts were generating fraudulent bot transactions through a Shopify source integration.

06 · THE PRODUCT PROBLEM

Finding the bots was only half the problem.

How do we stop them without disrupting legitimate users?

This is where the work stopped being analysis and became a product decision.

07 · OPTION A

CAPTCHA

What it solves: it creates an immediate barrier for automated scripts.

The product concern: it introduces an additional interaction into the checkout experience for legitimate users.

08 · OPTION B

Invisible bot detection

Detect abnormal script execution patterns in the background and mitigate suspicious transactions without requiring an interaction from genuine users.

09 · THE TRADE-OFF

The decision wasn’t about blocking bots. It was about where to place the friction.

Both options stop automated scripts. They differ in who pays for it.

CAPTCHA puts the cost on every legitimate user at checkout. Detection puts it on the traffic that looks abnormal.

10 · THE DECISION

Detect the abnormal behaviour. Don’t interrupt everyone.

CAPTCHA was the initial proposed solution. After evaluating the trade-off between mitigation and user friction, I pivoted toward implementing bot detection first.

11 · FROM DECISION TO DELIVERY

Turning the decision into a product.

I led problem definition, PRDs, tracking metrics, stakeholder alignment, sprint prioritization, cross-functional testing and post-launch monitoring.

12 · THE RESULT

The signal recovered.

Within one month of launch, SRT improved from 60% to 67%.

The bot traffic was mitigated without introducing an upfront CAPTCHA challenge into the checkout experience.

What this story says about how I solve problems

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Connecting the dots

“You can’t connect the dots looking forward; you can only connect them looking backward.”

— Steve Jobs