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“Rebuilding Checkout Without Tanking Conversion” (Product + Tech)

How we redesigned a legacy checkout, measured real impact through experimentation, and avoided false conversion conclusions when the experiment was interrupted.

Rebuilding Checkout Without Tanking Conversion

Overview

Checkout is one of the highest-risk areas of any commerce platform. Even small changes can have outsized impacts on conversion, revenue, and customer trust.

This case study covers how we rebuilt a legacy checkout experience, validated improvements through controlled experiments, and navigated a temporary conversion dip caused not by the new checkout itself, but by how traffic was reallocated during evaluation.

The Problem

Our existing checkout had accumulated years of incremental changes:

  1. Inconsistent UI patterns
  2. Fragile client-side logic
  3. Limited flexibility for experimentation
  4. Poor observability into where users dropped off

Despite “working,” it was difficult to evolve safely.

Goals of the New Checkout

Before writing new code, we defined what success actually meant.

  1. Reduce friction without introducing dark patterns
  2. Support controlled A/B experiments
  3. Improve maintainability and observability
  4. Preserve or improve conversion over time
  5. Avoid breaking trust for returning users

The New Checkout Experience

The new checkout focused on:

  1. Clearer information hierarchy
  2. Reduced cognitive load per step
  3. Better error handling and validation
  4. Cleaner separation between UI and business logic

Importantly, we did not assume the new version would automatically outperform the old one.

Experiment Design

To validate the new checkout, we ran a controlled experiment:

  1. Control: Legacy checkout
  2. Variant: New checkout
  3. Traffic split: 90% control / 10% variant
  4. Primary metric: Checkout conversion rate
  5. Secondary metrics: Drop-off per step, error rate, completion time

This allowed us to:

  1. Limit downside risk
  2. Collect real user data
  3. Iterate quickly on the variant

Early Results

Initial data showed:

  1. The new checkout performed as well or better on most sub-metrics
  2. Some friction points were identified and fixed quickly
  3. No catastrophic regressions were observed

At this point, the experiment was behaving exactly as expected.

The Conversion Dip (and the Misinterpretation)

During evaluation, traffic was temporarily shifted to 100% control, effectively pausing the experiment.

Shortly after, an overall conversion dip was observed and attributed to:

“Switching back to the old checkout must have fixed conversion — the new checkout hurt performance.”

This conclusion was incorrect.

Why 100% Control Invalidated the Signal

Several factors made the conclusion unreliable:

  1. The control version had little recent data at scale
  2. The observed dip coincided with traffic reallocation
  3. There was no longer a baseline to compare against
  4. Short-term variance was mistaken for causal impact

In short: we lost the experiment, not the conversion.

Communicating This to Stakeholders

One of the most important parts of this process was explaining why the data didn’t support the conclusion.

We focused on:

  1. Separating correlation from causation
  2. Explaining why waiting longer wouldn’t help without a variant
  3. Emphasizing that experiments require comparison, not patience

This reframed the conversation from:

“Which checkout is better?”

To:

“What can we actually prove with the data we have?”

What We Learned

1. Experiments Are Fragile

Even well-designed experiments can be invalidated by:

  1. Traffic reallocation
  2. Partial rollouts
  3. Executive intervention

Guardrails matter.

2. Conversion Dips Need Context

Short-term changes are often:

  1. Traffic-driven
  2. Seasonal
  3. Distribution-related

Without a control, they’re not actionable.

3. Product Judgment Matters as Much as Code

The hardest part wasn’t building checkout — it was:

  1. Knowing when data was insufficient
  2. Pushing back on premature conclusions
  3. Protecting long-term decision quality

Results (When Run Correctly)

When evaluated under a valid experiment:

Checkout Conversion
Maintained / Improved
Error Rate
Reduced
Iteration Speed
Significantly Faster
Experiment Safety
Improved

More importantly, we ended up with a checkout system that could be evolved safely instead of feared.

What I’d Do Differently

  1. Add stricter experiment safeguards
  2. Automate alerts when experiments are paused
  3. Document interpretation rules up front

The technical rebuild was successful — the lesson was ensuring the process was just as robust.

Why This Matters

Checkout isn’t just a UI — it’s a trust boundary.

This case study reflects a broader principle:

Strong systems require both technical rigor and disciplined decision-making.

Without both, even good data can lead to bad decisions.