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Roundforest
UX Design · A/B Testing · eCommerce

Data-Driven UX Funnel

Transforming affiliate funnels through iterative performance optimization - replacing guesswork with fast A/B testing to drive confident purchasing decisions at scale.

A/B Testing
Mobile UX
Optimization
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Role
Senior Product Designer
UX · Experimentation · Mobile
Timeline
Ongoing · 4 months
1 PM · 2 Data Analysts · 1 Engineer
Platform
Web · Mobile-first
Affiliate · BestReviews.Guide
Overview

About Roundforest

At Roundforest, we run high-volume Google traffic campaigns sending millions of global shoppers to data-driven "Top 10" product review lists. The platform earns a commission from Amazon on every purchase triggered by a user clickout - meaning even tiny friction points in the journey translate into massive revenue impact.

Top 10 list page
Overview section Google ranking vs engagement
Problem

High Traffic, Low Engagement - Low Google Ranking

Data showed that on-site engagement was too low relative to our Google ranking. To improve rankings, we needed to increase engagement - without harming more critical metrics like click-out rate and conversion.

Design Process
Observe
Funnel analysis, behavioral data, and engagement metrics to surface friction points.
Hypothesize
Break problems into small, testable UX hypotheses around intent, clarity, and scanability.
Design & Test
A/B test variants with live traffic - validate interaction quality over assumptions.
Iterate
Compound wins from each validated iteration - reject what hurts, scale what works.
Hands-On Design process
Narrow Down feature, Driving Engagement & Conversions

We are introducing "narrow-down" filter chips to simplify the shopping experience.

Our hypothesis: Giving users quick, clickable filters will spark higher engagement and guide them to faster buying decisions - instantly hitting two of our core product goals.
1st A/B test results:
  • •Significant EPU (earn per user) uplift ↑
  • •Positive engagement trend (not significant)
Filter 1
Pre-Iteration 2 – Decision Making
User Distraction, The Core Risk.

Historical data and user testing showed that heavy, engaging elements easily distracted users from their primary goals.

Key Objectives & Solutions
Streamline & Attract

Designed lean, minimalist filter chips that invite interaction without causing cognitive clutter.

Optimize Placement

Initial testing revealed users were scrolling past the filters. I redesigned the layout to catch their eye while maintaining a seamless user experience

Clarity over Content

Communicated the value of the filters instantly, removing unnecessary copy to prevent information overload.

Tweaking for Better Engagement

We noticed a really positive trend in clickout and EPU (earnings per user), but the overall engagement with the feature itself wasn't quite where we wanted it. To fix that, we ran another iteration focusing on the UI and messaging - making it more visually appealing and enticing for users to actually click on and try out.

Filter 2
Impact & Results
Better results, step by step

Small, targeted UX improvements that scaled across millions of monthly sessions.

↑ Engagement
Significant uplift in meaningful user interaction across desktop and mobile
↑ Interaction
Consistent increase in clicks on key decision elements throughout the funnel
↑ Clickout
Noticeable uplift in downstream conversion-related behaviors and Amazon clickouts