REVER®

Overview

REVER’s return flow had a critical drop-off: 38% of shoppers abandoned mid-process. I led the shopper-side redesign to fix that specific problem — not by overhauling everything, but by focusing on the exact steps where users churned. This case study covers how I identified the friction, designed targeted solutions, and improved return completion, trust, and exchange conversions within 6 weeks.

Goals

  1. Pinpoint the abandonment triggers
    Use session replays, analytics, and interviews to locate where and why users were quitting — not just guess.

  2. Design lean, targeted fixes
    Focus only on the friction points: reason selection, refund clarity, and trust gaps. No bloated redesigns, just surgical UX changes that move the needle.

  3. Prove impact within 6 weeks
    Launch fast, measure hard. Success was defined by a higher completion rate, lower return duration, improved NPS, and increased exchange conversions.

My Role + Responsibilities

Led UX redesign of REVER’s return flow. Identified key drop-off points, crafted shopper-first solutions, and boosted completion by 30% in 6 weeks.

Team

1 Product Manager, 2 Engineers. Support and CX teams provided ongoing insights. I led design; dev handled implementation.

REVER®

Overview

REVER’s return flow had a critical drop-off: 38% of shoppers abandoned mid-process. I led the shopper-side redesign to fix that specific problem — not by overhauling everything, but by focusing on the exact steps where users churned. This case study covers how I identified the friction, designed targeted solutions, and improved return completion, trust, and exchange conversions within 6 weeks.

Goals

  1. Pinpoint the abandonment triggers
    Use session replays, analytics, and interviews to locate where and why users were quitting — not just guess.

  2. Design lean, targeted fixes
    Focus only on the friction points: reason selection, refund clarity, and trust gaps. No bloated redesigns, just surgical UX changes that move the needle.

  3. Prove impact within 6 weeks
    Launch fast, measure hard. Success was defined by a higher completion rate, lower return duration, improved NPS, and increased exchange conversions.

My Role
+ Responsibilities

Led UX redesign of REVER’s return flow. Identified key drop-off points, crafted shopper-first solutions, and boosted completion by 30% in 6 weeks.

Team

1 Product Manager, 2 Engineers. Support and CX teams provided ongoing insights. I led design; dev handled implementation.

REVER®

Overview

REVER’s return flow had a critical drop-off: 38% of shoppers abandoned mid-process. I led the shopper-side redesign to fix that specific problem — not by overhauling everything, but by focusing on the exact steps where users churned. This case study covers how I identified the friction, designed targeted solutions, and improved return completion, trust, and exchange conversions within 6 weeks.

Goals

  1. Pinpoint the abandonment triggers
    Use session replays, analytics, and interviews to locate where and why users were quitting — not just guess.

  2. Design lean, targeted fixes
    Focus only on the friction points: reason selection, refund clarity, and trust gaps. No bloated redesigns, just surgical UX changes that move the needle.

  3. Prove impact within 6 weeks
    Launch fast, measure hard. Success was defined by a higher completion rate, lower return duration, improved NPS, and increased exchange conversions.

My Role + Responsibilities

Led UX redesign of REVER’s return flow. Identified key drop-off points, crafted shopper-first solutions, and boosted completion by 30% in 6 weeks.

Team

1 Product Manager, 2 Engineers. Support and CX teams provided ongoing insights. I led design; dev handled implementation.

Live Preview
Live Preview
Live Preview
Logo.
Logo.
Logo.

Timeline

Q1 2025

Client

REVER®

Category

SaaS | E-commerce | Product Design

Product Duration

3 Months
Problem
Problem
Research
Research
Development
Development
Results
Results

Problem

Problem

Problem

REVER had strong early traction in the e-commerce returns space, but internal data revealed a clear bottleneck:


Shoppers were abandoning returns mid-flow (38% dropout, 61% completion rate plateau)

Root causes included:

  • Confusing UX at reason selection and refund method steps

  • Trust issues due to REVER-branded experience feeling like a third-party layer

  • Refund speed — a key selling point — was buried or unclear


This case study focuses on solving that specific shopper-side problem, rather than tackling the entire product. I led the shopper flow redesign, collaborating with 1 PM and 2 engineers, with parallel feedback loops from support and CX.

Early Sketches.
Early Sketches.
Early Sketches.

Research

Research

Research

I focused on uncovering why shoppers dropped off mid-flow and how to rebuild trust during the return experience.


Methods selected for this goal:


Session Replays (FullStory / Hotjar)
Analyzed 40,000+ return sessions. Found consistent drop-off at:

  • Step 2: Reason selection

  • Step 3: Refund method
    Most common behavior: shoppers backed out when refund timing was unclear.


Customer Interviews (15 shoppers, 6 merchants)
Key insights:

  • Shoppers didn’t realize returns were instant

  • REVER branding caused skepticism and felt like a third-party risk

  • Stepper-style flow made the process feel long and frustrating


Event Analytics (PostHog)
Shoppers who saw an estimated refund time were 3.1× more likely to complete their return.


Design priorities shaped by research:

  • Reduce cognitive load during the process

  • Surface refund speed as early and clearly as possible

  • Rebuild trust at key friction points in the flow


To design an experience that truly addressed shopper frustration, I mapped four key personas based on interviews, analytics, and behavior patterns. Each represented a distinct return mindset — from speed-seeking first-timers to skeptical, security-conscious buyers — and helped guide targeted UX decisions.


Persona

Description

Pain Points

Design Opportunities

Ava – Impatient Shopper

First-time customer returning a gift. Mobile-first, expects instant results.

- Doesn’t understand refund timing- Thinks REVER is a 3rd-party scam

- Add refund countdown- Clarify refund policies early- Use trust badges

Marcus – Mobile-Only Millennial

Buys frequently via mobile. Familiar with returns but easily frustrated.

- UI feels long and clunky- Confused by reason/refund flow order

- Progressive disclosure- Simplified 2-click return- Auto-saved progress

Linda – Cautious Buyer

Older shopper, skeptical of unknown brands or processes.

- Distrusts REVER branding- Nervous about sharing personal/bank info

- Add visible PCI/GDPR compliance- Live chat fallback- Visual cues of legitimacy

Raj – Return-to-Exchange Opportunist

Shops often and prefers exchanging over refunding.

- Refund offered too early- Doesn’t see product alternatives he’d rather keep

- Exchange-first flow- Personalized AI suggestions- Pull past purchase data

I focused on uncovering why shoppers dropped off mid-flow and how to rebuild trust during the return experience.


Methods selected for this goal:


Session Replays (FullStory / Hotjar)
Analyzed 40,000+ return sessions. Found consistent drop-off at:

  • Step 2: Reason selection

  • Step 3: Refund method
    Most common behavior: shoppers backed out when refund timing was unclear.


Customer Interviews (15 shoppers, 6 merchants)
Key insights:

  • Shoppers didn’t realize returns were instant

  • REVER branding caused skepticism and felt like a third-party risk

  • Stepper-style flow made the process feel long and frustrating


Event Analytics (PostHog)
Shoppers who saw an estimated refund time were 3.1× more likely to complete their return.


Design priorities shaped by research:

  • Reduce cognitive load during the process

  • Surface refund speed as early and clearly as possible

  • Rebuild trust at key friction points in the flow


To design an experience that truly addressed shopper frustration, I mapped four key personas based on interviews, analytics, and behavior patterns. Each represented a distinct return mindset — from speed-seeking first-timers to skeptical, security-conscious buyers — and helped guide targeted UX decisions.


Persona

Description

Pain Points

Design Opportunities

Ava – Impatient Shopper

First-time customer returning a gift. Mobile-first, expects instant results.

- Doesn’t understand refund timing- Thinks REVER is a 3rd-party scam

- Add refund countdown- Clarify refund policies early- Use trust badges

Marcus – Mobile-Only Millennial

Buys frequently via mobile. Familiar with returns but easily frustrated.

- UI feels long and clunky- Confused by reason/refund flow order

- Progressive disclosure- Simplified 2-click return- Auto-saved progress

Linda – Cautious Buyer

Older shopper, skeptical of unknown brands or processes.

- Distrusts REVER branding- Nervous about sharing personal/bank info

- Add visible PCI/GDPR compliance- Live chat fallback- Visual cues of legitimacy

Raj – Return-to-Exchange Opportunist

Shops often and prefers exchanging over refunding.

- Refund offered too early- Doesn’t see product alternatives he’d rather keep

- Exchange-first flow- Personalized AI suggestions- Pull past purchase data

I focused on uncovering why shoppers dropped off mid-flow and how to rebuild trust during the return experience.


Methods selected for this goal:


Session Replays (FullStory / Hotjar)
Analyzed 40,000+ return sessions. Found consistent drop-off at:

  • Step 2: Reason selection

  • Step 3: Refund method
    Most common behavior: shoppers backed out when refund timing was unclear.


Customer Interviews (15 shoppers, 6 merchants)
Key insights:

  • Shoppers didn’t realize returns were instant

  • REVER branding caused skepticism and felt like a third-party risk

  • Stepper-style flow made the process feel long and frustrating


Event Analytics (PostHog)
Shoppers who saw an estimated refund time were 3.1× more likely to complete their return.


Design priorities shaped by research:

  • Reduce cognitive load during the process

  • Surface refund speed as early and clearly as possible

  • Rebuild trust at key friction points in the flow


To design an experience that truly addressed shopper frustration, I mapped four key personas based on interviews, analytics, and behavior patterns. Each represented a distinct return mindset — from speed-seeking first-timers to skeptical, security-conscious buyers — and helped guide targeted UX decisions.


Persona

Description

Pain Points

Design Opportunities

Ava – Impatient Shopper

First-time customer returning a gift. Mobile-first, expects instant results.

- Doesn’t understand refund timing- Thinks REVER is a 3rd-party scam

- Add refund countdown- Clarify refund policies early- Use trust badges

Marcus – Mobile-Only Millennial

Buys frequently via mobile. Familiar with returns but easily frustrated.

- UI feels long and clunky- Confused by reason/refund flow order

- Progressive disclosure- Simplified 2-click return- Auto-saved progress

Linda – Cautious Buyer

Older shopper, skeptical of unknown brands or processes.

- Distrusts REVER branding- Nervous about sharing personal/bank info

- Add visible PCI/GDPR compliance- Live chat fallback- Visual cues of legitimacy

Raj – Return-to-Exchange Opportunist

Shops often and prefers exchanging over refunding.

- Refund offered too early- Doesn’t see product alternatives he’d rather keep

- Exchange-first flow- Personalized AI suggestions- Pull past purchase data

Integrations Page UI (Desktop).
Integrations Page UI (Desktop).
Integrations Page UI (Desktop).

Design + Development

Design + Development

Design + Development


Shopper Flow Redesign (Web + Mobile Web)


A. Two-Click Returns

  • Replaced the dense, multi-step form with progressive disclosure (each step shown only after completing the previous one)

  • Added a real-time eligibility check before the return flow begins


Outcome:
87% of sessions completed in under 45 seconds, down from 2:43


B. Trust-Building UX and Microcopy

  • Added a visible live countdown showing refund processing time (e.g., “Funds arriving in ~59 seconds”)

  • Introduced a fallback call-to-action for live chat to reduce abandonment

  • Included trust signals at high-friction points: PCI, GDPR, and carrier logos


Outcome:
Refund-related NPS increased from +4 to +38


C. Exchange-First Flow (AI Suggestion Engine)

  • Redesigned the return path to suggest relevant exchange options before offering a refund

  • Integrated Shopify catalog sync and past shopper data to power personalized recommendations


Outcome:
Exchange conversion rate increased from 8.7% to 29.4 (3.3× improvement)

Results

Results

Results

In 6 weeks we've got these results:


Metric

Before

After

Delta

Return Completion Rate

61%

91%

↑ +30%

Exchange Rate

8.7%

29.4%

↑ +237%

Refund Experience NPS

+4

+38

↑ +34

Avg. Return Duration

2:43

0:44

↓ −73%


Visual Comparison Charts:


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PHILO

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