Case Study
Product Detail Re-architecture
Building a modular PDP framework during a live platform migration to improve conversion and support scalable merchandising.
Summary

The Need


Conversion was constrained by inconsistent hierarchy, unclear value communication, and mobile friction.

What I Led


Defined and executed a new PDP framework balancing merchandising goals, technical constraints, and user clarity.

How it Worked


Re-architected decision-critical content, simplified variation logic, and introduced modular components for scale.

Impact


Established a scalable PDP architecture that reduced evaluation friction and enabled ongoing optimization.

Redesigned PDP framework across desktop and mobile.

Context

This redesign took place during a major platform transition from a legacy in-house product data system to ProdX, a vendor platform built to manage product variations, substitutions, and related products at scale.

While ProdX introduced greater flexibility, it also imposed new structural constraints that directly shaped the customer experience.

At the same time, the existing PDP was already under strain. Variation logic surfaced invalid combinations, unrelated products appeared selectable, and small updates introduced outsized risk. The experience did not scale cleanly across complex categories or support merchandising effectively.

This became a system-level redesign of how product data, variation logic, and presentation worked together under real business and technical constraints.

Business Stakes

The redesign needed to strengthen customer confidence and engagement while protecting active revenue during a live platform migration. Any structural misstep risked invalid product states, broken purchasing flows, or measurable revenue loss at scale.

Success required improving performance without destabilizing production systems.

Team

Product, Engineering, Platform Vendor (ProdX), Merchandising, and Operations.

Cross-functional coordination across platform architecture and merchandising strategy.

Constraints

  • Live platform migration with active revenue
  • External vendor system (ProdX) with predefined data structures
  • High risk of invalid product states across complex categories
  • Limited tolerance for experimentation failures in production

Impact Snapshot

  • +1.49% increase in conversion rate (from 0.82%)
  • +84k additional add-to-carts from related products
  • −5% bounce rate
  • +31 seconds average time on page
Overview
  • My Role & Scope
  • The Problem
  • Discovery & Insights
  • System Design Strategy
  • Defining the Variation System
  • Experience Design & Execution
  • Validation, Experimentation & Outcomes
  • Strategic Impact & Learnings
My Role & Scope

I served as Lead UX Designer with end-to-end responsibility across strategy, research, system design, and delivery.

Beyond interface design, I led edge-case analysis, competitive and best-practice research, experimentation planning, and post-launch analytics to ensure the solution scaled technically and commercially. I also represented design in direct negotiations with ProdX, advocating for implementation changes needed to support real-world product complexity.

Key Leadership Contribution: Defined and negotiated the system rules governing product variation behavior across categories, aligning product, engineering, and the vendor around a shared foundation that reduced risk, supported scale, and informed future initiatives.

What I Owned
  • Framing the problem at a system level, not just an interface level
  • Leading discovery, including deep edge-case analysis across 100+ products
  • Defining the variation model and authoring all variation rules
  • Translating system constraints into usable, scalable experience patterns
  • Driving alignment across product, engineering, and external platform partners
  • Advocating for changes to the ProdX platform when constraints conflicted with experience needs
Leadership Under Platform Constraints

This work required leadership at the intersection of user needs, system architecture, and business risk.

Working closely within the product triad, I facilitated sessions, synthesized research into direction, and challenged solutions that introduced risk or failed to account for critical edge cases.

The goal was durable platform viability, not a visually improved experience built on brittle rules.

The Problem
Problem System
  • Symptoms: Confusing variation states, inconsistent hierarchy, and mobile friction reduced decision confidence.
  • Drivers: Legacy data rules and vendor constraints created invalid combinations and inconsistent content mapping.
  • Constraints: Live migration with active revenue and low tolerance for production errors.
  • Result: Higher drop-off risk, weaker merchandising performance, and limited scalability across categories.
A Broken Experience, Amplified by a Platform Transition

The existing product detail experience was already fragile before this initiative began.

Product variations were often incorrect, and items presented as variations were frequently unrelated products. Customers struggled to understand differences, compare options, or trust what they were seeing. Small changes introduced outsized risk, making iteration slow and confidence low.

At the same time, the organization was migrating product data from a legacy in-house system to ProdX, a more advanced platform for managing variations, substitutions, and related products at scale. While ProdX introduced greater capability, it also imposed structural constraints that would directly shape what experiences were possible at the interface level.

If those constraints were misunderstood or accepted too early, the resulting experience would fail regardless of visual polish.

Why the Initial Direction Would Not Work

Early exploration showed that the challenge was not simply adapting the interface to new constraints, but confronting how those constraints shaped the experience itself.

Auditing more than 100 real product examples made it clear that ProdX’s default variation model could not reliably represent how customers understood product differences. Variation categories lacked a clear primary anchor, secondary attributes were inconsistently scoped, and state changes triggered page and URL behavior that broke continuity.

These limitations were not immediately obvious to all partners, but it became clear that without redefining how variation rules were authored and governed, the experience would remain confusing and brittle.

What Was at Risk

If the variation system was implemented incorrectly, the organization would inherit a more complex platform without realizing its benefits. Customer trust would erode, teams would depend on fragile workarounds, and future product growth would be constrained by early system decisions.

This project required rethinking the system that powered the experience.

Existing Page & Variation Implementation
Discovery & Insight
Moving Beyond Assumptions

Discovery focused on how product variation logic actually behaved in the real world, not how it was intended to work on paper. Rather than starting with interface concepts, I focused on where the existing experience broke down and why. That meant looking beyond screens into how product data was structured, governed, and interpreted across systems.

Deep Edge-Case Analysis

A core part of discovery involved auditing more than 100 edge-case products across categories.

These were not theoretical scenarios. They reflected real products with complex combinations of sizes, flavors, bundles, substitutions, and near-duplicates that regularly surfaced incorrect or misleading variations in the current experience.

Patterns emerged quickly:

  • Products grouped as variations were often only loosely related
  • Legitimate variations were frequently split apart
  • Category-specific rules conflicted with global assumptions
  • Small data inconsistencies produced disproportionately bad experiences

This analysis made it clear that variation rules needed to be defined, validated, and enforced at the system level before interface decisions could succeed.

To understand how the new ProdX variation system would behave in real conditions, I conducted a deep audit of edge-case products across categories.

This analysis surfaced inconsistent category logic, ambiguous variation hierarchies, and failure modes that were not apparent in standard flows or vendor prototypes. These findings became the foundation for defining variation rules, pressure-testing system assumptions, and aligning Product, Engineering, and ProdX on what the experience actually needed to support.

Cross-Functional and Best-Practice Research

In parallel, I led structured discovery across internal teams, competitive platforms, and industry research to pressure-test assumptions and identify proven patterns.

This included:

  • Competitive analysis of platforms with complex variation, substitution, and grouping behavior
  • Baymard Institute research on product configuration, choice architecture, and error prevention
  • Collaborative ideation and story mapping with product and engineering
  • Validation of proposed approaches against known edge cases

This work surfaced a clear gap between platform defaults and customer expectations, reinforcing the need for a rules-driven system rather than ad hoc configuration.

Competitive patterns across major retail platforms revealed consistent best practices for variation clarity, anchoring, and error prevention.

While implementations varied, successful experiences clearly distinguished primary products from selectable options, avoided invalid states, and maintained a stable sense of place as customers explored variations.

Key Insights
  1. Variation complexity was a system problem, not a UI problem
    Across 100+ audited products, the breakdowns stemmed from how variations were modeled and governed upstream. Interface improvements alone could not compensate for misclassified attributes and brittle logic.
  2. ProdX’s default model assumed ideal data, not real commerce

    The platform worked well for simpler products, but broke down when applied to real-world catalogs involving substitutions, bundles, size hierarchies, and category-specific rules.
  3. Edge cases were not edge cases
    Many of the most complex products were also commercially important. Failure in those scenarios would directly affect customer trust, merchandising performance, and revenue.
  4. Incremental adaptation would increase long-term risk 

    Forcing ProdX’s defaults into the existing experience would create hidden dependencies, increase regression risk, and limit future flexibility.
  5. Clear variation rules were required before experience design could succeed
    Until variation definitions, precedence, and exclusions were explicitly defined, downstream design and engineering work would remain fragile and difficult to maintain.
Problem Identification & Story Map
System Design Strategy
Defining the System Requirements

The core challenge was not designing a better interface, but establishing a system that could accurately model product variation, scale across categories, and remain adaptable over time.

Based on discovery and edge-case analysis, the system needed to:

  • Represent variation types differently by product category
  • Enforce clear precedence rules when multiple attributes applied
  • Exclude non-variations from the variation experience
  • Support future expansion without requiring PDP redesigns
  • Remain understandable to merchants and operators, not just engineers

This reframed variation logic as product infrastructure rather than a design configuration problem.

System Model
  • Goal: Make product evaluation fast, accurate, and confidence-building across devices while staying within vendor constraints.
  • Inputs: ProdX data model, variation rules, substitutions, related products, pricing, and availability.
  • Core interactions: Evaluate, compare, select variation, validate state, and add to cart.
  • Decision support: Prioritized hierarchy, progressive disclosure, and trust signals such as availability, delivery, and returns.
  • Governance: Reusable modules, category scalability, and safe handling of invalid states.
Authoring the Variation Rules

I authored a comprehensive set of variation rules defining:

  • Which attributes qualified as true variations
  • Which attributes should never appear as variations
  • How attributes should be grouped, ordered, and labeled
  • How conflicts between attributes should be resolved
  • How different product families required different logic

These rules were derived from audited product examples and validated against real catalog data. They became the contract between product, engineering, and the vendor platform.

Influencing the Vendor Implementation

ProdX’s default implementation did not support several of these requirements.

I led a working session with ProdX’s founder, alongside our product owner and engineering counterparts, to walk through real product failures from the audit, show where default assumptions broke down, and advocate for changes to attribute handling and rule flexibility.

As a result, ProdX extended its model by adding attributes and adjusting behaviors to better support real product complexity. This shifted the relationship from platform consumer to active design partner.

Translating System Logic Into Experience

With the system rules defined, experience design could move forward with more confidence.

I worked with engineering to ensure that:

  • The design reflected system logic accurately
  • Customers were never exposed to irrelevant or misleading options
  • Complex products felt simpler without hiding necessary information
  • Edge cases behaved predictably across scenarios

Design decisions were now grounded in explicit rules rather than assumptions, reducing ambiguity across teams and lowering long-term maintenance risk.

Variation Rules for the System Design
Defining the Variation System

With the strategy aligned and platform constraints understood, the work shifted to formalizing the variation system as a durable foundation. This was not just a design pattern. It was a governance model for maintaining clarity, preventing invalid states, and scaling as the catalog evolved.

From Attributes to Meaningful Variations

Not every product attribute should be treated as a customer-facing variation.

A major failure of the existing experience was that true variations, descriptive attributes, and related products were handled the same way, creating confusing and unpredictable behavior.

I defined a clear distinction between three attribute types:

  • Primary variation attributes
    Attributes customers intentionally choose between, such as diaper size, count, or package quantity. Changing these should update the product without changing what the product fundamentally is.
  • Secondary attributes
    Attributes that provide useful context but should not drive selection, such as absorbency level, age range, or fit indicators.
  • Excluded attributes
    Attributes that should never appear as variations, even if they exist in the data. For example, pulling size or count values from similar products could unintentionally switch a customer from overnight diapers to standard diapers without them realizing it.

Making these rules explicit made the system easier to reason about across design, engineering, and merchandising. New categories and attributes could be introduced without redesigning the PDP or relying on UI-level exceptions.

Rule Precedence and Conflict Resolution

Many products qualified for multiple variation attributes at the same time. Without precedence rules, outcomes became unpredictable.

I established logic for:

  • Attribute prioritization
  • Conflict resolution
  • When variation groups should collapse or expand
  • How safe defaults were selected

These rules ensured each product resolved to a single, predictable variation structure.

Experience Design & Execution
Designing From Rules, Not Assumptions

With the variation system defined, experience design could move forward with more clarity. Decisions were no longer driven by edge-case guesswork or UI workarounds, but by explicit rules governing how products should behave across categories.

This allowed the interface to focus on clarity, predictability, and trust.

Experience Goals

The redesigned product detail experience needed to:

  • Help customers understand what they were buying
  • Make variation choices easier to evaluate
  • Prevent invalid or misleading selections
  • Extend discovery beyond the primary product without redundancy
  • Reduce friction that led to bounce and exit
  • Scale across categories without introducing inconsistency

The goal was not to expose system complexity, but to absorb it so customers could move forward with confidence.

From Concepts to Execution

Early concepts explored how to express variation logic without overwhelming customers.

Beyond variations, I partnered closely with product and engineering to define how adjacent systems surfaced related products. For “Similar items” and “Often bought with this item,” I proposed a looping logic that rotated product groups instead of showing near-duplicate items. This created more variety, reduced redundancy, and made these swimlanes feel more intentional.

Several principles guided execution:

  • Only surface meaningful choices
  • Let structure do the work
  • Favor predictable layouts over highly conditional patterns

These principles helped the experience stay consistent even as variation logic differed by product type.

Early concept exploring how variation logic could be made visible without overwhelming the interface. This concept informed the final interaction model but was intentionally simplified to validate logic, not visual polish.

The resulting experience preserved clear product identity, prevented invalid states, and introduced adjacent product discovery in a way that felt helpful rather than distracting.

Translating System Logic Into Interaction

Each interaction was designed to reflect the system’s intent:

  • Variation groups appeared only when valid
  • Option ordering reinforced precedence rules
  • Default selections were safe and explainable
  • Invalid states were eliminated rather than handled visually

Customers were never asked to interpret internal complexity. The experience behaved logically by design.

Collaboration With Engineering

Execution was tightly coordinated with engineering to ensure fidelity between system logic and interface behavior. Rather than handing off static designs, I worked iteratively with engineers to validate that:

  • The design accurately reflected rule resolution
  • Edge cases behaved consistently across scenarios
  • Changes to system logic propagated safely through the experience

This reduced rework and prevented late-stage surprises.

Outcome of the Design Approach

The final experience felt simpler even as the underlying system became more robust. Because design decisions were anchored in shared rules rather than individual interpretation, the experience was easier to justify, extend, and maintain.

The result was a PDP framework that could scale with more confidence.

Final Designs: Web & App
Validation, Experimentation & Outcomes
Unmoderated Usability Testing

Before exposing the redesigned experience to live traffic, I validated the product detail experience through unmoderated usability testing to ensure it was understandable without guidance.

Testing focused on whether customers could:

  • Understand the structure and hierarchy of the product detail page
  • Complete key tasks confidently without hesitation or backtracking
  • Interpret available options, constraints, and system feedback correctly
  • Move forward without needing outside explanation or support

Participants consistently navigated complex product configurations with confidence. They identified valid options, understood why certain combinations were unavailable, and completed selection tasks with minimal hesitation.

This confirmed that clarity extended beyond variation handling into overall product understanding and decision-making.

Unmoderated usability testing demonstrated consistently high task success across core product detail flows, validating clarity, navigability, and user confidence within the redesigned experience.
Experimentation

To validate a key system-level decision, we ran a focused A/B test on the redesigned product detail page.

Test

  • Compared two versions of the same page
  • Moved the “Similar Items” swimlane from below the product description to above it
  • Held all other page elements constant

Result

  • Swimlane conversion increased from 0.82% to 1.49%
  • The higher placement outperformed the lower placement and reached clear statistical confidence within one month

This confirmed that surfacing relevant alternatives earlier in the decision flow improved engagement and conversion.

A controlled experiment moving a similar items swim lane above the product details that showed a swimlane sales conversion rate increase from 0.82% to 1.49%, reaching 100% probability to be best design option after one month.
Outcome Summary

The redesign produced measurable gains across engagement, conversion, and revenue-adjacent behaviors within the first month of launch.

Engagement & Confidence

  • Time on page increased by approximately 31 seconds
  • Bounce rate decreased by approximately 5%
  • Variation interactions increased by approximately 31k events, indicating higher exploration and confidence

Conversion & Revenue Signals

  • Add-to-carts from similar items and often-bought-with swimlanes increased by approximately 84k
  • Add-to-carts for the primary product increased by approximately 4k
  • The A/B test showed swimlane conversion improved from 0.82% to 1.49% when “Similar Items” appeared above the product description

System & Platform Outcomes

  • Enabled a scalable ProdX implementation without fragmenting the customer experience
  • Reduced risk of invalid states and edge-case failures across categories

These results showed that the redesign improved more than page-level clarity. It changed how customers engaged with complex product configurations and created a stronger foundation for ongoing optimization.

Pre-launch analytics revealed high bounce rates, lower variation engagement, and limited cross-sell interaction, indicating confusion within complex product configurations.
Post-launch metrics showed increased variation interaction, longer time on page, higher add-to-cart activity, and reduced bounce rate, confirming improved clarity and engagement at scale.
Strategic Impact & Learnings
Customer & Business Impact
  • Increased customer confidence when navigating complex product configurations by making variation logic predictable and trustworthy
  • Enabled deeper product exploration without increasing cognitive load, supporting informed purchase decisionsImproved the effectiveness of cross-sell and related-item interactions
  • Improved the effectiveness of cross-sell and related-item interactions by clarifying how products related to one another

Rather than optimizing for a single conversion moment, the redesign strengthened the overall decision experience for customers managing high-variance products.

Platform & Organizational Impact
  • Established a governed variation model that scaled across categories without requiring redesigns
  • Reduced the risk of invalid states and merchandising-driven inconsistencies by making variation rules explicit and shared
  • Enabled faster onboarding of new attributes and product types without fragmenting the customer experience

The product detail page shifted from a fragile, case-by-case solution to a stable system the organization could confidently build on.

Leadership Learnings

Complex systems require shared rules, not just better interfaces.

This work reinforced that:

  • Design decisions must account for data, merchandising, and engineering realities, not just visual clarity
  • Making constraints explicit early prevents downstream rework and reactive fixes
  • Strong outcomes come from aligning teams around system behavior, not debating individual UI states

Leading this effort required balancing short-term performance pressure with long-term platform integrity, ensuring the experience could evolve without collapsing under its own complexity.

Applying This Approach Elsewhere
Interested in how this approach translates to other complex platforms?
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