KAY Inline Guided Filter Questions

A/B-Tested Inline PLP Experience

An inline product listing experience designed to guide high-intent engagement ring shoppers while integrating directly with the existing PLP filter system.

Objective

Reduce friction in engagement-ring discovery on the product listing page by introducing an inline, guided filter experience that translates shopper intent into active PLP filters while preserving existing filter functionality, performance, and analytics integrity.

Goals
  • Improve engagement with the product listing page by offering a guided discovery experience.
  • Help users reach relevant ring styles faster without overwhelming them with filters.
  • Test whether visual-first or intent-first entry points better support decision-making.
  • Integrate seamlessly with the existing filter system without breaking native functionality.
  • Ensure parity across desktop and mobile filter behavior.
Hypothesis

If shoppers are guided through an inline experience that converts their intent or style preferences directly into active PLP filters, they will engage more deeply and reach relevant products more efficiently than when using the standard filter interface alone.

Year

2025

Client

KAY Jewelers

Project Type

Front-End Development Interactive Experience Web Design

Role

End-to-End UX Design & Front-End Development

  • Defined the inline PLP concept and experiment structure.
  • Designed responsive user flows for desktop, tablet, and mobile.
  • Built guided questions that translated intent into active PLP filters.
  • Integrated seamlessly with the existing filter system and tag UI.
  • Developed and deployed multiple A/B test variants with cross-device parity.
Tools Used

Figma Dynamic Yield HTML CSS

Figma Links

Desktop

  |  

Mobile

Experiment Design

The experience was tested as an inline PLP experiment with a control variant and two guided entry variants designed to evaluate different decision-making approaches during product discovery.

  • Variant A – Control:

    Shoppers interacted with the standard PLP experience using the existing filter panel without guided prompts.

  • Variant B – Visual-First Entry:

    Shoppers began by selecting ring styles presented visually allowing them to browse based on aesthetic preference.

  • Variant C – Intent-First Entry:

    Shoppers began by selecting their purchase context (e.g., buying for a partner, for themselves, or just browsing) using text-based prompts.

Both experimental variants translated user selections directly into active PLP filters, updating results in real time while preserving the native filter UI, applied tags, and underlying filter logic.

Live Experience Walkthroughs

These recordings show the inline guided filter experience operating directly within the engagement-ring PLP. Each variant demonstrates how user selections dynamically apply native filters, update results in real time, and preserve existing filter tags and UI behavior.

Variant B: Visual style selection dynamically applies native PLP filters in real time.
Variant C: Intent-based entry translates shopper context into active filters without disrupting the PLP.

Technical Implementation & Challenges

This experience was built to operate on top of the existing PLP filter system rather than replacing it. Quiz selections programmatically triggered native filter states, including visual filter tags, while the default filter UI remained functionally intact.

Key challenges included:

  • Synchronizing quiz logic with native filter behavior without introducing conflicts or performance regressions.
  • Supporting divergent desktop and mobile filter architectures, which required separate handling to maintain consistent outcomes.
  • Preserving analytics accuracy during testing and ensuring experiment stability amid ongoing SPA and platform changes.

The solution required extensive debugging, conditional logic, and defensive handling to ensure the experience remained resilient across platform updates and test iterations.