PLP optimization for confident product discovery

Role: Product Designer & Design Owner
With: Product, Engineering, PM, Brand
Tools: Figma, ContentSquare, Power BI, Framer, ChatGPT

Context & Problem

KITS was expanding fast, but analytics showed serious friction:

• Mobile bounce rate at 65.4%

• Users back-toggling from PDP to PLP at 13%, a signal of uncertainty, not lack of interest
  
  

  

In other words, users saw products — but couldn’t quickly decide if they were right for them. This was our biggest UX bottleneck.

Research & Insights

Using quantitative analytics and behavioural tools, I identified three core problems:

1. Low discovery efficiency — 97% of mobile users never saw content below the fold.

2. Navigation fatigue — users clicked in, didn’t find confidence, and retreated.

3. Inconsistent attribute data across brands blocked unified filtering.

These insights didn’t just describe symptoms; they showed where decisions broke down and what users needed next.

Design Strategy & Execution

I reframed the PLP as a decision surface, centered on confidence and clarity instead of just inventory display.

1. Priority and Intent

• A new header set clear intent and supported SEO.

• Introduced collection tiles so users could narrow early and feel in control.

2. Product Cards with Confidence Signals

Instead of forcing clicks to PDP, I surfaced:

• Try-On indicator

• Ratings

• Colour swatches adjacent to visuals

3. Better Filters

In the previous experience, filters were hard to discover, dense once opened, and ordered in ways that reflected backend data rather than how people think. Sizing options jumped between Medium, Small, and Large, brand lists were long and unscannable, and it was difficult to tell which filters were active or how to undo a selection.

The redesign focused on making filters easier to understand at a glance and more trustworthy as a narrowing tool. Filters were reordered based on decision relevance, not raw click rate. Frame sizes now follow a clear small-to-large progression, with KITS frames mapped into standard sizes and designer frames honouring brand sizing when measurements aren’t available. Dense filters like Brand include search, and every selection provides immediate feedback through count indicators and removable tags.

4. Constrain

The biggest hurdle was organizational. Product Owners often used "Collections" as a dumping ground for seasonal promos, which cluttered the UI and confused the user journey. To consolidate 50+ disparate brands into one space, I established a Rule Table as a fallback logic. This allowed us to map inconsistent backend data, from eye shapes to materials. This gave the tech team full control over messy inventory without sacrificing the elegance of the user experience

Impact: Record high result

PLP → PDP view completion climbed to 40–45%

Desktop filter-engaged conversion at 17%