Filter & Sorting Feature

Increase product discoverability and support higher conversion by making it easier for users to narrow down large catalogs and find the right product faster.

Nov 27, 2025

Filter & Sorting Feature

Increase product discoverability and support higher conversion by making it easier for users to narrow down large catalogs and find the right product faster.

Nov 27, 2025

Filter & Sorting Feature

Increase product discoverability and support higher conversion by making it easier for users to narrow down large catalogs and find the right product faster.

Nov 27, 2025

CLIENT

Claro

Role

Senior Product Designer

Service

End-to-end Product Design

CLIENT

Claro

Role

Senior Product Designer

Service

End-to-end Product Design

CLIENT

Claro

Role

Senior Product Designer

Service

End-to-end Product Design

Yellow Flower
Yellow Flower
Yellow Flower

Overview

Overview

Overview

TL;DR
  • Business goal: Improve product discovery and support conversion by making filters and sorting easier and more intuitive to use.

  • What I did: Led benchmark research, behavioral analysis, and UX strategy for a new filters & sorting model on PLPs.

  • Impact+126% YoY increase in filter usage (131k → 298k interactions), indicating higher engagement, better discoverability, and stronger reliance on refinement tools.

Context & Business Goal

Claro’s e-commerce platform offers a wide range of devices and accessories. The Product Listing Page (PLP) plays a critical role in:

  • Product discovery

  • Campaign and promotion visibility

  • Conversion

  • Reducing support tickets related to product choice

The business needed a filters & sorting experience that supported fast, confident decision-making and could scale across categories and devices.

Business goal: Increase product discoverability and support higher conversion by making it easier for users to narrow down large catalogs and find the right product faster.

The Challenge

The existing PLP experience made it hard for users to:

  • Quickly narrow down options

  • Understand which filters were available

  • Use sorting in a meaningful, goal-driven way

  • Efficiently filter on mobile

From a UX and product standpoint, it was clear that this decision layer was underperforming, and friction here had direct impact on:

  • Drop-offs during product exploration

  • Misalignment with promo and commercial priorities

  • User confidence in the catalog

Key Constraints
  • Need to reuse and respect existing design system components

  • Performance requirements on mobile web

  • Legacy catalog & search infrastructure

  • Multiple stakeholder interests (commercial, marketing, product, CX)

Personas
Cold Audience (The Retail-Driven Shopper)

Users coming from Online Media and Google / CPC channels.

Their journey is primarily driven toward Acquisition and Number Portability. They show low trust and high sensitivity to price and fraud, often comparing the experience to major Brazilian retailers such as Magalu and Casas Bahia.

Warm Audience (The Loyal Customer)

Users arriving through the Minha Claro app and Direct Marketing (Existing Base) channels.

Their main journeys focus on Device Purchase and Plan Migration. They have high trust, prioritize benefits, and value an ongoing relationship with the brand. These users expect VIP treatment and personalized recognition.

Design Solutions

Design Solutions

Design Solutions

My Role & Responsibilities

I led the end-to-end UX work for this initiative:

  • Defined research objectives and plan

  • Conducted competitive & cross-industry benchmarking

  • Ran behavioral analysis (Hotjar heatmaps & session replays)

  • Synthesized findings into experience principles and product recommendations

  • Presented outcomes to Product leadership and C-level stakeholders

  • Collaborated with developers on feasibility and component logic

  • Supported QA and post-dev UX review

  • Contributed to the new PLP prototype and experimentation plan

Discovery & Research
1. Competitive & Cross-Industry Benchmark

I analyzed how leading platforms structure PLPs, filters, and sorting:

  • National: Arezzo, Samsung, Magalu, Casas Bahia, Mercado Livre, Riachuelo, TIM, Vivo, Buscapé

  • International: Walmart, AT&T, Vodafone

  • Vertical expert: Kimovil

For each, I mapped:

  • Filter placement and hierarchy

  • Filter types (price, brand, category, technical attributes)

  • Sorting logic and labels

  • How applied filters are shown and removed

  • Mobile behaviors and patterns

  • Visual treatments for promotions and offers

This benchmark gave us a pattern library of what works for large product catalogs serving different user intents.

2. Behavioral Analysis on Current PLP
To understand how users actually interacted with Claro’s PLP, I ran an in-depth behavioral review using Hotjar:
  • Click maps & scroll maps

  • Session recordings focused on filter/sort interactions

I focused on:

  • Most-clicked areas & dead zones

  • Attempts to interact with non-clickable elements

  • Mis-clicks and hesitation behaviors

  • Scroll depth and attention hotspots

Key UX Gaps Found
  • Misleading affordances: Users repeatedly clicked elements that weren’t interactive.

  • Confusion between filters and sorting: Users struggled to understand which control did what.

  • Low discoverability of advanced filters: Important decision attributes were hidden or nested.

  • Poor feedback on applied filters: Users weren’t always sure what was active or how to clear it—especially on mobile.

Key Insights

Synthesizing the benchmark and behavioral analysis, we landed on several critical insights:

  • Users rely heavily on filters, but the current UI made that reliance painful.

  • Technical attributes (e.g. specs) were essential to decisions but buried in the interface.

  • Mobile usage patterns required a different hierarchy and interaction model than desktop.

  • Sorting options were unclear and misaligned with common shopping intents.

  • Visual emphasis on promotions and offers significantly influenced how users navigated the PLP.

These insights became the backbone for the new PLP concept and informed both UX and product decisions.

Strategy & Experience Principles

I translated research into a set of experience principles that guided the redesign:

  1. Speed over density

    Fast narrowing is more valuable than showing everything at once.

  2. Intent-driven filters

    Organize filters based on how users choose products (goals, use cases), not just internal taxonomy.

  3. Mobile-first interaction

    Filters must feel effortless and easily reversible on small screens.

  4. Clear system feedback

    Users should always understand:

    • What’s currently applied

    • How to change it

    • How to reset to a broader set

  5. Integrated commercial visibility

    Promotions and best-sellers should be integrated into the exploration, not layered on top.

Design & System Thinking

Working with Product Owner and Engineering, I helped shape the new PLP prototype:

  • Reorganized filter hierarchy based on real decision attributes

  • Clear separation of filters vs sorting to reduce confusion

  • Improved mobile patterns: full-screen, easy-to-apply/remove filters with strong feedback

  • More meaningful sorting options (e.g. relevance, newest, best-selling, promotional priorities)

  • Stronger visibility of promotions and attributes within product cards & filters

  • Design system–aligned components to ensure scalability across categories and future features

We defined:

  • Component behaviors & edge cases

  • States for applied filters, empty results, and resets

  • Analytics hooks to track interactions and support further optimization

Stakeholder Collaboration
Executive & C-level Alignment

I delivered a summarized, business-focused presentation covering:

  • Current friction & impact on product discovery

  • Competitive positioning and missed opportunities

  • Key behavioral findings

  • Experience principles & strategic recommendations

This session created alignment between C-level, Product, UX, and Engineering around investing in PLP improvements as a strategic lever.

Partnership with Development & QA

Throughout implementation, I:

  • Clarified interaction behaviors and edge cases

  • Worked with developers to respect performance constraints, especially on mobile

  • Supported QA in reviewing flows, spotting UX regressions, and aligning the build with the intended UX

Images: Benchmarking, filters before redesign and new mobile version:

Benchmarking
Benchmarking
Benchmarking
Before
Before
Before

Results & Impact

Results & Impact

Results & Impact

Results & Impact

Using Google Analytics, we measured a significant behavioral shift after the new experience was released and adopted:

  • Filter interactions in November 2024: 131,392

  • Filter interactions in November 2025: 297,978

That’s a +126% year-over-year increase in filter usage, indicating:

  • Higher discoverability

  • Stronger user reliance on filters for decision-making

  • Increased engagement with product refinement tools

The redesigned PLP is now undergoing A/B validation to measure the impact on conversion and revenue.

Reflection

This project reinforced how “small” interaction patterns—filters, sorting, layout decisions—can deeply affect:

  • User confidence and satisfaction

  • Product discovery and relevance

  • Overall commercial performance

It also showed the value of combining:

  • Market and competitor benchmarking

  • Behavioral analytics

  • Clear experience principles

  • Cross-functional alignment

Next steps I’d pursue:

  • Personalized filters based on previous behavior or segments

  • Context-aware sorting (e.g. different default sort by campaign or category)

  • Deeper analysis of filter combinations and their correlation with conversion