E-commerce intelligence platform

Kiwiik Data

Kiwiik Data is a modular B2B SaaS platform built to help consumer brands and retailers collect, manage, and visualize product information from e-commerce platforms across Latin America. The system provides real-time visibility into:

• Pricing & Availability
• Content Quality
• Product Positioning
• Stockouts
• Keyword Visibility
• Competitor Monitoring

It replaced fragmented Excel-based workflows with centralized, automated insights for teams like Trade Marketing, E-commerce Analytics, and Category Management.

Problem & Opportunity

The challenge: Teams used spreadsheets and disconnected tools to manage product info, causing delays, human error, and missed market signals.

The opportunity: Build a centralized, modular platform with role-based access, real-time KPIs, and actionable alerts — tailored to each company’s e-commerce maturity.

‍

My Role

As the Senior Product Designer, I led the product design strategy and execution from discovery to delivery, scaling the team and collaborating with product, engineering, and business stakeholders. I was responsible for research, design systems, UX/UI delivery, and feature ideation.

Key contributions:

• Scaled the design team by 50%, mentoring UX/UI designers, researchers, and copywriters.
• Designed data dashboards and complex UX flows
• Conducted 30+ user interviews and usability tests, increasing onboarding completion by 25%.
• Built a design system that reduced dev time by 30%
• ‍Prototyped and pitched features with video demos, reducing approval time by 40%.
• Presented to stakeholders and supported the acquisition of major clients (e.g., Bimbo, Sigma).

Research & Insights

We interviewed 35+ users across 10+ companies: Trade Marketing Managers, Content and Data Analysts, Category Managers, and more. Methods included structured interviews, usability testing, card sorting, and heatmaps from beta users.

Key insights:

- Teams spent hours creating retailer-specific Excel files.
- Low data governance caused pricing/content errors.
- Platforms failed to alert on stock-outs or competitor changes in time.
- Teams responded too slowly to price drops or stockouts
- Content creators struggled to manually track imagery and SEO.
- Reporting processes were manual, repetitive, and slow.

These findings shaped our modular approach — companies could adopt only the tools they needed based on their digital maturity.

‍

Design Process

We started each module with:

1. ‍Design Sprints
2. Workshops with product and engineering
3. ‍Effort-impact prioritization tied to user pain and business goals

‍Iteration Workflow:

We followed an iterative design approach:

• Low- to high-fidelity prototypes in Figma
• Real-data user testing with beta clients
• Weekly stakeholder walkthroughs
• Team alignment via FigJam and Miro

‍

Complex UX Flows:

• Dashboards and custom reports
• Stock/pricing alerts
• Search filters
• Content scoring
• Competitor comparison
• Reporting tools

‍

Solution & Key Features

We designed a modular, scalable platform with role-based dashboards. Key features I led:

• Real-time dashboards for pricing and availability
• Content audit system with 1–5 scoring
• Automated alerts for stockouts and pricing anomalies
• Competitor price comparison across retailers
• SVG/PDF report builder with visual exports
• Advanced filters to explore product data

‍

Testing & Iteration

We ran iterative testing cycles with:

• Usability testing (core flows)
• Beta heatmaps
• A/B tests on CTA and layout

We used real client data to improve feedback quality.

‍

Key Outcomes:

1. Reduced report creation time by 50%
2. Improved dashboard clarity and feature discoverability
3. Boosted user satisfaction and task success rates

‍

‍

Results & Impact

Product & Business:

• Launched 5 modules within 12 months
• Supported acquisition of enterprise clients: Bimbo, Sigma, Grupo AlEn
• Increased product adoption and conversion by 15%

Team & Process:

1. Created design system and design/research culture
2. Reduced handoff and rework through stronger collaboration
3. Implemented a Design Sprint culture in the team

‍

‍

Reflection & Learnings

Working on Kiwiik Data was a transformational experience — both professionally and personally. It challenged me to operate at the intersection of business strategy, user advocacy, and design leadership, and it gave me the opportunity to shape a product from early concept to enterprise adoption.

Key Learnings

• Designing with data and complexity requires simplicity. Translating massive product datasets into intuitive dashboards taught me the importance of visual clarity, prioritization, and progressive disclosure.
  
• User research is a continuous loop, not a phase. We discovered that ongoing feedback — not just discovery-phase interviews — led to sharper, more relevant product decisions at every stage.
  
• Modularity improves scalability and adoption. Designing a platform where clients could adopt only the modules they needed increased flexibility and improved onboarding for users with different digital maturity levels.
  
• Team alignment is everything. Whether collaborating with engineers or presenting to stakeholders, clear communication and shared understanding were crucial to delivering value without friction.
  
  
  

This project pushed me to lead with vision while staying hands-on. I scaled the design team, improved collaboration structures, and introduced new tools and processes — all while remaining deeply involved in research, design systems, prototyping, and iteration. It sharpened my ability to balance user needs with business goals, and reinforced my passion for building products that are both strategic and human-centered.

‍