Client work · Data & analytics · Archive

Analytics for the Bottle: Infant Formula Research Dashboards

Worked with a data scientist on an analytics suite for researchers comparing infant formula products on the market: what is in them, what they cost, and how the market behaves around them.

RoleUI/UX, desktop · user flows · branding
PartnerA data scientist supplying the research data
ScopeDashboard · comparison · brand search · market analytics
AudienceFormula milk researchers
From the archive. This piece is restored from my old portfolio and lightly re-edited for the new site. The work predates the case studies on the homepage; the thinking held up, so here it is.

This one is niche in the best way: a tool for researchers who study infant formula, built with a data scientist who knew exactly what insight the research needed. My job was turning that data into screens a researcher could interrogate instead of scroll.

The dashboard

The core view has search, filtering by data source, region, rating, and infant age, and an extensive dashboard of top results by nutrient, ingredient, and more. The graph designs came out of working sessions with the data scientist: the charts show what the study needs to compare, not what looks impressive in a demo.

The analytics suite: brand name search trends, brand analytics with volume shares, an Enfamil brand overview with nutrient counts, and the product comparison view with ingredient bubbles
The suite at a glance: brand search trends, share-of-volume analytics, the Enfamil brand overview, and the comparison view with its ingredient bubbles. Research questions, screen by screen.

Product comparison

Researchers pick two or three products off the market and see them side by side: pricing, nutrients, ingredients. The feature every spreadsheet secretly wants to be.

Brand search

Search any formula brand and see every product under it, so brand-level patterns are one query away instead of a manual list.

Supplementary analytics

Around the product data sits the market context a researcher might need: regional use, sales, web search traffic for brands, keyword research, and backlink traffic. The study compares products; this layer explains what the market is doing about them.

The data scientist knew what to measure. The design's job was making the measuring feel like asking questions.

Yow, what's up? 👋

Got data that needs to become decisions?

raymarlobaton@gmail.com