Personalised
Nutrition App

Client
ZOE

Year
2019-20

Credits
Kostantinos Frantzis (Product design manager)
Chris Georgiou (Design Director)

App
App Store 

 


ZOE is a nutritional science startup on a mission to help people eat with confidence. They combine science, large-scale data and machine learning to help people find the right foods based on their unique metabolism. Based on over 20 years of research on genetically identical twins (the largest nutritional science study in the world), ZOE’s goal is to share their research and develop a product that will help people eat the right foods for their bodies.

ZOE-in-hand

Problem statement

People don’t know what food is good for their bodies. Nutritionists, books, social media and news offer contradicting advice about nutrition and often blanket answers for everyone. Each person is unique, from their DNA to their gut microbiome, and they should have access to personalised nutrition.

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Discovery & ideation

Through over 80 user interviews and workshops, we started looking at trends and clustering key focus points for our MVP. Most people knew what was good or bad for them, yet they wanted a simpler way to check products and see how it would affect their body compared to others.

ZOE_Image3_cluster of 3

Validating & testing

By continuing interviews with real users as well as testing low-fidelity prototypes, we were able to refine and iterate ideas continuously, making changes and generating ideas at a very rapid pace. Through this testing we were able to start sketching out what the MVP could look like.

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Design exploration

We knew early on our product would be changing very rapidly, and that we needed a very simple design system that would allow design and development to continuously adapt and morph the more we learnt.

ZOE-Design-Exp-L1
ZOE-Design-Exp-L5
ZOE-Design-Exp-L4 – ALT
ZOE-Design-Exp-L2
ZOE-Design-Exp-R1
ZOE-Design-Exp-R2
ZOE-Design-Exp-L3 ALT

Designing the MVP

Once we launched in the App Store and Play Store, we started getting even greater insights into our users; how they used it and which additional features would be beneficial. This qualitative data allowed us to make quicker adaptations to the design.

MVP – Image 1
MVP – Image 2 – Alt
MVP – L1
MVP – L2
MVP – R1
MVP – R2

© Nils Bellemans 2024

© Nils Bellemans 2024