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The Problem
The Problem
Fast-moving consumer goods (FMCG) corporations are companies that target everyday consumers with low-cost, fast-selling products. One major part of their operations is to collect consumer data to better understand their products.
Currently, they struggle with capturing, organising, and extracting insights from consumer data. Inefficient data transfer, non-textual feature extraction, and inadequate visualization hinder optimal product understanding and decision-making.
User Personas
User Personas
After engaging in insightful discussions and consultations with our client, we identified personas which correspond with 3 main tasks taken on by FMCG researchers on a day-to-day basis. These personas have crystallized into focused individuals driven by efficiency and value. These personas reflect a keen interest in seamless experiences, seeking user-friendly solutions that align with their daily routines.
Market Researcher (Capture)
Market Researcher (Capture)
User Persona: Meet James
Market researcher
8 years of experience
Academic background in business. MBA holder.
Formulates, collects, and deals with customer surveying
Consolidates survey data to upload it to the right places for other researchers
Pain Points
Manually upload and record different data types from different consumer surveys
Uploads to different software platforms (not in a central place)
Not able to merge different data types when uploading (one user with image and video data cannot be merged under that particular user)
R&D Researcher (Curate)
R&D Researcher (Curate)
User Persona: Meet Sarah
Research & Development (R&D) researcher
10 years of experience
PhD in chemical engineering and has a strong educational background.
Tech-savvy and stays up to date with the latest tools and technologies in her field
Most familiar with facial care products. Labels image data of participants, populates data fields such as "acne severity" or "skin moisture levels"
Pain Points
Slow and manual process. Has to individually scroll through images to tag
Unable to use online AI tools as they are not always able to extract the specific features required
Has to manually select which user and product ID to tag data under
Additional Attributes and characteristics of data entries will still need to be tagged manually
Data Analyst (Comprehend)
Data Analyst (Comprehend)
User Persona: Meet Kumar
Data Analyst at
9 years of experience
PhD in data analytics, very familiar with Jupyter, R, and JMP
Wants to generate both routine and new insights, and present it to other researchers
Occasionally needs to generate user case studies from their data
Works with a whole range of products and needs the flexibility to switch between these quick
Pain Points
Difficult to manually select/choose from vast amount of non-unified data
Slow to identify correlation from selecting numerous combinations of data
Cannot easily visualize the data of one person individually
Existing Software Issues: Not proficient at dealing with multimodal data, visualisations are over-standardised (inadequate for new insights).
This leads us to our problem statement:
As a researcher at an FMCG company, currently
I have to process multimodal product and consumer data in order to generate meaningful, data-driven insights.
How might we make the process of analysing product and consumer data more efficient?
Storyboards
Storyboards
These storyboards serve as visual roadmaps, outlining the user journey step by step. Through thoughtful illustrations and annotations, we depict the user's motivations, actions, and emotions, allowing us to align our design and development efforts with real-world scenarios.
Feature Identification
Feature Identification
We decided to focus on toothpaste as the product of interest for the scope of this project. From there, we chose 2 types of surveys to process
Our Project
Our Project
Repo Link: https://github.com/Service-Design-Studio/Team11-Consumer-Conflux
Deployed Webapp Link:https://frontend-team11-service-gpdj3735qa-as.a.run.app
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