Cargo Market Research Analysis
Role: UX Researcher / Data Analyst
Team Size: Independent
Tools: R (ggplot2, tidyverse, dplyr, stringr) , Tableau, GitHub , google forms
Duration: 5 days
This analysis provided the foundation for insights used in the Cargo App design case study.
My Role
Clean and transformed raw survey data from Google Forms for analysis.
Created new variables and standardized responses to uncover spending and saving patterns.
Organized messy survey responses into clear categories, making spending patterns easy to identify and act on.
Built visualizations to show insights
Problem Statement
We wanted to understand the shopping habits of potential users and determine if they even cared about price comparison in the first place. As the sole statistician on the team, I worked independently to ensure the data was analyzed both properly and clearly, and presented in a way that would make the responses easy to understand.
Research Process
Survey Design
Created survey questions that captured the potential needs of users, and users spending habits.
These survey questions allowed us to see potential competitors and if there was a gap in the market for our product to succeed.
Had a balance of quantitative (closed ended) questions and qualitative (open ended questions).
Distributed survey through social media, gathering over 50 responses within 4 days.
Cleaned and prepared survey data in R (standardized categories, renamed columns, filtered using regex).
Created separate csv file for reproducibility.
Data Collection
Data Cleaning and Preparation
Analysis
Visualized findings in Tableau using charts and graphs to show needs of users.
Applied descriptive statistics to identify trends.
There was a heavy interest in savings in the following categories
78% of respondents monthly expenses included groceries → groceries are the top priority category.
Gender of Respondents
Respondents Monthly Expenses
Respondents Saving Interests
Pie Chart
Gender
29.4 %
70.6 %
Female
Male
Groceries
Gasoline
Clothing
Electronics
Household Items
Health & Beauty
Entertainment
Bar Chart
78%
43%
37%
18%
14%
12%
22%
Groceries with 76% interest from respondents
Clothing with 88% interest from respondents
Electronics with 60% interest from respondents
Gasoline with 60% interest from respondents
Health and Beauty with 67% interest from respondents
Household Products with 70% interest from respondents
Results and Impact
1
Found that groceries and clothing were top spend categories, suggesting cost-saving recommendations should prioritize these areas first.
Found that groceries and clothing were top spend categories, suggesting cost-saving recommendations should prioritize these areas first.
2
Found categories that users were interested in seeing in the app. Some including Groceries, Health and Beauty and Household Products
Found categories that users were interested in seeing in the app. Some including Groceries, Health and Beauty and Household Products
4
Provided a foundation for future product recommendations by discovering where users most want cost-saving solutions
Provided a foundation for future product recommendations by discovering where users most want cost-saving solutions
3
Provided the team with structured, reliable data that made design decisions faster and easier.
Provided the team with structured, reliable data that made design decisions faster and easier.
Deliverables
Code Repository : Github
Tableau Dashboard : See Dashboard
Reflection
This project taught me how to work effectively under pressure with a tight deadline. I had less than a week to clean and analyze all survey responses while balancing many other on-campus responsibilities. As the only team member with experience in R, it made me prioritize clarity and interpretability. I wanted to make sure the data was easy for my group to understand. This approach allowed us to base our app concept, and design on real user insights rather than just assumptions.
This project taught me how to work effectively under pressure with a tight deadline. I had less than a week to clean and analyze all survey responses while balancing many other on-campus responsibilities. As the only team member with experience in R, it made me prioritize clarity and interpretability. I wanted to make sure the data was easy for my group to understand. This approach allowed us to base our app concept, and design on real user insights rather than just assumptions.
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For the best experience:
Please view this analysis on a desktop or tablet.