Personal Project
Customer Data Analysis
Dashboard
Data Visualization
Turning customer data into making smarter market decisions
A dashboard tool to guide smarter decisions, reduce marketing risk, and help businesses stay ahead.
Time
3 weeks
Role
Data Analysis, UI Design
Tools
Figma, Flourish, RAWGraphs, Google Sheets
Problem
Customer behaviour doesn't stand still, making it harder to preditct.
Solution
Data-driven dashboard that uncovers buying pattern for better business decisions.
Impact
MarketLens turns customer data insights into business advantage.
Users
Gain foresight into market trends, and make confident, data-backed decisions.
Business
Identify new revenue opportunities and enter markets with reduced risk.
Product
Grows into a long-term value driver that benefits both users and businesses.
Background
Customer trends change faster than our campaigns can keep up.
In business environment, business owners and marketing managers have always been questioned all the time that
Are we investing in the right place?
Are we missing trends before they happen?
Markets become competitive and it's getting harder to predict what customers want and which products to sell.
Timeline
The project is scheduled over a three-week timeline.
The first two weeks are allocated for data collection, filtering, and organization. The final week is dedicated to the design and development of the dashboard UI interface, incorporating the organized data into a functional and user-friendly visual format.
Data Filtering
I started working on dataset first.
I have gathered as many relevant datasets as possible so that nothing important is missed. All data is sourced from Kaggle and Our World in Data.
In this project, all data is based on 200 customers who live in different cities.
During this phase, relevant datasets are sourced, reviewed for quality and relevance, and structured for analysis.
All Collected Datasets
Featured Datasets
Visualizing Data
I sketched and mapped out data into visual format before polishing.
To represent the data more effectively, I organized each category into subcategories and explored various visualization methods to find the most suitable formats. After experimenting with different approaches, I selected the following chart types for the final dashboard:
donut chart
column chart
sunburst chart
heatmap
radial network graph
radar chart
simple circles, and packed circles.
HMW
How does MarketLens help business owners and marketing managers to make smarter decision?
Product Purchase Rate
Based on the location users selected, users can check purchase rate for each product categories based on the seasons along with showing how customer engage with different marketing channels.
Why it matters: Since product demand varies by season, purchasing rates across product categories are also affected.
Each product category is shown with an icon and color-coded bars for the four seasons. Hovering over a bar reveals the total customer purchase amount for that season.
Radial Network Graph
Clicking a product category reveals seasonal purchase rates by location, sorted from highest to lowest.
Sunburst Chart
To ensure accessibility and avoid gender color stereotypes, I use patterns instead of color to represent gender making the visualization inclusive for users with color vision deficiencies.
Column Chart (Grouped)
Instead of showing numbers, I use varying circle sizes to represent engagement rates. The larger the circle, the higher the customer engagement for both channels and product categories.
Radial Network Graph with packed circles
Customer Purchase Amount
Users can check customer purchase amounts based on product categories, income levels and cities.
Product Preferences
Users can check which products are preferred in which locations and by customer segments.
Why it matters: Especially for global launches, understanding product preferences by location helps improve decision-making and market prediction.
Customer segments are categorized based on their purchasing behaviours and personalities.
Curious Explorers
Try new products often, shop across categories, influenced by trends.
Royal Big Spenders
Frequent buyers, high spending, brand loyal.
Occasional Indulgers
Buy only during promotions, prefer small luxuries, not loyal.
Returners & Swappers
Frequently return or exchange items, indecisive or size sensitive.
Last Minute Buyers
Purchase out of urgency, often for occasions or deadlines.
Value Seekers
Focus on discounts, bundle deals, loyalty points.
Conscious Consumers
Buy with purpose eco-friendly, cruelty-free, or ethical sourcing focused.
Bulk Buyers
Purchase in large quantities, usually less frequently.
Customer Satisfaction
Users can check how each of customer segment react feedback in the market.
Why it matters: Understanding customer behaviour is key to launching a product and helping marketers adjust their strategies effectively.
Data points of yellow lines represent number of customers reactions for each satisfaction level.
Radar Chart
Using emojis helps visualize customer satisfaction levels in a simple and easily understandable way.
Level 1 - Disappointed
Level 2 - Sad
Level 3 - Neutral
Level 4 - Happy
Level 5 - Satisfied
Style Guide
Sans-serif font for clean, modern look
Akzidenz-Grotesk Next belongs to traditional of general purpose and its neutral, modern look carries a classic, trustworthy feel.
Regular/ Medium
ABCDEFGHIJKLMNOPQRSTUVWXYZ
abcdefghijklmnopqrstuvwxyz
0123456789[{]}\|;:’”,<.>/?`~
!@#$%^&*()-=_+
Number Value
20px Medium
Subheader
16px Regular
Key Label
14px Regular
Colors and patterns
Different colors are used to reflect related data values, while patterns are applied to avoid stereotypes and ensure inclusivity.
Accessibility (Contrast Check)
Icons with outline styles
Outlined icons are chosen for a clean, minimalist look that enhances clarity and easy recognition for users.
Reflection
What I have learned from this project
Data visualization isn’t just about charts.
I’ve realized that turning raw data into clear visualizations can be the foundation for a project with tangible user benefits.
Ensuring accessibility is essential in data visualization work.
I’ve learned that because people have different abilities, working on data and choices to represent them need to be designed to be clear and understandable even at a glance.
It is not about PINK or BLUE.
I’ve learned to be mindful of gender stereotypes when choosing colors for representation. Traditional color choices, accepted for decades, often create bias in how we represent gender.
Next Steps
What I will do for future steps
Make testing with marketing persons.
Add AI generated insights based on the customer data.
Make customize visualizations for added features.
