MarketLens
A data visualization dashboard that turns customer behavior into clear, actionable business insights. It helps startup founders, business owners, and marketing managers see what's actually happening in their market.
TIMELINE
3 weeks
PLATFORM
Desktop / Web
INDUSTRY
Marketing
TOOLS
FIgma
Google Sheets
RoLE
Sole Designer
Overview
The gap between data and decisions is where businesses lose.
Most businesses collect customer data but struggle to read it fast enough to act on it. MarketLens bridges that gap, converting purchasing patterns and behavioral data into visual insights that make the next move obvious.
Problem
By the time a campaign launches, the market has already moved.
Customer trends shift constantly, but most marketing decisions are still based on gut feel, last quarter's numbers, or scattered spreadsheets. Business owners and marketing managers are always asking the same questions:
Which product should we push right now?
Which channel actually works?
Are we targeting the right people?
Without a clear view of the data, every answer is a gamble.
Business Value
See the market clearly and move at the right moment.
My goal was to give decision-makers one place to answer their most critical questions:
When to launch?
What to promote?
Where to spend?
Who to target?
Each insight in MarketLens is designed to reduce a specific type of marketing risk: timing, channel selection, audience fit, and investment confidence.
Planned Timeline
Two weeks of groundwork, one week of design.
With only three weeks, I had to be deliberate about where time went. The first two weeks were entirely dedicated to sourcing, cleaning, and structuring the data because no amount of design polish can fix a weak data foundation. The final week was where it all came together: turning clean, organized data into a dashboard that felt simple to use.
Data Filtering
Start with good data, or don't start at all.
The dataset covers 200 customers across multiple cities, broad enough to reveal patterns, specific enough to stay meaningful. I sourced data from Kaggle and Our World in Data, then reviewed each dataset for quality and relevance before structuring it for analysis. The goal was to ensure every insight in the dashboard had a trustworthy foundation.
Visualizing Data
The right chart type isn't obvious and it takes testing to find it.
Before opening Figma, I sketched each data category by hand, mapping out how information was grouped, what relationships mattered, and what a user would actually need to see at a glance. From there, I tested multiple chart types for each category and landed on the formats that best balanced clarity with density:
donut chart
column chart
sunburst chart
heatmap
radial network graph
radar chart
simple circles, and packed circles.
Each choice was made to match the shape of the data, not just to look interesting.
Main Features
Product Purchase Rate
Which product sells, where, and in which season, all in one view.
Product demand isn't constant; it shifts by season, and it shifts differently depending on where the customers are. This view lets users select a location and immediately see how each product category performs across seasons, alongside which marketing channels customers in that area respond to. Launch timing and channel decisions stop being guesswork.
Detail Breakdown
Radial network graph, built for density without clutter.
A radial layout fits multiple categories and seasons into a compact space without overwhelming the viewer. Colored bar lengths make seasonal peaks scannable at a glance, and hover interaction surfaces the detail only when needed, keeping the default view clean.
Sunburst chart for hierarchy made visual.
The sunburst shows how data layers: category, season, location, purchase rate, nest inside each other. It makes it easy to compare segment sizes across seasons and spot where the largest concentrations of purchasing activity actually live.
Packed circles for engagement without numbers.
Rather than displaying raw engagement figures, varying circle sizes communicate relative performance instantly. Patterns replace color fills to avoid gender color stereotypes and keep the visualization accessible for users with color vision differences.
Patterns to avoid gender colour stereotypes and ensure accessibility for users with colour vision deficiencies.
Male
Female
Main Features
Customer Purchase Amount
Who spends the most, on what, and where.
This view breaks down purchase amounts by product category, income level, and city, giving marketers a clear picture of their highest-value audience segments. When you know which income bracket spends the most on which category in which city, audience targeting stops being broad and starts being precise.
Main Features
Product Preferences
Know what sells where before you spend a dollar on it.
Product preferences vary significantly by location, and for businesses planning regional or global launches, getting this wrong is expensive. This view maps product preference by location and customer segment, so decisions about what to stock, promote, or localize are grounded in real demand signals rather than assumptions.
Detail Breakdown
Eight customer segments, each with a distinct buying identity.
Customers aren't one audience; they're at least eight. From Curious Explorers who chase trends, to Value Seekers who wait for discounts, to Conscious Consumers who buy with purpose. Each segment behaves differently and responds to different marketing signals. Knowing which segments dominate a market shapes everything from messaging to channel choice.
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.
Main Features
Customer Satisfaction
What customers feel about your product by segment, not by average.
Aggregate satisfaction scores hide the real story. This view breaks down satisfaction reactions by customer segment, revealing which groups are delighted, which are neutral, and which are quietly disappointed. That breakdown is what tells you where to improve and which segments are worth doubling down on.
Detail Breakdown
Emojis as data. They are fast to read, hard to ignore.
Satisfaction levels are mapped to five emoji states from Disappointed to Satisfied, with yellow data points showing the volume of customer reactions at each level. It makes the emotional signal scannable in seconds, no decoding required.
Level 1 - Disappointed
Level 2 - Sad
Level 3 - Neutral
Level 4 - Happy
Level 5 - Satisfied
How It Works: Components & Prototyping
One component system, not hundreds of screens.
Rather than designing every state as a separate screen, I built the dashboard using Figma components and variants keeping the prototype clean, consistent, and easy to update. Both light and dark themes were designed with accessibility checked at every step.
Accessibility
Color contrast check
WCAG Grading (AAA)
7.48:1
7.25:1
8.09:1
7.96:1
9.3:1
WCAG Grading (AA)
5.04:1
5.5:1
5.5:1
5.5:1
5.78:1
Style Guide (Color)
The primary purple ties the dashboard together without competing with the data colors, keeping the visual hierarchy intact.
Primary
#BA7DF0
Spring
#6CBF84
Summer
#FF8888
Fall
#F4A261
Winter
#8CB9D6
Style Guide (Typography)
Its clean geometry keeps the focus on the numbers and charts, not the letterforms.
Regular/ Medium
ABCDEFGHIJKLMNOPQRSTUVWXYZ
abcdefghijklmnopqrstuvwxyz
0123456789[{]}\|;:’”,<.>/?`~
!@#$%^&*()-=_+
Number Value
20px Medium
Subheader
16px Regular
Key Label
14px Regular
Challenges
The hardest part wasn't the design, it was the data.
Finding the right chart for each dataset took more iterations than expected. What looked good in a sketch sometimes fell apart when real data was plugged in. Data handling was also a steep learning curve early on.
The turning point was asking for guidance instead of grinding through it alone, which accelerated the work significantly and shaped how I approach ambiguous problems now.
Takeaways
Three things this project changed about how I design.