Ominchannel

Data to grow sales

Omnichannel data to grow sales

Data to grow sales

Walmart is a global retail leader with thousands of stores and a growing online marketplace, focused on affordability and efficient supply chains.

My role

UI/UX Intern

Contains

Data analysis, product design

Team

1 Senior designer, 2 marketing,

30%

Smoother cross-dept collaboration as everyone views the same metrics

50%

Richer customer insights by merging online and offline behaviour data.

Solution summery

What did I do?

(Use these for quick navigation)

Problem identification

User research

solution ux

Final experience

impact

Problem identification

Ux strategy

Ux research

Problem statement

ideation

user testing

Final experience

Branding

Gamified it

Impact

Problem identification

Strategy

User research

Final experience

Problem identification

current problems with data

Problem identification

current problems with data

Too Much Data, No Direction

Walmart collects lots of data from multiple sources, but there’s no meaningful analysis drawn from it.

Tough to Decode

Finding insights or patterns becomes time-consuming and complicated.

Insights Stuck in Silos

Teams see only part of the data, so insights stay limited and hard to scale.

UX RESEARCH

DATA ORGANISATION

UX research

Data organisation

UX research

data organisation

1. What the data really tells us?

Drivers are caught between the road and their phones. Messages keep pinging while they’re told to “stay on schedule.” They face traffic, no safe spots to stop, and sometimes messages that don’t even go through.
They just want clear, quick updates that don’t make them choose between safety and staying informed.

2. What It Means Structurally

From social media scrolls to website clicks, TV ads, Black Friday events, and in-store experiences, every channel tells a part of the customer story.
Each medium can be used to collect unique data.

3. Where It Happens in Practice

Every medium speaks a different language of data.
Social media shows who’s engaging and why, billboards show where attention lives, TV tells how far reach goes, and stores reveal what actually sells.
Together, these signals help Walmart close the loop from awareness to action creating a full-circle view of the customer journey.

1. What the data really tells us?

Every number hides a story.
Location data reveals 'what sells where'.
Demographics show 'who’s buying and why'.
When combined, these scattered data points turn into patterns which help predict needs, personalise offers, and sell smarter across every channel.

2. What It Means Structurally

3. Where It Happens in Practice

Problem statement

How might we create an omni-channel tool which help us analyse and leverage data collected to boost Walmart’s growth

Omni-channel

Boost sales

Data analysis

Ideation

Data visualisations explored

Flexible Comparison for Deeper Insights

The feature lets users mix and match parameters (e.g., Gender F 10–14 vs Gender M 10–14) to analyze how the same data point behaves across segments.

Compare Multiple Metrics Side-by-Side

By comparing several data points together, users get an overview of how different segments behave and can add more metrics for deeper detail.

Compare Multiple Metrics Side-by-Side

By comparing several data points together, users get an overview of how different segments behave and can add more metrics for deeper detail.

PERSONA PAGE

Flexible Comparison for Deeper Insights

Users can be grouped by buying patterns, interest areas, product types, and platform interaction.
The system auto-generates persona names, which users can customise.
Each persona can be explored in detail, and two personas can be compared side-by-side for deeper analysis.