How Starbucks Uses Data to Pick Every Store Location — And What It Means for Your Business

They Don't Pray. They Calculate.

Starbucks has over 38,000 stores worldwide. Not a single one was placed by luck.

Behind every new Starbucks location is a proprietary tool called Atlas — an internal system that ingests real-world data and tells the company exactly where the next store should open, long before anyone signs a lease.

And here's the thing that surprised us most: the data Atlas uses isn't secret. It's the same data available to any business willing to look.

The Atlas Data Pipeline

Atlas works by layering multiple data inputs until the numbers either line up — or they don't. No gut feeling. No "I think this corner looks good." Just data.

Here's what feeds into every location decision:

1. Foot Traffic Volume

How many people walk past a specific corner, broken down by hour and day of week. This is the single most predictive factor — if nobody walks by, nobody walks in.

2. Household Income

Average household income within a one-mile radius. Starbucks doesn't just need traffic; they need traffic that can afford a $6 latte regularly.

3. Competitor Proximity

Where the nearest coffee competitor already sits. Sometimes competition validates demand. Other times, saturation kills margins. Atlas knows the difference.

4. Property & Rent Trends

Current listing prices, rent trajectories, and lease terms in the area. A great location at the wrong price is still a bad decision.

5. Transit & Accessibility Scores

Walkability index, proximity to public transit stops, parking availability. Convenience is a massive driver for daily coffee purchases.

How Much Each Factor Matters

Not all data inputs carry equal weight. Based on publicly available research and Starbucks' own 10-K filings, here's an approximate breakdown of how these factors influence the final decision:

Foot traffic dominates. But notice: even lower-weighted factors like zoning and permits can be deal-breakers. Atlas doesn't ignore anything.

The Real Insight: This Data Is Not Secret

Here's what most people miss about the Starbucks story.

They don't have access to data that the rest of us can't get. They just actually use it — consistently and at scale, across 38,000 stores and counting.

Foot traffic data? Available from providers like Placer.ai and SafeGraph. Household income? Public census data. Competitor locations? Google Maps API. Property trends? Listing platforms publish this daily.

The difference between Starbucks and most businesses isn't access. It's discipline.

What This Means for Indonesian Businesses

If you're opening a retail store, a restaurant, a co-working space, or any location-dependent business in Indonesia, you can apply the exact same framework:

• 1.Map foot traffic around your candidate locations
• 2.Analyze household income in the surrounding neighborhood
• 3.Check competitor density — are you filling a gap or entering a bloodbath?
• 4.Track property listing trends — is the area getting more expensive or cooling down?
• 5.Score accessibility — can your target customers actually get there?

The data exists. Indonesian property listings, neighborhood demographics, pricing trends — it's all available if you know where to look.

Start With Property Data

At DataProjects, we aggregate Indonesian property listing data that covers pricing trends, location attributes, and neighborhood-level insights across major cities.

Whether you're doing site selection for a new store, analyzing market entry opportunities, or building a location intelligence model of your own — the data is ready.

Starbucks proved the model works. The question is: are you putting the same kind of data to work before committing to your next location?

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Sources: Starbucks 10-K Annual Report 2024, Harvard Business Review — Location Analytics in Retail