With nearly 3,000 corporate-owned locations spanning street-side restaurants, malls, airports, and more, Panda Express operates at a scale where growth decisions require discipline.
Each new site must balance real estate availability, operational feasibility, and market demand – across highly varied regions and formats. To navigate that complexity, Panda Express relies on a structured approach that brings together strategy, real estate, and operations – supported by location intelligence.
We spoke with members of Panda Express’s strategy team about how they use data and Kalibrate Location Intelligence (KLI) to guide growth, evaluate opportunities, and align decisions across the business.
Tell us about your role and how location intelligence fits into your day-to-day responsibilities.
Mike McDermott, Senior Data Analyst:
I’m a Senior Data Analyst on the restaurant development team. While we sit within strategy, we work closely with both real estate and operations.
Location intelligence is embedded across our workflow – from long-range planning to evaluating individual sites. We use it to understand trade areas, forecast performance, and support decision-making at every stage.
Caroline Martina, Data Analyst:
At a strategic level, we provide white space analysis to support long-range planning. On a day-to-day basis, we evaluate individual deals – looking at cannibalization, retail activity, competitors, and how a trade area performs relative to others.
Why is location intelligence important to Panda Express?
Mike:
Our process is built around collaboration between real estate, operations, and strategy. Real estate identifies opportunities, operations evaluates feasibility, and strategy provides an objective, data-driven perspective.
Location intelligence enables that alignment. It allows us to quantify trade area potential, assess risk, and determine how many locations a market can support.
How has working with location data changed the way you think about your business?
Caroline:
The sales forecasting model has been one of the biggest shifts for us. With KLI, we’re able to take a baseline sales forecast and adjust it based on our own brand performance.
We incorporate our own analog adjustments and market-specific inputs, which allows us to tailor forecasts by region rather than relying on a one-size-fits-all model. That’s been really important in determining what will work in certain markets.
Mike:
Forecasting has become foundational to how we think about growth. We use it alongside our internal analytics to guide both short-term decisions and long-term planning.
How does location intelligence influence your overall business strategy and long-term planning?
Mike:
We conduct a long-range planning process each year where we evaluate trade areas across the country.
KLI supports that by helping us assess trade area potential and run forecasts on target markets and tiered locations. It gives us a clearer picture of what’s possible in a market – especially when we’re looking to enter new areas.
Tell us something interesting you’ve learned about your customers, competitors, or markets.
Mike:
One of the biggest insights has been how much performance varies by format and customer behavior – especially when it comes to digital ordering.
For example, non-drive-thru locations – particularly in urban or high-density areas – tend to have a much higher mix of digital orders. In some cases, digital can make up a significant portion of total sales. In contrast, drive-thru locations can outperform in other markets, sometimes by a wide margin.
We also see strong digital adoption and seasonality in college markets, where convenience plays a bigger role.
Which Kalibrate features or insights do you find most valuable in your daily work?
Caroline:
Sales forecasting is the most valuable capability for us, especially because we can customize it with our own inputs and adjustments.
We also use KLI to evaluate the broader landscape – understanding nearby retailers, comparing trade areas, and analyzing how different factors influence performance.
Mike:
On the field side, Site Ride reports are critical. Teams use those while they’re on site tours to quickly understand forecasts, key metrics, and how a location compares within the market.
We also rely heavily on KLI’s mapping functionality. Field teams use it to lay out and visualize their tiers – labeling Tier 1 and Tier 2 opportunities directly on the map. That makes it easier for both field and strategy teams to align on priorities and review new sites.
Can you walk us through a recent project where location intelligence changed your approach or outcome?
Caroline:
We used KLI’s Bulk Closest Stores tool to analyze how our locations perform near specific types of anchors, starting with Costco.
Using that tool, we identified Panda Express locations in close proximity to Costco stores across the country and then evaluated performance by region. What we found was that the impact isn’t consistent—some regions show a clear sales lift, while others don’t see much difference.
That helped us refine our approach. Instead of applying a broad co-tenancy assumption, we now evaluate those relationships at a regional level and incorporate that into our site selection strategy.
We’ve applied the same Bulk Closest Stores methodology to other location types, including hospitals. In those cases, we found that proximity to hospitals can drive strong daytime demand – particularly from hospital staff like nurses ordering during lunch hours.
That type of analysis helps us better understand not just where we want to be, but why certain locations perform the way they do.
Location intelligence is often associated with real estate. Have you used spatial analytics in other ways at Panda Express?
Caroline:
Yes – one example is how we support our Panda Cares team, which is our philanthropic arm.
We used the same Bulk Closest Stores tool to identify which Panda Express locations are closest to Boys & Girls Clubs. That allows the team to understand where we’re currently supporting communities, how far our reach extends, and where there are gaps.
From there, Panda Cares can identify Boys & Girls Clubs that aren’t yet supported and prioritize opportunities to expand their impact.
It’s the same core analysis – understanding proximity and relationships between locations—but applied in a completely different context.
At Panda Express, location intelligence isn’t just about identifying the next site – it’s about creating alignment across teams, uncovering patterns in complex markets, and supporting real estate investment decisions at scale. From long-range planning to on-the-ground site tours, data plays a central role in turning strategy into action.
