Quick answer: Flow AI’s Areas experience groups zones together so shopping centers and mixed-use properties can report on a wing, dining precinct, or event hall as a single unit, tracking total devices, average dwell time, visits, unique visitors, and visits per device for the group rather than reconciling individual zones by hand.

A shopping center leasing team doesn’t think in individual zones. They think in wings, in the dining precinct, in the wing anchored by a department store. Flow AI’s zone-level data has always supported that kind of analysis, but getting there meant pulling multiple zones and reconciling them by hand every time the question came up.

The new Areas experience turns that manual step into a saved view.

The gap between zones and the questions people actually ask

Flow AI tracks visitor activity at the zone level: a specific tracked space within a property. That’s precise, but most of the questions leasing, marketing, and operations teams actually ask aren’t about a single zone. They’re about a wing, a dining precinct, or an event hall, which is to say a group of zones that together make up a place people think of as one destination.

Answering those questions used to mean pulling each zone’s numbers separately and combining them by hand, every time, for every report. That’s the same manual-reconciliation problem that shows up across retail property analytics generally, whether the question is about tracking dwell time or retail leasing metrics: the underlying data exists, but assembling it at the right level takes work that has to be repeated every time.

What an Area is and what it reports

An area is a group of zones. The Areas experience lets you analyze a wing of a shopping center, a dining precinct, or an event hall as a single unit rather than reconciling its zones by hand.

For each area, Flow AI reports five metrics:

• Total devices detected within the area
• Average dwell time across all zones in the area
• Visits, counted at the area level rather than per zone
• Unique visitors, deduplicated across the zones that make up the area
• Visits per device, a rough proxy for how often the same visitors return to that area

Areas can be compared side by side in a sortable table, expanded to show daily visit and dwell time trends, and exported for further analysis.

How to analyze a shopping center by area instead of by zone

For a property team setting this up for the first time, the shift is mostly conceptual rather than technical: instead of asking “how did zone 14 perform,” the question becomes “how did the west wing perform,” where the west wing might be made up of zones 12 through 18. In practice, that looks like:

1. Identify the destinations your stakeholders actually ask about. A leasing team asks about wings and anchor-adjacent zones. An F&B operations team asks about the dining precinct. An events team asks about the event hall. These are usually a handful of natural groupings per property.
2. Group the relevant zones into an area for each one. This is a reporting grouping, not a change to how zones themselves are tracked or configured.
3. Review the five core metrics for that area the same way you would for a single zone, but now representing the destination as a whole.
4. Compare areas side by side when the question is relative, such as which wing is outperforming the other, or expand an area to see its daily trend when the question is about a single destination over time.

Where this fits into leasing and operations conversations

Shopping centers and mixed-use properties run on questions that are naturally area-shaped, not zone-shaped:

Leasing performance by wing. Comparing the wing anchored by one department store against the wing anchored by another is now a saved comparison instead of a manual pull, which matters directly for tenant mix optimization and anchor tenant strategy conversations with ownership. This is the same question at the center of how to optimize tenant mix in malls using visitor data: tenant mix decisions are area-level decisions, and Areas gives that analysis a permanent home in the dashboard instead of a one-off export.

Dining precinct performance. F&B courts and dining precincts have their own visit and dwell patterns, distinct from the retail wings around them. Areas make it possible to track that precinct on its own terms, separate from the retail traffic passing through nearby.

Event hall utilization. A property with a dedicated event space can now measure that space’s visits and dwell time as a unit, separate from everyday retail traffic, which supports the kind of retail leasing metrics conversations that come up with prospective tenants and investors, and the broader question of what metrics matter for retail leasing in the first place.

Areas and multi-site analytics are two different things

It’s worth being precise here, because these features solve related but distinct problems. Areas group zones together within a single property, so you can analyze a wing or a precinct as one unit. Flow AI’s separate multi-site analytics capability compares whole properties against each other across a portfolio. One is about the inside of a building; the other is about the relationship between buildings. A property team might use both: Areas to understand how a single shopping center’s dining precinct performs, and multi-site analytics to compare that shopping center against others in the portfolio.

Frequently asked questions

Want to see how your property’s wings, precincts, or event spaces perform as areas? Request a demo.

 Shopping centers, mixed-use properties, and any site with distinct sub-destinations, such as a dining precinct or event hall, that leasing and operations teams already think about as a unit rather than as individual zones.

Suggested internal links: How to Track and Optimize Retail Leasing Metrics for Better ROI, How to Optimize Tenant Mix in Malls Using Visitor Data, Shopping Centers Industry Page, Tenant Mix Optimization, Anchor Tenant Strategy, Retail Leasing Metrics, Flow AI, September 2026 Product Release