A Solution That Transforms the Value of Commercial Spaces with Movement Data Without Privacy Concerns

Who Is This For?

A story for organizations that need to explain the value of their space in numbers.

• Operators of commercial facilities such as shopping complexes, department stores, and large wholesale malls

• Real estate asset managers that operate and develop retail assets, along with property management (PM) and asset management teams

• Departments operating public multi-use facilities such as libraries, cultural centers, and exhibition halls

• Merchandising (MD) planning and leasing teams, as well as trade area analysis and retail consulting organizations

• Businesses building smart buildings and spatial data platforms

What they all need is the same fact: "How is this space actually being used?" Every decision, from setting rents and arranging stores to deploying staff, must be built on that fact.

The Problem on the Ground

Visitor counts, zone-level traffic, dwell time, and movement paths: securing all four at once is harder than you might think.

• Decision-making: The basis for setting rents, reorganizing merchandising, and selecting event zones remains stuck at managers' experience and intuition.

• Cost: Getting accurate figures requires deploying field surveyors, and the results are valid only for the few days the survey was conducted.

• Operations and safety: Without knowing which zones are crowded at which times, guidance, cleaning, and safety staff end up being deployed evenly regardless of time or location.

• Technology: Dwell time and movement paths, the most important metrics of all, have often been abandoned altogether because of the privacy burden involved in collecting them.

Why Were Existing Methods Insufficient?

The root of the problem lies in the structural limitations of each measurement method.

• Manual counting and surveys are limited in duration and location, and their results ultimately remain estimates.

• Entrance gate counters only record total entries and exits. There is no way to know where people went once inside.

• Wi-Fi and beacon-based measurement relies on the assumption that visitors carry specific devices with specific settings enabled.

• Video cameras are vulnerable to occlusion and changes in lighting, and above all, their potential to identify individuals makes it difficult to collect individual-level data such as dwell time and movement paths.

• Sales and POS data only records what happens after a purchase. The majority who passed by without buying remain invisible.

There have been many attempts to collect offline data, but not a single technology has satisfied all four requirements at once: no wearable devices needed, point-level precision, continuous collection, and personal de-identification. Satisfying one has always meant giving up another.

How LookOut Solves It

LookOut reads the space in 3D using LiDAR sensors and generates visit data through the following steps.

1. Detection and tracking: People are detected as anonymous point-coordinate objects, and their movement trajectories are tracked continuously. The system recognizes position and movement, not faces or physical appearance.

2. Area integration: The detection areas of multiple sensors are merged into one, so an entire large floor is observed as a single, seamless space.

3. Virtual analysis zones: Key points such as storefronts, aisles, and entrances/exits are designated as zones in software, and traffic and dwell time are calculated separately for each zone.

4. Path linking: Entry and exit points are connected into a single trajectory, mapping the flow structure of which entrance visitors came in through and which one they left from.

5. Accumulation and integration: Values aggregated in 5-minute intervals are accumulated by time of day, day of week, and period, then delivered through a dashboard and integrated with operations and control systems.

6. De-identification: Because the system uses 3D coordinate data rather than raw video, exposure of personal information is minimized.

LookOut satisfies all four challenges the market has been unable to solve (contactless measurement, precise space-level data, a continuous data pipeline, and complete privacy protection) at once, setting a new standard for offline space analytics.

What Changes

When the measurement method changes, the criteria for operational and leasing decisions change with it.

• Rent pricing and merchandising layouts can be justified based on zone-level traffic and dwell time.

• Staff and guidance/cleaning resources can be concentrated where and when they are needed, based on hourly congestion data.

• Signage and promotion locations can be decided by identifying main entrances and dominant movement paths.

• The before-and-after effects of renovations and events can be quantitatively compared using the same metrics.

• Pre-purchase behavior, which sales data cannot capture, can be secured while reducing the privacy burden.

Featured Use Case

Pilot on a Single Floor of a Large Wholesale Mall

We installed two LiDAR sensors on a single floor of a large domestic wholesale and fashion mall and observed 8 stores and 5 entrances/exits over three days (Thursday to Saturday, 09:00–18:00 daily). The figures below are not survey results or estimates; they are actual measurements observed through anonymous coordinates on that floor during that period.

Crowdedness came from activity levels, not headcount.

Zone visitors on Saturday totaled 23,085, 1.46 times Thursday's 15,804. However, storefront traffic reached 124,573 passes, 3.02 times Thursday's 41,221. This is because the number of stores each visitor passed rose from 2.61 to 5.40. A measurement that only counted visitors would never have revealed this difference.

Attracting many visitors and keeping them there were different metrics.

On the same floor, an anchor store with a 27.3% traffic share saw its dwell capture rate rise to 31.2%, while a pass-through store with an 18.9% traffic share saw its dwell capture rate drop to 14.4%. The character of each store, invisible from traffic rankings alone, emerges from the gap between these two metrics.

Movement patterns remained stable regardless of visit volume.

Of the 15,468 entrance-to-entrance movements observed over the three days, the upper west entrance accounted for 35.7% of all entries, effectively serving as the main entrance. The usage ranking of each entrance remained the same on all three days, and on weekdays, about 77% of visitors exited through a different entrance than the one they came in through, passing through the floor.

Average dwell time shortened from 93 seconds on Thursday to 54 seconds on Saturday.

On busy days, visitors quickly browsed multiple stores rather than lingering in one place. This, too, was observed using only anonymous coordinates that do not identify any individual.

That said, these results reflect a sample of 3 days, 1 floor, and 9 daytime hours. Generalizing to other periods or floors would require additional observation, and in full-time operation, this sample is replaced by continuous, uninterrupted data.

Streamlining Offline Operations, LookOut Is With You

If you need a detailed brochure on LiDAR-based foot traffic and congestion analysis tailored to the commercial or public space you operate, please submit a request through the "Request a Custom Consultation" form. Tell us about the layout of your space and your observation goals, and we will propose a sensor placement plan along with the metrics we can deliver.