Rooted in the nationwide physical device network, SitePulse used machine learning to develop in-house AI + Statistical models to cross validate between different data collection methods to increase confidence and reduce error.

Most foot traffic and location intelligence platforms rely solely on mobile data sets collected from mobile devices to generate location-based insights. There are many downsides to this collection method, namely:

• Mobile datasets come from device pings. Devices must be at a specific setting, such as using a certain app and/or allowing certain permissions, to send out pings. This means that only a portion of devices are visible in the dataset.
• Pings are sent out sparsely, sometimes once every hour or couple of hours. This characteristic makes visitor count, dwell time, and visit frequency data much less reliable.
• Aggregated from various apps, services, and other providers, mobile dataset have inconsistent data quality.

By augmenting the mobile datasets with real-world high-resolution device data and AI statistical models, SitePulse can "upscale" the low-resolution datasets into high-quality insights, anywhere standard mobile datasets reach.