Estimated Visits 2.0—an exciting development in visitation volume reporting— is now being offered to Spotzi clients worldwide in our Spotzi Professional and Spotzi Enterprise dashboards. This visitation report uses a proprietary algorithm to estimate precise traffic counts by address, combining an analysis of each location's unique characteristics with human movement data.
The Estimated Visits report already offers coverage across the US, Canada, Australia, and New Zealand. This allows users to compare location performance across markets, verticals, and even borders. 11 additional countries are also in the process of being added to this growing roster.
Estimated Visits is an invaluable tool to marketers and business owners alike; an accurate insight into true visitation volumes is crucial to the evaluation of store/business performance, the assessment of real estate value, and the optimization of retail business labour and stocking schedules.
Human movement data—sourced from intermittently observable smartphone devices with user consent—is currently being used as the equivalent of foot traffic data. This data provides a valuable starting point for the estimation of true visitor traffic, but fails to be a suitable representation of retail foot traffic alone. This is because:
The Estimated Visits 2.0 report uses a machine-learning model to analyze real-world data and provide insights into true visitor traffic. This model improves upon existing methods of foot traffic data collection in both accuracy and privacy—with data projection being a far more privacy-friendly method of visitation reporting.
This ML model has been trained to recognize and extract complex patterns from movement and traffic data; It combines these travel behaviour patterns with an observability metric to make foot-traffic estimations. Access to ground-truth data is continuously being used to hone the precision and accuracy of these predictions.
The Estimated Visits model uses a probability measurement, and takes the following points into account when extrapolating true visitation volumes from device movement data:
A proprietary device observability metric is used to quantify the overall probability that a device will be observed at any given location.
The probability of observing any device at its location increases with dwell time. Therefore, the average dwell time must be calculated at every location in order to accurately assess the relationship between its movement data and actual foot traffic. Each location’s average dwell time is paired with every visitor’s device observability ranking (see above) to achieve an unmatched assessment of overall observability.
A demographically and geographically-balanced set of panels are created each month based on active device observation and the latest available census data. Movement data is then filtered down to these panelist devices, and they provide the basis of a location’s overall estimated visitation. This ensures a consistently-sized sample of devices and corrects for obstacles in internet connectivity, as well as geographic and demographic bias.
In the past, research into foot traffic predictions and estimations was obstructed by the limited availability of real-world data. This lack of ground-truth data access was due to businesses heavily regulating access to store POS data, and exclusively using it for internal foot-traffic calculations.
Today, partnerships with a variety of businesses and data sources have allowed us to obtain this missing data. We can hone our machine-learning models by calibrating each algorithm against purchase data, POS data, and data obtained from beacons/cameras. Our machine-learning models are able to provide the most accurate visitation estimates possible based on ongoing pattern-extractions and ground-truth comparisons.
Additional ground-truth data is continuously being sought out and utilized across all current and future available countries. The tireless training of the algorithms behind this product will lead to a continuous rise in estimate accuracy, and will allow clients to gain the most precise insights into visitation volumes across the world.
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