In a scenario where MAC randomization is observed, and where the core service provider (WiFi vendor collecting the data) does not filter randomized addresses, the Presence metric can appear as a higher value than physically present. As presence data includes both authenticated and associated data, the effects of this will diminish as the number of associated clients approaches the total devices present.

The repeat/new visitor metric is one of the most affected by MAC randomization. There is particular bias towards new visitors (as freshly generated randomized addresses will not have been previously observed) thus contributing to potentially inflated numbers of new visitor observations.

The probability of skew towards a randomized MAC overlapping with a genuine visitor is extremely improbable, as vendors are typically always using local-bit markers in the MAC address. The probability of overlap in 2 randomized addresses is extremely low, within the constraints of single venue space and short visit time window.

The filtering of randomized MAC address will for the most part clean this data. It is expected that the trend seen through the proportions of new vs repeat will be genuine and relatively accurate. Both metrics may appear lower than the actual value by a proportion based on a combination of the authenticated user set, and the dwell time of individuals (as the opportunity to generate genuine MAC increases over time). E.g. a convenience store/fast food restaurant would see more impact than a supermarket/restaurant.

MAC randomization effects on dwell data is a little less transparent. Overall there is a bias towards lower dwell metrics for the short observations caused by new devices. Where devices still present genuine MAC addresses over the longer periods, however, (e.g. randomization only while in sleep mode) the genuine dwell time can still be present and accurate for the device.

Typically filtering out low dwell times, and filtering out randomized addresses can improve the quality of dwell metrics.

Regardless of any randomization, the measured signal from the devices remains visible. It is therefore still effective to compute the relative location of an individual device.

As the movement of a mobile device is largely relative to the data window, for unassociated devices there is little advantage to knowing a prior location for the individual device. 

For connected devices there is far more data throughput and we are guaranteed to have the genuine MAC address, and no randomized MAC will overlap a genuine MAC address, so there is no real impact of randomization for this service.

Finding your MAC address on mobile 

Turning off MAC Randomization

iOS

1. Open the Settings app, then tap Wi-Fi.

2. If you aren’t currently connected to the network, tap Edit in the upper-right corner of the screen.

3. Tap the information button next to the network name.

4. Configure the setting:

   • If using iOS 18 or iPadOS 18, tap Private Wi-Fi Address, then tap Off

   • If using an earlier OS, turn Private Wi-Fi Address off

Android

This varies slightly depending on the make and model but typically follows the process of:

1. Open the Settings app

2. Select Network & Internet, then Wi-Fi

3. Select the network to configure

4. Select Advanced, Privacy, then disable Randomized MAC

Mac

1. Choose Apple menu > System Settings, then click Wi-Fi in the sidebar

2. Click the Details button or More Info button next to the network name

3. From the menu next to Private Wi-Fi Address, choose Off

Windows

1. Select the Start button, then Settings

2. Go to Network & Internet then Wi-Fi

3. Go to Manage known networks and select a network, then Properties

4. Disable "Use random hardware addresses for this network"

Where we have genuine authenticated users, we will observe accurate data.

It therefore follows that the higher proportion of authenticated users onboard, at the venue, the higher the accuracy you will see in your data. As we strive to attain further accuracy into the customer behavior within venues, it follows that with more users directly connected to the WiFi service, you will have a deeper insight, finer granularity, and a more engaged and participatory audience.

As Android / iOS currently have different strategies for randomization, it is important to assess the proportion of iOS vs Android devices actually present for the interval of the metric. The bias of measurable data to actual data relates to this proportion.

Due to trampling of vendor codes, prevalent in Apple iOS devices, we cannot realistically use the vendor component in the randomized MAC address to identify proportions of Android/iOS. It would be skewed in favor of non-iOS. It is therefore more reliable to relate the proportion to the ratio observed by actual authenticated users(& attempted authentications during that window). For larger data sets this approach can yield more accuracy, unless there is a known underlying reason why this may be skewed (e.g. mobile phone showroom, or inaccessibility for a device type to authenticate).

Where proportions of non-randomized versus randomized MACs are known, and there is a gauge of the split between iOS and Android, we can produce a new estimate of bias based on:

• This quantifiable observational evidence
• The device susceptibility to randomization
• The measured dwell (opportunity weighting)
• Tempered coefficients to the probability of randomization over time, clamped by the observational space, and based on the prior device classification

Where there is no vendor data available on the set (quantity) of randomized data, we must rely on typical bias, measured and computed at alternate venues to give a current gauge as to the proportions of randomization likely in the environment.

Generally, and for the purposes of statistical observation, the traffic shapes and time of day patterns will largely remain the same and project the same trends in overall data.