In a world where visitor attractions have previously had little insight into the big unknown of what visitors actually do onsite, location analytics provides a unique insider’s perspective to understand visitor behavior. There are several different technologies that can be used to track onsite movements, with various tradeoffs when it comes to the level of visitor intrusion and network requirements versus the granularity and accuracy that can be achieved.
WiFi is a preferred method for most as it utilizes a likely existing infrastructure with no visitor intrusion and as a result, offers a high sample size. Visitors’ WiFi capable devices (such as their mobile phones) emit a background signal as they look for a WiFi network to connect with, able to be traced throughout the site and therefore used as location analytics. This relies on the visitor having a device (which though some young children won’t, presumably their supervising adult will) and that device being turned on with WiFi enabled – on average, true for over 90% of visitors.
By nature, this signal doesn’t identify the visitor, only that a device is present. The device emits this signal every so often as it calls out for potential networks to connect to, which can be listened for if the WiFi network is data capable. This signal is known as a MAC address – similar to an IP address on your computer, but different in that it belongs to the device hardware rather than the network connection. Though this signal doesn’t identify the visitor themselves, various additional safeguards can help further deidentify, anonymize and additionally protect visitor privacy.
Operating systems such as Apple, Android, iOS and Windows have been thinking about this too, with many modern devices spoofing (faking the MAC) and randomizing (changing the spoofed MAC) as an approach to enhance perceived user privacy. Over time, spoofing has become more prevalent as modern devices make their way into the market – a trend more noticeable in some visitor attractions than others, depending on demographics. Randomization has also become more frequent too – where initially many manufacturers only changed the MAC every so often, now some devices are randomizing multiple times within a visit. To identify if this is an issue at your venue, check your WiFi penetration rate visualization (which compares the number of unique address signals to your visitation) – if this number is consistently and unexplainably over 130%, meaning most visitors are supposedly carrying multiple devices, it’s likely due to randomization. Conversely, you can also look at your data without short visits (under 30 minutes) – if this penetration rate is exceptionally low, meaning very few long visitor trails are visible with a decent dwell time, again this may be a symptom of randomization.
Where randomization impacts the quality of your location analytics to a significant degree, or simply to look at your WiFi data in another way, it’s possible to filter for just devices connected to the network. Where visitors have chosen to connect to the venue’s WiFi, the MAC address becomes identifiable offering a superior quality signal and avoiding the impacts of randomization. The tradeoff for data quality here is simply sample size – for most venues, fewer than 30% of visitors connect to the WiFi network and this sample may be skewed, for example towards international or more technology savvy visitors. Improving this sample size is a compelling reason to increase the conversion metric of those that connect to WiFi, for example through additional signage, a special mention by guest services at admission, or a note on the ticket itself – or simply by making the captive portal user experience easier.
In Dexibit, we’ve recently added new WiFi filtering functionality giving you the power to choose what data is best for your venue. In the rules section of Dexibit’s venue management module, you can set the default data set you wish all your users to see (between connected, all devices or unconnected devices), or as an individual user, change this on the location analytics insight model or any related visualization. You can then compare each side by side – for example, to look at the difference in your repeat visit rates, which is the metric most likely to be impacted by randomization.
Though the world of MAC signals is changing, WiFi remains an excellent option to understand important aspects such as dwell time, trail route and more through location analytics – and the connected devices sample size is still a valid representation of visitors. Though WiFi data is notoriously noisy and requires significant data cleansing and transformation behind the scenes to provide meaningful insight, with this new functionality, stronger and cleaner signals mean deeper insights with higher integrity – all bringing us closer to the visitor.