Disrupting traditional methods for recording visitation in the museum, WiFi presence detection presents a convenient, cost effective and real time data source. Unlike other methods, which focus on entry only, it also provides data into onsite visitor behavior once the visitor passes through the door, rather than just recording footfall into the museum itself.
Detecting presence using WiFi relies only on the visitor having a device with WiFi turned on, commonly more pervasive than Bluetooth®. The visitor does not need to connect to the network (though this helps data quality) and does not need to take any steps to enable their presence, such as downloading an app, accepting permissions, actively look at their WiFi list or turning ‘ask to join’ on. Detection works because as visitor devices are surveying the field for possible networks, they send out a signal in doing so, known as a MAC address.
A MAC address is not linked to the visitor’s identity and does not have a reverse look up facility. However, privacy treatments are still important. This is why Dexibit’s data is only available in aggregate volumetrics and the raw address is not accessible. If required, the address can be hashed at capture, though this reduces the data quality.
Detection can be performed by the venue’s own WiFi network using most mainstream providers such as Cisco, HP or Ruckus, or with a hardware accessory. Ranges vary by vendor, and some (including Dexibit’s accessories) can be adjusted to scan an isolated area, or to limit exposure to passers by. Dexibit’s accessories should be plugged in with a WiFi connection available to enable data to report back remotely. Battery power is possible, but not desirable as these rechargeable devices only last a week or so without power. They can be hidden from sight, but perform better at height and also need to be weather proofed. Dexibit then streams this data, combining it alongside other relevant data sources, such as online, social, transactions, weather etc.
Raw WiFi presence data requires cleansing, which Dexibit’s solution performs in real time using machine learning methods. This accounts for influencing factors which can vary by city and site, adjusting for the percent of visitors carrying a WiFi enabled device, multiple device penetration, scanning interval, randomization, fixed equipment, staff movements are more. A sample manual count can help validate the overall scaling factor.
Additionally, Dexibit then analyzes WiFi data to reveal zone activation, trail routes, dwell times, repeat visitation and more. This insight on visitor behavior in engagement is equally as valuable as measuring overall visitation. A venue can use this understanding of visitor behavior from presence whilst taking visitation counts from ticketing or elsewhere, or rely upon presence to report visitation itself, as well as behavior.
The value of understanding visitation and engagement over and above validating annual reported audiences is to determine the impact of levers within the institution’s control alongside influences to otherwise allow for. This insight can then be tracked against the museum’s capital plan or used for operational purposes. For example, this might include discovering optimal channels and campaigns for promotion, highlighting seasonality, deriving the impact of weather in order to determine offers, managing successful events, working in with what’s on in the local area, planning new facilities and prioritizing maintenance.