Foot Traffic Analytics from WiFi: Understanding Your Venue's Heartbeat

What WiFi Foot Traffic Data Captures

There are two ways WiFi can count people, and they are very different.

Passive counting. Phones looking for networks send probe requests, and some access points and analytics tools log them to estimate how many devices are nearby, including devices that never connect. This used to give a rough headcount. It is much less reliable now. Apple devices use a randomised MAC address when scanning for Wi-Fi while not connected to a network (Apple Platform Security, Wi-Fi privacy), and Android devices have used randomised MAC addresses when probing for new networks since Android 8.0 (Android Open Source Project, MAC randomisation). One phone can look like several devices, and probes also reach you from the pavement and the shop next door. VoqadoWiFi does not do passive counting.

Login counting. When a guest joins your WiFi and completes the portal, you get a timestamped login tied to a guest record. This is what VoqadoWiFi counts. It is accurate for the guests it covers and blind to everyone else: guests on mobile data, children without phones, the person who came in to collect an order.

So WiFi logins are a sample of your footfall, not a headcount. The sample is useful because it is consistent: if the share of guests who log in stays roughly the same, changes in logins track changes in visits. Check that against your covers or till transactions for a few weeks before you lean on it.

Reading Your Daily and Weekly Charts

The Analytics page in VoqadoWiFi shows logins by hour, by day of week, and as a day by hour grid, all in each venue's local time. It will mostly confirm what you already know. The useful part is where it does not.

What to look for:

Peak timing: guests usually log in soon after they arrive, so the login peak tends to come a little before your busiest service moment. The chart is in hourly buckets: it tells you that 12:00 to 13:00 is the peak hour, not the exact minute.

Shoulder periods: the hours either side of the peak. If they are much quieter than the peak, that is where an offer or an event has room to work, and where a staff member could start later or finish earlier.

Dead zones: hours that are quiet every week, such as a Tuesday afternoon. These are the windows to aim a campaign at, because you can see exactly which hours to fill.

Read at least four weeks together. One week includes too much weather, holidays and local events to trust on its own.

New vs Returning Visitor Ratio

This answers a simple question: are you growing by finding new guests or by bringing the same ones back?

In VoqadoWiFi, a login counts as returning when the guest was already in your guest list before it, and the dashboard shows the returning share of logins over the last 7 days. That is counted by guest, not by device. Tools that count devices have a problem here: on networks with weak or no security, Apple's default is a private Wi-Fi address that rotates every 2 weeks (Apple, private Wi-Fi addresses). An open guest network fits that description, so a device based tool can count a regular as new.

There is no single healthy ratio. It depends on the venue: a hotel lobby will be mostly new guests, a neighbourhood café mostly returning. Read the trend, not the level:

• The new share climbs while total logins stay flat: guests are trying you and not coming back. Look at the experience before you spend more on marketing.
• The returning share is high and total logins are flat or falling: a loyal base that is not being topped up. Events, referrals and local social posts are worth more than another email to the same people.
• Both rising together: keep doing what you are doing, and find out what it is.

The dashboard's guest segments (New, Returning, Regulars, At risk, Lost) give you the same picture by guest rather than by login.

Staffing Optimisation

Hourly login data is a good starting point for a staffing conversation, because it is consistent from week to week and nobody has to count by hand.

Illustrative example: if Tuesday evening logins are a fraction of Friday evening's week after week, and your till data agrees, that is a case for a lighter Tuesday rota. We do not have a figure for how much venues save this way, and it depends on your wage structure and minimum staffing for safety and service.

Before you cut a shift, check three things: that the login pattern matches your covers or transactions, that the quiet hours are not quiet because you are understaffed, and that you still have enough people for prep, deliveries and cleaning in that window.

Using Foot Traffic Data in Lease Negotiations

Login trends can support a conversation with your landlord, for example if visits have fallen since a neighbouring anchor tenant left, or grown since you opened later in the evening. How much weight a landlord gives it is up to them. Be ready for the obvious question: logins count only guests who used your WiFi.

Make it hold up:

• Show trends over months, not a single week, and keep your portal settings the same across the period you compare.
• Put logins next to figures the landlord already trusts, such as your turnover.
• Share aggregate counts only. Your guest list is personal data; a landlord needs totals by week or month, not names or email addresses.

To pull the numbers, export the Analytics view as CSV for the date range you want, then chart it yourself.