Phil Clemas
August 2021

Why Real-Time Data Is Critical

It seems a long time ago since our government imposed Covid-19 alert level 4, essentially consigning us all to stay at home for four weeks. Who would have thought such restrictions were going to be imposed just a fortnight earlier.

The impact on the Out-Of-Home (OOH) industry was immediate. We witnessed a considerable decline in traffic volume across our digital network and it is highly likely all OOH vendors have been dramatically affected in some way. We must not understate the amount of change that has occurred nor should we assume that all will return to normal.

The reality is we simply don’t know what the new normal for OOH will look like.

International travel will likely be disrupted for quite some time with domestic air travel also in for a slow recovery. Printed OOH formats may take time for advertiser confidence to return mainly due to longer lead times. Public transport and commuter services will be somewhat restrained by social distancing guidelines. However, retail OOH is well positioned to assist with the recovery as are roadside digital formats. Digital OOH has the added advantage of immediacy and flexibility. But the key question is, how can we verify the speed in which our roadside audiences return?

LUMO Digital Outdoor invested in building its own traffic-camera infrastructure across its digital media network from when it was established in 2016. Integrated with clever software, the network captures in real-time, vehicle speeds and volumes that travel towards and passes each of its screens. The software was developed by US company, AdMobilize and LUMO has partnered with them from the beginning.

During the lockdown period, LUMO has been in the fortunate position of being able to leverage that traffic data to update agencies and advertisers on the changes in its vehicular-based audience volumes and travel patterns by day.

How does it work?

In simple terms, whilst the camera network continuously streams live data, the traffic software uses an AI algorithm to identify individual vehicle movement heading towards each camera. It then captures speed of each vehicle and counts them as they travel over a cross-line detection zone.

The data offers an accurate, tech-based measure of vehicular traffic volumes using a methodology that is uncomplicated and transparent, no smoke and mirrors or tricky data modelling. LUMO’s traffic analytics platform simply records and reports only those road traffic volumes heading towards and in view of its screens, as they occur. To help illustrate this, I have inserted a GIF showing the technology in action.

Compared to other platforms, this solution doesn’t rely on mobile device data to determine a ‘modelled’ traffic total. Whilst there are clear benefits in the application of mobile data for OOH when modelled correctly, it lacks the granularity and functionality a camera-based solution has available to it, to accurately record traffic counts, at site level, in real time. This is by large due to an absence of device density (the number of devices that are opted in to app-based, location tracking), data recency (difficult to get location data playback in real time) and data fidelity (difficult to get sufficient volume when geofencing devices at an OOH site, and determine whether the device is actual facing the advertising screen).

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