Applying analytics to physical spaces is quite challenging compared with online. While it is a breeze to collect almost unlimited data points in online stores by using cookies, it is a huge challenge in brick-and-mortar. Unfortunately, there is nothing comparable with an offline cookie today. However, various technologies and software solutions enable retailers and digital signage integrators to collect valuable insights about shopper behaviour. And to determine the ROI of digital signage, some form of analytics is mandatory.

Contrary to popular belief, (retail) analytics is not limited to brick-and-mortar retail but increasingly also implemented in enterprise environments (meeting room and open space desk occupancy), smart city applications (sensors in roadside totems, e.g. LinkNYC) and in transportation (passenger flow, queuing time applications).

The emergence of IoT solutions has been a tremendous boost for analytics as hardware (sensors) have become a commodity and on-device connectivity makes it simple to implement. But the real driver for analytics is artificial intelligence. For the first time analytics has also become affordable for SMB, as AI platforms enable customers and integrators to manage and analyse millions of data points with the push of a few buttons.

First generation analytics was primarily based on proprietary surveillance camera solutions and was excessively expensive. Only top-of-the line CCTV-cameras were able to support analytics solutions. Today CCTV cameras are rarely used in new analytics projects as high performance 2D and 3D sensors are available for a fraction of the price. Microsoft Kinect was one of the first mainstream solutions enabling analytics for the masses. The Redmond-based company stopped offering Kinect two years ago, but a new generation of Kinect sensors has already been announced. In the interim Intel launched its Realsense sensor solution and commoditised the 3D sensor market (street prices starting at US$150).

In addition, many new sensor players have entered the market. Some of the solutions are quite smart. e.g. sensors with built-in computing power which directly plug into light rails for power supply and mounting. Sweden-based Modcam is one of these providers.

But cameras and sensors (2D/3D) are not the only tools available for analytics. Wi-Fi and other radio tracking solutions are very attractive at first sight. Tracking mobile phones seems to be the most efficient approach; unfortunately, retail reality shows that any mobile tracking-based solution lacks precision mostly due to legal/privacy restrictions. Most analytics concepts still integrate some form of Wi-Fi tracking, but seldom as the only analytics technology. Especially since Apple started changing the MAC address regularly on iOS devices, it has become impossible to track returning customers via mobile devices. Google has just announced that Android devices will soon also hide their identity by regularly changing MAC addresses.

But the challenges of analytics are not so much about technology and more about solution-agnostic concepts and customised data analytics. What can be measured and, even more importantly, what kinds of insights are customers able to obtain? The most popular use-cases are:


  • The most popular metric is footfall: how many of the shoppers passing by the entrance enter the store?
  • Next most popular is shopper-to-buyer conversion rate: how many of the shoppers made a purchase?


  • Occupancy of meeting rooms
  • Average usage time / staff in the room


  • Passenger flow
  • Queuing time

But taking analytics further by analysing multiple points along the customer journey is where clients usually obtain the best insights. That requires collecting data points not only with sensors but also at digital touchpoints (e.g. interactions at kiosk terminals) and online/mobile. Connecting these insights along the customer journey is essential for today’s customer centric businesses. The goal for most businesses is to connect detailed online shopper profiles with typical personas’ in-store behaviours. The ideal is to identify the customer, but this is not legal anymore in many countries around the world. Nowadays the limit of analytics is often not technology but legal restrictions (e.g. GDPR in Europe) and cultural acceptance. Camera-based face recognition may be fully accepted in China, but it is a no-go in many Western societies.

But even with these limits, analytics offers incredible insights if done right. Most analytics concepts focus mainly on understanding historic customer behaviour. More advanced concepts focus on supporting businesses in real time. For example, travel retailers at airports use sensors to detect real-time passenger flow, enabling them to open enough check-outs before customers start queuing. The latest AI-based concepts even support businesses in using analytics to improve new store designs or airport layouts before they are built.

Turning Data into Dollars is not rocket science. Standard analytics use-cases are easy to implement. Especially interesting for the digital signage integrator is to prove to clients the effectiveness of visual touchpoints and content via analytics. Enabling integrators to answer one of the most frequently asked questions: What is the ROI of digital signage?

In the long tail, analytics can offer actionable insights that enable physical stores to better compete against online retailers, boost real-time usage of meeting rooms in enterprise organisations and improve passenger flow and reduce queuing time for users of transportation facilities.

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