How to measure your in-store data like your website

Published on
September 8, 2021
Thor Turrecha
EVP of Global SaaS
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If you are spending on digital signage content for your in-store campaigns, you’re most probably wondering whether your content is delivering the results you crave. Being able to gain in-store customer insights instantaneously used to be a difficult task, but using a camera as a sensor leveraging  computer vision technology can help you to see the data you need, exactly as you need it!

Savvy retailers are continuing to see the benefits of Artificial Intelligence and Computer Vision across both their online and in-store experiences. Creating great in-store customer experiences is more than just providing excellent customer service and great deals. Here are the 3 secrets behind the success of modern retail leaders. 

Getting to know your audience

Based on my research and conversations with retailers, many struggle with measuring how digital signage adds value to their business. Why can’t we track visitors in-store just like how we track websites?

Fortunately, modern digital displays are packed with smart software and cameras to track traffic in an establishment and can do more than just count visitors and buyers. Isn’t it also great to know if people watched your ad and for how long?

Using computer vision, you can capture a multitude of audience data anonymously, such as

  • Views and Dwell Time (how many viewed the content and how long they viewed it)
  • Time (time of day when content is viewed)
  • Demographics (gender, age, physical attributes of viewers)
  • Behavior In-store (i.e. browse, buy, etc.)

Armed with this knowledge, retailers can further tweak and optimize their content to increase the impact and are able to display optimized content that hits the ideal demographic at the ideal time, all anonymously.

Anonymized data

The application of this technology has raised privacy issues, but keep in mind that there is a difference between facial recognition vs. detection.

While face detection can characterize many attributes of a person, it is limited to building random ID or profiles without getting personal information, keeping it anonymous.

The bottom line is that no personal identification or tracking of individuals will be performed when implementing the system, especially in retail, and no images will be saved in the database.

Delivering personalized experiences

Now the rich data on your audience is a great groundwork for creating differentiated and personalized experiences for customers in-store.

With machine learning, data is stored for neural networks to emulate how the human brain would process it and create patterns, so that AI can make a recommendation - allowing you to deliver the right content, to the right person, at the right time. 

For instance, a 21-year-old male, who is wearing basketball shoes in front of your store was profiled. You can then trigger content on basketball apparel on your digital signage display. That way, you can invite him to your store or show targeted offers based on what you know about him.

Anonymous audience measurement is an important ingredient to provide contextual content to your audience. Relevant content makes them feel important, elevates their experience, and increases message recall.

Tracking in-store traffic in brick-and-mortar retail presents great benefits - if done right. But will many adapt and consider the benefits or will they just see it as too complicated? 

Article original posted as Tracking in-store traffic like your website

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