According to a study conducted by the marketing consultancy Nielsen, the use of Deep Learning in digital signage increases sales. In fact, consumers who were exposed to personalized ads thanks to Deep Learning were up to 50% more likely to buy the products shown than those who were not exposed to personalized ads.

 

 

But what is Deep Learning?

 

It's a form of artificial intelligence based on a neural network model. What's unique about Deep Learning is that its networks are made up of dozens or even hundreds of layers of neurons. The more layers of neurons there are in the network, the "deeper" it is considered to be, and therefore the more efficient it is if sufficiently trained.

 

No, you don't train a neural network like you train an athlete for the Olympics. Although ...

 

So how and why must a neural network be trained to be viable and reliable?

 

 

Let's take an example from image recognition. We want our neural network to be able to recognize a dog in a photo. For this to be the case, it needs to be able to recognize all types of dog, whatever their breed, color, size, hair length, lighting, angle...

 

We're going to train this network using tens of thousands of dog photos, each of which must be different to ensure that the network is as accurate as possible. As the training progresses, the network will have collected so much dog recognition data that it will be able to recognize a dog in any other image.

 

What is Deep Learning used for?

 

- Image recognition,

- Image classification,

- Voice recognition,

- Language processing,

- Robotics,

- Cybersecurity,

- Bioinformatics,

- Medical diagnostics,

- Assisted driving ...

 

How can Deep Learning optimize digital signage?

 

Thanks to Deep Learning, digital OOH advertising is becoming more precise and sharper when it comes to targeting campaigns. Indeed, several metrics (attention time, traffic, gender, age...) are taken into account, such as capturing attention time by gender and age, in order to assess the effectiveness and relevance of the campaign.

 

With Deep Learning, the display solution can adapt content accordingly if it proves irrelevant, or on the contrary, if it proves highly effective. This data is then stored and reused to optimize and perfect subsequent campaigns.

 

This solution can also be of interest to your CRM. Indeed, your CRM categorizes your customers according to their demographic characteristics, which can help you target your campaigns (only on your digital channels). By using a Deep Learning solution for your in-store digital signage, you can take advantage of this same segmentation and targeting directly on your physical shops.

 

What's more, this form of AI opens up a wide range of possibilities. As explained above, all you need to do is train the neural network properly for a given scenario, so that it can produce the desired responses and expected results. It's a highly effective tool for personalizing your campaigns. This solution can be continuously optimized, regardless of demand, needs or objectives.

 

Finally, by associating the right CMS with Deep Learning, you can remotely manage (add visuals, determine the duration and target of your campaign, read and analyze metrics...) all your campaigns, whatever the size of your screen park and their locations.

 

In conclusion, combining digital signage, audience targeting and Deep Learning is the best solution for optimizing your POS campaigns and making them profitable:

 

- Make sure your campaigns are properly targeted

- The solution adapts to your needs and objectives

- Real-time tracking of results and performance

- Adapt and optimize campaigns according to audience behavior, thanks to metrics analysis

 

If metrics are poor, act quickly to revise your campaign message and adapt your strategy remotely and at the snap of a finger (deactivate the irrelevant campaign and quickly replace it with the new one).