Artificial Intelligence has been a hot topic since OpenAI's ChatGPT took the world by storm. Almost every industry has been shaken by this revolution and digital signage is at the top of the list.
Survey results indicate that 71% of consumers are not only impressed by digital billboard ads but are also more influenced by them than other forms of advertising. With an advertising medium that’s already highly effective, AI will only amplify its impact and open up new possibilities for marketers.
Understanding AI in Digital Signage
Definition of AI
Artificial intelligence involves teaching machines to think and act in ways that resemble human behavior. The intention is to develop systems that can intelligently pursue and achieve specific objectives much like humans do.
AI in digital signage uses intelligent technologies to enhance the functionality and effectiveness of digital display systems. AI can analyze viewer demographics, behavior, and engagement to tailor content in real-time to improve relevance and impact.
AI Technologies Used In Digital Signage
Machine Learning: Machine learning is a field of artificial intelligence where computers learn from data without being explicitly programmed. Machine learning algorithms are used to identify patterns in data, and then use those patterns to make predictions about new data.
There are many different types of machine learning algorithms, but they all share the common goal of learning from data to make predictions. Some of them include:
- Supervised learning: This is a type of machine learning where a computer program learns to make predictions or classifications based on labeled examples.
For example, in image recognition, the program might show pictures of cats and dogs, each labeled with the correct animal. The program analyzes the data and identifies patterns that distinguish cats from dogs.
It learns to associate certain features, like pointy ears or a wagging tail, with a specific label. Once trained, the program can be given new, unlabeled data (e.g., a new picture of an animal). It uses the learned patterns to predict whether the new animal is a cat or a dog.
- Unsupervised learning: In unsupervised learning, the computer program learns to find patterns and relationships in data without being given instructions or labeled examples.
For example in customer segmentation, the program might be given data about customers' purchase history, demographics, and browsing behavior. It will look for similarities, differences, or patterns, and then uncover hidden structures and relationships in the data that were previously unknown.
- Reinforcement learning: In reinforcement learning, a computer program, called an agent, learns to make decisions by interacting with an environment. The agent learns by, receiving rewards for good actions and penalties for bad ones.
For example, a machine learning model could be trained to identify images of dogs by rewarding the model when it correctly identifies a dog and penalizing the model when it makes a mistake.
Computer Vision: Computers can react much faster than humans to visual inputs, which means they are more accurate in identifying objects in images. Computer vision is a form of AI spearheading this technology through pattern recognition. Its algorithm is trained on a vast amount of image data which it then processes to identify the objects within them.
The accuracy of object identification has grown from 50% to 99% in recent years. The digital signage industry is adopting this technology in audience analysis through facial recognition and attention tracking.
Natural Language Processing: NPL involves the interaction between computers and human language. It allows computers to understand and respond to human language in a way that is similar to how humans communicate with each other. ChatGPT, which you most likely have interacted with is a language model based on natural language processing.
A digital signage application for NLP is an AI-powered voice recognition system. Digital signage equipped with microphones can capture voice input, which is then analyzed by AI algorithms to trigger specific actions, such as displaying relevant content, adjusting settings, or initiating a transaction.
AI-Powered Predictive Analytics: Predictive analytics models use historical and current data to forecast trends and behaviors seconds, days, or years into the future with a great deal of precision. In digital signage, predictive analytics can help optimize scheduling and content placement by predicting when and where specific audiences are most likely to be present.
How Marketers In Digital Signage Are Using AI
Media Buying
Any DOOH marketer before the early 2000s will tell you that media buying was a pain. It involved direct negotiations between advertisers and publishers, often through intermediaries like media agencies. The process was inefficient and lacked transparency. It wasn’t until the mid-2010s that AI automated decision-making on where and when ads are displayed, a process known as programmatic media buying.
This system uses real-time bidding (RTB) to purchase ad space. AI algorithms analyze and react to changes in audience composition such as audience characteristics and behaviors, time of day, and other contextual factors. As a result, ads are more precisely targeted and personalized for the right audiences.
Forecasting Sales
Retailers collect vast amounts of consumer data from every touch point. In digital signage, that could be from POS systems, QR codes, facial recognition software, mobile device interactions, and even social media interactions. Predictive analytics uses this wealth of information to forecast future sales with remarkable accuracy.
Once they analyze historical sales data, customer demographics, and external factors like seasonality and economic trends, predictive models can identify patterns and correlations that drive consumer behavior. Retailers can then anticipate demand fluctuations, optimize inventory levels, and tailor their marketing campaigns to specific customer segments.
Image Generation
When you hear about AI image generation, Dall-E or Midjourney are some tools that may come to mind, and the truth is, they’re shaking up the visual appeal of digital signage. Whatever image your mind can conjure up, turn it into a prompt and the AI will do the rest for you. Imagination is the limit, literally.
Improving Customer Experience
Just by giving them a great customer experience, 93% of consumers will make repeat purchases. Where AI comes into play with this is by enhancing the efficiency of digital signage in wayfinding, personalization, and interactive engagement. For starters, facial recognition technology can improve ad relevance by analyzing user demographics and emotions, and then pulling up relevant ads and recommendations to shoppers.
When it comes to wayfinding, agent-based simulations are used to model how people will navigate through an environment to identify the best places to place wayfinding signage. AI will then show the shortest routes to specific destinations and even take into account weather and accessibility.
Campaign Planning
Most, if not all campaigns that have moved entire audiences to action had a common denominator: creativity, something that AI is not capable of. With the help of AI algorithms that can create detailed customer profiles and identify customer preferences and purchase patterns, marketers can channel their efforts and creativity to come up with groundbreaking campaign ideas.
It’s important to keep in mind that the quality of the output will depend on the quality of the input. AI works with the data you feed it, therefore inaccurate or incomplete data can lead to misguided campaigns that miss the mark and result in wasted opportunities.
Challenges and Ethical Considerations
Privacy Concerns
AI is powered by consumer data. For this reason, around 60% of consumers express concern about how organizations apply and use AI today, and 65% have even lost trust in businesses over their AI practices.
AI algorithms can be complex and opaque which makes it difficult for users to understand how their data is being used to target them with personalized ads and recommendations. To add onto this, AI-powered marketing tools can track and monitor consumer behavior across various platforms which can feel invasive and infringe on individuals' privacy rights.
Accuracy and Bias
Studies have shown that the accuracy of AI use in marketing depends on the quality and representativeness of the data they are trained on. If the training data is biased, incomplete, or flawed, the resulting AI model will likely perpetuate those biases, leading to discriminatory outcomes and inaccurate predictions.
Even in the case the data is unbiased, AI algorithms can introduce their own biases due to the inherent limitations of the models themselves. For instance, if an algorithm is designed to optimize click-through rates, it may prioritize sensational or misleading content that generates more clicks, even if it is not accurate or relevant to the user.
Conclusion
We have discussed the profound impact of artificial intelligence (AI) on digital signage marketing and its potential to reshape the industry. AI provides marketers with data-driven insights that enable them to create more impactful and efficient campaigns.
Even as AI becomes more integrated into digital signage, it's important to address the ethical considerations surrounding data privacy, algorithmic biases, and data accuracy. Striking a balance between innovation and responsible use will be essential for building consumer trust and ensuring the continued success of AI in digital signage.
We can expect AI to further revolutionize digital signage with dynamic content adaptation, immersive experiences, and even more sophisticated audience targeting.
