Abstract
This chapter summarizes the role that artificial intelligence and machine learning (AI/ML) are expected to play at every stage of advertising development, assessment, and execution. Together with advances in neuroscience for measuring attention, cognitive processing, emotional response, and memory, AI/ML have advanced to a point where analytics can be used to identify variables that drive more effective advertising and predict enhanced performance. In addition, the cost of computation has declined, making platforms to apply these tools much less expensive and within reach. The authors then offer recommendations for 1) understanding the clients/customers and users of the products and services that will be advertised, 2) aiding creativity in the process of designing advertisements, 3) testing the impact of advertisements, and 4) identifying the optimum placement of advertisements.
TopIntroduction
This chapter explores the resources and climate for creating more impactful advertising through the potential use of Artificial Intelligence (AI) and Machine Learning (ML) in three parts:
- 1.
The Vision: Why is AI/ML important to advertisers? What might it be able to deliver now and, in the future, given what is known about the impact of advertising on brain and behavior?
- 2.
The Theory: What evidence do we have concerning the key variables that we can use to inform and optimize our advertising (measurable image and sales driving) and, in turn, predict performance? How are academics and marketers creating data bases and conducting meta-analyses to determine the most powerful drivers for implementing AI/ML?
- 3.
The ‘Know How’: What are the tools and methods of AI/ML that show the greatest potential for identifying drivers and predictors of advertising excellence?
Key Terms in this Chapter
Functional Near-Infrared Spectroscopy (fNIRS): Is a non-invasive imaging method involving the quantification of chromophore concentration resolved from the measurement of near infrared (NIR) light attenuation or temporal or phasic changes.
Facial Action Unit Coding (FAC): FAC refers to a set of facial muscle movements that correspond to a displayed emotion.
Supervised Learning (SL): The machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
Machine Learning: Is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Sentiment Analysis: The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine positive, negative, or neutral attitudes associated with test element.
Electroencephalography (EEG): EEG is a test used to evaluate the electrical activity in the brain. An EEG tracks and records brain wave patterns that are highly time locked to a stimulus.
Unsupervised Learning: A machine learning technique in which the users do not need to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data.
Emotion: A natural instinctive state of mind deriving from one's circumstances, mood, or relationships with others.
Electrodermal Activity (EDA): A psychophysiological measurement of skin moisture (i.e., sweat) that enhances electrical conductance across the skin. EDA can be measured in as a response to either external or internal stimuli and is believed to reflect how physiologically arousing the stimulus is perceived to be.
Consumer Neuroscience: Is the combination of consumer research with modern neuroscience. The goal of the field is to find neural explanations for consumer behaviors. Unlike traditional market measures which rely on conscious recall of past experience or deliberative beliefs regarding future behaviors, consumer neuroscience relies on in-moment measures of central and peripheral nervous system processes that often occur outside of conscious awareness.
Artificial Intelligence: The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Deep Learning: A subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.
Functional magnetic resonance imaging (fMRI): Functional magnetic resonance imaging or functional MRI (fMRI) measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases.
Cognitive Neuroscience: The scientific field that is concerned with the study of the biological processes and aspects that underlie cognition, with a specific focus on the neural connections in the brain which are involved in mental processes.