Electronic evaluation for video commercials by impression index

Consistent movement of viewers' facial keypoints while watching emotionally evocative videos

Neuropsychological research aims to unravel how diverse individuals' brains exhibit similar functionality when exposed to the same stimuli. The evocation of consistent responses when different subjects watch the same emotionally evocative stimulus has been observed through modalities like fMRI, EEG, physiological signals and facial expressions. We refer to the quantification of these shared consistent signals across subjects at each time instant across the temporal dimension as Consistent Response Measurement (CRM). CRM is widely explored through fMRI, occasionally with EEG, physiological signals and facial expressions using metrics like Inter-Subject Correlation (ISC). However, fMRI tools are expensive and constrained, while EEG and physiological signals are prone to facial artifacts and environmental conditions (such as temperature, humidity, and health condition of subjects). In this research, facial expression videos are used as a cost-effective and flexible alternative for CRM, minimally affected by external conditions. By employing computer vision-based automated facial keypoint tracking, a new metric similar to ISC, called the Average t-statistic, is introduced. Unlike existing facial expression-based methodologies that measure CRM of secondary indicators like inferred emotions, keypoint, and ICA-based features, the Average t-statistic is closely associated with the direct measurement of consistent facial muscle movement using the Facial Action Coding System (FACS). This is evidenced in DISFA dataset where the time-series of Average t-statistic has a high correlation (R2 = 0.78) with a metric called AU consistency, which directly measures facial muscle movement through FACS coding of video frames. The simplicity of recording facial expressions with the automated Average t-statistic expands the applications of CRM such as measuring engagement in online learning, customer interactions, etc., and diagnosing outliers in healthcare conditions like stroke, autism, depression, etc. To promote further research, we have made the code repository publicly available.

Radio, Podcasts, and Music Streaming-An Electroencephalography and Physiological Analysis of Listeners' Attitude, Attention, Memory, and Engagement

Whilst radio, podcasts, and music streaming are considered unique audio formats that offer brands different opportunities, limited research has explored this notion. This current study analyses how the brain responds to these formats and suggests that they offer different branding opportunities. Participants' engagement, attitude, attention, memory, and physiological arousal were measured while each audio format was consumed. The results revealed that music streaming elicited more positive attitudes, higher attention, greater levels of memory encoding, and increased physiological arousal compared to either radio or podcasts. This study emphasises the importance for brands of utilising diverse audio channels for unique branding and marketing opportunities.

DeePay: deep learning decodes EEG to predict consumer's willingness to pay for neuromarketing

There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers' subjective valuations and predict responses to marketing campaigns. However, the properties of EEG raise difficulties for these aims: small datasets, high dimensionality, elaborate manual feature extraction, intrinsic noise, and between-subject variations. We aimed to overcome these limitations by combining unique techniques of Deep Learning Networks (DLNs), while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DLN to predict subjects' willingness to pay (WTP) based on their EEG data. In each trial, 213 subjects observed a product's image, from 72 possible products, and then reported their WTP for the product. The DLN employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 0.276 test root-mean-square-error and 75.09% test accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Network visualizations provided the predictive frequencies of neural activity, their scalp distributions, and critical timepoints, shedding light on the neural mechanisms involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.

Do you like it or not? Identifying preference using an electroencephalogram during the viewing of short videos

Accurately predicting whether a short video will be liked by viewers is a topic of interest to media researchers. This study used an electroencephalogram (EEG) to record neural activity in 109 participants as they watched short videos (16 clips per person) to see which neural signals reflected viewers' preferences. The results showed that, compared with the short videos they disliked, individuals would experience positive emotions [indexed by a higher theta power, lower (beta - theta)/(beta + theta) score], more relaxed states (indexed by a lower beta power), lower levels of mental engagement and alertness [indexed by a lower beta/(alpha + theta) score], and devote more attention (indexed by lower alpha/theta) when watching short videos they liked. We further used artificial neural networks to classify the neural signals of different preferences induced by short videos. The classification accuracy was the highest when using data from bands over the whole brain, which was 75.78%. These results may indicate the potential of EEG measurement to evaluate the subjective preferences of individuals for short videos.

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

Introduction

The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking.

Methods

Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review.

Results

Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour.

Conclusions and implications

FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.

Consumer Behaviour to Be Considered in Advertising: A Systematic Analysis and Future Agenda

In the past decade, neurophysiological and physiological tools have been used to explore consumer behaviour toward advertising. The studies into brain processes (e.g., emotions, motivation, reward, attention, perception, and memory) toward advertising are scant, and remain unclear in the academic literature. To fill the gap in the literature, this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to extract relevant articles. It extracted and analysed 76 empirical articles from the Web of Science (WoS) database from 2009-2020. The findings revealed that the inferior frontal gyrus was associated with pleasure, while the middle temporal gyrus correlated with displeasure of advertising. Meanwhile, the right superior-temporal is related to high arousal and the right middle-frontal-gyrus is linked to low arousal toward advertisement campaigns. The right prefrontal-cortex (PFC) is correlated with withdrawal behaviour, and the left PFC is linked to approach behaviour. For the reward system, the ventral striatum has a main role in the reward system. It has also been found that perception is connected to the orbitofrontal cortex (OFC) and ventromedial (Vm) PFC. The study's findings provide a profound overview of the importance of brain processes such as emotional processes, reward, motivation, cognitive processes, and perception in advertising campaigns such as commercial, social initiative, and public health.

An EEG-Based Neuromarketing Approach for Analyzing the Preference of an Electric Car

This study evaluates consumer preference from the perspective of neuroscience when a choice is made among a number of cars, one of which is an electric car. Consumer neuroscience contributes to a systematic understanding of the underlying information processing and cognitions involved in choosing or preferring a product. This study aims to evaluate whether neural measures, which were implicitly extracted from brain activities, can be reliable or consistent with self-reported measures such as preference or liking. In an EEG-based experiment, the participants viewed images of automobiles and their specifications. Emotional and attentional stimuli and the participants' responses, in the form of decisions made, were meticulously distinguished and analyzed via signal processing techniques, statistical tests, and brain mapping tools. Long-range temporal correlations (LRTCs) were also calculated to investigate whether the preference of a product could affect the dynamic of neuronal fluctuations. Statistically significant spatiotemporal dynamical differences were then evaluated between those who select an electric car (which seemingly demands specific memory and long-term attention) and participants who choose other cars. The results showed increased PSD and central-parietal and central-frontal coherences at the alpha frequency band for those who selected the electric car. In addition, the findings showed the emergence of LRTCs or the ability of this group to integrate information over extended periods. Furthermore, the result of clustering subjects into two groups, using statistically significant discriminative EEG measures, was associated with the self-report data. The obtained results highlighted the promising role of intrinsically extracted measures on consumers' buying behavior.

Effectiveness of Electricity-Saving Communication Campaigns: Neurophysiological Approach

Public communication campaigns are among the tools for promoting electricity saving. A crucial task in the process of creating a campaign is to design a simple message to effectively reach the average consumer. It is a beneficial practice to create alternative messages and pretest them to find the most effective. The research methodology during pretesting includes both quantitative and qualitative methods. However, it is believed that the outcomes obtained with the use of conventional techniques are not fully reliable. Therefore, the following question arises: What additional research methods should be applied at the stage of testing the message of a communication campaign so that its effectiveness can be assessed more reliably and/or improved even before its broadcast? In this study, we aim to present the possibility of applying cognitive neuroscience methods in conjunction with a questionnaire to experimentally check the effectiveness of the message using the example of selected electricity-saving communication campaigns. The key results of this study indicate that merging conscious and subconscious reactions to media messages allows us to gain new knowledge that can be used in the future to improve the communication campaign effectiveness. Our investigation showed the benefits that can be obtained by synergizing traditional research methods with neuroscientific approaches.

Neuroimaging Techniques in Advertising Research: Main Applications, Development, and Brain Regions and Processes

Despite the advancement in neuroimaging tools, studies about using neuroimaging tools to study the impact of advertising on brain regions and processes are scant and remain unclear in academic literature. In this article, we have followed a literature review methodology and a bibliometric analysis to select empirical and review papers that employed neuroimaging tools in advertising campaigns and to understand the global research trends in the neuromarketing domain. We extracted and analyzed sixty-three articles from the Web of Science database to answer our study questions. We found four common neuroimaging techniques employed in advertising research. We also found that the orbitofrontal cortex (OFC), the ventromedial prefrontal cortex, and the dorsolateral prefrontal cortex play a vital role in decision-making processes. The OFC is linked to positive valence, and the lateral OFC and left dorsal anterior insula related in negative valence. In addition, the thalamus and primary visual area associated with the bottom-up attention system, whereas the top-down attention system connected to the dorsolateral prefrontal cortex, parietal cortex, and primary visual areas. For memory, the hippocampus is responsible for generating and processing memories. We hope that this study provides valuable insights about the main brain regions and processes of interest for advertising.

Cognitive Neuroscience Methods in Enhancing Health Literacy

The aim of the article is to identify the usefulness of cognitive neuroscience methods in assessing the effectiveness of social advertising and constructing messages referring to the generally understood health promotion, which is to contribute to the development of health awareness, and hence to health literacy. The presented research has also proven useful in the field of managing the processes that improve the communication between the organization and its environment. The researchers experimentally applied cognitive neuroscience methods, mainly EEG measurements, including a metric which is one of the most frequently used to measure the reception of advertising messages, i.e., frontal asymmetry. The purpose of the study was to test cognitive responses as expressed by neural indices (memorization, interest) to the reception of an advertisement for the construction of a hospice for adults. For comparative purposes, a questionnaire survey was also conducted. The research findings have confirmed that there are significant differences in remembering the advertisement in question by different groups of recipients (women/men). They also indicate a different level of interest in the advertisement, which may result from different preferences of the recipients concerning the nature of ads. The obtained results contribute to a better understanding of how to design advertising messages concerning health, so that they increase the awareness of the recipients’ responsibility for their own health and induce specific behavior patterns aimed at supporting health-related initiatives, e.g., donating funds for building hospices or performing preventive tests. In this respect, the study findings help improve the organizations’ communication with their environment, thus enhancing their performance. The study has also confirmed the potential and innovativeness of cognitive neuroscience methods as well as their considerable possibilities for application in this field.

Is EEG Suitable for Marketing Research? A Systematic Review

Background: In the past decade, marketing studies have greatly benefited from the adoption of neuroscience techniques to explore conscious and unconscious drivers of consumer behavior. Electroencephalography (EEG) is one of the most frequently applied neuroscientific techniques for marketing studies, thanks to its low cost and high temporal resolution. Objective: We present an overview of EEG applications in consumer neuroscience. The aim of this review is to facilitate future research and to highlight reliable approaches for deriving research and managerial implications. Method: We conducted a systematic review by querying five databases for the titles of articles published up to June 2020 with the terms [EEG] AND [neuromarketing] OR [consumer neuroscience]. Results: We screened 264 abstracts and analyzed 113 articles, classified based on research topics (e.g., product characteristics, pricing, advertising attention and memorization, rational, and emotional messages) and characteristics of the experimental design (tasks, stimuli, participants, additional techniques). Conclusions: This review highlights the main applications of EEG to consumer neuroscience research and suggests several ways EEG technique can complement traditional experimental paradigms. Further research areas, including consumer profiling and social consumer neuroscience, have not been sufficiently explored yet and would benefit from EEG techniques to address unanswered questions.

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.

Investigating the Optimal Baseline Positioning to Maximize Cognitive Experimental Outcome

In emotional and cognitive research, the baseline is commonly used for standardization purposes in order to have a reference for the identification of the activation. Since no previous studies have investigated which moment of the experiment could be considered optimal for baseline evaluation, we designed an experimental protocol to analyze which time interval could be considered more effective in highlighting differences between the baseline state and the cognitive effort exhibited during tasks (specifically, reaction and working memory tasks). Several indexes were extracted from EEG signals during the visualization of the considered baseline stimuli and the execution of tasks. From our results, as regards to the considered Global Field Power (GFP) indexes (Attention and Memorization indexes), the last baseline stimulus seems to be the best one to highlight the difference in cognitive workload between the individual baseline condition and the two cognitive tasks. Instead, in terms of Engagement index (EI), the difference between Reaction Task (RT) and the individual baseline condition seems to be best highlighted by the relaxing video right after performed tasks. In conclusion, the best baseline position to maximize the differences in cognitive workload may vary among the considered indexes because of confounding effects and individual differences, but further analyses are required to validate this result.

Consumer Neuroscience and Digital/Social Media Health/Social Cause Advertisement Effectiveness

This research investigated the use of consumer neuroscience to improve and determine the effectiveness of action/emotion-based public health and social cause (HSC) advertisements. Action-based advertisements ask individuals to 'do something' such as 'act', 'share', make a 'pledge' or complete a 'challenge' on behalf of a brand, such as doing 'something good, somewhere, for someone else'. Public health messages as noncommercial advertisements attempt to positively change behavioural intent or increase awareness. Australian health expenditure was $180.7 billion AUD (Australian dollars) in 2016/17 with $17 million AUD spent on government health campaigns. However, evaluation of health advertisement effectiveness has been difficult to determine. Few studies use neuroscience techniques with traditional market research methods. A 2-part study with an exploratory design was conducted using (1) electroencephalography (EEG) using a 64 channel EEG wet cap (n = 47); and (2) a Qualtrics online psychometric survey (n = 256). Participants were asked to make a donation before and after viewing 7 HSC digital/social media advertisements and logos (6 action/emotion-based; 1 control) to measure changes in behavioural intent. Attention is considered a key factor in determining advertising effectiveness. EEG results showed theta synchronisation (increase)/alpha desynchronisation (decrease) indicating attention with episodic memory encoding. sLORETA results displayed approach responses to action/emotion-based advertisements with left prefrontal and right parietal cortex activation. EEG and survey results showed the greatest liking for the ManUp action/emotion-based advertisement which used male facial expressions of raw emotion and vulnerability. ManUp also had the highest increased amount donated after viewing. Lower theta amplitude results for the International Fund for Animal Welfare (IFAW) action/emotion-based advertisement indicated that novel (possessing distinct features) rather than attractive/conventional faces were more appealing, while the rapid presentation of faces was less effective. None of the highest peak amplitudes for each ad occurred when viewing brand logos within the advertisement. This research contributes to the academic consumer neuroscience, advertising effectiveness, and social media literature with the use of action/challenge/emotion-based marketing strategies, which remains limited, while demonstrating the value in combining EEG and neuroscientific techniques with traditional market research methods. The research provides a greater understanding of advertising effectiveness and changes in behavioural intent with managerial implications regarding the effective use of action/challenge/emotion-based HSC communications to potentially help save a life and reduce expenditure on ineffectual HSC marketing campaigns.

Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement

Television (TV) programming attracts ever-growing audiences and dominates the cultural zeitgeist. Viewership and social media engagement have become standard indices of programming success. However, accurately predicting individual episode success or future show performance using traditional metrics remains a challenge. Here we examine whether TV viewership and Twitter activity can be predicted using electroencephalography (EEG) measures, which are less affected by reporting biases and which are commonly associated with different cognitive processes. 331 participants watched an hour-long episode from one of nine prime-time shows (~36 participants per episode). Three frequency-based measures were extracted: fronto-central alpha/beta asymmetry (indexing approach motivation), fronto-central alpha/theta power (indexing attention), and fronto-central theta/gamma power (indexing memory processing). All three EEG measures and the composite EEG score significantly correlated across episode segments with the two behavioral measures of TV viewership and Twitter volume. EEG measures explained more variance than either of the behavioral metrics and mediated the relationship between the two. Attentional focus was integral for both audience retention and Twitter activity, while emotional motivation was specifically linked with social engagement and program segments with high TV viewership. These findings highlight the viability of using EEG measures to predict success of TV programming and identify cognitive processes that contribute to audience engagement with television shows.

When a new technological product launching fails: A multi-method approach of facial recognition and E-WOM sentiment analysis

The dual aim of this research is, firstly, to analyze the physiological and unconscious emotional response of consumers to a new technological product and, secondly, link this emotional response to consumer conscious verbal reports of positive and negative product perceptions. In order to do this, biometrics and self-reported measures of emotional response are combined. On the one hand, a neuromarketing experiment based on the facial recognition of emotions of 10 subjects, when physical attributes and economic information of a technological product are exposed, shows the prevalence of the ambivalent emotion of surprise. On the other hand, a nethnographic qualitative approach of sentiment analysis of 67-user online comments characterise the valence of this emotion as mainly negative in the case and context studied. Theoretical, practical and methodological contributions are anticipated from this paper. From a theoretical point of view this proposal contributes valuable information to the product design process, to an effective development of the marketing mix variables of price and promotion, and to a successful selection of the target market. From a practical point of view, the approach employed in the case study on the product Google Glass provides empirical evidence useful in the decision making process for this and other technological enterprises launching a new product. And from a methodological point of view, the usefulness of integrated neuromarketing-eWOM analysis could contribute to the proliferation of this tandem in marketing research.

Global Electroencephalography Synchronization as a New Indicator for Tracking Emotional Changes of a Group of Individuals during Video Watching

In the present study, we investigated whether global electroencephalography (EEG) synchronization can be a new promising index for tracking emotional arousal changes of a group of individuals during video watching. Global field synchronization (GFS), an index known to correlate with human cognitive processes, was evaluated; this index quantified the global temporal synchronization among multichannel EEG data recorded from a group of participants (n = 25) during the plays of two short video clips. The two video clips were each about 5 min long and were designed to evoke negative (fearful) or positive (happy) emotion, respectively. Another group of participants (n = 37) was asked to select the two most emotionally arousing (most touching or most fearful) scenes in each clip. The results of these questionnaire surveys were used as the ground-truth to evaluate whether the GFS could detect emotional highlights of both video clips. The emotional highlights estimated using the grand-averaged GFS waveforms of the first group were also compared with those evaluated from galvanic skin response, photoplethysmography, and multimedia content analysis, which are conventional methods used to estimate temporal changes in emotional arousal during video plays. From our results, we found that beta-band GFS values decreased during high emotional arousal, regardless of the type of emotional stimulus. Moreover, the emotional highlights estimated using the GFS waveforms coincided best with those found by the questionnaire surveys. These findings suggest that GFS might be applicable as a new index for tracking emotional arousal changes of a group of individuals during video watching, and is likely to be used to evaluate or edit movies, TV commercials, and other broadcast products.

A Dynamic Programming Model for Operation Decision-Making in Bicycle Sharing Systems under a Sustainable Development Perspective

Maintaining a balance between revenue and expenditure is the key to the sustainable development of a bicycle sharing system (BSS), and is a challenge for almost all systems worldwide. This article proposes a dynamic programming approach to obtain the optimal strategy to maximize the revenue of overall BSS. The Variable Granularity-Depth First Search (VG-DFS) algorithm is designed to speed up the solution. A numerical experiment is presented to verify the applicability of the model through a comparison with real data from the BSS in Hangzhou. Results indicated that the BSS could achieve break-even, or even obtain a substantial income by utilizing our model to make operational decisions, especially when the region it is located in has a relatively high GDP. Moreover, the best investment strategy proved is to involve stations in the initial construction period of the BSS as much as possible.

Prediction of subjective ratings of emotional pictures by EEG features

OBJECTIVE

Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli.

APPROACH

To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings.

MAIN RESULTS

Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization.

SIGNIFICANCE

The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.

Event-related potentials elicited by social commerce and electronic-commerce reviews

There is an increasing interest regarding the use of electroencephalography (EEG) in social commerce and electronic commerce (e-commerce) research. There are several reviews in the field of social commerce or e-commerce; these have great potential value and mining them is fundamental and significant. To our knowledge, EEG is rarely applied to study these. In this study, we examined the neural correlates of social commerce reviews (SCRs) and e-commerce reviews (ECRs) by using them as stimuli to evoke event-related potentials. All SCRs were from friends through a social media platform, whereas ECRs were from strangers through an e-commerce platform. The experimental design was similar to that of a priming paradigm, and included 40 pairs of stimuli consisting of product information (prime stimulus) and reviews (target stimulus). The results showed that the P300 component was successfully evoked by SCR and ECR stimuli. Moreover, the P300 components elicited by SCRs had higher amplitudes than those elicited by ECRs. These findings indicate that participants paid more attention to SCRs than to ECRs. In addition, the associations between neural responses and reviews in social commerce have the potential to assist companies in studying consumer behaviors, thus permitting them to enhance their social commerce strategies.

From the eyes and the heart: a novel eye-gaze metric that predicts video preferences of a large audience

Eye-tracking has been extensively used to quantify audience preferences in the context of marketing and advertising research, primarily in methodologies involving static images or stimuli (i.e., advertising, shelf testing, and website usability). However, these methodologies do not generalize to narrative-based video stimuli where a specific storyline is meant to be communicated to the audience. In this paper, a novel metric based on eye-gaze dispersion (both within and across viewings) that quantifies the impact of narrative-based video stimuli to the preferences of large audiences is presented. The metric is validated in predicting the performance of video advertisements aired during the 2014 Super Bowl final. In particular, the metric is shown to explain 70% of the variance in likeability scores of the 2014 Super Bowl ads as measured by the USA TODAY Ad-Meter. In addition, by comparing the proposed metric with Heart Rate Variability (HRV) indices, we have associated the metric with biological processes relating to attention allocation. The underlying idea behind the proposed metric suggests a shift in perspective when it comes to evaluating narrative-based video stimuli. In particular, it suggests that audience preferences on video are modulated by the level of viewers lack of attention allocation. The proposed metric can be calculated on any narrative-based video stimuli (i.e., movie, narrative content, emotional content, etc.), and thus has the potential to facilitate the use of such stimuli in several contexts: prediction of audience preferences of movies, quantitative assessment of entertainment pieces, prediction of the impact of movie trailers, identification of group, and individual differences in the study of attention-deficit disorders, and the study of desensitization to media violence.