E-Matura | Reading | Multiple Choice | B2 | Facial recognition in market research

Read the text from 2016 about how marketing uses new digital technologies. Then choose the correct answer (A, B, C or D) for each question (1-7). The first one (0) has been done for you.

Facial recognition in market research

One of the hottest areas of technology development in marketing research (MR) for 2016 is that of facial and emotion recognition. Understanding emotions is hugely powerful in MR but notoriously difficult to achieve. Facial expressions are strongly linked to emotions, and research organizations have used human observation of recorded videos to try to assess emotional response for many years. Human assessment has many limitations, however, and facial expression recognition technology offers an opportunity to overcome some of these limitations, delivering a much greater level of insight about personal sentiment and reactions.

According to research by Dr. Paul Ekman, a pioneer in the study of emotions and facial expressions and Professor Emeritus of Psychology at the University of California Medical School, brief flashes of emotion displayed on the respondent’s face – or “micro-expressions” – reveal a person’s beliefs and their propensity to act or buy.

The scope for this technology goes beyond pure research. Customer experience leaders have declared 2016 “The Year of Emotion,” continuing the trend for MR and Voice of the Customer (VoC) to become increasingly omplementary disciplines. This trend is also likely to fuel demand from enterprises who expect their MR providers to offer the most cutting-edge research technologies.

Emotions drive spending and loyalty. Organizations managing research programs and customer experience activities can use emotion detection technology to analyze people’s emotional reactions at the point of experience. This knowledge not only gives researchers a greater understanding of behavior patterns but also helps predict likely future actions of those consumers.

The result? An unprecedented level of insight into what affects customer emotions. Such valuable information can drive better business decisions, resulting in improved product and service offerings and experiences.

Marketing researchers are under increasing pressure to deliver real business value to their customers. Adding to that pressure, however, are the ongoing decline of survey response rates and challenges with collecting data from specific demographic groups. The challenge to find ways to complement panels, focus groups and surveys is on and emotion detection provides some real opportunities.

As with many leading-edge technologies, the range of applications out there is vast, but will start from relatively niche or specific beginnings. The primary use case for those researchers implementing emotional detection is advertisement testing. Within a survey, an advertisement can be shown during which the respondent’s webcam will record their reaction. Traditionally, respondents would answer questions about the advertisement, rating it on various scales. While broadly effective in most cases, results are dependent on the respondent’s ability to recall what they’ve just been shown, their interpretation of their own emotions, and their ability to put those emotions into words. Researchers can also observe and record emotions while the video content is being shown, but great skill is required and consistency is difficult to achieve.