Harnessing 5 Cutting-Edge Emotion AI Tech to Use with FACS For Market Research

Decoding and harnessing emotional insights of consumers has been a dominant goal for market researchers, and yet somehow, has evaded their grasp despite multiple efforts. However, recent advancements in AI offer a definitive solution. With emotion AI tools like FACS and cutting edge emotion AI tech like facial and vocal analysis, researchers can extract new layers of data and use them to drive better decisions at the stakeholder level.

With emotion AI, every word spoken, every facial expression is an ocean of insights that unlocks new levels of business intelligence. However, one needs to know what AI technologies to use to be able to extract these insights in the first place. And to be honest, it depends on the medium of research and the type of responses the user gives, but it is usually a basket of these 5 emotion AI technologies coupled with FACS that gives feasible results in most cases. 

With this blog, we’re trying to answer some key questions like-

Can the subtle nuances of audio responses reveal consumer sentiment?

How can emotion AI help in differentiating between genuine and non-genuine responses?

How can modular surveys adapt to unveil deeper layers of consumer insight?

And the only way to do this is to unearth the hidden utilitarian traits of these 5 cutting edge AI technologies alongside FACS. So let’s get started.

Exploring Synergies: FACS and Emotion AI Technologies

Although FACS is a potent AI subset and is capable of extracting valuable insights on its own, clubbing it with other emotion AI technologies during research increases its efficacy exponentially. Here are top 5 emotion AI technologies to use with FACS.

1. Eye Tracking

   Eye tracking uses a camera to identify the eyeballs of a subject and traces their movement across the display to generate attention heatmaps. These heatmaps help identify areas that attract maximum attention and those that attract none. However, attention heatmaps generated through eye tracking have a major flaw. You see, all the elements on a website or a product packaging have a specific purpose, to elicit a specific emotion and influence the decisions of consumers, hence boosting conversions. You can read more about how emotion AI can be used for market research here.

   But how does one verify whether or not the element drives the desired emotions when the consumer interacts with it?

   There’s no definitive way of doing this using eye-tracking alone, and that’s where FACS steps in. By tracking where consumers look when presented with stimuli such as advertisements, product packaging, or websites, eye tracking reveals patterns of visual attention. When combined with FACS, which analyses facial expressions, researchers can correlate where participants are looking with their emotional reactions. For example, if a participant’s gaze is drawn to a specific product feature and their facial expressions indicate interest or surprise, it suggests that the feature resonates positively with them.

2. Voice Transcription with FACS

   Voice or Speech Transcription is used in surveys or interviews involving vocal responses from the participant. It refers to converting speech into text without any manual inputs. It capitalises on the high processing power of modern computers and uses a speech recognition system to analyse the text. In other words, the model passes the audio through advanced speech recognition algorithms to identify and interpret the spoken words. This involves breaking down the audio signal into smaller segments and analysing them for patterns that correspond to phonemes, the smallest units of sound in a language.

   To improve accuracy, the speech recognition system incorporates language models that help predict the most likely sequence of words based on the audio input. These language models are trained on mountains of textual data to understand the syntax, grammar, and vocabulary of a language.

   Researchers can use voice transcription with FACS and gain access to some astonishingly valuable insights. When you take the text generated through voice transcription and map the emotional density received through FACS onto that text, you get a text transcription that is incapable of dishonesty. Different parts of the text will have different colours, with each colour representing a certain emotion. This helps researchers identify whether or not a response was genuine.

3. Tonal Analysis

   Although tonal analysis may have a similar ring, it’s not the same as voice transcription. Where voice or speech transcription is a way of converting speech to text using advanced speech recognition, tonal analysis is a way of extracting the emotional profile of a person using only his/her voice. It involves analysing the pitch, rhythm, intonation, and other acoustic features of speech to infer the speaker’s emotional state or attitude.

   It examines variations in pitch, like “highness” or “lowness” of a sound, the rhythm and intonation of speech, the pattern of stressed and unstressed syllables, and the rise and fall of pitch contours. Tonal analysis algorithms are trained to recognize patterns in speech that are associated with specific emotions, such as happiness, anger, sadness, or neutrality. These algorithms use machine learning models to analyse acoustic features and classify speech segments into different emotional categories.

   Researchers can use the results from tonal analysis and correlate them with those obtained from FACS to verify the emotional heatmap.

4. Text Sentiment Analysis with FACS

   Text Sentiment Analysis, also referred to as Opinion Mining, is a language processing technique that analyses text data to uncover the sentiment or tone expressed in it. It serves a similar purpose to tonal analysis, but instead of analysing the voice or speech, it extracts emotional insights using just the text. It is more suited for research avenues where the medium of delivery is text.

   The goal with text sentiment analysis is to classify text excerpts (such as reviews, social media posts, survey responses, etc.) as expressing positive, negative, or neutral sentiments. This is done by passing the data through steps such as tokenization (breaking text into individual words or phrases), removing stop words (commonly occurring words that typically do not carry significant meaning, such as “and,” “the,” “is”), and stemming or lemmatization (reducing words to their root forms). After which the machine learning models analyse the text and assign a sentiment value to them.

   Heatmaps from both text sentiment analysis and FACS can be used simultaneously to reaffirm areas of positive feedback from respondents.

5. Modular Surveys

   Modular surveys are a flexible way of gathering data through surveys by facilitating customisable questionnaires and modules based on the characteristics of the respondents, previous responses, or specific research goals. Using AI to design modular surveys helps researchers automate the process and focus on more crucial tasks like insights analysis.

   AI algorithms analyse demographic data, previous survey responses, and other relevant data to dynamically create customised survey modules tailored to individual respondents or segments. These modular surveys adapt in real-time based on respondent characteristics to make sure that the questions are relevant.

   Modular surveys combined with FACS can help researchers with longitudinal analysis by tracking shifts in emotional engagement, brand perception, or product preferences across multiple survey waves, allowing for deeper insights into consumer behaviour dynamics and market trends. This longitudinal perspective enables businesses to adapt their strategies proactively and stay ahead of evolving consumer needs.

Conclusion

Using emotion AI for market research offers multiple benefits like automating manual tasks, tapping into greater efficiency, higher speeds, more accuracy, and richer insights thanks to the emotional data that helps researchers eliminate dishonest responses from the data. This helps drive better decisions at the stakeholder level and at the same time makes the overall market research process more efficient.

Although these tools may be used discretely and researchers can combine them based on their needs, a smart way to incorporate these technologies in both qualitative and quantitative research would be to use Insights Pro, our premier product, that helps users tao into the unmatched potential of emotion AI without having to manually pick, what parts of the technology to use. We have separate tools for both qualitative and quantitative research that can be used for analysing both live and recorded interactions. It automatically picks the right group of technologies based on the mode of survey and deploys them for the best possible results.