Three Key Benefits of Adding
Non-Verbal Responses

Uncovering Hidden Emotions

Market research respondents often don't reveal their emotions verbally. This is sometimes when they judge the emotion to be inappropriate, or simply plain embarrassing.
But in a more profound way, fleeting emotions can come and go without the person being aware, not even unable to put it into words.

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Capturing System 1 Responses

By capturing facial expressions and body language on first exposure to an image or statement we are able to capture those fleeting, involuntary signs of emotion.

Generating Quantitative Metrics

One of the limitations of qualitative research, by definition, is the inability to put numbers on responses to give, at the least, an indication of volume of, say, approval of a concept. Even with a sample of 30 the numbers can be meaningful.  Showing or not showing an emotion is an important metric (see chart for an example).

Facial Expression Analysis

Based on hundreds of interviews categorising subtle emotions, our facial expression analysis help identify those emotions relevant to commercial projects and your products or service.

Body Language Interpretation

We capture and analyse not just changes in facial expressions, but crucially changes in body language.

Interpret Using Context

One reason why machine learning facial coding does not work well is that interpreting expressions without any context is unreliable. When we test communications often there is a maximum of six emotions we look for, each making sense in its context.

Some Applications

Where Non-Verbal Responses Are Especially Helpful

TV and Online Video Ads

We can track the emotional response while a viewer is watching an ad second-by-second. We can identify a range of subtle emotions, but many ads don’t aim to provoke emotions like humour or sadness. The aim is to keep the viewer engaged, or even better provoke Active Engagement when we see movement and signs of thinking (leaning forward, or tilting the head). It’s impossible to be really interested in something and not show it through bodily energy being activated. A key metric for success is whether the ad kept the attention over 30 or 60 seconds. Our measure of Disengagement (when the viewer looks away from the screen, even for less than a second) is critical in measuring the success of an ad.

Packaging

When testing mock-ups of packaging, body language provides invaluable clues as to how consumers will respond to the different packaging in the real world. We can observe how the person handles the different packaging options. Do they look closely at the object for more than a second or two? How close do they hold it to their body? Is it held in both hands, or at a distance with one hand? When they place it down is it nearby within eye shot, or further away out of sight? These bodily responses, done nonconsciously, tell us much more about how attractive an object is to the consumer. If it’s a case of unboxing we can observe the process, noting surprise, pleasure, frustration and even anticipation.

Marketing Communications

A key tool in testing marketing material is Active Engagement. When shown for 2 or 3 seconds, does the image or proposition grab attention? We can very quickly test multiple versions of taglines, or logos, in this way. Do they catch the eye, or more accurately catch the nonconscious brain. More questions will then tell us why they were eye-catching and engaging.  In the real world where a brand must grab attention, this test is very valuable. We can get a positive, considered response in a discussion, but will it work out on the supermarket shelves or on the website?
We are not restricted to finished art work in non-verbal testing.  It’s helpful to disaggregate a static ad, for example, to test separate images and words at an early stage in the creative development.

UX Testing

When a consumer is clicking around  a web page we can pick up fleeting emotions at each second of the journey – from moments of puzzlement, interest, even uncertainty and intrigue. These feelings are too subtle and fleeting to be accessed by the respondent and related to us verbally as the respondent navigates the website. Tracking the emotional  ‘journey’ on a website enables us to identify precisely those fleeting moments where the user is puzzled, uncertain or conversely show pleasure and satisfaction in successful navigation.

Strengths of Non-Verbal Data

Granular

The words which respondents utter are only part of what they are telling us.  Below lies the rich collection of non-verbal responses generated by the automatic brain.  Facial expressions and body language reveal the hidden emotions which can be combined with verbal information to produce a fuller picture.

Truthful

Microexpressions, those fleeting emotions, often on the face for less than a second, are involuntary and cannot be controlled. This is quite different to ‘socially directed’ expressions, which are slower and designed to communicate an emotion to others. The face and body reveal emotions which the respondent might wish to conceal. In some cases these are emotions which the respondent might be unaware of, or ones she finds uncomfortable.

Predictive

The fleeting involuntary emotional responses shown in our facial expressions and body language are not influenced by the research setting, or by other respondents. These are exactly the same responses which will be activated out in the supermarket or any other location where choices are being made. Tapping into their automatic brain is arguably more reliable than gaining a considered verbal response only.

Supported by hundreds of hours of analysis and a comprehensive empirical study of emotions

We conducted a systematic trial over the course of a year to identify the 55 subtle and mixed emotions.  We used material designed to arouse a whole range of emotional responses, from positive emotions, to cognitive and mind/body split. To elicit strong negative emotions we read out a list of the most stressful life events and asked the respondents to describe the experience of divorce, bereavement etc. The trial underlined the importance of knowing the context when coding emotions. Knowing how the respondent is likely to react is a key advantage in interpreting signs. Studies have shown that an emotion out of context can be hard to identify (which is one of the weaknesses of machine learning technologies).

Valid

Responses tested by results scrutinised by clients, showing a very high level of agreement about the interpretation of non-verbal signals.

Reliable

Multiple instances showing the same results over time - unike conventional qualitative research

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