Emotional
testing, neuroscience and community based measurement are expanding the
boundaries of traditional research by offering new, insightful tools to predict
audience and consumer behavior. A particularly interesting example is the work
being done by Emotient which is a San Diego based research company that has
been operating in the emotional measurement space for about three years. Their position
is that emotions drive spending and that by focusing on facial recognition and
registering an emotion via a facial expression, it is possible to predict the
strength of any creative such as an advertising message or even a political
candidate.
There are
several companies using biometrics to help marketers ascertain the non-verbal
and unstated impact of their content. Tools like Galvanic Skin Response (GSR)
which measures changes in the skin and Functional Magnetic Resonance Imaging
(FMRI) which measures brain activity through the blood flow, as touted as good
methods to measure subconscious consumer response. But according to Marian
Bartlett, Ph.D., a Co-Founder and Lead Scientist of Emotient, GSR’s are noisy
and slow while FMRIs are slow and cumbersome.
Emotient’s
IP has the ability to capture hundreds of facial expressions at one time in a
non-intrusive manner which, they say, offers the capability to amass big data
by individuals’ gender and age. There are nine basic emotions, each offering an
insight into the desirability (or lack thereof) for the messaging and creative,
and twenty more facial muscle movements that can be captured. And, with
technology, facial expressions can be captured anywhere and, once opted-in,
passively, not requiring a visit to a laboratory and donning a gel cap or
wearing a measurement vest. Imagine gathering data via a tablet or smartphone
or in a car with a sensor pointed at the driver.
The result
is an ability to scale and to create a range of baselines based on creative or product
type, gender and age, for example. Joshua Susskind, Emotient’s Co-Founder, Lead
Deep Learning Scientist, explained that via their methodology, P&G’s Tide detergent
was able to more accurately predict intent to purchase across three different
types of detergent fragrances using facial recognition while the survey the
client fielded at the same time, could not.
One of the
more interesting applications of Emotient’s methodology was in predicting the
winner of the first Republican debate on Fox News. According to the measurement
of audience reactions through the debate, Emotient projected that Donald Trump
would be, by far, the winner. This was based on a preponderance of facial
expressions of “Joy” for Trump while the other contenders elicited expressions
of “Anger” for Scott Walker and “Disgust” for Chris Christie and Rand Paul.
This
certainly makes for some compelling arguments for the expanded use of facial
recognition to help move the media needle. Such methodology might become a tool
for creatives that can be applied to advertising messages so that the ads can
be placed in program content with the greatest emotional match. It can also be
used to fine tune a message so that it better resonates with a desired target
audience and can be placed in compatible content. A fast food company whose
commercial angers women may want to tweak their message for a more engaging and
positive response from them or place their ad in more male dominant content. Interestingly,
not all negative emotions are bad for messaging, according to Susskind. An ad
that elicits “Disgust” may be perfect for detergent but not for food.
But the real
question is, if actual cause and effect can be measured through the scalability
of emotional reactions, do we need to rely on our current, historical data-centric
stats that are less nuanced and predictive? Isn’t it time to integrate these
new neuroscience-based measurement capabilities and approaches into our current
measurement toolkit?
My opinion
is that, as an industry, media has been historically wedded to delivery metrics
such as ratings and GRPs. These measurements have been embedded in the system
since inception. While new technology and neuroscience applications are
certainly exciting and can produce impactful results, I am not sure they will
ever replace standard metrics as the next currency. These new solutions will more
than likely remain as a consultative, supportive measurement tools. Our
industry moves slowly and is loath to change from those data-driven metrics
that have become the bedrock of our business. But, who knows? I could be wrong.
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