The cliché claims that hell hath no fury like a woman scorned.
So a PepsiCo subsidiary, Frito-Lay, tested this in an ad. It shows two women competing to use a washing machine. The loser gains revenge by dumping her orange-coloured crisps into the dryer with her opponent’s white clothes.
The majority of votes cast in a focus group labelled it as mean-spirited, so the company shelved it.
But when another test group were hooked up to electroencephalogram (EEG) sensors, the results were very positive. Women found it hilarious, even if they didn’t admit it.
The ad was aired and the subsequent ‘Orange Underground’ campaign was born and went on to help NeuroFocus (the company that conducted the EEG research) win a Grand Ogilvy award.
This is neuromarketing, and it’s thriving. Intel and Google are investing, and there are more than 100 companies worldwide trying to capitalise.
Actions speak louder than words – and so the line between what consumers say and what they do has had marketers baffled for decades.
But the science is not infallible. There are four limitations to consider when evaluating neuromarketing research:
1. The Hawthorne Effect
A research subject knows that they are being observed meaning they may have a positive or negative reaction to this which is unrelated to the marketing and therefore the outcome could be misleading. Even with a high-tech brain-scanning gadget, the Hawthorne effect can mask a subject’s true feelings.
2. The Observer-expectancy effect
It has been said that most of our communication is through body language, therefore although attempting to remain neutral; researchers will probably provide subtle verbal and non-verbal cues to participants and these can, of course, influence a subject.
3. Limited Sample Sizes
Like fingerprints, every brain is different, what works for one person may not work for another. We still know so little about the inner workings of the brain and new discoveries are still being made all the time.
4. Sampling and self-selection biases
It’s often hard to get people to do something for nothing, or even something for something so the people that sign up for your research may not be the target audience you’re after and so you won’t get the data that you really need.
Neuromarketing can be extremely useful, but to maximise it’s usefulness you do need to acknowledge it’s limitations.
It can only produce qualitative data. You can’t strap an EEG to thousands of subjects at one time and while qualitative feedback is important, it can be misleading if it is your only source of information.
If you proceed with haste based on limited or misinterpreted data, neuromarketing strategies are naturally ineffective, but also damaging.
Incorporate current neuroscience studies and other forms of qualitative data to form hypothesis to test and evaluate properly and you’ll be taking the marketing world by storm in no time.
[This is an edited version of a story first published on online-behavior.com]
Photo (cc) Sybren Stüvel on Flickr. Some rights reserved.
Thanks Vicky – always good to see Neuromarketing get some exposure and help shed light on initial interpretations resulting in new experiences like Orange Underground.
However, EEG is not the only form of Neuromarketing. And the challenges listed here are very much EEG problems, not necessarily Neuromarketing ones. Other methods include Facial Coding, Voice Pitch Analysis, fMRI, Galvanised Response and other psychometric profiling. Would be interested to see a follow-up to this around the evaluation and challenges of each method.
Neuromarketing research has been restricted to the lab, but some of these new methods completely remove the barriers/challenges mentioned making EEG a good, scientific validation method, but perhaps not a super scalable capture method. Given the youth of Neuroscience as a science (less than 30 years old), it’s bang on to call out methods and their challenges so we can put the industry to the test on which combinations provide the most accuracy for given human understanding situations.
One more thing – we had a pretty big debate internally for machine learned facial coding – Quant or Qual measure? Curious to some thoughts on this.
Look forward to more and hearing other views from your reader base.
Emotionally yours – CrowdEmotion.