UK businesses will have spent over £20bn on advertising this year. A significant proportion of that amount is wasted on ineffective ads. Now we can use biometric technology to predict in advance whether an ad will be successful at converting customers or not.
Ad pre-testing has been a part of advertising for decades. Before an ad is released it is tested on chosen panels consisting of target demographics. This gives advertisers an idea of how it may be performing. This method, however, is based on shaky foundations as it involves asking people questions directly about their opinions and feelings. This assumes that people are rational beings who can make logical decisions and adequately describe what they think and feel. Behavioural science provides a large body of evidence to show that this is not the case. When we ask people “Do you support reducing voting age from 18 to 16?” – 37% of people say yes. If we ask the same question in a different way: “Do you support giving 16 and 17 year olds the right to vote?”, the percentage of people who support it raises to 52%. Labour party voters are more likely to agree with a political opinion if they are told that it represents a Labour policy. The opposite is true for Conservative voters. We do not give our honest opinions not because we are not honest, but because our opinions are heavily influenced by heuristics, biases and contextual information. This has implications for advert testing – panel members instead of giving answers predictive of real purchase behaviour, provide answers which are consistent with heuristic context of the experimental situation.
Biometrics offers a different perspective. Instead of asking questions about the ad directly, we use implicit measures, which do not require participants’ conscious input. These measures include EEG (electroencephalogram), A.I. emotion recognition, GSR (galvanic skin response) as well as eye tracking. The first three methods give us a full picture of what is happening in a person’s mind: motivation, emotion and arousal, while eye tracking shows what parts of the adverts capture people’s attention.
Figure 1 The cube of biometrics – each method gives us a distinct angle of analysis
providing a full picture of customer reaction to the adverts.
EEG, which involves measuring brain wave activity, tells us whether customers experience positive or negative motivation. A higher level of activity in the left hemisphere signifies a positive, approach motivation. This translates into higher probability of purchase behaviour. Conversely, if the right hemisphere activity dominates, the subject experiences more negative, avoidance motivation, predicting low level of purchase behaviour.
Emotion recognition uses artificial intelligence to track subjects’ facial expressions while watching the ad. It gives us information on the valence of the emotions (whether they are positive or negative). The more positive emotions customers experience while watching the advert, the higher is the probability of subsequent purchase behaviour. Studies also show that on top of the overall rate of positive emotions, purchase behaviour can be predicted from a rising slope of positive emotional reactions along the time of the video ad.
GSR gives us information on the level of physiological arousal that customers experience. Arousal should be looked at in combination with the valence. Studies show that high arousal (of both positive and negative emotions) translates into higher memory encoding. However, only positively-valenced emotions result in higher purchase intention.
Each of the three biometric methods can be used individually, but a combination provides a much higher predictive power. Research by Jha and Ghoshal (2017) demonstrated that combined biometric measures are well over 3 times as effective in predicting effectiveness of an ad as traditional non-biometrics measures (77% vs 24%). If we combine biometric and traditional measures the predictive capacity raises to 84%. This gives us an excellent way of knowing in advance which adverts are likely to perform well and which are likely to fail in generating sales.