We find neuropsychology fascinating at Matogen AI, so have made a short study of it here. With Black Friday ad campaigns circulating, it seemed appropriate to relate it to consumer behaviour. Behavioural Data Science, in a nutshell, is using computers to understand humans – and influence their behaviour.
For decades, “behavioural analytics” has been the term used to describe the process of using theory from non-financial fields such as psychology, neuroscience, economics, sociology and anthropology, to understand consumer purchasing behaviour. Within the South African market research landscape, the “Conversion model”, a seminal psychological model to measure the strength of the relationship between consumers and brands, was developed from theory relating to religious conversion. For many years, it was used with great success to predict churn and quantify customer loyalty.
Social science theory: ‘The why behind the panic buy‘
Recently, many retailers (and consumers!) were caught unawares by the waves of “panic buying” at the onset of the COVID-19 pandemic. Traditional timeseries purchasing data was useless in predicting this highly disruptive phenomenon, whereas theories from the realms of economics and psychology could have proven useful in predicting and mitigating these occurrences.
For example, Nobel prize winning economist Daniel Kahnemann’s “loss aversion” states that humans experience financial loss more profoundly than financial gain. In the context of panic buying, people were alarmed at the anticipated pain of not having access to products they normally would. Such risk aversion caused people to buy anything they set their eyes on for fear of not being able to do so later. Another Economics Nobel prize winner, Richard Thaler, wrote extensively about behaviour-nudging heuristics, a.k.a. “copycat behaviour”. During the early stages of the pandemic, consumers observing others buying large amounts of a particular item, were inclined to jump on the same bandwagon. No doubt this phenomenon was exacerbated and entrenched by the constant stream of social media and news reports showcasing this herd like behaviour.
In addition, psychologists have contributed theories regarding the human need for control during periods of stress. As such, stockpiling could be considered an amplified form of retail therapy in an attempt to assert control. Social scientists have even highlighted the soothing effects of buying utilitarian products specifically, i.e. items that “give you something to do”, which coincidentally tied in very well with actual types of products (food and cleaning products) that were being stockpiled!
“Game Theory” is another Nobel prize winning economics contribution, formulated by John Nash which famously entered the public lexicon in the Oscar-winning film, “A Beautiful Mind”. It assumes rational (and self-preserving!) reasons that could explain panic-buying: Out of two competing strategies, act normally or panic buy, normal purchasing behaviour would result in a state of equilibrium. However, once the panic buying has already started, the optimal strategy would be to do the same.
Enter algorithms and big data
With the emergence of AI technologies, the insights provided by behavioural theory can now be leveraged further using a range of methodological tools from statistics, computer science and engineering. As described by the Alan Turing Institute, “Human behaviour is a major source of data in the current digital economy. At the same time, it is also one of the main ‘objects’ of data science in the sense that many data science and artificial intelligence models are aimed at influencing human behaviour (e.g. through ‘nudging’, personalisation, and behavioural segmentation).”
Applying behavioural science to increasingly large datasets, human behaviour can now, better than ever, be understood and predicted across a variety of applications ranging from health, telecommunications, finance and development. Behavioural data science can help cities, businesses, governments and individuals understand why and how human decisions are made and inform how to optimise behaviour to achieve better outcomes.
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