Economic theory works on a simple underlying principle - that individuals act according to rational self-interest. From the products we buy and the price we will pay for them to the decisions we make at work, the models rely on an assumption that we will follow rules and stay within set parameters.
That is great news for data and analytics - they can provide the rational evidence, framework and forecasts for everything, from which other products a customer is likely to buy through to what impact a change in interest rates will have on the housing market. It is why decision makers are embedding these tools into their organisations to ensure they understand and optimise objectively.
Except that human beings are not like that. Psychologists will tell you that six or seven out of every ten decisions we make as individuals have no rational explanation. Even within the constraints imposed by a business, the best rate you might achieve is half. Emotion, ego, desire are as likely to inform the CEO’s choices as they are a customer’s at point of sale.
It is not surprising, then, that the Nobel Prize for Economics has just gone to one of the founding fathers of behavioural economics, Richard Thaler. As a result of his thinking, the UK has a government-funded Behavioural Insights unit which uses Thaler’s theories to encourage people to take more exercise, give up smoking, take out a pension and other socially-desirable activities.
What Thaler brought to light was the irrational choices which we all make and the mechanisms to nudge us towards more appropriate decisions. Marketers have become excited by these ideas, especially in retail environments or face-to-face where the human element can be brought to bear.
And this is where data and analytics has been losing out. Irrationality is not a data point, or at best appears as an outlier. When viewing behaviour en masse, such as on site navigation or journeys from first click to purchase, the randomness will get filtered out in order to leave a manageable picture of those who follow more predictable pathways. Data is made to fit the model because most models assume rationality.
For data scientists, this is potentially exciting new territory to explore. If indicators of the irrational can be found, then some patterns could start to emerge. It may be that grocery shoppers never buy milk on a Tuesday, for example, or that insurance conversions among women are highest on Friday afternoons. Anything that appears improbable is actually telling you something about human behaviour. Once you recognise that, you can explore methods to encourage the actions you really desire.
Tragically, the people who have already understood this and put it into practice as a formal discipline are “playas” - those creepy men who use behavioural economics when hitting on women, usually by applying pressure to points like negative self-esteem. Single-minded in pursuit of an outcome, they see behaviour as a system they can game for their own ends. It may be unpleasant, but that is exactly what any economic model does.
Of course, you do not want to turn your data and analytics resource into a machine that gets your brand perceived as a playa. But you are trying to optimise outcomes from behaviours that appear random, yet can be nudged towards something more predictable. Thaler sits at the acceptable end of this new discipline and his Nobel Prize shows we are getting closer to accepting that how humans make decisions is not always with their heads.