method.
A hypothesis based on existing evidence, followed by a deduction, and tested through observation. Then repeat.
And yet, in business, little work is scientifically based. In fact, most of it involves no experimentation and an awful lot is based on outdated assumptions. Isn’t it time we removed the guesswork?
One of the characteristics of governmental behavioural teams like the BIT is their use of best practice, scientific methods. Often, this means using randomised controlled trials (RCTs). David Halpern, CEO of the BIT, calls them the “gold standard of evidence-based policy”.28
RCTs are a methodology developed by medical science, where the efficacy of a medicine (for example) is tested by evaluating that medicine against a control condition, that is, one where no medicine (or an existing medicine or placebo) is given. Patients are allocated at random (hence the name), with the aim of eliminating bias. Data is then analysed to look for statistically significant differences between the two groups’ outcomes to determine not only effectiveness, but also potential side effects.
Social (including behavioural) science experiments generally involve testing a behavioural intervention (nudge) against a control condition – which is usually no change. In one BIT tax experiment for HM Revenue and Customs, they tested a social proof letter saying, “Most people pay their tax on time”. This was then evaluated against the existing HMRC letter, with taxpayers receiving one of the two letters at random. The test letter resulted in a 15% increase in the number of people paying before the deadline.
Having seen the overall success of this new letter in getting people to pay their tax on time, the BIT team interrogated the data a bit further. They found that amongst the top 5% of taxpayers, the social proof message had actually reduced the likelihood of paying on time by 25%.29
Amongst this group – it failed.
To find out what did work for this group, the BIT ran another RCT in 2015 to see what message worked best amongst the highest taxpayers. This turned out to be a loss aversion30 message based on the effect on public services of not paying tax – this increased payment rates by 8% amongst the highest taxpayers.
And, thus, progress was made. Hypothesis, deduction, observation.
Or, to put it another way: test, learn, adapt. In this case, it was only possible to understand how behaviour was heavily influenced by this context (i.e. the choice architecture) by sciencing the shit out of the problem.
Loss aversion
You may have experienced FOMO (fear of missing out), or the realisation that you want to do something (e.g. going to a party or seeing a movie) not because you especially desire it, but because you fear regretting not doing so. This is a manifestation of a particularly prevalent behavioural bias: loss aversion.
Put simply, loss aversion is the tendency for our behaviour to be more influenced by the risk of a negative outcome (loss), than the chance of gain. We generally feel the loss of £5 more keenly than gaining £5. It explains related biases like the endowment effect (that we value something we own more highly than an identical item we don’t) and scarcity bias (our increased desire for things we think are in short supply).
Evolutionary psychologists explain this in terms of our survival instincts. In a world of scarce resources, we needed to ensure that we harness and keep as much as we can, while we can, for fear it might be gone tomorrow. Even in a world of relative abundance, this still guides our behaviour.
Consequently, we also have a present bias: we value things more today than in the future, largely because we can better visualise what we can do with resources (e.g. money) now. It explains why we are generally so bad at saving for retirement, and often reach the end of the month with less money in the bank than we expected.
This bias is used frequently by businesses, particularly in marketing, through limited time offers, closing down sales and the like. In the digital world, when buying tickets or hotel rooms, for example, we often see it through nudges like ‘only 5 left at this price!’ Instinctively, this makes us want the product more and we become more likely to purchase immediately. But, note the clever phrasing here: ‘at this price’, means the price could just as well go down as up! Our inherent bias makes us assume that if we don’t grab it now, it will cost us.
When working with the call centre described later in this chapter, we experimented with using loss aversion for customer benefit. When talking about the benefits of moving to online banking – specifically that it is safer, quicker, and better for the environment (because it removes the need to send out paper statements) – we found we could significantly increase the likelihood of customers taking up the service by simply suggesting that if they didn’t, they would miss out. Previously, customer service representatives (CSRs) had quite rationally talked through these benefits, but found customers tended to ignore the information or cut them off before they finished.
Simply saying that a customer would miss out by not changing worked much better – often without the need to even say what the benefits are (suggesting they already knew what they were, they were just too cognitively lazy/uninterested to change). If a customer queried this, the CSR could then talk them through the advantages, but, in most cases, the decision had already been made.
This is yet another example of how much faster our instinctive system-1 processes are than our more conscious system-2 ones – and more influential.
Learning from failure
In his book Black Box Thinking, the journalist Matthew Syed contrasts two industries – medicine and aviation – in terms of how they encourage best practice and avoid the negative outcomes of failure (which is often death). In contrast to how medicines are tested using RCTs, he found that the medical industry does not generally adopt a scientific approach in its response to failure. Medical mistakes are often covered up, ignored, or worse – usually for fear of reprisal and legal action.
But as Syed puts it: “science [is] a discipline where learning from failure is part of the method.” This is why the introduction of black boxes in the 1960s – that retain all data pertinent to an air crash (which is then shared globally amongst the entire industry)31 – has had such an impact on aviation safety.
Indeed, 2017 was the first year on record where there was not a single fatality as a result of a commercial airline crash. A total of 399 people died globally solely in freight and military crashes that year – by contrast, in 1972, a total of 3,346 people were killed.32
Syed categorises this scientific approach to failure as characteristic of a ‘growth mindset’ (as opposed to a ‘fixed mindset’) for organisations, and the best way to deliver incremental improvements through marginal gains. He quotes the philosopher and scientist Karl Popper: “The history of science, like the history of all human ideas, is a history of … error. But science is one of the very few human activities – perhaps the only one – in which errors are systematically criticised and fairly often, in time, corrected. This is why we can say that, in science, we learn from our mistakes and why we can speak clearly and sensibly about making progress.”
If a business wishes to progress, grow and succeed, then understanding the merits of a scientific approach, and the value of testing, is critical. It must also recognise that this process is an iterative one, where we can learn as much from failure as from success, as with the BIT HMRC experiment. In this way, behavioural science – by requiring testing to establish what works in a realistic context – can increase the effectiveness of established management techniques based on continuous improvement and marginal gains, such as kaizen, lean thinking, and agile processes.
