Behavioural economics for business: the Williams behaviour change model

“An ounce of action is worth a ton of theory” Ralph Waldo Emerson

Applying behavioural science to your daily practice isn’t always straightforward.

Academic writers can be strong on the evidence, but weak on the application. The distance from ivory tower to supermarket shelf is all too often vast.

Business writers can be strong on the application, but weak on the evidence. You’d be right to question how generalisable of some of their models and case histories are.

Bri William’s self-published book Behavioural Economics For Business is less polished than most behavioural science books; the second half of the book is essentially a collection of blogposts. But I like her behaviour change model. It is simple: you can put to work immediately. For me that’s enough to justify the purchase price.

1) Defining the behaviour change: from A-B

We want to change an existing behaviour (e.g. a non-buyer) to a desired behaviour (e.g. becoming a buyer). This is point A to point B.

2) Barriers

Williams describes three reasons people don’t take action:

  1. Apathy – they can’t be bothered  
  2. Paralysis – they are overwhelmed  
  3. Anxiety – they are worried about proceeding  

A combination of factors may be in play, for example when buying a car you may have far too much choice and be scared of committing to such a large purchase.

3) Enablers

Address each barrier using behavioural principles.

  1. Apathy – reduce effort and maximise reward (e.g. we’ll drop the car off at your home for a test drive).
  2. Paralysis – clarify choices (e.g. narrowing their options within a budget, or having a default options)
  3. Anxiety – reducing concerns about taking action – addressing the potential fear of loss (e.g. Cazoo offers a money back guarantee).
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The UK savings ratio is at a record high – but it only tells us half of the story

The pandemic has transformed the personal finances of UK households.

ONS data shows the UK savings ratio is now touching 30%, meaning the average UK household is saving around £1 in every £3 they take home.

This has skyrocketed from £1 in every £14 last year, and is double the previous record high.

Locked down consumers can’t drop a few quid on a whim. Many are paying down debt and building in a margin of safety. 

Two questions occurred to me from this data.

Firstly, will a savings habit persist?

Is the pandemic retraining our habits for permanent change? Lots will be getting a kick out of their new found savings-pot prudence. Feels good, doesn’t it?

Secondly, how much is this UK average hiding from us?

For every white collar worker smugly squirreling cash into their ISA, there’s another household having much harder time.The IFS report that poorest households have taken a hit of £170 a month

The debt charity StepChange today report that 1.2 million people face severe problem debt, having to borrow to meet debt repayments.

Younger people are far more likely to work in hardest hit parts of the economy like hospitality and retail. Shockingly, data from the Resolution Foundation show the majority of the under 25s had been furloughed or made redundant by June.

One average: a multitude of stories. 

…..

Postscript

Differential in savings rates between US homeowners vs. renters Q3 2020; Source: @urbaninstitute, h/t @adam_tooze

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Book review: Good habits, bad habits by Wendy Wood

Wendy Wood is the world’s leading expert on habits. Her book “Good Habits, Bad Habits: The Science of Making Positive Changes that Stick” is a high quality overview relevant to anyone who is interested in behaviour change. Here’s a quick rundown of the parts which resonated with me.

1) If you repeat something and get rewarded for it, you’re learning a habit

Her shorthand definition of a habit is something that makes behaviour automatic without conscious motivation. Your situation triggers a response from memory – you act without having to think about it.   

2) Habits are the mental equivalent of admin-only files on your computer

They free our minds to do more pressing tasks, because brainpower is a limited, depletable resource. Wood quotes A.N. Whiteheard here:

“Operations of thought are like cavalry charges in battle – they are strictly limited in number, they require fresh horses, and must only be made at decisive moments”

But day to day, we are not aware of this. We are unduly confident in our own thoughts. She writes: “…we often don’t realise what our habits are doing. It is as if they are operating parallel to us, just outside our consciousness.”  

3) We spend nearly half our day on autopilot – and overestimate the strength of conscious thought  

How much? That sounds a lot. Her research into the daily experience of habit shows on average 43% of our day we are doing things without thinking about them: repeating actions in the same context, responding automatically.

One example is eating, which has three components of habit formation: frequent, performed in similar contexts and provides immediate reward.

4) Many activities – like driving – blend conscious thought and habitual response

Most of us will have experienced a time where we’ve been driving home on our commute – then suddenly realised we can’t remember driving for the past 5 minutes. You can drive the same route so frequently you’re able to respond to it automatically. Here driving is a trade-off “between reacting to the unexpected (conscious thought e.g. cut up) and habit (context-triggered responding when driving a familiar route)”

Expanding on the theme, she describes how 1 in 3 US drivers admit to texting behind the wheel. This:

“…showcases the extraordinary potential inherent in habit. It can take one of the most dangerous things we do everyday and seamlessly transform it in the background of our lives. Only new drivers, relying on their conscious decisions, feel the adrenaline rush of fear that all of us rationally should experience on the road. As driving habits form, the wide range of skills required to operate an incredibly complex machine become a background hum behind what we are thinking about. Good or bad, habits emerge with practice, and conscious-decision making recedes”

5) We rely on one part of our brain to make initial decisions and another to persist

Our brains evolved in a piecemeal fashion over thousands of years. Newer mental functions – which joined the party relatively recently in evolutionary time – work alongside pre-evolved ones.   

When we learn a task we rely on prefrontal & hippocampal regions, brain areas associated with decision-making and executive control. When we repeat them the putamen & basal ganglia are at work.

“Habits live in resilient, deep seated neural structurers – ones that are fundamental to mammalian life. Our core mental competencies have as much to do with making habits as to making plans… Our goal directed and habit neural systems are interconnected, and they often work together”

The upshot? We should focus on the right strategy to change our habits.

“Imagine if we “made the decision” to go to gym every time: you’d be forcing your mind to go through same exhausting process of engaging with the reasons you felt you should be going in the first place. Because our minds are adversarial you’d be running through the reasons not to go too…. We should skip the debate chamber and get to work”

6) Let your habits take the strain  

We should not rely on willpower if we are trying to change our ways. It’s about situational self-control rather thanself-control. It’s about changing the context rather than conscious effort.

“…our habitual selves can take on much of the drudgery needed to achieve the goals set by our conscious selves. It is a more efficient and a happier way to live… It is when you stop and think that you might stray away from your goals”

The proof comes from experiments with people who rate highly for self-control. When you look at how they achieve their goals it is about exerting less effort rather than more:  

“The good effects we ascribe to self control are it seems more accurately captured by situational control… High self-controllers achieved desired outcomes by streamlining not struggling”

Changing your own habits is thus more about controlling your situation so that you don’t need to exert self-control. Make the default choice the right choice:

  • Choose what you want to change (e.g. run 4 times per week)
  • Create a routine – regular times, places, and pattern of action (e.g. going at 5pm Monday to Thursday when you finish work)
  • Make it easy to repeat – consider the context & your surroundings (e.g. buying 4 sets of running gear so there’s always some clean, finding shorter & longer running routes from your doorstep which you can choose depending on how you feel etc.)
  • You can also be opportunistic with timing – transitional moments / life events disrupt your routine (e.g. moving house) – plan to derail the bad and create the new.

7) Better habits lead to a better life

We should think of our “habit selves” as second selves that we need to train.  

Wood describes how we start to prefer the things we experience regularly: repeating actions changes what we desire. It’s a feedback loop. Thus we can “hack” our habit selves here so that they become aligned with our desired selves: before long they will be dragging us out for a run.

“If you know how to form a habit, then beneficial actions can become your default choices. Your best self, your habit, is uppermost when you are not thinking.”

In summary? A challenging reminder that we don’t think the way we think we think.

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Is the easy to measure, but less effective, favoured over the hard to measure but more effective?

Is the easy to measure, but less effective, favoured over the hard to measure but more effective?

A fascinating article from Faris Yakob on the lessons we can learn from Adidas’ admission they over-invested in digital & performance marketing, and under-invested in brand.

Relying on attribution modelling from Google and Facebook but with no brand tracking in place they fell prey to “McNamara fallacy” – ignoring what they didn’t measure.

“When Adidas brought in econometric measurement they learned that 65% of sales were being driven by brand activity, performance online was driving sales offline, and that it needed to invest more in tv, outdoor and cinema to create a balanced communication strategy”

Financial imperatives can lead to a short term focus.

You can’t just pick the apples, you need to water the tree.

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Consumers Are Becoming Wise to Your Nudge

I know exactly how the conversation will go.

I’m interviewing Chris, a 52-year-old man living a small coastal town, for the second time. We’ve been exploring the new checkout process for a client’s redesigned website. The new site isn’t performing as well as the company thought it would, so I’m exploring why and seeing what we can learn from competitors.

“Only 2 rooms left? They don’t expect me to believe that do they? You see that everywhere.”

I leave with a wry smile. The client won’t be happy, but at least the project findings are becoming clear. Companies in certain sectors use the same behavioral interventions repeatedly. Hotel booking websites are one example. Their sustained, repetitive use of scarcity (e.g., “Only two rooms left!”) and social proof (“16 other people viewed this room”) messaging is apparent even to a casual browser.

For Chris the implication was clear: this “scarcity” was just a sales ploy, not to be taken seriously.

My colleagues and I at Trinity McQueen, an insight consultancy, wondered, was Chris’s reaction exceptional, or would the general public spot a pattern in the way that marketers are using behavioral interventions to influence their behavior? Are scarcity and social proof messages so overused in travel websites that the average person does not believe them? Do they undermine brand trust?

The broader question, one essential to both academics and practitioners, is how a world saturated with behavioral interventions might no longer resemble the one in which those interventions were first studied. Are we aiming at a moving target?

This was the basis for a research project we completed in February 2019 examining reactions of the British public to a range of behavioral interventions. We took a nationally representative sample of 2,102 British adults, and undertook an experimental evaluation of some of marketers’ most commonly used tactics.

We started by asking participants to consider a hypothetical scenario: using a hotel booking website to find a room to stay in the following week. We then showed a series of nine real-world scarcity and social proof claims made by an unnamed hotel booking website.

Two thirds of the British public (65 percent) interpreted examples of scarcity and social proof claims used by hotel booking websites as sales pressure. Half said they were likely to distrust the company as a result of seeing them (49 percent). Just one in six (16 percent) said they believed the claims.

The results surprised us. We had expected there to be cynicism among a subgroup—perhaps people who booked hotels regularly, for example. The verbatim commentary from participants showed people see scarcity and social proof claims frequently online, most commonly in the travel, retail, and fashion sectors. They questioned truth of these ads, but were resigned to their use:

“It’s what I’ve seen often on hotel websites—it’s what they do to tempt you.”

“Have seen many websites do this kind of thing so don’t really feel differently when I do see it.”

In a follow up question, a third (34 percent) expressed a negative emotional reaction to these messages, choosing words like contempt and disgust from a precoded list. Crucially, this was because they ascribed bad intentions to the website. The messages were, in their view, designed to induce anxiety:

 “… almost certainly fake to try and panic you into buying without thinking.”

“I think this type of thing is to pressure you into booking for fear of losing out and not necessarily true.”

For these people, not only are these behavioral interventions not working but they’re having the reverse effect. We hypothesize psychological reactance is at play: people kick back when they feel they are being coerced. Several measures in our study support this. A large minority (40 percent) of the British public agreed that that“when someone forces me to do something, I feel like doing the opposite.” This is even more pronounced in the commercial domain: seven in ten agreed that “when I see a big company dominating a market I want to use a competitor.” Perhaps we Brits are a cynical bunch, but any behavioral intervention can backfire if people think it is a cynical ploy.

Heuristics are dynamic, not static

Stepping back from hotel booking websites, this is a reminder that heuristics are not fixed, unchanging. The context for any behavioral intervention is dynamic, operating in “a coadapting loop between mind and world.” Repeated exposure to any tactic over time educates you about its likely veracity in that context. Certain tactics (e.g., scarcity claims) in certain situations (e.g., in hotel booking websites) have been overused. Our evidence suggests their power is now diminished in these contexts.

Two questions for the future

In our study, we focused on a narrow commercial domain. It would be unwise to make blanket generalizations about the efficacy of all behavioral interventions based on this evidence alone. And yet nagging doubts remain.

#1: Like antibiotic resistance, could overuse in one domain undermine the effectiveness of interventions for everyone?

If so, the toolkit of interventions could conceivably shrink over time as commercial practitioners overuse interventions to meet their short-term goals. Most would agree that interventions used to boost prosocial behavior in sectors such as healthcare have much more consequential outcomes. In time, prosocial practitioners may be less able to rely on the most heavily used tactics from the commercial domains such as social proof and scarcity messaging.

#2 : How will the growing backlash against big tech and “surveillance capitalism” affect behavioral science?

Much of the feedback from the public relates to behavioral interventions they have seen online, not offline. Many of the strategies for which big tech companies are critiqued center on the undermining of a user’s self-determination. The public may conflate the activities of these seemingly ubiquitous companies (gathering customer data in order to predict and control behavior) with those of the behavioral science community. If so, practitioners might find themselves under much greater scrutiny.

Feedback loops matter

There probably was never an era when simple behavioral interventions gave easy rewards. Human behavior—context-dependent, and driven by a multitude of interacting influences—will remain gloriously unpredictable.

The lesson I take from our study? Feedback loops affect the efficacy of behavioral interventions more than we realize. Just because an intervention was successful five years ago does not mean it will be successful today. Practitioners should pay as much attention to the ecosystem their interventions operate in as their customers do. There’s no better place to start than spending time with them—talking, observing, and empathizing.

We should also consider our responsibilities as we use behavioral interventions. Marketers should design nudges with more than the transaction in mind, not only because it is ethical or because they will be more effective over time but also because they bear responsibility toward the practitioner community as a whole. We owe an allegiance to the public, but also to each other.

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Originally published in Behavioral Scientist magazine, June 12th 2019.

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How powerful is anchoring?

shutterstock_142974694

We decided to explore the effects of anchoring (relying too heavily on the first piece of information we see when making a decision).

The experiment

Everyone reading this will have had the same email. It’s a staple of office life.

“I’m taking part in a charity run, will you sponsor me?”

If I asked you what would influence your donation amount, you’d probably ask, “Who’s asking for sponsorship?”, “What’s the charity?” or even “How skint am I this month?”

But what about the amounts other people had sponsored? Would seeing that others donated large amounts on an online giving portal nudge you into giving more?

We created a behavioural experiment with matched nationally representative samples of 1,000 UK adults to explore this.

  • We mocked up two charitable giving pages and asked people seeing them how much they would donate towards their friend’s charity run. Like in real life, these had names, pictures, good luck messages and donation amounts.
  • The only difference between the control and the test versions was that test version used higher average donations (£20, £50, £100) than the control (£5, £10).
  • We used a monadic survey design to isolate the influence of the anchors; matched nationally representative samples of 1,000 UK adults saw either the test or control version.

Anchoring 1What we found

You can get people to double their donation merely by showing them other people donate more. Mean donations were £8.81 when people were anchored low (£5, £10) but £18.17 when they were anchored high. This was a 106% uplift.

Anchoring 2.jpgKnowledge = immunity?

As this was part of a larger behavioural experiment examining the effectiveness of a range of different behavioural interventions, we also had the opportunity for some mischievous subgroup analysis. Later in the survey we asked participants a range of profiling questions. You might be surprised to learn that almost half of the UK population agreed that “I am more intelligent than the average person” (49%) and around 1 in 8 “have heard of behavioural economics” (13%).

Neither inoculate you against anchoring, as it operates unconsciously.

Anchoring 3.jpg

Giving is inherently social

Thinking more broadly about charitable giving, social pressure plays an important role.

Sponsoring someone bonds us closer to them. It’s a tangible signal that you care about them and their cause. When you listen to people talk about sponsorship, many admit to feeling a subtle pressure: for example “will the person asking be offended if I say no?” There’s also pressure not to step out of line with our peers: “what if everyone else gives and I don’t?”

My interpretation? In our experiment anchoring is exacerbated by social pressure: an interaction effect applies.

Isolating anchoring from social pressure – experiment 2

  • We asked a simple question: “You want to make a one-off donation to a cancer charity. You go to their website and find the donation screen below. How much would you donate?”
  • We mocked up a donation web page, again with two versions. In each you could select from three suggested donation amounts, or enter a sum of your own choosing. The test version prompted people to donate larger amounts (£20, £80, £150) than the control (£10, £50, £100). This was the only difference.

What we found

  • Anchoring increased charitable donations in this experiment also, but the experimental effect was not as large (a 45% uplift).
  • As before, a higher anchor not only meant larger donations, but more people donating and greater variance in giving (choosing to donate a bespoke amount rather than picking from the amounts suggested).  

Anchoring 4.jpg

So what have we learnt?

Anchoring is powerful

Anchoring is a subtle signal that drives our choices. Context is the driving factor here, not the individual. If you control the context, you control the choice.

Anchoring affects all of us 

As our cheeky subgroup analyses indicate, self-proclaimed intelligence and/or knowledge of behavioural economics does not inoculate you from cognitive biases. Think hard on this the next time an estate agent or car salesperson takes you through a few options in an order of their choosing.

The effects of anchoring are unconscious

Anchoring works unconsciously. Applying it ethically should be our first concern.

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10 takeaways from Nudgestock 2019


Nudgestock 2019Nudgestock, Ogilvy’s annual festival of behavioural science, is highly unusual in its ambition and scope. The day is about developing creative solutions to human problems. We’re here to understand people in all their complexity and consider what unites them in their decision making.

Behavioural science is a young discipline, still mapping its boundaries. So instead of being spoon-fed a narrow diet of industry veterans re-presenting case studies, we’re invited to harvest our inspiration from thought-leaders from across domains of expertise. It’s a challenging approach, demanding your attention and sapping your brainpower.

A succession of anthropologists, economists, academics, marketers, brands and campaigners take the stage. I turn to the people either side of me to make sense of it all: I’m sat next to an evolutionary anthropologist and a food entrepreneur. I can’t even rely on industry jargon to bridge the gap in my understanding our as we chat through our impressions of each speaker.

I’m out of my comfort zone, and loving it. Here are a few things that stuck in my head:

1) Many problems are better solved using “psycho-logical” rather than logical thinking

Rory Sutherland anchors the event. Beaming from the stage in a Hawaiian shirt, his talk is a whistle stop tour of his new book, Alchemy. His argument: logical thinking is a straight-jacket when you are solving human problems. We should all consider “using psycho-logic to conjure up value from nowhere.” A host of examples show us how looking through a psychological lens enables magical creative solutions:

  • Instead of spending £80bn on HS2 to reduce journey times to Birmingham by 30 minutes, spend a fraction of that making the experience more pleasurable and productive;
  • Make paying tax less painful by keeping rates constant (we become accustomed to the rate we pay) and instead giving an annual rebate. You then have option of donating a proportion to causes of your choice, like the NHS (feelgood factor). Those sharing over 50% could have their names publicised (an opportunity both for kudos, and to signal identity).

2) There is nothing wrong with doing good by stealth

Huge improvements to human hygiene in the 20th century were in a large part caused by a growing urge to maintain the appearance of cleanliness, unconscious status-seeking. Soap was sold on its ability to increase your attractiveness, rather than its hygiene powers. Positive ends derived from selfish means.

There is much we can learn from “Scenting the soap”. If you can change the story, you change the meaning and value of the product. In the present day Beyond Meat is framed not around a rational benefit or hairshirted denial, but exceeding (not just imitating) what it is replacing.

Rory Sutherland Nudgestock 2019

3) Make rational benefits irrationally appealing & “Don’t mess with Texas”

These themes were echoed by Richard Wise, a “global brand anthropologist” with a nice line in dry humour. He galloped through “five legendary case histories which made rational benefits irrationally appealing” which were all for “utterly worthless and contemptible categories”.

In each example, rational benefits were red herrings. Previous communication efforts often failed due to logical linear thinking. Brand owners struggled to see past them to connect to the human truths.

Anti-littering in Texas, for example, relied on Keep Texas Beautiful as a message, which failed to change behaviour (or in Wise’s words, “didn’t do shit”). Who litters most? Young men aged 16-35: pick-up driving, beer-drinking wrestling fans. The solution for the Lone Star State was to appeal to a more rebel instinct in the tone of their message, provoking tribal pride. This was about speaking to a lower self about a higher message. You can imagine the arguments getting it signed off.

The result? A 72% reduction in littering on highways over 4 years: the single greatest anti-littering campaign on record, subsequently adopted into culture, emblazoned on t shirts, meme-ified and parodied.

Nudgestock 2019 don't mess with texas

4) The 3 psychological hacks which Uber rely on

Taxis aren’t new. You’ve been able to summon a cab to pick you up for over a hundred years. What makes Uber different – and crucially feel different – is the user interface:

  • The dot on the map reduces the uncertainty of waiting, reducing frustration. You can see where you are, where your driver is, and feel reassured. None of this changes objective reality: it changes the way you feel waiting for a cab.
  • No money changes hands physically at the end of the ride – payment is pre-arranged and intangible, reducing the pain of payment.
  • Chunking the customer journey into granular steps. The Uber Pool customer journey is longer, meaning people drop out. Showing updates like “now finding a driver going in your direction” and “finding other riders going in your direction” along the way resulted in a 11% reduction in ride cancellations. Chunking helps.

5) Stand up to quantification bias – unconsciously valuing the measurable over the immeasurable

Tricia Wang bounded on stage with barely suppressed energy. Wang is a tech ethnographer – someone who watches what people do with technology for a living. She coined the term “thick data” (which essentially means qualitative data) – “the most direct, unmediated data from humans which captures the full context of their emotions and stories”. As she put it “you cannot put tears and smiles into spreadsheet.”

Without paying close attention to human experience you miss emergent behaviours, unseen opportunities and strategic shifts – meaning your strategy will be wrong.

Wang believes companies make bad decisions because of quantification bias. They fall into a trap that their huge datasets hold the answer to what they should do next, rather than understanding people. She is famous for advising Nokia to change their reliance on featurephones after observing how smartphones were transformational to the lives of people living in the developing world. Nokia rejected the advice as their models predicted otherwise. Big data gives you an artifice of certainty: her experience taught her that without the right model you are not listened to.

6) Re-balance marketing functions to be more holistic

In Wang’s view, there needs to be a culture change in organisations. Thick data – qualitative insight from real people:

  • Rescues the context loss that results from relying on big data alone;
  • Inserts the human back into the loop that has become invisible undetected;
  • Changes your perspective, allowing you to focus on the right things.

You need a holistic approach, scale and depth, to drive businesses forward. It is interdisciplinary: we need to work together. The marketing function should balance thick data, big data and creativity. In her view this is why Apple outperform Nokia, Fender outperform Gibson and Zara outperform Gap.

7) Simple rules of thumb outperform complex prediction models 

A lot of people are here today to hear Gerd Gigerenzer speak; he’s spent 40 years studying how people make decisions in the real world. In his view: less is more.

  • The world we live in is complex: we have imperfect knowledge of future states of the world, their consequences and probabilities;
  • It’s counter-intuitive, but your aim should be to make things simple;
  • Rules of thumb are more effective than all other approaches.

He compared two models of predicting whether a customer will make a future purchase:

Strategy 1 – Less is more – experienced managers used a rule of thumb: ”if a customer has not bought in the past 9 months classify them as inactive” (the “hiatus heuristic”).

Strategy 2 – A complex problem needs a complex solution. Use a pareto negative binomial distribution model.

The former was a more accurate strategy than the latter, across many categories (see pic). As Gigerenzer put it: “…simple solutions are out there, we just need to look for them. Often just a single variable is important…”

A simple “hiatus heuristic” required less information, was fast and saved effort. The implications are clear: speak to the doers, people on the front line more likely to have skin in the game experience. We should aspire to fast and frugal heuristics, not be ashamed of them.

Nudgestock less is more

8) The sentence that can predict flu with more accuracy than Google

Another example related to a flu prediction project at Google:

  • Google created an algorithm to predict spread of flu. Their hypothesis was the volume of searchers would correlate with the number of ill people. They used 50 million searches centring on 45 search terms, with a 4-year calibration period. The model failed to predict the swine flu breakout in April 2009 – a black swan event with no precursors in the data: big data is backward looking. If the future is not like the past, this is a problem.
  • To improve the model, they re-engineered the algorithm, with even more data: 160 predictors. Again, it failed, even with another 4-year calibration.
  • What turned out to be more accurate was a simple rule of thumb, based on recency: comparing the number of flu related doctor visits 2 weeks ago to the number of doctor visits one week before flu diagnosis, in the previous year.

9) A lot of management decision making is about “arse-covering”

Defensive decision making is the notion people choose the option which protects themselves, rather than the best decision for their organisation. Many organisations have a culture of fear and blame: why take a risk? Far better to “fail conventionally than to succeed unconventionally” as John Maynard Keynes put it.

Gigerenzer said that many CEOs admit to him privately they approach decision making this way. This has serious implications:

“Defensive decision making strangles us because people can no longer do their best for you”

When it comes to medicine this can be the difference between life and death. He cited research showing 93% of doctors admitted to defensive decision making. If your doctor is motivated by protecting themselves from litigation rather than healing you, they end up recommending a different – interventionist – course of treatment. His rule of thumb? Ask them what they would do if they were choosing a treatment for themselves.

Nudgestock 2019 Gigerenzer

10) Organisations are monitoring people to death – decentralise and empower to win

Professor Sir Paul Collier from Oxford University closed the day. Talking without notes, he covered the birth of capitalism, how it developed, diagnosing where it is failing, before prescribing solutions – all in 30 minutes.

So I’ll focus on a single point. The annual jobs and skills survey has shown a sustained drop in agreement to the statement “Do you have enough discretion in your job to do it properly?” Since the 1970s there has been a 40% drop, and now most people disagree. People don’t feel trusted – but are micromanaged, targeted, tested and controlled. This simply does not work, in business (GM vs Toyota) or in the public sector (teachers in the UK vs Finland).

As our world becomes more complex, the amount of behaviour that can be codified reduces. So much of what we do at work relies on tacit knowledge. The lesson? Decentralise decision making, empower people and trust them. Work provides us with dignity, respect and esteem – we shouldn’t forget it.

In summary

Reflecting on it all, the common thread is our obligation to argue for, justify and reassert human connection.

Abstraction and data have a role to play, but in isolation they feel cold, insufficient. The relentless logic of businesses scaling-up risks humans being engineered-out completely. As Sutherland writes in Alchemy:

“It is impossible to quantify many of the psychological factors which people care about… there are no S.I. units for what really matters.”

So here’s to using our judgement, in business and in life.

——

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How defaults reinvented workplace pensions

It’s easy to get downhearted when you think about politics. So let’s take a moment to be cheerful in these hyper-partisan, Brexity times. Sometimes politicians do leave legacies that make a real difference to people’s lives.

In 2012 there was a crisis in pension saving. Fewer than half (47%) of UK workers were saving towards their retirement in a workplace pension scheme, and many were facing an uncertain future due to their lack of provision. Speaking to people across the income spectrum at the time on a project for the National Employee Savings Trust it was clear were lots of reasons for it. For some, affordability was the issue. Others didn’t think much about the future – retiring seemed a long way off and they assumed “things will work out.” Many more however fell into the “I always meant to but never got round to it” category.

Inertia is a powerful force. Our attitudes and intentions may clearly point in one direction, yet our behaviour won’t necessarily follow. Stacked on top of each other the friction costs involved in opting-in to a pension act as a barrier. Thinking about the future is hard. People find finances boring (shock horror!) so considering a pension provider, deciding which funds to invest in and then filling out the paperwork is not fun. All this meant a significant proportion of people who wanted a pension never took one out.

That all changed in 2012 when new pension rules were enacted, and large employers became responsible for auto-enrolling staff into workplace pensions by default. The decision point – the barrier – was removed.

The results were profound. ONS data (see figure below) showed that by 2018 more than three-quarters of us (76%) had a workplace pension, a significant increase.

Proportion of employees with workplace pensions in the UK - 1997 to 2018

This ‘nudge’ was transparent: people could opt-out if they wanted to. But when we are presented with a default option already set we tend to accept it, going with the flow. My interpretation? There were a pool of people with a latent intention to save who were helped by this intervention – and they’ll end up happier retirees as a result.

Takeaways:

  • Like with a lot of examples in behavioural science, findings are counter-intuitive. The person in control of the decision is the person who designs the “choice architecture”– not you.
  • Little “friction costs” add up. If you make it hard to do something, fewer people will do it.
  • Defaults are probably the biggest behavioural lever you can pull. Their use can quite easily backfire, so should be carefully considered.
  • In this instance there was a huge amount of supporting education, communication and stakeholder engagement that complemented the pension rule changes. Interventions are not binary: educative nudges which “strengthen the hand of System 2 by improving the role of deliberation and people’s considered judgements” (Sunstein & Reich 2019) supplement the non-educative nudge (the default opt-in) in this example

 

 

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Using behavioural science to unlock customer opportunities: keeping it SASSY at Leeds Digital Festival

This month Trinity McQueen were delighted to take part in the fourth annual Leeds Digital Festival. Comprising 200+ events over eleven days it’s a great showcase for innovation in the city.

leeds-digital-festival.gif

My presentation focussed on applying behavioural science. It’s something we’re talking to lots of our clients about: it helps us see problems in a new light, and unlocks value for marketers at little cost. We went through seven case studies from work over the past decade covering everything from pensions to breakfast cereal.

One challenge we always face is summarising key principles in a way that makes them stick in your head. Let’s face it: as a topic behavioural science can intimidate. Marketers are busy people and not everyone is comfortable with terms like “heuristics” or “salience”. So when people ask “isn’t this all a bit complicated?” we say “no – it’s SASSY.”

Behavioural Science is SASSY.jpg

We also revealed results from a recent experiment, examining the effectiveness of different behavioural tactics with a nationally representative sample of 2000 UK adults.

Questions afterwards were wide-ranging, thinking about how principles stretch to different domains, whether some tactics become less effective over time and the emerging ethics of nudging.

Thanks to all who attended and contributed, and to iView Leeds for hosting.

 

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Stick or twist? Supermarket buying patterns from 5 years of Clubcard data

A team of psychologists and data scientists from UCL worked together to interrogate Tesco Clubcard data, exploring hidden patterns in how people buy. It is powerful study because of its:

  • Scale (283,000 people);
  • Scope (longitudinal data covering 5 years, 89 store visits per person on average)
  • Remit (purchase data, as opposed to attitudinal data)

I’m always keen to get a meta-level perspective of buying behaviour, stretching horizons beyond short-term client projects to long-term trends. The study promises a great deal.

The findings are a mixture of the obvious and the new. It’s a reminder that you can throw terabytes of computing power at an ocean of data and end up mostly confirming what we already know. That’s OK – it’s why it is called research.

Clubcard buying patterns

The academic paper is dense and has lots of jargon. The diagram makes the pattern clear. Each dot is a shopping trip: green dots are when people twist and blue dots are when people stick.

Coherency maximising_Clubcard

I find all this stuff useful for a couple of reasons:

A reminder that real-world decision making differs from the lab

The authors describe how lab experiments modelling similar choices often show people are systematic and rational. They exhibit system 2 strategies in these system 2 conditions, relying on objective criteria in comparable trade-off environments.

The real world is full of subjective evaluations (“ooh that looks tasty”) in varied trade-off environments (e.g. stores that change layout or merchandise).

As Professor Dilip Soman put it in his book The Last Mile:

“When you’re in a store, absolutely everything around you could influence what you buy: the display, the price presentation… the presence of crowds… What’s more, these factors could interact with each other… Theory can show us the way but without testing… we risk failure because of something in the background context that trips up the effectiveness of our intervention.”  

Data can only take us so far into the subjective world of human taste. How do you reverse engineer a whim?

Hidden patterns that shoppers may not be aware of  

People like things more the more often they choose them (the authors call this ‘coherency-maximising’). They hypothesise that “people come to like what they purchase, out of a need to “make sense” and explain their choices to themselves and others.”

This aligns with other sources – like Sharp’s How Brands Grow – showing how people bring their attitudes into line with their behaviour. As Sharp writes “…since brands aren’t very important to us, brand buying tends to have a strong effect on our rather weak attitudes.” 

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