Unlearning in Behavioral Science

Recently I came across various materials* which state that many of the things I learned in grad-school at Erasmus University aren’t as I’ve learned them. Among them are the bodies of knowledge on choice overload, priming, ego-depletion etc. (Just as a note, Erasmus Research Institute of Management is in top 3 European research institutes in its field, so I got top level education from highly qualified professors.)

* This piece by Jason Collins I found particularly interesting. 

Nowadays, there is serious doubt casted upon ego-depletion, studies on priming have been challenged on a wide scale, apparently, choice overload doesn’t manifest each and every time and, it seems that, the endowment effect doesn’t manifest in isolated groups of hunter-gatherers, which means that it is not an innate (evolved) human feature.

It looks like I have to unlearn many of the things I have learned.

That’s interesting.

Yet, I can’t simply delete from memory knowledge (information) on ego-depletion (just as an example). Even if I could delete information from my memory, in the case of priming there is no clear list or criterion on which results hold and which don’t.

The situation isn’t as grim as it seems at first glance, particularly for the applied part of behavioral science.

The fact that not every result found in a scientific study (lab or field) doesn’t hold in different contexts is not exactly news. Moreover, people who profoundly understand behavioral science know that there are some strong and some weak phenomena. For example, we know that loss aversion and mental accounting are strong phenomena and we also know / knew that priming is a weak one.

When doing applied behavioral science work, you want to rely on the strong phenomena. Sure, you can use weaker ones such as priming, but you can’t rely on them.

For example, when designing an intervention intended to keep a space clean, it is essential to rely on convenience of trash-cans and social proof (social imitation – more broadly). Yes, you can dispense a citrus scent (olfactory priming) to promote cleanness, but that is more of an add-on and not the core of the intervention.

We knew that priming studies aren’t exactly at the top of reliability and replicability, but most behavioral science specialists have learned about ego-depletion and many (most?) took it as a given.

Apparently it is not.

However, this doesn’t bring huge changes to applied behavioral science work. The most important practical learning from the entire body of knowledge on self-control and ego-depletion is:

Design is more powerful than self-control

And it holds true.

Maybe the self-control issues that we face are not due to ego-depletion; they could be due to fatigue, forgetfulness, lack of (availability of) attention etc. Yet, how we solve them remains unchanged.

The fact that choice overload doesn’t manifest every time and failures of choice architecture aren’t exactly news, either.

Choice architecture works ONLY when there is no clear pre-existing preference (i.e. the chooser faces ambiguity).

I have been saying this in my training on choice architecture since 2013 (when I joined the field).

If in shop A (men) shoes come in sizes 40-45 and in shop B they come in sizes 41-49, I will buy 43 in either shop, simply because that is my shoe size.

For more on this topic read To Be Clear on Ambiguity 

  

Fewer options to choose from is simply easier for the consumer (chooser), but choice is about a lot more than being simple to choose.

Take the example of Total Wine shop(s). This is what they say on their “about” page:

Our typical store carries more than 8,000 different wines from every wine-producing region in the world, including more than 2,000 wines not available in any other store. (source)

Through the lenses of choice overload 8000 different wines sounds crazy. According to the choice overload principle they should have went out of business a long time ago. Here are a few explanations why they’re still doing well.

First, when wanting to be “the place to go to for wines”, you need to have lots of wines.

Second, many wine enthusiasts seek variety and want to explore. (Others simply want to get drunk and probably have an existing preference for the wine with the best alcohol/ price ratio).

Third, a wine bottle is a low-impact purchase. If you buy something you don’t like it’s just a few (more) dollars spent for an interesting* experience.

*interesting is a word used when you don’t like something and either you don’t admit it or you don’t want to say it out-loud.

For full fairness, Total wine uses choice architecture and offers a lot of structured choice.

Just as a rhetorical question:

Why do we put choices about houses, retirement plans in the same category with ones about jams, wine and coffee?

This, however, is another story.

Coming back to the applied part of behavioral science: We have to test if our interventions work or not, thus we can assess in each particular case how the number of choices offered influences the outcome.

When doing applied behavioral science we have to take into account the bigger picture.

For example, if in a coffee shop reframes the “bring your own cup” discount into a surcharge for paper-cups behavior shifts towards the desired direction: fewer paper cups used. However, it may also lead to a decrease in clients who simply don’t like the idea of paying explicitly for paper cups, even if the total price is the same as at the coffee shop across the street where the paper cup is included.

Moreover, clients who intended, but forgot to bring their own cup will feel bad (due to loss aversion) for having to pay for a paper cup. Moreover, they will experience regret, which is one of those emotions you don’t want to have associated with your business.   

One of the beauties of science is that it evolves and if we are to be professionals in a field of (applied) science we need to keep up with it and, occasionally, unlearn things. 

Look Beyond What You See! Loosing on One Hand, sometimes, Comes with Larger Gains on the Other

Not very long ago, I wrote a post regarding the not so fortunate habit of small entrepreneurs of focusing on loses on small mental accounts and ignoring the wider picture: Shooting yourself in the foot with loss aversion and mental accounting.

Recently I came across a very interesting article about the psychology of returning products (open in a new tab and read later).

Getting returns can be the worst thing possible for a small-shop keeper because she loses the profit on a previous transaction and, often, has to incur a larger loss due to the impossibility of re-selling the returned product. Moreover, receiving returned products involves some additional costs such as a dedicated employee, shipping etc.

In my country of birth – Romania – for several years after the fall of communist dictatorship, many shop keepers held a no return policy even if the product was faulty. In many cases this happened even after it was illegal to do so. Merchants didn’t want to take any loss on previous transactions.

Yet, according to the article mentioned above, things aren’t as bad as a (psychology naïve) shop keeper might think. Actually, making easier for customers to return a product (even if it has no “technical” fault), is good for business. Longer time spans in which a buyer can return a product and no questions asked policy are, in fact, good for business.

The psychological mechanisms at play are numerous and rather complex:

Endowment effect: the longer I have a product the more I value it because it’s mine,

Managing anticipated regret: because it is easy to change my decision – return the product – I feel less potential future regret with the purchase, thus I go along with it,

The affect heuristic: I don’t feel bad when I return a product, thus I like the merchant more etc.

In a nutshell, adopting more customer friendly return policies will increase the number of occasional transactions that are unprofitable (bring loses), but overall, more people will buy and profits will increase.

On a different line of thought, in Romania (my country of birth) smoking was banned in all public indoor spaces (except for jails). I know that in most civilized countries such bans existed for many years, but we’re a bit behind.

When the bill was still under debate, many restaurant and bar owners complained that if such a ban would be enforced they will lose a lot of business.

While it is very plausible that some smokers will go less frequently to bars and restaurants, there is another side to the story. Many of the 75% of Romanians who don’t smoke avoided going into smoky bars and restaurants. Some of them might go out more often and restaurants and bars can get business from them.  

We don’t know yet how restaurant and bar businesses will be affected by this complete ban on smoking (the law will be enacted starting March), but judging by the base rates – 25% of the population smokes while 75% doesn’t – I think that there is a good chance that the ban will be good for business.

The two situations described in this post: returning products and a complete ban on smoking in restaurants and bars can be seen as unrelated. Yet, there is an underlying commonality: We humans have a (bad) tendency to think that what we see is all there is. When a change takes place, we focus on the immediate imaginable things that will happen.

Both shopkeepers and restaurant owners focus(ed) on the immediate losses their businesses (would) suffer. Only after scientific research and deliberate thinking the opportunities (gains) became visible.

Look beyond what you see!

Do I Really Need a Financial Incentive to Recommend a Service / Product?

Shortly after my wife and I moved to the USA, I noticed an announcement in the apartment building we live in that said: “recommend a friend to move here and you get 250$ when they sign the rental contract” (citing from memory). From an economic point of view this made perfect sense: you bring a client to a business and you get something in return.

Only later I realized that this type of incentive made sense when I saw it simply because, at the time, we barely knew anyone on this side of the Atlantic. A few months later, two former colleagues from Erasmus University moved from The Netherlands to the Washington DC area and they were looking for a place to live. We wanted to help them and showed them around the neighborhood. They were curious about the place we lived in and they came over to our place. To make a long story short, I got a business card from the leasing office of the building and gave them the information. The leasing officer (a very nice lady) mentioned that the offer of 250$ was still valid, so if our “friends” leased an apartment from them, we would get the incentive.

That was the moment when it struck me that this type of incentive scheme was faulty. Although I wouldn’t mind getting 250$, my motivation for recommending the apartment building wasn’t financial. We can pay the rent and I think, considering market conditions, that we get a reasonably good deal. We wanted our former colleagues to enjoy the same price-quality ratio. Moreover, the prospect of getting some cash out of the whole thing made me feel guilty. I truly, deeply hate the multi-level marketing approach. The relationship with our former university colleagues was social, not economical.  In fact, as someone who recently made the move from The Netherlands to the USA, we knew the costs and inconveniences it involved. If anything I would have preferred for our former colleagues to get the 250$.

Our former colleagues picked an apartment in a different neighborhood and the 250$ never left the real-estate company.

A similar case happened with a meal-delivery service we use. At the recommendation of my friend Arjan Haring (from The Netherlands) we tried Hello Fresh – a meal delivery service. In a nutshell, we pay each week 70$ and we receive a box with ingredients for three meals for two. This (type of) service is fantastic for foodies such as myself and my wife. We enjoy cooking and eating new stuff, but aren’t actively looking for new recipes and ingredients.  For our food experience, Hello Fresh is a blessing.

As we were very excited about this service, we talked about it with our few acquaintances in the US. Most of them seemed intrigued and curious about it.

In the first month(s) of using this service, Hello Fresh had an option for existing clients to “give a box” for free. It was an (a)typical approach for bringing in new business based on (existing) customer recommendations.

A bit later, however, they changed this “give a box for free” approach to a split incentive scheme. Basically, if we recommend the service to a “friend” and she subscribes, we get 30$ discount for our next order and the recipient gets 40$ off their first order.

While there is some economic sense in this split benefit approach, I began feeling uncomfortable recommending Hello Fresh. I wouldn’t mind 30$, but the financial incentive doesn’t match my motivation for recommending the service.

I recommend something because I want others to enjoy the service we think is great, not to make money out of it.

While in the case of Hello Fresh there might be some evidence-based reason for changing the approach to generate leads from existing clients from “give a free box” to split-benefit, there’s a big lesson to learn, particularly for marketers.

If you want to leverage your existing clients’ social relationships for your business, you need to understand their nature: SOCIAL.

Most people make a reasonably good distinction between social norms and market norms. The element that makes multi-level marketing utterly disgusting is that it perverts social relationships into (wannabe) market / business relationships.

Social relationships are based on imitation, reciprocity, status and alliances. Once you understand this, you can properly leverage them for your business’ benefit.

Simply put, if you want me to recommend your service to a friend (acquaintance, colleague etc.) help me enhance my social relationship with her/him. If you allow me to make a gift in the form of a discount, voucher or even allow me to offer them a full experience for FREE, that makes me look good, gain reputation etc. with the person with whom I am having a social relationship. This gain in strengthening my social status or relationship with someone I know (well) is, for me, more valuable than (the relatively small amount of) money you are offering as an incentive.

Marketing & Behavioral Science: www.naumof.com 

What Is NOT Happening: The Wisdom of the Insect Screen

When we analyze a situation (or simply are awed by it), we look at what is happening and, naturally, try to understand why. When designing something or when working on a new product / service, we focus on what it enables us (people) to do.

This is all perfectly natural, yet it is only (I dare say, small) part of the picture. When analyzing a situation we need to try and see What is not happening. Of course, pigs don’t fly and aliens don’t land in your backyard, but these are extreme examples. Whenever something happens, many (related or not) other things don’t happen. If it is too abstract, it will make lots of sense in a couple of paragraphs.

When designing / working on a new product or service the focus is on what it will enable people to do (better). Very rarely, we focus on what the product or service will prevent people from doing. This is only natural, but, nonetheless, what a product / service prevents from happening is at least (equally) important.

The Wisdom of the Insect Screen

On a personal note: For a long time I had in mind the notion of what isn’t happening and non-events on a very abstract level, but only recently found a great, down to earth, illustration: The Insect Screen.

When my wife and I moved to the Washington DC area (USA), we didn’t fully realize the issue with insects (it was early March and there was snow). Virginia is a warm and humid area – a paradise for bugs. When we picked the apartment in which we now live, we took for granted the insect screens at the windows. For those who don’t know, an insect screen is a fine-metal-wire-grid fixed on the outside of (opening) windows that allows for the circulation of air and prevents insects from entering.

That’s all good and rather simple. It’s not rocket science and makes perfect sense. Moreover, the product – insect screen – does what it is supposed to do: it prevents insects from entering the house. It also enables people to open windows without having to be concerned about flies, mosquitos and other bugs creating nuisance.

The insect screen, however, has further implications. While it was designed to keep insects out, it also makes it difficult for things to get out of the apartment through the windows.

A couple of days ago, a Facebook friend posted that her cat took a dive from the 6^th (7^th by American standards) floor. While I’m glad that the cat is alive (though a bit shook-up), I have to say that this accident wouldn’t have happened if the windows had (fixed) insect screens.

In my country of birth (Romania), though not only, many people living in apartment buildings have a habit of throwing out trash out the window. While most often this restricts to cigarette buds, shaking out carpets, blankets or table cloths, sometimes it happens to be larger items such as trash bags. Many apartment buildings have small yards around them, but these behaviors happen even in buildings that are facing directly to the sidewalks of large streets and passers-by might get some breadcrumbs on their heads.

These negligent, inconsiderate and even anti-social behaviors would be impossible if windows would have (fixed) insect screens.    

On a more positive note, while insect screens are designed to keep out insects, they also keep out other things such as leaves, flying plastic bags, birds and even large rain drops. Insect screens are impotent when there’s a large rainstorm, but if the rain is mild, you can still keep windows opened without having to worry about moping the floor.

If a banal product such as the insect screen has so many non-event implications, shouldn’t you think creatively on what your product’s non-event implications are?  

When to Fire a Cannon and When to Use a Precision Knife in Behavioral Design

When designing and testing a behaviorally informed intervention, there are two extreme approaches: (1) Precision Knife Approach and (2) Firing a Cannon.

In the Precision Knife Approach we design a simple intervention that uses only one or two features that vary (independent variables). We subsequently run an experiment (Randomized Control Trial – RCT) to investigate each feature’s effect on the target behavior (Dependent variable).

The Precision Knife Approach is rooted in rigorous academic research. In order to conduct proper (experimental) research, scientists need to investigate the effect on the target behavior (Dependent variable) of each feature that is manipulated (independent variable) and, if more than one, their interaction effect(s).

The advantage of using a Precision Knife Approach is that you get to know how each feature in your intervention works. You know which features used together generate positive interaction effects (i.e. 1+1 > 2) and which features used together generate negative interaction effects (i.e. 1+1 < 2). 

The downside of the Precision Knife Approach is that it faces behavioral designers with a choice between simplistic interventions (i.e. one or two features) that can easily be tested and complex interventions (i.e. 4 and more features) that are incredibly difficult to test.

The difficulty of testing complex interventions (using the Precision Knife Approach) comes from how a correct experimental research design is done. If we have an intervention based on one feature, then we need two experimental conditions (test cells): Control and Intervention. Once we introduce another feature in the intervention the number of test cells doubles. If we introduce a third feature it doubles again (from 4 to 8) and so on.

Having such hyper-complex research designs is impractical for many reasons including costs of designing different variants of the intervention, acquiring a large enough sample to “fill in” all test cells etc.

The other extreme approach is the Firing a Cannon. In a nutshell, this means that when designing the behaviorally informed intervention, you put everything (reasonable) in it and, subsequently, test the entire intervention against a control (do nothing) or / and against the current material used.  

From the point of view of scientific research methodology this is really sloppy. Moreover, it comes with the risk of generating negative interaction effects (1 + 1 < 2).

From a design / practical point of view, the Firing a Cannon Approach is highly useful because behavioral design has the main goal of improving an existing situation through cost-effective and subtle means (interventions). Finding the best – most effective – intervention can be a later goal.

Moreover, the Firing a Cannon Approach requires fewer resources and smaller samples to test the effectiveness of the intervention.

Another reason for which the Firing a Cannon Approach is advantageous is the increased chances of actually getting things done or proving the worth of behavioral design.

Imagine that you go to a (prospective) client or beneficiary with a complex intervention and an extremely complicated RCT design (such as in the Precision Knife Approach). Because most people are scared of complex things, there’s a good chance that the proposition will be rejected.

Imagine that you go to a (prospective) client or beneficiary with a simple intervention using one or two features and the proposition is accepted. You implement the intervention and run the RCT. You find nothing – the intervention doesn’t work. Subsequently you meet with the beneficiary (client) and present the non-results and ask to run another try, this time using different features (tools) in the intervention. Although this is perfectly correct from a methodological perspective, (real) people are not eager to keep investing in things that don’t produce (desired) results.

In the early stages of the behavioral design process (after the research), the Firing a Cannon Approach is superior to the Precision Knife Approach. In the beginning it is important to show that cost-effective behavioral interventions produce results that are equivalent or superior to what is happening at the current stage.

If the project allows for refinement of behavioral interventions, it is possible to use the Precision Knife Approach to fine-tune the materials used.

For more on Behavioral Design take a look at www.naumof.com 

Shooting Yourself in the Foot with Focus and Loss Aversion

My wife and I are (re)visiting The Netherlands for a few days and we rented an apartment via booking.com. When we arrived, the owners asked us to pay the city tax in cash since they didn’t want to pay a processing fee to the credit card company. You can imagine how that felt like, particularly after 24+ hours without sleep and a trans-Atlantic flight.

Just to be clear, the city tax is about 15 Euros. I’m not sure how much they would have had to pay for the payment, but even a fee as high as 5% would have resulted in a cost of less than one Euro.

Unfortunately, this is not the only situation I encountered in which (small business) people shoot themselves in the foot because they focus on avoiding (small) losses on narrow mental accounts.

Some restaurant owners want each and every table (seating) to be profitable and, occasionally, become sales-aggressive or impolite.

A print-shop owner stopped handing out candy because someone took a hand-full from the candy jar.

A business owner wants to make a profit on each and every transaction, thus refusing to make some small deliveries.

A small-shop keeper refuses to install a bankcard-payment POS because the bank charges him 3% of every transaction.

Each of these examples makes intuitive sense: nobody likes to lose money.

The focus on making a profit on each and every transaction might sound like good management, but it simply isn’t.

Each such decision is like shooting yourself in the foot and later wonder why you can’t run.

Sure, a restaurateur might squeeze another few dollars or euros from a client, but there’s a good chance that person will never set foot again in the restaurant. A shop keeper will avoid paying the bank the transaction fees, but some existing clients might start avoiding the shop, while potential clients will not even consider it since they can’t pay with their bankcards.

You might think that such problems occur only for small businesses, but this isn’t exactly the case.

Let’s do a thought experiment:

Imagine that a friendly alien comes from the sky and proposes the following gamble: a fair coin will be flipped and if the space-ship side comes up, you will win 150$, while if the other side comes up you will lose 100$. Each side has a 50:50 chance to come up.

Would you take this bet?

I’m not sure what you would do, but I have shown this situation to hundreds of participants in my training programs in applied behavioral economics, many of them in pretty large businesses.

Only about 10% of them say that they would take this bet. For all the others, taking a bet in which you can either win 150$ with 50% probability or lose 100$ with 50% probability is unacceptable.

This holds even after I compute the expected value of the gamble (which is +25$)

One participant asked me if the bet is played only once and I answered yes. He said that if it is only once he doesn’t take it, but if it would have been played several times, he would take it.

Here’s the key: if you play this bet several times, on average your chance of overall gaining money increases, even if you will sometimes lose. If you play ad infinitum it is certain that you will end up gaining money.

Imagine a conference room with 20 people having to decide if each of them would take the above mentioned bet. If all of them are from the same company, it is like the group (company) would play the bet 20 times.

Up to now, no one realized this. Every person makes a decision on their own and, usually, the decision is to not take the bet. Overall the group (company) loses because everyone thinks individually…

I am not criticizing anyone, but it might be a good idea to take a look on the rewards and penalties systems. If, during an evaluation period of one year, an (each) employee has to make one such decision she will most likely not take the risk because there’s a good chance (50%) that she will lose money for the business and her evaluation will be bad, her bonus will disappear and so might her job.

Small businesses shoot themselves in the foot because they focus on avoiding loses on each and every transaction.

Large(r) business shoot themselves in the foot because they focus on evaluating each employee / department / manager etc.   

Behavioral Science Meets Marketing Communication and UX Design

On September 21^st I gave two workshop sessions on how behavioral science can improve marketing communication and UX design, respectively.

I very much enjoyed giving the two half-a-day workshops to the very nice audience at LiveHealthier – a corporate wellness company just North of Washington DC. Both workshops were well received by the audience in both enjoyment of the sessions and usefulness.

Here’s what Amy Troop SVP, Consumer Experience at LiveHealthier said about the training:

Nick Naumof conducted two sessions for our consumer marketing and product development teams on applying the theories of behavioral science to marketing communications and UX design.

Participants found it to be time well spent and came away with immediate applications for the learnings in their day-to-day work.

Thanks, Nick, for all your efforts in crafting a meaningful program for our team!

Thank you Amy, Demetrius and Sasha for the great support in organizing this session! Thank you to all participants who made the day delightful.

Here you can find details on my Learning Programs on how behavioral science and behavioral design can make your products & services work with human nature.

You can take a look at my dedicated programs on behavioral design for banking  and health & wellness.

The Nudge is Not Enough! The Love Story Between Behavioral Science and Practical Applications

A couple of weeks ago I published this post on BehavioralEconomics.com 

Thank you Alain Samson for the invitation.

A romantic relationship goes through various stages from early dating to marriage and, in about half of all cases, divorce. It begins with flirting and continues with that essential first date. If that goes well, it is followed by more dates. If things go OK and the chemistry is good, the relationship will go to the next level: one partner offering the other a shelf in their closet. Sooner than many realize, this leads to the natural question of Why pay two rents? followed by a de-facto living together. After a while, one of the partners pops the BIG question: Will you marry me?

The relationship between academic or theoretical behavioral science (let’s call him THEORY) and applied behavioral science (let’s call her PRACTICE) is not much different from a romantic relationship.

It was quite hard for THEORY to get that first date with PRACTICE, but luckily it happened.

In hindsight, the seminal papers of Kahneman and Tversky on heuristics and biases and on prospect theory published in mid and late 1970s were not enough, at the time, to get PRACTICE to accept the first date.

Fortunately, after about 20 years of flirtation, that first date happened. It was in mid and late 1990s, when Thaler and Benartzi developed and analyzed early implementations of the Save More Tomorrow program which helped (American) employees to save more for retirement by bridging the intention-action gap. In very brief, at every pay raise a person’s savings rate automatically increased (e.g. from 3% to 4%). The automated escalation of savings rates helped most people keep their commitment to save more, while the coupling with pay raises eluded the miserable feeling of losing money out of one’s current paycheck (i.e. loss aversion identified by Tversky and Kahneman).

Occasional dates happened between THEORY and PRACTICE after that, but neither side was taking the relationship too seriously.

The book Nudge (2008) by Cass Sunstein and Richard Thaler showed that THEORY and PRACTICE have a shot at a serious relationship. The establishment of the Behavioral Insights Team (UK Nudge Unit) in 2010 was equivalent to PRACTICE offeringa shelf in its closet to THEORY. As in any romantic relationship, THEORY brought in more and more of its things into PRACTICE’s apartment. Now in 2015, they have (almost) de-facto moved in together.

Throughout their relationship, THEORY and PRACTICE have enjoyed making nudges… those small, relatively inexpensive, supposedly irrelevant changes in choice architecture that lead to potentially large changes in behavior – tax collection, college enrollment rate, savings rate, sales etc. Simply put, nudges are small changes that have a large impact on behavior. The result of THEORY and PRACTICE’s union.

However, the Nudge is Not Enough!

Indeed nudges or behaviorally informed interventions have (considerably) improved several areas of public and private services. Most of the time, these small interventions are more than welcomed. Simplifying and structuring choice related information is great simply because everyone hates filling in endless forms and making complicated choices between things they are clueless about (such as Ethiopian food).

Nudges are, most often, great! Nonetheless they are not enough.

The shortcoming of nudges is that most often they are simply tweaks augmenting a pre-existing service or policy.

While they can be beautiful, intriguing and occasionally elegant, nudges are just augmenting (improving) an existing service / policy regardless of that service’s (policy’s) quality, appropriateness or fitness.

For example, an education institution optimizes the choice architecture of its forms in order to smooth the actual application and enrollment processes, resulting in more students joining the institution’s programs. This nudge does not change the service provided. The additional students will attend the exact same courses, go through the exact same stages (from enrollment to graduation) as before the nudge was applied. While for the additional students who joined because of the improved choice architecture attending more education might be beneficial, it is possible for them to be rather unhappy since the courses might be boring and irrelevant.

Getting more people into schools or other forms of (adult) education is generally beneficial for everyone involved. We can use behavioral science insights to increase enrollment and decrease drop-out rates. But what if we could use the same knowledge to design better education services?

For example, night-school or other forms of evening-learning are rather popular among adults. However, after a full day at work, System 2 is fatigued and self-control resources are almost depleted. Therefore, it might be a good idea to adapt both the content and teaching methodology to this cognitive reality.

Applying nudges to traffic tickets in order to increase payment compliance (i.e. voluntarily paying the fine) will not solve the issue of traffic safety. If anything, it will continue to feed a carrots-and-sticks approach to influencing human behavior. What if we could use existing knowledge in behavioral science to design safer roads? OK. That would cost a lot of money and will take a lot of time. But what if we could (re-)design insurance services that encourage safer driving behavior?

It is time for THEORY and PRACTICE to take their relationship to the next level: from Nudging to Behavioral Design.

In his book “Slim by Design” Brian Wansink says: it is better to work with human nature than against it. The main thesis of his book is that instead of emphasizing on counting calories and self-control reliant diets, it is much better to (re-)design eating spaces, homes and shops. This way, eating better (healthier) is the natural thing to do and not an eternal fight between temptation and self-control.

In the same line of thought, we can design public policies and (private) services that work with human nature and not against it. While nudges add a (thin) layer of human-friendliness, these behaviorally designed policies and services incorporate behavioral science knowledge in their very core.

Car Insurance

Insurance companies truly and deeply hate when their clients have car accidents, leading to expensive repairs, because insurers have to pay the bills. Although this is the very nature of the insurance business, your insurer would love to take your risk of minor accident from 2% to 1.9% and/or have to cover the damage of a broken bumper than that of a full-frontal collision, while at the same time keep on charging you the same $400 / 6 months.

To some extent, behavioral design can create a car-insurance service that promotes safe(r) driving behavior. Part of the risk is purely random, while another part is (to some extent) related to behavior. Behavioral design can address the latter. Car manufacturers are already doing a lot to prevent drivers from not wearing a seat-belt or driving way above the speed limit. Insurance companies, too, can contribute to encouraging preventive behavior.

For example, in Europe cars need to go through regular maintenance and mandatory checks. An insurance company has the possibility of sending out customized reminders (nudges) when the check date is near. Such an approach will decrease the risks associated with unfit vehicles on the roads. Similarly, insurance companies can provide as a default option tracking devices that monitor driving behavior, provide real-time feedback, implement social-benchmarking on risky driving (e.g. 63% of drivers drive safer than you) and offer financial incentives (i.e. lower rates) for safe driving behavior. Moreover, the device can locate the car if it is stolen.

Health and Well-being

Health is a broad area in which nudges are popular — and for very good reasons. There are many examples of nudges for hand-washing, treatment adherence, in-store interventions for purchasing vegetables etc. The major challenge is to design health insurance and health-care services that incorporate behavioral science knowledge in a systemic manner.

As in the car-insurance situation, health-insurance companies (public authorities) hate having to pay large bills on treatments for conditions that could have been prevented or are delivered in a sub-optimal manner (e.g. emergency rooms overcrowded by non-emergencies).

For example, the treatment for diabetes is quite expensive and has to occur for a lifetime. Promoting more appropriate eating behaviors in order to prevent the disease actually makes (economic) sense for health-insurance companies (authorities). Nudges can be useful and are welcomed. However, things are a bit more complicated; simple augmentations of pre-existing frameworks might not do. Rather complex preventive programs that have behavioral science at their core are needed. Texting individuals in high-risk (of diabetes) populations reminding them to eat more fruits might be useful. However, a more direct approach such as fruits for junk-food exchange program might do more. Services that provide regular home-delivery of easy to prepare (eat) healthier food already exist (e.g. Hello Fresh) and can be an inspiration for preventive health services provided by insurance companies.

Another behavioral design approach to decreasing health-related expenses and increasing health well-being is to improve the financial well-being of the most vulnerable population groups. It may seem a bit awkward for a health insurance company (authority) to care about the financial well-being of the poor. However, health and financial wellbeing are inter-related to some extent. Moreover, a critical situation in one will lead (sooner or later) to serious problems in the other.

Unlike middle class or more affluent people, the (very) poor cannot absorb financial shocks such as car-repairs, replacing a broken fridge etc. Short-term money lenders are eager to offer loans for such emergencies, but the interest rates are skyrocketing (e.g. 500% per year). Since most of these individuals live from one pay-check to another it is virtually impossible to repay the loan, leading to a vicious cycle of debt and misery. When caught in such a debt-trap, it is very likely that some will neglect their health, eat cheaper and less healthy food, work 16 hours a day etc. All of these behaviors will, ultimately, result in health problems and high healthcare bills.

Offering financial safety-nets for vulnerable categories might be a good idea for preventing serious health-problems and subsequent large medical bills.  One solution would be to offer emergency small loans (e.g. up to $1000) with zero interest that would be repaid throughout one year in the health-insurance bill.

Applying nudges – augmenting existing service or policy frameworks – constitutes considerable progress similar to that of going from dating to de-facto living together in a romantic relationship.

Since the relationship looks and feels good, there is no reason for not taking it further. Behavioral science is so rich in potential applications that we should not restrict ourselves to highly effective, yet superficial, applications.

Soon the time will come to ask the BIG question:

Will you do Behavioral Design with me?

Check out my new website www.naumof.com 

The Short Bucket List: A tool for making memorable gifts

This week I attended a Design Thinking workshop in Washington DC. The aim was to improve the gift giving experience. As I am quite bad with picking gifts, I had a chance to work on a tool that would help me and others who face the same challenge.

 While gifts are usually material objects, probably the most memorable gifts are the experience ones such as learning to fly an airplane, parachute jump etc.

Many people have “Bucket Lists” – things to do before they die.

So, I created this prototype of a tool for making memorable experience gifts.

The gift giver asks the future recipient of the gift to fill in this short bucket list with up to 5 things they would like to do before passing away. (Each item on the list is written on a post it)

Subsequently, the items on the list are removed from the piece of paper and put into the “Randomizer”

After mixing the options, one is picked at random and that is the gift:

Now the gift giver knows what to offer as a gift, the gift receiver doesn’t know what she will get – surprise element, but she will get for sure something she wants because she picked the options.

The gift giver has the option of sharing the experience with the receiver (e.g. do a parachute jump together).

Of course, the gift giver can cheat and draw again if she doesn’t like what was randomly selected.

Naturally, everything could be done digitally. 

Explicit and Implicit Physical Cues for Social Norms

In many models describing human behaviour, including my own 4D model, social influences and the physical environment are seen distinctly. However, there are situations in which there are physical cues of social norms.

Sometimes these cues can be explicit and prescriptive. They are physical objects that clearly state what the (formal) norm is. In this example, the signs clearly means: 

Your dog shouldn’t poop in my front yard.

Other times, elements of the physical environment represent cues of descriptive social norms. If there’s trash on the street, then it is socially acceptable to throw some more trash. 

The presence of lots of cigarettes buds suggests that

 it is OK to smoke here.

Which one do you think is stronger? The Explicit Prescriptive norm or the Implicit Descriptive Norm?