How behavioral science reveals what traditional market research misses

In this episode, MichaelAaron and Richard challenge some of marketing’s biggest assumptions about consumer research. They explore why people often can’t explain their own decisions, why A/B tests outperform surveys, and how observing real behavior leads to stronger insights and better marketing decisions. 

Transcript

MichaelAaron Flicker: [00:00:00] Welcome back to Behavioral Science for
Brands, a podcast where we bridge the gap between academics and practical
marketing. Every week, we sit down and go deep behind the science that
powers great marketing today. I'm MichaelAaron Flicker.
Richard Shotton: And I'm Richard Shotton.
MichaelAaron Flicker: And today, we are discussing research, a critical part
of the marketing ecosystem.
Let's get into it.
So Richard, you and I have talked about research methodologies, great ways to
get consumer insights for as long as we've been chatting together. And we
decided, let's put it together in an episode that we can really engage people and
you were just commenting before the show, bring some brand-new studies to
the table.
Richard Shotton: Yeah. Yeah, absolutely. We've got some really good studies
coming up that we've not discussed before, and then we're gonna open with a
familiar one to set the scene. But yeah, later on we've [00:01:00] got some
brilliant studies involving things like the bogus pipeline, which I can't wait to
talk about.
MichaelAaron Flicker: Yeah. Yeah. It's name alone- yeah ... gets the- Makes it
worthy of a whole episode ... gets the palate watering.
Richard Shotton: Yeah. Yeah,
MichaelAaron Flicker: yeah. Yeah. And as we were preparing for the episode,
something about the influx of AI and the need to move faster just makes the
field of research feel even more alive than it has.
And what I was reflecting on is just how fast the industry of research seems to
be moving. AI's changing workflows, teams are under pressure to have tighter

turnaround on insights, and businesses are getting more and more used to
having data in real time dashboards that give them feedback in real time.
And I was thinking how speed is not the same thing as truth, and in some ways
the faster [00:02:00] research gets, the more important it is to be asking almost a
basic question: What kind of evidence are we actually collecting underneath the
research? How true is the research that we're getting?
Richard Shotton: Yeah, absolutely.
Thinking on a really basic level, if you and I had a super fast car, it goes 1,000
miles an hour, it gives us the potential to get to LA very quickly. But if we're
using a map that tells us LA's on the East Coast, we can have the fastest car in
the world and it's not gonna help. We're just gonna get to the wrong place
quicker.
And I th- I do have a bit of skepticism sometimes about our obsession as
industry with real time. Real time and hyper sped up processes are only good if
they are based on an accurate collection of data and a data that is representative
of what you're trying to achieve. And often a lot of data is based on a flawed
map [00:03:00] of human motivation.
MichaelAaron Flicker: And with that, technology has brought everybody so
much closer together. The world's a smaller place, and I was reflecting how the
brands are closer to consumers than almost ever before. You've got post-
purchase surveys, you've got feedback dashboards, you have this unending
world of social media commentary.
And so you have, on the one hand, the speed at which getting to insi- getting to
research and getting to consumer feedback is happening so much faster, and on
the other hand, you're so much closer to what feels like the potential for
insights, 'cause you're in the social media threads as a brand. You're seeing
people react to your product.
You're seeing people talk about your brand in real time. So you have the, you
the sense of being close. But As you and I like to do, [00:04:00] we want to talk
about what's effective. And so one of the big questions that organized how we
approach today's episode is not just how do we get to insights faster, but really
how do we get closer to what actually motivates consumer action?
How do we not just take what we've heard people say, but how do we translate
it into make sure we're understanding what's truly motivating consumer

behavior as you rightly just said. So the conversation on research really feels
timely, and it's a chance for us to ask not just what are the right methodologies
to do research, but how can we have more confidence that what we're hearing
and what we're g- receiving will translate into a- things that we can use to
change buying behavior, to change consumer insight and action.
A fair setup for today's episode, that's what we're really goaling for.
Richard Shotton: Yeah. That's, I think a very fair setup. Perfect. [00:05:00]
MichaelAaron Flicker: So we're gonna start, as you teased, with a study- ...
that many people may know, or at least an author that we all have come to know
and love. Go ahead.
Richard Shotton: Yeah. So there's a b- bit of a twist on this actually.
The f- the original study's very famous. So it's a 2008 study run by one of the
absolute premier people in behavioral science, Robert Cialdini. So he was a
professor at Arizona State University. And in one of his most famous studies
back in 2008, he works with a hotel chain in southwest USA, and he persuades
them to alter the messages that are running in guest rooms, encourage them to
reuse their towels.
I'll simplify the study slightly, but let's imagine rooms are divided into two
groups, and half the rooms have a very standard door hanger message. Now,
there's a door hanger in there that says, "Please reuse your towels. It's good for
the environment." And 35% [00:06:00] of guests comply. And then the other
half of the rooms have a door hanger message which says Please reuse your
towels.
Most guests who've stayed in this room have done so." And there you get 49%
of people reusing their towels. Now, we've discussed this study before, and
generally we've talked about the fact that this is a great experiment, a realistic
experiment into the power of social proof. That if you stress what most people
do, it makes that behavior more likely to occur.
And what Cialdini is showing in the hotel experiment is often social proof is a
more effective way of changing behavior than just giving people a long list of
rational reasons about why they should change. But what we often don't talk
about is the follow-up study that Cialdini did. So to begin with, he's worked
with the cleaners.

He has identified what effectively influences people in their rooms when they
don't think anyone's watching. Now what [00:07:00] he does is a more standard
market research approach. Gets a new group of participants, sends them a
survey and says to them, "Look, imagine you're in a hotel and imagine you saw
one of these two door hanger messages," the social proof one or the
environmental one.
And he says to people, " what would influence you to change your behavior?"
And the interesting thing is that what people claim would influence their
behavior is the exact opposite of what actually influenced their behavior. People
say, "Oh, I wouldn't be influenced by social proof. I don't do what other people
do.
I don't copy. I'm not a sheep. I would be influenced by the environmental
message." So people say environmental messaging in this situation would be
influential, but when we-- or when Cialdini does the real-world study, he finds
in actuality it's the complete opposite.
MichaelAaron Flicker: So they, they are lying about this? No.
They are [00:08:00] misrepresenting. This is the question at hand, right? What's
happening- Yeah ... here that their claimed behavior and their actual behavior
are so much apart?
Richard Shotton: I think it's both. I think- Yeah ... some people straight out lie.
We don't wanna get to politics, but I think for a big group of the population,
they feel like there is a socially acceptable way of answering.
Yeah. And for other pe- Social pressure. Yeah, e-exactly, social pressure. So
maybe let's focus on that one. We can come to the other, the group later on. But
it's this strange situation in which even though you get sent a survey online,
you're never gonna meet the res-researcher face to face, we want to project a
positive image to this anonym-anonymous researcher.
We even wanna project a positive image to our-ourselves. It's very hard to say
something that we think reflects poorly on ourself. So there's a social
desirability bias. The-- When [00:09:00] we are asked a survey, we often
answer in the way that reflects well on us rather than what we think would
actually motivate us.
MichaelAaron Flicker: And I think that gets to a lot of the challenges betwe-
behind research that ask what would you do rather than structured research that

looks at what did you actually do? That this is the problem. Is-- Would you call
that the problem of claimed data? Is that a fair comment?
Richard Shotton: A-a-absolutely. So there's a fundamental issue that claims,
like what people say in a focus group, what people say in a survey, they are
often quite misleading.
What is much better, I think we'll come to this later on, is setting up simple test
and control experiments like Cialdini did. Don't even bother asking people. Get
straight to running a field experiment. Now, of course, you can't always do that.
But again, even when it comes to a survey, there are tactics you can employ that
I [00:10:00] think get slightly closer to the truth.
They help avoid this problem of lying.
MichaelAaron Flicker: So then the question becomes, what tactics can we use
to overcome this? Yeah. What, what's in our marketer's tool belt that we can use
to overcome this compulsion to not to not know thyself to not represent what
they're gonna do?
Richard Shotton: Yeah.
E- especially if you're in a situation which is morally charged, like charitable
behavior, volunteering, environmental behaviors, one of the tactics you can do
is ask people not what they would do, but ask people what they think others
would do. So there's a brilliant study, it's about 25 years old by Epley, who was
at University of Chicago, and David Dunning at Cornell.
And
at Cornell, there is a regular annual fundraiser. So people will go around selling
daffodils, and all that money goes to, to, [00:11:00] to charity. Now, it's a
regular fundraiser for the American Cancer Society. So five weeks before the
annual fundraise was gonna happen Epley and Dunning go out and ask 251
people, first of all, to predict how many daffodils were they gonna buy if at all.
And 83% of people say they'd buy a daffodil or they're going to buy a daffodil,
and on average, they say they're gonna buy two apiece. The researchers then ask
those same people to have a think about what others, other people on the
campus are gonna do, and the answer changes quite a lot. People estimate that
on average, 56% of others are gonna buy a daffodil, and on average they'll buy
1.6.

So we've got people are claiming for themselves 83% at an average of
[00:12:00] two, others 56% at an average of 1.6. The psychologists then wait
five weeks, and once the fundraiser has happened, they then go and ask people
what did they actually do? And the actual donation level, only 43% of people
bought a daffodil.
On an average, they bought 1.2. So they massively overestimate their own
generosity. They slightly overestimate the generosity of others. But our
prediction of what other people are gonna do is much closer to the truth than our
prediction of our own behavior. So if you're operating any kind of morally
charged area, I think one of the simplest things you can do as a market
researcher is stop asking people what they're gonna do and ask them what they
think others are gonna do.
MichaelAaron Flicker: It strikes me that this way of asking probably has two
key [00:13:00] benefits. The first is that we remove this socially acceptable
question, right? We don't have to answer- Yeah ... it a way that reflects on
ourselves. So I think that's one thing this achieves. But two, it-- we as humans
are pretty good estimators of others' behavior.
We, we observe, we, we adjust, we adapt because of our stimulus around us. So
I think we are better at guessing what the group may do than any individual. So
I think this kind of brings two benefits to the table and could be used in a lot of
instances when, as you say, there's a morally charged element.
Or whenever there's something that could reflect negatively on you, you might
wanna consider this as a way to get to closer estimates of the results.
Richard Shotton: Absolutely. So we're still relying on claim data, but with this
little twist, we're making it a bit more accurate. And it doesn't cost [00:14:00] a
brand or a research agency any more to do it this way.
It's no more complicated than asking people what they're gonna do. It's a very
small twist, but in these specific areas, I think it, it really helps.
MichaelAaron Flicker: Based on that insight that we're good at guessing what
the group might do you think that this would be recommended even in instances
where there's not a morally charged concern that it reflects negatively on us?
If I were to guess how how many how many meals out in a month your group
of friends may do versus how many meals out am I willing to do? That's not

really a morally charged question, but do you have any thoughts on would that
help us get to maybe a more accurate projection of what might actually occur?
Richard Shotton: Do you know what? I haven't seen any studies on that. I
think there is a stronger case that it's in these morally charged areas where you
get the greater benefit [00:15:00] because I guess you'll get-- There's two things
that work to your favor- You're knocking
MichaelAaron Flicker: down if you do that, yeah ...
Richard Shotton: one, yeah, people are reasonably accurate at knowing what
others are gonna do, and then secondly, they don't have the self-interest of
needing- Yeah
to protect, project a particular image. So you still get the first benefit, you don't
get the second one. So I think there's a very solid case for areas of ethics. The
jury is out I think would be a fair- Yeah,
MichaelAaron Flicker: I think that's fair. It strikes me that if I if the question
were posed to me I take a very different perspective if I'm answering what
might I do.
I start as- asking what's my commitments, what's my family situation, what's in
my pocket, versus if I'm trying to guess what my community might do, I start
answering with, " what have I seen other people do? What do I-- What's my
community doing?" Yeah. Yeah. So a- again, no data to back this up, but it does
change the the agency [00:16:00] from which I'm answering.
Richard Shotton: Yeah. I th- I think you're right in that. I hadn't thought of it in
that way. If I was asked how likely is it that I will be late for this podcast
recording, I would think, "Oh what are my commitments today? What are my
travel times?" All this kind of stuff. If someone asked how likely is it
MichaelAaron's gonna be late, I wouldn't think about any of that.
I'd just think, " okay, we've done 120 podcasts. He's been late twice. Okay, it's
about a 2% chance." And I think it's that approach, what has previously
happened, not trying to judge the the kind of supposed factors that are drawing
on things. It's just looking at what is the base rate, go with that as a pretty good
answer.
So you're right.

MichaelAaron Flicker: That leads to- And of course, as a-- And as a brand, we
don't have access to either of those. We don't know what they would consider in
their own decisions hat, but we also don't know their lived experience, their
communities, their... That, so either way, you're collecting really valuable data.
You're just collecting it from a different perspective. We'll put a pin in it, and
we'll say- Yeah ... [00:17:00] maybe you and I will run some research of our
own to see if that yields us any better outcomes, but I think that'd be very
interesting. Yeah.
Richard Shotton: You know what? The... I've often found with the
experiments that academics have done, the best way to treat them is not these
amazing gifts that come from down high, and you have to treat them-
With kid gloves. If you find a research study that's quite useful but not spot on,
like in this case it's about the charity world and we wanna look at it in
elsewhere, you can take the methodology and rerun it- Yeah ... in a slightly
different setting to get to those answers. All of us as marketers, we, with our
websites and our store footprint, we've got essentially laboratories.
Most academics would give their right arm to have a medium-sized corporate
website. It's an amazing laboratory to test hypotheses.
MichaelAaron Flicker: I, yeah. So I went to, maybe you and I would run it. If
anyone listening wants to [00:18:00] run this and see what you find, let's share it
and we'll we'll, we'll- Richard and I on air, will commit to- ... running your
research results should you share them with us, and we can and we can help
other people learn from it.
Richard Shotton: It's a dangerous commitment here. Dangerous commitment
involving the public. Who knows where we're gonna end up? Yeah.
MichaelAaron Flicker: And yet somehow it feels so safe.
Yeah. So let's see what happens. Okay. But yeah, lo- love to invite everybody,
but put it to practice in what you're doing and see what you learn. Okay. So we
have a second tactic that we think could be- Yeah ... really interesting for those
looking to learn more in research could consider.
Richard Shotton: Absolutely. And I love this one.

So this is one with a ridiculous name. It's called the bogus pipeline, and I'm not
100% sure why it's called that. But essentially, it's a technique to boost honesty.
And what you do is, before people take part in the research, is you tell them that
they are gonna undertake their [00:19:00] survey while they are condu-
connected to a lie detector.
Now, in reality, it's just a box with flashing lights. There is no lie detector. But
crucially, people believe that it's active. Now, that all sounds a little bit strange.
You might think that this will be a one-off study done in the university of the
back of beyond, and it's a bit of a dubious finding to, to report back on.
But actually, this idea has been repeatedly experimented on. There's a
psychologist called Neil Rose Northwestern University, and as far back as 1993,
he did a meta-analysis. So he found 31 different experiments- Wow ... of
reasonable quality, and then looked at what the general findings were. And he
found again and again that if people think they are connected to the lie detector,
they will answer more honestly.
Now, [00:20:00] many people might be thinking, "How do you know they're
answering more honestly? Maybe they were answering honestly before." what
researchers do is they think about identifying some really horrible behaviors,
and it it could go from mildly horrible to really horrible. So the mildly horrible
could be Did you re-gift something one of your family gave you?"
You could ask Did you pretend to put money into the church donation box, but
not actually?" All the way down to genuinely horrific Did you have racist
thoughts?" the kind of full-on nasty stuff. And they judge whether people have
become more truthful by seeing if more people answer that, yes, they've done
some of those nasty to very nasty behavior.
So that's how they can get to what's actually the truth. They don't know who's
lying or who's telling the truth, but when you see the percentage admission
against unpalatable behaviors, you know there's a general swing towards
truthfulness. So that [00:21:00] I think is a slightly bizarre but interesting
opportunity to get people to become more honest, make them believe that they
will be held to account, that they will be noticed if they are telling untruths
MichaelAaron Flicker: So I think that really gets to the heart of when people
are intentionally telling an untruth.
They're intentionally- Yeah ... lying. But as we started to get into in the
beginning of the episode, sometimes people just don't know what their

motivations are. They're honestly trying to- Yeah ... answer the researcher's-
Yeah ... question, but we call it a con- a confabulation. Yes. They they are
strangers to themselves.
Timothy Wilson has a book called "Strangers to Ourselves," and I was reading
a summary by James Clear who who did a great book summary. I think we
[00:22:00] should-- We'll put it in the show notes. And he summarizes Wilson's
point by saying, "We do not realize how much the non-conscious mind impacts
our behaviors and personalities.
In many cases, the non-conscious mind influences our behavior more than our
conscious thoughts do, and the two minds will often conflict with one another."
So that, to me, that's really an insightful point, that sometimes we're choosing to
lie because of either social pressure or moral guidance, and sometimes we don't
know why we did what we did, and that's really the strangers to ourselves
theory.
Richard Shotton: Yeah. You're right. Lying's one thing, and we've talked about
two tactics to avoid that. A bigger, more fundamental issue is if people don't
know what motivates them, yet they feel pressure to answer a survey, often
what people will do is give you lots of plausible explanations of their behavior.
They [00:23:00] think these explanations are true, but a simple contest and
control methodology will show that actually people are misguided.
So this phrase confabulating is a brilliant one, and it as you say, it describes this
idea of trying to be honest, but sending the research off in the wrong direction.
MichaelAaron Flicker: And just before you consider that maybe your research
doesn't fall into this category, I've n- I don't think I've ever done a brand
research project in 28 years that did not give a financial award for completing
the survey.
So you have these people that have started And they have and there is a gift card
or financial benefit at the end of the survey. There's an incentive to get to an
answer and click next. I maybe in Europe it's different- Wow ... but in America,
almost every res- branded research study has [00:24:00] a benefit a pr- a
financial or otherwise meaningful benefit at the end.
Richard Shotton: Yeah. And again, I think this is gonna take us back down to
that line route, because the problem often with a sample is if you're given some
screening criteria, if let's say you're a bit short of cash, maybe you're a student,
you might think to yourself, " which of these criteria is most likely to get me to

getting my free gift card, which will get, make me valid to enter the the research
study rather than what actually reflects my beliefs?"
And so I think there's all sorts of dubious incentives that once you start
scratching the service, you are actually quite worrying. So we have a way to-
Yeah ... so we have a way to move through this.
Yeah. So the, go back to Cialdini's study the towel study. What he did in his
original experiment is essentially called a field experiment.
He gets a naturalistic [00:25:00] setting. He then, so in this case, the hotel and
people have to use their towels or not, that's a standard part of a hotel stay. He
then randomizes the rooms into two groups. He changes one variable, that's in
this case, the message on the door hanger, and then any change in behavior he
can attribute back to that single change variable.
That is a much better way to get to the truth than artificially putting front,
someone in front of a survey and asking them direct questions about what they
do or not do.
MichaelAaron Flicker: And so we're talking about prioritizing observed data
rather than claimed data. We're saying if you can set up a good field study, it
will likely give you more insight than just asking what people would do.
And if you were to follow the wildly unofficial but absolutely thoughtful
[00:26:00] MichaelAaron and Richard approach to building a good field study,
w- to recap what you said, Richard, four things. One, choose a representative
and reasonably sized sample. Two, only change one variable within each cell
with- w- within the samples.
Three, critically do not let them know that they're taking part of the-- what the,
what part of the experiment we're absolutely observing for. And four, we call it
a naturalistic setting. Don't put them in a lab. Put them in as close to the real life
that you can, because that's where they're gonna naturally behave the way they
otherwise would.
Richard Shotton: Yeah.
MichaelAaron Flicker: Those are the four things to a good field study
experiment.

Richard Shotton: Absolutely, and I would always be pragmatic, and
sometimes you have to cut your clooth- cloth to suit your means. Yeah.
Sometimes you can't do all of those four things, but that's what we should
always be aiming for. [00:27:00]
MichaelAaron Flicker: And so maybe you can talk about an example of a field
study-
Richard Shotton: Yeah
MichaelAaron Flicker: that really brings this to life.
Richard Shotton: C- Absolutely, 'cause it sounds so simple, but there's actually
sometimes real creativity, I think from psychologists and behavioral scientists,
in trying to get some of this naturalism. So one of my favorite studies comes
from Deborah Small, who was at the University of Pennsylvania.
And in two thousand and seven she recruits a group of participants. So it's
starting as a very standard survey, and she asks them if they'll complete a short
survey on their use of technology, and they will be given five dollars for
contributing. Now, all this is a smokescreen. People think the study is
answering half a dozen questions about tech.
But actually, it's only once she hands over the fee, the five dollars, that the real
experiment starts. She then tells a little [00:28:00] white lie, and she says to the
participants, Deborah Small says to the participants, "Look, thanks for taking
part. What we always do after giving people their fee is give them the
opportunity to donate some money to a charity.
So why don't you have a read of this little laminated info pack, and you can
decide to give as much or as little as you want?" Some people get a little card
that looks like it comes from Save the Children, which talks about the number
and the scale and the stats of food shortages in Africa and the hundreds of
thousands of people who are suffering from malnutrition And when they come
to decide whether they're gonna give any of their money away, on average they
give $1.17.
The other half of the people are given a short info pack, which again looks like
it comes from Save the Children. But this time it doesn't follow lots of statistics,
[00:29:00] it just tells the reader about an individual girl, a seven-year-old girl
fr- from Mali in Africa called Rockia, who's suffering from malnutrition.

Now, amongst that group, the average donation is $2.83. So we've gone from
statistics leading to $1.17 being donated. Individual story, it leads to a $2.83
donation. Wow. So yeah. Wow. More than doubling. Exactly. Wow. Now,
interesting study in and of itself. It became known as the identifiable victim
effect, and it's essentially the argument that stats and facts leave people cold,
doesn't really create an emotional reaction.
But an individual story with a place and a character, you can tug on people's
heartstrings and therefore they'll become more generous. So there's an
interesting insight to generated by the methodology, but I think what's really
interesting for us is her technique. So two big [00:30:00] things. Even this kind
of question and answer, this survey approach, she makes it far more naturalistic
by allowing people to think they've come to the end of the survey.
They are then given real cash. So this isn't just, oh, a fictional would you donate
theoretically. They are actually handing back some of their fee. So she gives
people real cash. That makes it a bit more insightful. And then the third bit is
people are not given the two descriptions side by side.
Half the people get the statistical one, half the people get the individual story.
It's this randomization. And of course, no one knows that the researcher from
their bird's eye view is judging the generosity comparing these two groups.
They don't know that they are taking part in an experiment weighing up
statistics [00:31:00] versus a story.
They think they are just gonna be judged on how generous they are. They don't
know that other factor that's going on. So I think it's that mix of naturalism,
subterfuge, real money, and then randomization to two groups that make it a
really good example of a field experiment.
MichaelAaron Flicker: I think it really brings it to life for us.
And, It's so often when you sit down with a research group as a brand or as an
agency and you say to your- to them, "This is what we really don't know and
what we want to discover," that the most common approaches would be a
qualitative, a quantitative, an ethnography. But these field experiments are not
often at the at the front of what these research agencies are most programmed to
do.
So it gives us, as brand leaders and agency leaders, a chance to say, " do I need
to ask questions [00:32:00] of claims that will-- that could give me some

insight? Or do I need to have a true understanding of behavior?" In which case,
a field experiment will yield us a lot more output.
Richard Shotton: Yeah. And you could think to yourself, "Oh we've got to run
these focus groups.
The client or my boss is completely wedded to them." why don't you add on a
little bit of a layer of subterfuge as well? So let's say you're working for a brand
who is testing, I don't know, a low-calorie cream cheese spread. You can invite
people to come in and answer questions about it.
Maybe before they even get into the room you leave them in a vestibule for 10
minutes. Some people have the cream cheese labeled as low fat. Other people
just get labeled it as tasty cream cheese. And the second group that come in
have a different label. You could question those people afterwards about how
much they liked the sandwiches.
That would tell you something genuine [00:33:00] about how changing the
label but keeping the food the same still created very different tastes. That
would be a way of applying subterfuge around a focus group. People think
they're just being entertained and fed before the research starts. You could
actually use that as the real research.
It's a simple thing to do.
MichaelAaron Flicker: And what I specifically love about this
recommendation is it costs no more money. It takes no, it, it takes no
incremental approval, let's say. You're not commissioning another project, but
it-- you can get a lot more out of what you're already spending money on by just
bringing these principles to the table.
Richard Shotton: Yeah. Yeah. I think it's that moment when you've, just before
people go into your research group or just after they've been paid their fee, these
are really powerful moments where suddenly people think everything's, all the
research is done, then they're gonna start acting naturally. Let's, let-let's use
those.
MichaelAaron Flicker: Yeah. It's funny that you ca- [00:34:00] that you just
tagged on and when they get paid, because to me, one of the things that we've
looked for in experiments, o- on top of it being a field experiment, on top of it
showing true behavior, is when there's real money involved. Even a small
amount of real money somehow brings out more- Yeah

real behavior. And you and I have talked about that off camera before. May-
maybe it's worth just sharing our reflections on this, because even a small
amount of money seems to bring a lot of truth to the situation.
Richard Shotton: Yeah imagine if someone came up to you and said Imagine
you're in a restaurant and you're with a friend, and someone brings the wine list
out.
Would you go for the very expensive wine or the cheap wine?" Who's gonna
answer, "I'd go for the second cheapest wine on the menu"? However, in reality,
I know what I'd be doing. I- if we're asked to speculate theoretically, we're
gonna answer in a way it's costless. Why not pick the thing that makes us look
really generous and and [00:35:00] exorbitantly kind to other people?
Of course we're gonna answer in that way. If there's actual money at stake, then
there's an opportunity cost to doing that. It doesn't mean we're gonna behave
perfectly naturally. Like frankly, I cannot believe that if people going back to
the Deborah Small study people donate, what was it, $1.17 or $2.83 on average.
Now, if they were in an anonymized booth and were asked to put some of their
fee and no one would ever possibly know what they'd given we-- the amount
that people have donated would've collapsed. It's not that it gets us completely
honest but it helps gets us somewhere that way.
So I think it's the fact that it adds genuine opportunity cost to your answer that
makes it more, more believable.
MichaelAaron Flicker: So with that, why don't we summarize our episode
today?
Richard Shotton: Yes. Yes. Okay. So three studies were covered. We talked
about the danger of people [00:36:00] lying in surveys or focus groups. So we
talked about three different tactics you could em-employ.
The first was, especially in morally charged areas, don't ask people what they
would do, like how much they would donate to a charity. Instead, ask people
what they think others would do.
So that was the Daffodil study, the Epley and Dunning study. And asking
people what others would do was a more accurate guide to behavior than asking
people what they would do.

We then moved on to the brilliantly named bogus pipeline, which is essentially
the meta-analysis by Rose. Those 31 studies showed that if people think they
are wired up to a lie detector, they will answer more honestly than that lie
detector not being there at all. The point of that is that if you insinuate people
might be caught out by their lie, they're more likely to answer truthfully.
Now, it's not gonna happen [00:37:00] that a research agency or a brand actually
does the specific bogus pipeline study. But what they could do is at the
beginning of a survey, say something, and you could make this honestly true,
say, " we screen these answers using an AI tool to see whether they're honest or
not."
Now, as long as you actually go and do that, it doesn't matter whether your
attempts to judge honesty are useful or not useful. But if you tell people that
you're going to do that, you should benefit in the same way that you benefit
from the bogus pipeline. And then the third study, that was the field experiment,
was that brilliant study by Deborah Small, and it's essentially the argument that
if you use these kind of randomized AB-style tests, you get much closer to the
truth than a direct questioning of people.
And I think maybe the final point, just to reiterate, is your one around the power
of having actual money at stake. That to me is so true, and [00:38:00] therefore,
the implication for that is if you're a brand, think about your website or think
about your store network as the ideal laboratory. Now, we talked earlier about
academics would cut off their right arm to get access to a medium-sized
website.
The reason is it's naturalistic. The reason is it's got massive scale. The reason is
money is being handed over. A simple AB test on your website, I think can
often be more authoritative than a grandiose study done by a world-leading
psychologist. We have these amazing opportunities, let's use them
MichaelAaron Flicker: It's a great call to action for all of us.
And if you are a brand, you may be asking yourselves, "What are the ways I can
get closer to consumer insights, to customers' actual actions and behaviors,
rather than running large research studies that are more [00:39:00] claimed?" If
you're an agency, you may look for getting credit by showing the brands how
they can run A/B tests, drive to insight, rather than just commissioning more
studies or more work.

So I think there's benefits on both sides of the proverbial aisle, whether you're
an agency or you're a brand, and most importantly, they give us much greater
tools to getting to real insights that can be actionable in the market.
Richard Shotton: Excellent.
MichaelAaron Flicker: And with that, we say thank you for listening to this
week's episode of Behavioral Science for Brands.
If you found it interesting and engaging we always ask that you share it with
others that can learn from it. And please consider liking, following, and
commenting on our post. It helps the algorithm find more people just like you.
Until next time, I'm MichaelAaron Flicker.
Richard Shotton: And I'm Richard Shotton.
MichaelAaron Flicker: Thanks for listening.[00:40:00]
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