Dads, and their role in society and the family, are changing.  Schools have noticed it.  I, as a researcher who regularly conducts studies specifically among dads, have noticed it.  And marketers have noticed it.

Dads are becoming increasingly important to marketers, with good reason.  They play a crucial role in purchasing and family decision-making, and ignoring them could be bad for business.  However, marketers and advertisers continue to make the same mistakes with dads that they’ve been making for decades, including:

• Treating them like moms

• Treating them like other men

• Treating them like dads of the past

• Treating them like idiots

Having done research among dads for many years now, they continue to surprise me.  They continue to change, and the pace of change seems to be increasing.  Some of these changes are widely reported – such as their greater involvement in parental and household tasks and greater involvement in family decision-making) – while others are less well-known.  Here’s one, for instance; dads feel much more pressure lately to be nurturers as well as providers, and these pressures clash with each other.  As noted clinical psychologist Dr. Gail Winbury has observed, “fathers feel they’re being encouraged to attach emotionally to their children to a much greater degree than even a few years ago.”  And this pressure brings internal conflict—while they’re expected to be nurturers at home, dads still feel clear pressure to leave the house every day and slay dragons.

Winbury again: “Fathers are profoundly conflicted.  They’re receiving complex and often contradictory messages.”  These messages come through the media and their own social networks, and create a sense that nothing they do will ever be entirely correct.  As one recent focus group participant dad observed to me, “I feel like, no matter what I do, I’m going to be told I screwed up.”  

So, here’s what I think marketers need to do:

• Stop making outdated assumptions. Realize that fathers today bear little resemblance to dads of the past, that they’re significantly different from moms in their attitudes and behaviors, and they don’t like being portrayed as being clueless.  The biases and misconceptions that marketers continue to build into their marketing strategies and tactics toward dads often render them ineffective.

• Do some research. Get to know the dads of today.  This can be done in a variety of ways.  Qualitative tools and custom quantitative approaches are worth the resources.  In addition, social media listening and syndicated data can also inform your understanding of dads.  But, as with the previous point, be careful of the assumptions you might be making when conducting this research.  Also, bear in mind that, as dads continue to change, you’ll probably need to refresh this research regularly.

If marketers really listen to dads, avoid stereotypes, and are sensitive to the tightrope fathers are being asked to walk, they can become an important stakeholder to a brand.

The response to the blog articles I’ve been writing for several months has been quite gratifying.  Thanks for all the questions, comments and suggestions people have emailed, posted and texted.  One request that I’ve gotten repeatedly is for tools and techniques that can be used right now.   So, I’ve decided to write a few pieces about mindsets.  These are underused concepts that can take your understanding of marketing and consumer behavior issues to a much higher level.

So, let’s talk about maximizing and satisficing mindsets.  Herbert Simon, the Nobel Prize winning economist, introduced the concept of satisficing in 1956.  For those of you who think you’ve already caught two typos, not so!  ‘Satisficing’ is a word—it’s Simon’s concept of seeking an adequate, rather than optimal, outcome.  Technically, this is called the ‘theory of bounded rationality’ which questioned the then-accepted wisdom among economists (and still accepted today) that individuals always seek an optimal outcome. It points out that there is a significant cost to continued assessment and information gathering once one has identified a satisfactory solution.  The problem is that this cost is difficult to quantify.

If you want to see this in real life, go to an electronics store and watch people shop for TVs.  You’ll be able to distinguish very easily between those who seek the television with the very best picture quality vs. those who just want the cheapest set that’s good enough to watch Hogan’s Heroes reruns.  Simon theorized that people tend in general to be one or the other—maximizers or satisficers— in terms of their overall disposition.  However, keep in mind any particular decision-making challenge can influence one’s mindset.  For instance, a person seeking the very best TV may later visit a grocery store where he’ll buy store brand breakfast cereal because … “it’s good enough.”

Why should we care about this?  Because, in my experience, marketers tend to assume a maximizing mindset when trying to understand and describe consumer decision-making processes.  We take for granted that individuals seek to maximize quantifiable economic utility in all situations, and lose sight of the fact that often this isn’t at all how people behave.  This assumption can significantly skew our research results, and so we need to stop making it.  For instance, when talking with vacationers about a recent trip, instead of asking them what was the best part of it, ask them instead just to name some of the things they enjoyed.  There’s nothing wrong with following up with a question that asks them to identify their favorite, but answering it should be optional.  Similarly, every time I see a survey questionnaire that asks respondents to rank brand attributes or experiences, it makes me uneasy—why are we assuming that this is how an individual perceives this issue?  Forcing people into a maximizing mindset creates the risk of generating data that appears to be meaningful, but actually quantifies nonexistent perceptions.  A better approach would be to ask individual respondents to rate those elements, and then to derive a ranking across the entire dataset.

So here’s my big tip: when delving into consumer attitudes, perceptions or decision-making, one of the first things you should do is establish the respondent’s operative mindset.  This isn’t difficult.  If you’re conducting qualitative research, simply ask people to describe their desired outcomes … “what are you hoping for here?”  In quantitative research, it’s a simple matter to begin with some questions that give people an opportunity to describe their mindset – “on a scale of 1 to 5, with 1 meaning ‘OK is fine’ and 5 meaning ‘I want the very best,’ what is your goal for this purchase?”  Beginning with inquiries like these will enable you to understand the respondent’s intentions and state of mind, thus establishing a context for understanding their attitudes, perceptions and behavior.

One final thought: these principles can be applied to circumstances outside of market research.  When evaluating alternatives in business situations, remember to ask yourself if it’s worth the time and effort needed to identify the optimal solution or course of action — it very well might be, as long term success is often contingent upon excellence.  However, occasionally it’s appropriate to identify a sufficient solution, thus enabling you to move ahead more quickly.   And as for your personal life, it’s well documented that satisficers are generally happier than maximizers.  Everything in your life doesn’t have to be perfect– seeking ‘the best’ is often a fool’s errand, and contentment can often be found in adequacy.

I recently came across something called Brandolini’s Law.  It was first stated in 2013 by Alberto Brandolini, an Italian software engineer, and it says that the amount of energy needed to refute bullsh*t is an order of magnitude greater than the energy required to produce it.  It’s also called the Bullsh*t Asymmetry Principle.  Mr. Brandolini is hardly the first person to notice this.  Winston Churchill is often credited with having observed that “a lie gets halfway around the world before the truth has a chance to get its pants on.”  (In a beautiful irony, it seems that this attribution itself is BS.  The remark was probably made by Cordell Hull, FDR’s Secretary of State, but people continue to credit it to Churchill.)

If Brandolini is correct that accurate information is at a fundamental disadvantage to nonsense, this has significant implications to market research.  I often find that one of the most important priorities I face when conducting research is avoiding jumping to conclusions.  I know my clients struggle with this as well.  It’s unsurprising that this is a challenge.  We are pattern seekers by nature, and so we look for explanations when presented with data.  Humans are also naturally uncomfortable with not understanding something, and will sometimes prefer a bad explanation to no explanation.  From an evolutionary standpoint, these traits have clear value.  However, in this modern world, they don’t often serve us well.  And exacerbating that problem is the fact that we’re usually under pressure to deliver findings and implications as quickly as possible—often on the spot.

With market research, rushing to judgement can work against you—particularly in the case of qualitative approaches, which allow us to watch findings accumulate over time.  If you’ve ever sat in a focus group back room, or observed an online bulletin board, you’ve had the opportunity to see data be created in real time.  Not only is it important to resist drawing conclusions before all the information is in, but, once you do have the data, it’s wise to give yourself some time to mull things over – psychologists call this ‘consolidation’ – before forming opinions.  Doing so too hastily can lead to poorly-thought-out implications and unsound recommendations.  And once these flawed ideas are articulated, they can spread like wildfire, and abandoning or revising them after the fact is nearly impossible – hence Signore Brandolini’s observation.

So, here are some practices I follow to avoid this problem:

• I make a point of distinguishing very clearly in my own mind between the tasks of determining what I have heard and considering what I think it means.

• While I’m conducting research, and for at least a few days afterward, I restrict myself to the first task, and hold off on the second. I strongly encourage my clients to do the same.

• I’ll often schedule a debrief call with clients a few days after the research, the express purpose of which is to allow ourselves to engage in the second task.

• I consciously give myself permission to change my mind about things in the days following the research.

• I also strongly encourage all members of the research team to disagree with each other and me. As George Patton used to say, “If everyone is thinking alike, then somebody isn’t thinking.”

One final point. It’s important to bear in mind when looking at quantitative research results that the information you’re reviewing is incomplete.  As we all know, quant gives you a lot of ‘what,’ but not much ‘why.’  It’s qualitative that will provide the story behind the numbers.  So, before you start drawing conclusions based on quantitative data, try to work some qualitative information into your analysis.

I’d love to know your thoughts on this topic.  Feel free to email me or leave a comment on the blog.

Recently, I was reviewing data on keyword searches conducted on Google regarding qualitative research.  Here’s what I learned – a lot of people don’t seem to understand what qualitative research is, how it differs from quantitative, and how it can add value.  This is consistent with my own recent experience.  Many long-standing clients who are experienced research professionals increasingly find themselves defending the necessity of qualitative to their colleagues in marketing, top management, finance and corporate purchasing.  They’re constantly forced to respond to remarks like, “what can this stuff tell me that the numbers can’t?”

Whenever I hear about this, I think about the McNamara Fallacy –  named for Robert McNamara, Secretary of Defense for John F. Kennedy and Lyndon Johnson, and architect of Johnson’s escalation of the Vietnam War.  This phenomenon was described by pollster Daniel Yankelovich – a man who made his living quantifying things – in the early 1970s.  It describes a progression of thinking that starts reasonably, and ends up in near total dysfunction.

Step 1: Measure what can be easily measured.  Nothing wrong here —we should be quantifying what we can.

Step 2: Conduct an analysis that is either based entirely what can be measured, or that assigns estimated or arbitrary values to those things that can’t be.  Nothing inherently wrong here either, as long as you remember the limitations of such an analysis.  But it’s also risky, as it can easily lead to…

Step 3: Decide that what you can’t easily measure is unimportant.  Yankelovich says, “This is blindness,”  and, it will take you right to…

Step 4: Conclude that what you can’t easily quantify doesn’t exist.  Yankelovich calls this “suicide”.

This fallacy is named for McNamara because of his approach in Vietnam, in which he decided that objective, quantitative measures – most notably body counts – would be a proxy for success.  McNamara’s focus on these metrics told him the US was winning the war.  But by 1968, it was clear that the numbers were telling a misleading story.

The lessons here:

• Numbers provide essential information. However, by themselves, they only tell what can be quantified – that something can’t be quantified doesn’t mean it’s non-existent, or that it isn’t important.

• The numbers themselves must be questioned. Had Bob McNamara taken a closer look at the body count figures he was receiving by using tools that dig beneath numbers to help them tell a more accurate and enriching story, he might have interpreted them very differently.   It’s important to remember that every data point represents something: a person, an event, a memory, a perception, and so on.  If we are to truly understand the numbers in aggregate, and make good decisions as a result of that understanding, it is imperative that we spend time looking at those things the numbers represent.  This is where qualitative tools –  such as conversation, observation and creative exercises – can add so much value.

• It’s valuable to remind ourselves periodically why we use metrics in the first place. We do this to simplify—to make something understandable that might otherwise be too complex to grasp.  However, we must be very careful in our selection of metrics, as every measure contains assumptions about causality.  McNamara and his staff assumed – obviously incorrectly – that there was a causal link between success and killing more soldiers than then enemy.

Today, it seems we may have forgotten the cautionary tale of Robert McNamara.  With so much data available to us – and it’s often of such high quality –  we can forget the power of data lies in the stories it tells and the humanity it describes.  And it’s qualitative tools that help us find those stories and that humanity.  Often, we are presented with qualitative and quantitative as an either/or decision.  But this is a false choice.  The two must work together to uncover the truth.  And so we – as those who are responsible for interpreting data for the purpose of informing decisions – must always remember to dig deeply into that data and the assumptions that underlie it by creating research approaches that meld numbers and stories.

I recently attended NeuroU 2019, and It was a fascinating two days during which I immersed myself in the world of neuromarketing.  One key thing became abundantly clear; biometric data is about to become a thing, and marketers better get ready for it.  Here are two key takeaways I think you’ll find interesting.

Biometrics Can Provide a Valuable Augment to the Data We Already Collect

When we combine the types of data typically provided by marketing research—survey responses, syndicated data and qualitative learnings—and combine them with sources that measure physiological response to research stimuli, we can add valuable insight to our findings.  These physiological metrics can document respondent attention and engagement.

For example, we show a visual stimulus to qualitative research participants such as a print ad, a webpage, a package mockup or a retail shelf set.  In addition to discussing the stimulus, we could augment the findings with some eye tracking which would tell us what people actually looked at, when, and for how long.  “Hold on,’ you say, ‘eye tracking has been around for decades; what’s new and different about that?’ Now, we can also add in measures like heart rate, pupil dilation and galvanic skin response so we can determine which elements correlate with a physiological response.  This informs us as to what elements were actually engaging, and which ones merely elicited attention but no real interest.

Perhaps the stimuli are more dynamic – a TV commercial, or shoppers explore a retail environment.  We can now measure EEG response continuously during the exposure period, or gather facial coding data.  Both can provide significant insight into the nature of an individual’s emotional responses to a stimulus.  When we combine this information with traditional quantitative measures (such as recall and persuasion) and insights gathered during qualitative discussion, we can substantially increase our understanding of how consumers are responding to messages and environments.

The Hardware and Software Are Pretty Much Ready for Mainstream Usage

While biometric data was always interesting in theory, significant logistical challenges made it impractical for typical applications. The software was not user-friendly, the hardware clunky and temperamental, and costs usually prohibitive.  Over the past few years, suppliers have devoted significant resources to address these challenges, and now offer turnkey hardware and software suites that can provide reliable data at an extremely reasonable cost.

The upshot—it might be time to start dipping your toe into this end of the pool.  Used appropriately, biometric data has the potential to be a major problem-solver for researchers.