See you there!

If you have been keeping up with the Republican primary race, then your head is probably spinning.  I know mine certainly is.  We have already seen the rise, shine, and fall of several political stars – Michelle Bachmann, Rick Perry, Tim Pawlenty, and the latest of whom is Herman Cain.  As quickly as they come into the national spotlight they fade away into those dreaded side positions at the debates.

I am sure we have all encountered an instance in our life where we could not break out of our comfort zone to accept the truth of  a situation.  Go ahead, think about it.  Maybe you are a board member faced with a company wide restructuring after you have been doing things your way for 20 years.  Or maybe you’re dealing with the fact that technology is changing so fast that you decide not to keep up with it anymore.

We as humans experience this because we are creatures of habit.  We are socialized at a young age to expect the norms of a situation or construct, and it becomes very hard for us to break free of those norms – even if it is harming you or others.  This is called the Semmelweis Reflex, and it happens when humans fail to accept new knowledge because it goes against what they believe to be true.  The paradigms that we live within, those that define our professional and personal lives, are hard to break down in light of change – and unless that change comes in a manner we can come to accept, then it is very difficult for us to break out of our normalcy.  I previously posted on the Affect Heuristic, whereby action is taken contrary to evidence.  Here, though, we talk more about not acting at all in light of something new.

Let’s not take this too lightly.  Because we are creatures of habit, our tendencies to accept the status quo regardless of the consequences associated with it becomes a psychological issue.  If we have known something to be true our entire life, or career, then we become less likely to abandon that in light of something new and potentially paradigm shattering.  The psychological barriers embedded in our ways of thinking are very firm.

To put this into context, let’s take a look at who Semmelweis was and why he has a behavior heuristic named after him.  Ignaz Semmelweis was a Hungarian physician known largely for his antiseptic procedures.  However, it was his use of antiseptics that created the initial controversy.  Semmelweis found that the instances of childbed fever could be greatly reduced if doctors washed their hands in a chlorine solution before gynecological examinations.  His theory largely challenged the medical community’s longstanding practices and beliefs surrounding such fever, and despite the compelling evidence that Semmelweis presented, he was ridiculed and rejected within the medical community.

One may ask why the medical community did not accept or at the very least look into the claims of sterilization submitted by Semmelweis.  While it may seem commonsense now, this is a clear example of how paradigms can be so difficult to break down.  Simply put, it was not accepted because people did not want to accept it.

Let’s look at another and perhaps more relevant example:  the debate surrounding man’s contribution to global warming.  Never mind for a moment which side of the fence you are on regarding this position; that is immaterial when trying to explain why so many do not accept the evidence surrounding the issue.  Even though 97% of climate scientists believe that global warming is indeed caused by humans, around 40% of the American public believe these effects are being exaggerated.  But once we take into consideration the psychology behind such a paradigm shifting topic such as this, then the disparity between the evidence and beliefs becomes more easily understandable.  When people have to change their behaviors to accept the evidence, such as being more environmentally conscious, then the longer it takes us to shift our world views.

The real key then becomes how to better sell new ideas when they challenge existing norms.  Norms do have a way of changing over time (as we know that all doctors these days sterilize their hands before procedures).  In terms of research, this is a very big challenge.  Research, by definition, is the attempt to find the unknown answers to our questions.  Because these answers are not known, they oftentimes challenge the status quo.  And because habits die hard, the answers must be packaged in such a way as to avoid the conflicts that can come about from catching someone off guard.

In light of this, the burden exists not just on one party; real change only comes when both presenter and receiver of the information can come to an understanding.  Every single one of us has our comfort zones.  Semmelweis’ message did not work because he did not package it correctly.  ”Do this or people die” is not a good advertisement to get others to catch on.  It also didn’t work because people did not want to move away from what they knew for so long.  Thus, the message must be made real to those needing to accept it.  Conclusions carefully presented with evidence can be a more “marketable” idea.

New ideas that challenge existing norms must be gently presented in a way that is not threatening to those who would benefit from the knowledge – lest those ideas become rejected and ridiculed.  And in those situations, everyone loses.

In this particular post, however, I would like to speak more on how new technologies are allowing social media research to become more meaningful and available.  Let’s take Facebook as a convenient example.  If you are a marketer, for instance, and would like to understand basic demographics such as age and gender of a particular group, then there is no real challenge there.  Many social media sites offer such insights.  However, what if you wanted to better understand what is being said about a particular product?   This may present much more of a challenge, especially if your product is being distributed internationally or even inter-regionally in the U.S.

Enter text analytics.  Only until recently has technology caught up with the fast pace social media world to better analyze and understand what is being said.  The crux behind Facebook and Twitter, for instance, are feeds and tweets – what people are saying about their life, experiences, and the like.  This is what makes being involved in such discourse so appealing: the status update serves as our personal soapbox.  We can easily, without fear of stage fright or retaliation, say what we feel – about politics, about things we buy, and about our daily moods.  Think how valuable this is from  product assessment point of view.  And now we have the analytical means to assess this.

The capabilities of text analytics vary greatly and, as with anything, are multifarious in function.  The true value, however, exists in its ability to quickly quantify qualitative data.  This is how the researchers at Cornell University analyzed emotions.  Using such software enables the researcher to assign value to a group of comments (such as comments about mood and emotional tone, or about a product) in sentimental value.

As technology becomes smarter and more capable, a researcher’s ability also is increased.  Of course, before text analytics came on the scene we certainly could analyze and quantify qualitative feedback, but it took longer and there was more room for individual error.  But in the case of social media, where millions of data points are waiting to be mined, this was not necessarily a possibility.

Let’s take a look at some of the benefits that have been gained as a result of text analytics software:

1. All voices can be heard.  Traditional methods of analyzing qualitative data relied on researchers manually reading through comments and subjectively picking up on emerging themes.  While good researchers could perform this task exceedingly well, it was time consuming and was open for missing of important ideas.  Analytical software uses a series of algorithms that can pick up on consistent themes, thereby eliminating this potential error.  Thus, the voice of your customer or whoever you are trying to gauge will not go unheard.

2. Textual data sources are a good for picking up nuances.  Quantitative measures cannot capture the specific reasons why someone is unhappy or why they are satisfied.  That is why in depth research employs both quantitative and qualitative measures.  Comments allow you to pick up on key nuances that may be driving particular opinions.

Here at IQS Reseach, our text anlytics also employs something known as sentiment, which allows for a better understanding of not only what is being said, but also whether what is being said is positive or negative in nature.  If a common theme is “cost,” this enables us to understand if what is being about cost is good or bad.

Using these pieces of information as a background for the capabilities of text analytics can help us better understand the true value it provides when analyzing millions and millions of comments in social media avenues.  As our forums for thought open to new audiences and are expanded to all areas of the globe, our capability to measure these audiences must also expand.  And that is why researchers and consumers of research alike are now beginning to understand the full value of nontraditional methods of analyzing data.  How else could we measure something like human emotion on a platform as open and wide as Twitter?

I have to say, as the former Lead Quality Analyst for the Ford Explorer Vehicle line and as a bit of a Ford history buff I absolutely love this story.  As the founder and president of a market research and data analytics company I love it even more. 

So let’s start with the facts and look at the first misstatement. Henry Ford didn’t actually make this statement. There was an interesting article on the Harvard Business Review blog that explores the statement and confirms that it was not made by Ford.  So step one, even though most people give Ford credit for this statement we should probably acknowledge that it was really made by someone else. 

Regardless of who said it though, there are some important points about this statement that should be considered by anyone trying to understand their marketplace or the world in general. 

Furthermore, there is likely some truth to the literal interpretation of the saying.  In the early days of the automotive industry vehicles (horseless carriages) were competing with horses and horse-drawn carriages for share of wallet and also share of the road.  Had someone asked the typical non-vehicle driving, horse owner what (s)he wanted, a likely response may have been “faster horses”.  So that part we can assume is probably true. 

The real question becomes what is meant by “faster horses” ? 

Taking the answer literally provides very little information and brings about a unique set of problems.  For instance we would need to consider:

• What are the safety concerns of faster horses? 
• Will the carriages have to be changed to handle the increased speed?
• Will wider roads be needed?

Good research would also go on to explore the challenges with the current process.  This could include questions like:

• Why are horses currently going slow?
• Have you tried to go faster before? What happened?
• What things worked when you did try to go faster? 
• What are you feeding the horses?

While interesting, none of those questions or answers really provides a deeper insight into the problem than what was available before asking the question. 

As researchers and as consumers of research, we need to go farther.  To really understand the needs of the marketplace we need to focus on the meaning behind the words.  We hear the words the consumer is using but what are they really saying?  What does the consumer mean by faster horses?

In this case, we may infer that faster horses really means that the respondent wants to get from point A to point B more quickly or even more to the point, in less time.  If we focus on the end goal of the consumer (getting from point A to point B in less time) then our whole way of thinking can change. We can begin to explore why the user wants to go faster.  This could include questions like:

• What are the benefits of going faster?
• What losses are incurred by the inherent slowness of the current process?
• What compensation methods does the consumer currently use to cope with slowness in the current system?
• What opportunities could be realized by going faster?
• What is the opportunity cost of going slow? 
• Is the desire to go faster or just take less time?   

These questions allow us to fully understand the customer’s problem and help put their proposed solution (faster horses) into context.  But without these questions, we are forced to work within a linear framework for problem solving.

It is true that consumers are typically not able to describe the ideal solution to their problem.  However, they are often very capable of telling us about the problem itself.  When research focuses on the underlying problem then the potential solutions can be more fully understood.  

By the way, if you really want to read some interesting information about Henry Ford, I recommend looking into the Oscar II.  As a professed pacifist Mr Ford was strongly opposed to the war and the Oscar II was part of his plan to help bring it to a close.  But that is for another post.

But before going more into selection bias, let’s talk a bit why social media is such a goldmine for data.  Facebook and Twitter have hundreds of millions of users, and considering the entirety of the world’s population (just under 7 billion) this is a sizable representation.  Marketers have already begun using information posted on personal accounts to direct expansive and valuable product campaigns, and researchers are only now beginning to understand the potential these sources have for their own work.

Let’s take Facebook as an example.  There are around 800 million active users on Facebook worldwide.  And subscribers to Facebook are more than forthcoming when it comes to posting personal information, such as favorite music, foods, and movies, as well as the places they plan to visit.  On top of this, they tend to throw in the kitchen sink in their status updates.  Think of the vast amount of data that can be extrapolated from what people post online.

Even though social sites such as Facebook and Twitter have been around for years now, the technology to analyze these sources of data are only now beginning to catch up.  Text analytic software now has the capacity to handle such large amounts of data and meaningfully subscribe value to what is being said in qualitative response, such as Facebook and Twitter posts.  More on the capabilities of text analytics will be in our next post.

But how does this relate to selection bias?  Selection bias occurs when a sample is drawn from a population without regard to who that sample may be excluding.  If you are familiar with statistical sampling, then you know that samples must be taken randomly from the population in order for that sample to be statistically representative of the population.  But social media enters a whole other quandary of selection bias; because even if the Cornell researchers, as an example, randomly selected their sample of Twitter users, there is still selection bias occurring.  The problem does not lie in the fact that they used Twitter as their population source – the problem is that their findings neglect the fact that there are entire segments of the world population that does not use or has no access to social media.

The researchers took information that they gathered from Twitter, a social media platform, and stated conclusions based on all human beings.  How is this a problem?  It’s a problem because of who uses social media – typically younger, more affluent individuals.  Thus, how can we know for certain that all humans undergo these uniform emotional swings, such as third-world citizens who don’t have access to social media or the elderly who haven’t overcome the technological divide.  The simple answer is that we can’t.  And that is why selection bias is a problem here.

But regardless of this, if researchers take the time to understand the population of which they analyze and account for that, then the sky is the limit with new knowledge that is only now beginning to be extracted from this rapidly growing means of discourse – the social media sphere.

This growing field of Neuromarketing was the subject of a recent session at the Idea Festival in Louisville, KY last week.  Patrick Renvoise, a French marketing expert and former salesman of supercomputers, spoke on Thursday about whether or not there exists an intrinsic “buy” button in our brains.  If one does exist, imagine the potential marketers will have to sell their products.  Once they know how to tap into the part of the brain that controls our impulse to buy, then marketing campaigns can powerfully relate to those impulses.

Renvoise, author of the book Neuromarketing: Understanding the Buy Buttons in Your Customer’s Brain, spent an hour discussing the evolution of our brain, and understanding how the brain works is key in developing a good marketing campaign.  The premise is this:  our brain is actually three smaller brains through which the course of evolution has developed into one comprehensive, interrelated organ.  Though one organ in our current makeup, it acts as three in its functioning.

1. The new brain is the most recent evolutionary addition to our brain, and it controls our ability to rationalize decisions and thoughts.

2. The middle brain processes our ability to feel and have emotions.  Our gut feelings reside here.

3. The old brain, or the reptilian brain, is where our instinctual decisions are made.  Imagine homo sapiens roaming around pre-civilization searching for food, wary of wild beasts and predators – this was the extent of our brain during that time, and it’s main function was to prevent pain, thus preserving the body.

It is within the reptilian brain where most of our decisions, at least the important ones, are made.  Renvoise states that this part of the brain is 45,000 times older than written communication, and thus written words don’t necessarily impact it.  It is visual in nature, and it reacts on basic instinct.  Thus, it responds to what it sees.  But when we make decisions, we use all three parts of the brain, leading to emotional and rational choices.  Hence we now have complex, rational (at times) societies.

But let’s focus on this reptilian part of the brain for a moment, because this is where Renvoise believes the most effective marketing must be focused.  Take into consideration this case study – as it is illustrative on how marketing aimed at this reptilian functionality can be very effective.  A pizza delivery company fielded a survey to its customers in an attempt to discover what they were most concerned about in the delivery of their pizza.  One would assume the obvious – cold pizza, cardboard taste, cheese melted to the box.  Rather, they found that the biggest concern customers had was the anxiety they felt around when their pizza would arrive.  The company was Dominoes, and as a result, they implemented their 30-minute delivery guarantee, launching the company into the fastest growing pizza chain the country.  Nevermind whether it was the best tasting pizza – they figured out the pain their customers felt, and they targeted it.

We as humans believe that what we buy is therefore necessary; thus at the core of a particular buy is a primitive reaction.  If we think about it too long, we begin to rationalize away our desire to buy something – it’s too expensive; I already have something similar to this; there are other things I need first.  Sound familiar?  There is nothing wrong with this, as this type of rationalization is why we have survived in our complex societies.  However, if a marketer is trying to push a product or service, they must find a way get past this.  After all, people don’t like being sold to – they like to buy!

You have to figure out what kind of pain your audience is undergoing, because at the crux of our decision making for buying is pain avoidance.  What is it they truly need or want?  And how can you relieve that pain?  This is how, according to Renvoise, you access those ever elusive “buy” buttons of your customers.

If you are interested in Renvoise’s book, here is the info:
Neuromarketing: Understanding the Buy Buttons in Your Customer’s Brain, by Patrick Renvoise and Christoph Morin: Thomas Nelson, Publisher: 2007

There has been a lot of talk in the recent Republican primary race about straw polls.  And each of these polls seem to declare a different victor.  Mitt Romney won the New Hampshire poll, Rep. Ron Paul won both the Washington, D.C. and the California polls, Herman Cain won the Arizona poll, Michele Bachmann was victorious in the Iowa poll.  So many polls, so many different winners.  This begs the question, what exactly are straw polls and should we as potential voters listen to them?

Let’s begin with the first question – what is a straw poll?  There are two broad categories of polling: scientific and unscientific.  Scientific polling uses random sampling controls so that the results from a sample that is drawn is statistically representative of the population.  Previous posts have discussed this greater detail.  Unscientific polling, on the other hand, has no systematic sampling controls in place that would allow for representation of a population.  Historically, a lot of straw polls in the United States have been political in nature, and are usually fielded during election season by a particular political party.  The very name “straw poll” alludes to their nature – it is thought that this idiom alludes to a piece of straw being held in the air to determine which direction the wind is blowing.

Most straw polls are very targeted, very narrow surveys of opinion.  Their main purpose is to take a “snapshot” of a general opinion during a particular point in time.  This seems valid enough, but the difference between scientific and straw polls exists within the methodology.  Most straw polls use a form of convenience sampling that is a bit unorthodox, and the selection bias associated with can be extreme.

It is hard to assign a broad methodology to all straw polls (as each one is different in its own right), but many of them have candidates, such as in the Ames Straw Poll in Iowa, attract voters to cast their vote on who they believe should be the Republican candidate.  If it sounds like political grandstanding, it’s because it is to some degree.  It uses somewhat of an “honor system” whereby anyone can vote (within the parameters), which opens up a whole argument regarding the validity of the polls.

This brings us to our second question – should we pay any heed to the results of these polls?  I previously stated many of the recent straw polls and their victors.  There have been many polls, and there have been many different winners.  But to answer this question, we only need to look at the candidates themselves.  And they certainly place weight on these polls.  Tim Pawlenty dropped out of the Republican primary because of the lack of support the Iowa poll showed for his campaign.  Entire strategies are formulated based on results of straw polls.  That is because these polls show the weaknesses of particular candidates.  And for this reason, candidates are perhaps wise to take caution to what the polls are telling them.

However, are they good predictors of ultimate outcomes?  In answering this question, we are reminded of the 1936 presidential election.  The Literary Digest conducted its own straw poll, which showed Franklin Delano Roosevelt being defeated by a large majority.  We all know this was not the case, and the reason for this catastrophic (as it led to the downfall of the Digest) miscalculation was in the methodology of the poll, which is the main criticism of any straw poll.  The Digest used their mailing list to administer the poll, which consisted of motor vehicle registries and telephone books.  The problem here?  It was the Great Depression – many Americans were too poor to own a car or telephone, and thus a large sector of the population was neglected in this poll (selection bias at its finest), the very sector that was more likely to vote for FDR and his economic reforms.

The point of this post is this: take what you hear from these straw polls with a grain of salt.  They do little to predict outcomes, but can be very valuable to the candidates themselves in adjusting and fine tuning their campaigns.  Although there is a vast expanse of difference that exists between a lot of straw polls and scientific research, it can be surprisingly easy to muddle the reliability of each. However, knowing how to digest the results of research, both good and bad, will help you to avoid unsettling surprises.

Look at the financial crisis of 2008, where our lavish expenditures and comfort led to our own demise in many respects – a perfect example of how our society was blinded by the comfort we had come to inhabit.

We have seen the effects of what can happen when we let our guard down – when we start ignoring the signs that may be staring us in the face.  Instead of letting the data or trends tell us what to expect and how to prepare for what may be approaching, we continue on our path of neglect and foolishly act surprised when the situation hits us hard.

Let’s take a look at a specific example from the recent past – the decline of sales in the U.S. auto market.  The 1990s was a time of great economic success and excess.  People had more money to spend, and they spent it on lavishly large vehicles such as SUVs, which the auto market in America was providing plenty of.  One could argue that the events of the past decade were not foreseeable by the U.S. auto industry, and thus their inability to react was excusable and understandable – hence the bailouts.  However, it was less than 30 years ago that the industry suffered much of the same declines as they did in the past decade.

A report released in 1980 by Natural Resources and Commerce Division of the Congressional Budget Office indicated that the auto industry in America was suffering unprecedented decline.  The reasons cited for this decline may sound very familiar:

• Jump in gasoline prices
• Rise in interest rates and enactment of credit controls
• Economic recession

The impact that followed may also sound familiar – consumers switching to compact cars that met their needs which were more readily available by foreign automakers.  The suggestions and predictions of the CBO stated that in order for the U.S. industry to become viable and competitive again, they would have to produce more compact cars.  Perhaps it is just me, but I think there is not a clearer example of “history repeating itself” than this.

You may ask yourself how this affects you and your own situation.  The U.S. automakers failed to listen to the data that was undoubtedly available to them.  Are you in tune with what the data are telling you?  Are you listening to it to make decisions for the future?  Or are you blinded by perhaps recent success and letting that bias your decisions and direction for the future.

This isn’t a situation where ignorant people made foolish decisions.  More so, it is simply a lack of understanding of how valuable data can be.  Decisions can’t simply be made on gut instinct; and while we should all listen to our gut, using it as a sole means of direction can be misleading and dangerous to our own condition.

If history tells us anything, it says that it will visit us again – and the only way to overcome those reenactments is if you stay in tune with what has happened.  A good consumer of research has the ability to take in is happening and be proactive in addressing it.  Use the data to recycle what has succeeded and reevaluate what has failed.  In short, don’t fall victim to your own comfort bias – but be objective and deliberate in your approach.

While those trends provide some good news we also know that the smoking rate would decrease much faster if teenagers and young adults didn’t start smoking in the first place.  After all, if the supply of new smokers is stopped then the smoking rate would only continue to decline as a result of other attrition elements.  However, this opens up a much larger question about why do kids start smoking in the first place.  There are myriad opinions and thoughts about this subject and unfortunately most of the conversations never progress beyond the opinion stage.

Based on prior research we have been involved with through the Drive Cancer Out program we know that school age children exhibit strong predictive patterns around their likelihood to try smoking.  Those predictive patterns center around their beliefs that:

• Kids can smoke once in a while without getting addicted or suffering any harmful effects
• People who smoke are cool

The stronger a child’s association with these two statements, the more likely that child will be to try smoking as they become older…even when they tell you that they know smoking is harmful.  This becomes powerful because when we can identify these children, intervention and deterrent programs can be provided.  However, without the aid of statistics to isolate the key predictive drivers of smoking then all efforts to curtail the problem become subject to opinions and whims. 

If the goal is to reduce the number of smokers then the only real path to success involves understanding why people start smoking and deter them prior to the habit taking shape.   Data can help make this a reality whereas opinions often only succeed in expending needless energy and precious resources.