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Should you use DIY market research tools?

A market research innovation has occurred over the past decade that is talked about in hushed tones among research suppliers:  the rise of DIY market research tools. Researchers and clients need to become more educated on what these DIY tools are and when it is appropriate to used them.

DIY tools come in a number of flavors. At their core, they allow anybody to log into a system, author a survey, select sample parameters, and hit “go.” Many also provide the ability to tabulate data and graph results. These tools reduce the complexity of fielding studies. For the most part, these tools are created by outside panel and research technology companies but some end clients have invested in their own tools.

Many research suppliers view DIY tools as an existential threat. After all, if clients can do all this themselves what do they need us for? Will our fielding and programming departments become obsolete? Will we have a large portion of what we do automated?

Maybe. But more likely our fielding and programming departments will become smaller and have to adapt to a changing technological world.

There is a clear analogy here to DIY household projects. The tools and materials needed for most home improvement projects are available at big box retailers. Some homeowners are well-equipped to take on projects themselves, others are not, and the key to a successful project is often understanding when it is important to call for professional help. The same is true for market research projects.

Where the analogy fails is when you take on a project you aren’t equipped to handle. If it is a home project you will probably discover that you got in a bit over your head along the way. In market research, however, you can complete an entire project that has serious errors in it but never really notice. The project will result in sub-optimal decision making and nobody may really notice.

In days gone by, the decision of whether to use a research supplier or not was straightforward. If the project was meaningful or complex, clients used suppliers. For many projects, the choice used to be between using a supplier or not doing the project at all. The rise of DIY tools has changed that.

Here are some instances where DIY research makes sense:

  • If the project is relatively simple. By simple, we mean from both a questionnaire design and a sampling perspective.
  • If the risk of making a suboptimal decision based on the information is low. Perhaps the best aspect of DIY tools is they permit clients to research issues that otherwise may have gone unresearched because of time and budget considerations.
  • When getting it done quickly is important. For many projects, there is something to be said for getting it 90% right and getting it done today rather than taking months to get it perfect.
  • If you have someone with supplier-side experience on staff. Suppliers are likely to be a bit more attuned to the nuances of study design and may notice mistakes others might miss.
  • If you have thought through the potential limitations of the DIY approach and have communicated this to your internal client.
  • When you are using the DIY project to pre-test or pilot a study. This is an excellent use of DIY tools: to be sure your questioning and scales are going to work before committing significant resources to a project. A DIY project can make the subsequent project more efficient.

Here are cases when we would caution against using DIY tools:

  • If a consequential decision will be made based on the results. Having the backing of a third-party supplier is important in this case and the investment is likely worth it.
  • When research results need to motivate people internally. Internal decision makers will typically listen more to research results if the study was conducted by a third-party.
  • When a broader perspective is needed. As a client, you know your firm and industry better than most suppliers will. But there are many times when having a broader perspective on a project provides substantial value to it.
  • If the sampling is complicated. If your target audience is obscure and hard to define in a few words, suppliers can be very helpful in getting your sampling right. In a previous post we mention that it is the sampling aspects of projects that most clients don’t think through enough. We have found that the most serious mistakes made in market research deal with sampling, and often these mistakes are hard to notice.
  • If you are conducting a business-to-business study. DIY sampling resources aren’t yet of the same quality for b-to-b research as they are for consumer studies.

DIY studies clearly have their place. They will augment current studies in some cases and replace them in others. I don’t see them as a threat to the highly-customized types of studies Crux Research tends to conduct. Market research spending will continue to grow slowly, but less will be spent on data collection and more on higher value-added aspects of projects.

In the 30 years I have worked in research, the cost of data collection has dropped considerably – I’d say it is about one-third what it used to be. But, during this time the price of research projects has increased. The implication is that clients have come to value the consultative aspects of studies more and have become more reliant on their suppliers to do things that previously clients did for themselves.

That presents a bit of a conundrum: clients are outsourcing more to suppliers at a time when tools are being developed that allow them to do many projects without a supplier. For many clients, money and time would be saved by hiring someone on staff that knows how to use these tools recognizes when a third-party supplier is necessary.

Should we get rid of statistical significance?

There has been recent debate among academics and statisticians surrounding the concept of statistical significance. Some high-profile medical studies have just narrowly missed meeting the traditional statistical significance cutoff of 0.05. This has resulted in potentially life changing drugs not being approved by regulators or pursued for further development by pharma companies. These cases have led to a much-needed review and re-education as to what statistical significance means and how it should be applied.

In a 2014 blog post (Is This Study Significant?) we discussed common misunderstandings market researchers have regarding statistical significance. The recent debate suggests this misunderstanding isn’t limited to market researchers – it appears that academics and regulators have the same difficulty.

Statistical significance is a simple concept. However, it seems that the human brain just isn’t wired well to understand probability and that lies at the root of the problem.

A measure is typically classified as statistically significant if its p-value is 0.05 or less. This means that there is a less than 5% probability that the result came from chance or random fluctuation. Two measures are deemed to be statistically different if there is a 19 out of 20 chance or greater that they are.

There are real problems with this approach. Foremost, it is unclear how this 5% probability cutoff was chosen. Somewhere along the line it became a standard among academics. This standard could have just as easily been 4% or 6% or some other number. This cutoff was chosen subjectively.

What are the chances that this 5% cutoff is optimal for all studies, regardless of the situation?

Regulators should look beyond statistical significance when they are reviewing a new medication. Let’s say a study was only significant at 6%, not quite meeting the 5% standard. That shouldn’t automatically disqualify a promising medication from consideration. Instead, regulators should look at the situation more holistically. What will the drug do? What are its side effects? How much pain does it alleviate? What is the risk of making mistakes in approval: in approving a drug that doesn’t work or in failing to approve a drug that does work? We could argue that the level of significance required in the study should depend on the answers to these questions and shouldn’t be the same in all cases.

The same is true in market research. Suppose you are researching a new product and the study is only significant at 10% and not the 5% that is standard. Whether you should greenlight the product for development depends on considerations beyond statistical significance. What is the market potential of the product? What is the cost of its development? What is the risk of failing to greenlight a winning idea or greenlighting a bad idea? Currently, too many product managers rely too much on a research project to give them answers when the study is just one of many inputs into these decisions.

There is another reason to rethink the concept of statistical significance in market research projects. Statistical significance assumes a random or a probability sample. We can’t stress this enough – there hasn’t been a market research study conducted in at least 20 years that can credibly claim to have used a true probability sample of respondents. Some (most notably ABS samples) make a valiant attempt to do so but they still violate the very basis for statistical significance.

Given that, why do research suppliers (Crux Research included) continue to do statistical testing on projects? Well, one reason is clients have come to expect it. A more important reason is that statistical significance holds some meaning. On almost every study we need to draw a line and say that two data poworints are “different enough” to point out to clients and to draw conclusions from. Statistical significance is a useful tool for this. It just should no longer be viewed as a tool where we can say precise things like “these two data points have a 95% chance of actually being different”.

We’d rather use a probability approach and report to clients the chance that two data points would be different if we had been lucky enough to use a random sample. That is a much more useful way to look at data, but it probably won’t be used much until colleges start teaching it and a new generation of researchers emerges.

The current debate over the usefulness of statistical significance is a healthy one to have. Hopefully, it will cause researchers of all types to think deeper about how precise a study needs to be and we’ll move away from the current one-size-fits-all thinking that has been pervasive for decades.

Did Apple just kill telephone market research?

A recent issue of The Economist contained an article that describes a potential threat to the accuracy of opinion polling. The latest iPhones have a software feature that doesn’t just block robocalls but sends all calls from unknown callers automatically to voice mail. This feature combats unwanted calls on mobile phones.

Matching sampling frames to populations of interest is increasingly difficult to accomplish in survey research, particularly telephone studies. I will always remember my first day on the job in 1989 when my supervisor was teaching me how to bid projects. The spreadsheet we used assumed our telephone polls would have a 60% cooperation rate. So, at that time about 6 in 10 phone calls we made resulted in a willing respondent. Currently, telephone studies rarely achieve a cooperation rate above 5%. That is 1 in 20 calls. If you are lucky.

The Do Not Call Registry took effect in 2003. At this time, most survey research was still being conducted by telephone (online research was growing but still represented only about 20% of the market research industry’s revenues). Researchers were initially relieved that market research and polls were exempt from the law but in the end that didn’t matter. People stopped cooperating with telephone studies because they thought they had opted out of research calls when they signed up for the Registry. Response rates plummeted.

The rise of mobile phones caused even more headaches for telephone researchers. There was initially no great way to generate random numbers of cell phones in the same way that could be done for land lines and publicly-available directories of cell phone numbers did not exist. For quite some time, telephone studies were underrepresenting mobile phone users and had no great solution for how to interview respondents who did not even have a land line. Eventually, the industry figured this out and methods for including mobile phones became standard.

This new development of automatically routing mobile calls to voice mail could well signify the end of telephone-based research. If consumers like this feature on iPhones it won’t be long until Android-based phones do the same thing. It will preclude pollsters from effectively reaching mobile-only households. Believe it or not, about 45% of US households still have a land line, but the 55% who do not skew young, urban, and liberal.

Pollsters will figure this out and will oversample mobile only households and weight them up in samples. But that won’t really fix the problem. Samples will miss those that have the latest phones and will eventually miss everybody once all current phones are replaced. Oversampling and weighting can help balance under-represented groups, but can’t fix a problem when a group is not represented at all. Weighting can actually magnify biases in samples.

Implications to this?  Here are a few:

  1. More polls and market research project will be conducted online. This is a good thing as there is evidence that in the 2016 election the online polls were more accurate than the telephone polls. It is hard to believe, but we are at a stage where telephone polls are almost always slower, more expensive, and less accurate than their online counterparts.
  2. Researchers will use more mixed samples, using both telephone and online. In our view this tends to be needlessly complicated and introduces mode effects into these samples. We tend to only recommend mixed-mode data collection in business-to-business projects, where we use the phone to screen to a qualified respondent and then send the questionnaire electronically.
  3. Costs of telephone polls will go up. They are already almost criminally expensive and this will get even worse. For those not in the know, the cost per interview for a telephone poll is often 20 to 30 times the cost of an online interview.
  4. Address Based Samples (ABS) will gain in popularity. As telephone response rates decline, systematic biases in telephone samples increase. ABS, when properly operationalized, is a good alternative (although ABS has its limitations as well). ABS still isn’t really probability sampling, but it is the closest thing we have.
  5. The increased cost of telephone polls will spur even more investment in online panels. The quality of online research will be better off because of it. If there is a silver lining for researchers, this is probably it.

Technology has always tended to move faster than the market research industry has been able to adapt to it, probably because researchers have an academic mindset (thorough, but slow). Research methodologists always seem to eventually come up with a solution, but not always quickly. For now, we’d recommend against trusting any opinion poll that is based on a telephone sample, unless the researchers behind it have specifically made a case for how they are going to address this new issue of software blocking their calls to mobile phones. The good news is push polls and robo polls will soon become almost impossible to conduct.

Among college students, Bernie Sanders is the overwhelming choice for the Democratic nomination

Crux Research poll of college students shows Sanders at 23%, Biden at 16%, and all other candidates under 10%

ROCHESTER, NY – October 10, 2019 – Polling results released today by Crux Research show that if it was up to college students, Bernie Sanders would win the Democratic nomination the US Presidency. Sanders is the favored candidate for the nomination among 23% of college students compared to 16% for Joe Biden. Elizabeth Warren is favored by 8% of college students followed by 7% support for Andrew Yang.

  • Bernie Sanders: 23%
  • Joe Biden: 16%
  • Elizabeth Warren: 8%
  • Andrew Yang: 7%
  • Kamala Harris: 6%
  • Beto O’Rourke: 5%
  • Pete Buttigieg: 4%
  • Tom Steyer: 3%
  • Cory Booker: 3%
  • Michael Bennet: 2%
  • Tulsi Gabbard: 2%
  • Amy Klobuchar: 2%
  • Julian Castro: 1%
  • None of these: 5%
  • Unsure: 10%
  • I won’t vote: 4%

The poll also presented five head-to-head match-ups. Each match-up suggests that the Democratic candidate currently has a strong edge over President Trump, with Sanders having the largest edge.

  • Sanders versus Trump: 61% Sanders; 17% Trump; 12% Someone Else; 7% Not Sure; 3% would not vote
  • Warren versus Trump: 53% Warren; 18% Trump; 15% Someone Else; 9% Not Sure; 5% would not vote
  • Biden versus Trump: 51% Biden; 18% Trump; 19% Someone Else; 8% Not Sure; 4% would not vote
  • Harris versus Trump: 48% Harris; 18% Trump; 20% Someone Else; 10% Not Sure; 4% would not vote
  • Buttigieg versus Trump: 44% Buttigieg; 18% Trump; 22% Someone Else; 11% Not Sure; 5% would not vote

The 2020 election could very well be determined on the voter turnout among young people, which has traditionally been much lower than among older age groups.

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Methodology
This poll was conducted online between October 1 and October 8, 2019. The sample size was 555 US college students (aged 18 to 29). Quota sampling and weighting were employed to ensure that respondent proportions for age group, sex, race/ethnicity, and region matched their actual proportions in the US college student population.

This poll did not have a sponsor and was conducted and funded by Crux Research, an independent market research firm that is not in any way associated with political parties, candidates, or the media.

All surveys and polls are subject to many sources of error. The term “margin of error” is misleading for online polls, which are not based on a probability sample which is a requirement for margin of error calculations. If this study did use probability sampling, the margin of error would be +/-4%.

About Crux Research Inc.
Crux Research partners with clients to develop winning products and services, build powerful brands, create engaging marketing strategies, enhance customer satisfaction and loyalty, improve products and services, and get the most out of their advertising.

Using quantitative and qualitative methods, Crux connects organizations with their customers in a wide range of industries, including health care, education, consumer goods, financial services, media and advertising, automotive, technology, retail, business-to-business, and non-profits.
Crux connects decision makers with customers, uses data to inspire new thinking, and assures clients they are being served by experienced, senior level researchers who set the standard for customer service from a survey research and polling consultant.

To learn more about Crux Research, visit http://www.cruxresearch.com.

Truth Initiative wins Ogilvy for Opioid Campaign

Truth Initiative has won two 2019 Ogilvy awards for its campaign against opioid misuse.

The ARF David Ogilvy Awards is the only award that honors the research and analytics insights behind the most successful advertising campaigns. Crux Research, along with our research partners at CommSight, provided the research services for this campaign.

A case study of the campaign can be found here

You can view spots from the campaign here and here.

We are very proud to have provided Truth Initiative with research support for this important campaign.

How to be an intelligent consumer of political polls

As the days get shorter and the air gets cooler, we are on the edge of a cool, colorful season. We are not talking about autumn — instead, “polling season” is upon us! As the US Presidential race heats up, one thing we can count on is being inundated with polls and pundits spinning polling results.

Most market researchers are interested in polls. Political polling pre-dates the modern market research industry and most market research techniques used today have antecedents from the polling world. And, as we have stated in a previous post, polls can be as important as the election itself.

The polls themselves influence voting behavior which should place polling organizations in an ethical quandary. Our view is that polls, when properly done, are an important facet of modern democracy. Polls can inform our leaders as to what the electorate cares about and keep them accountable. This season, polls are determining which candidates get on the debate stage and are driving which issues candidates are discussing most prominently.

The sheer number of polls that we are about to see will be overwhelming. Some will be well-conducted, some will be shams, and many will be in between. To help, we thought we’d write this post on how be an intelligent consumer of polls and what to look out for when reading the polls or hearing about them in the media.

  • First, and this is harder than it sounds, you have to put your own biases aside. Maybe you are a staunch conservative or liberal or maybe you are in the middle. Whatever your leaning, your political views are likely going to get in the way of you becoming a good reader of the polls. It is hard to not have a confirmation bias when viewing polls, where you tend to accept a polling result that confirms what you believe or hope will happen and question a result that doesn’t fit with your map of the world. I have found the best way to do this is to first try to view the poll from the other side. Say you are a conservative. Start by thinking about how you would view the poll if you leaned left instead.
  • Next, always, and I mean ALWAYS, discover who paid for the poll. If it is an entity that has a vested interest in the results, such as a campaign, a PAC, and industry group or lobbyist, go no further. Don’t even look at the poll. In fact, if the sponsor of the poll isn’t clearly identified, move on and spend your time elsewhere. Good polls always disclose who paid for it.
  • Don’t just look to who released the poll, review which organization executed it. For the most part, polls executed by major polling organizations (Gallup, Harris, ORC, Pew, etc.) will be worth reviewing as will polls done by colleges with polling centers (Marist, Quinnipiac, Sienna, etc.). But there are some excellent polling firms out there you likely have never heard of. When in doubt, remember that Five Thirty Eight gives pollsters grades based on their past performances.  Despite what you may hear, polls done by major media organizations are sound. They have polling editors that understand all the nuances and have standards for how the polls are conducted. These organizations tend to partner with major polling organizations that likewise have the methodological muscle that is necessary.
  • Never, and I mean NEVER, trust a poll that comes from a campaign itself. At their best, campaigns will cherry pick results from well executed polls to make their candidate look better. At their worst, they will implement a biased poll intentionally. Why? Because much of the media, even established mainstream media, will cover these polls. (As an aside, if you are a researcher don’t trust the campaigns either. From my experience, you have about a 1 in 3 chance of being paid by a campaign for conducting their poll.)
  • Ignore any talk about the margin of error. The margin of error on a poll has become a meaningless statistic that is almost always misinterpreted by the media. A margin of error really only makes sense when a random or probability sample is being used. Without going into detail, there isn’t a single polling methodology in use today that can credibly claim to be using a probability sample. Regardless, being within the margin of error does not mean a race is too close to call anyway. It really just means it is too close to call with 95% certainty.
  • When reading stories on polls in the media, read beyond the headline. Remember, headlines are not written by reporters or pollsters. They are written by editors that in many ways have had their journalistic integrity questioned and have become “click hunters.” Their job is to get you to click on the story and not necessarily to accurately summarize the poll. Headlines are bound to be more sensational that the polling results merit.

All is not lost though. There are plenty of good polls out there worth looking at. Here is the routine I use when I have a few minutes and want to discover what the polls are saying.

  • First, I start at the Polling Report. This is an independent site that compiles credible polls. It has a long history. I remember reading it in the 90’s when it was a monthly mailed newsletter. I start here because it is nothing more than raw poll results with no spin whatsoever. Their Twitter feed shows the most recently submitted polls.
  • I sometimes will also look at Real Clear Politics. They also curate polls, but they also provide analysis. I tend to just stay on their poll page and ignore the analysis.
  • FiveThirtyEight doesn’t provide polling results in great detail, but usually draws longitudinal graphs on the probability of each candidate winning the nomination and the election. Their predictions have valid science behind them and the site is non-partisan. This is usually the first site I look at to discover how others are viewing the polls.
  • For fun, I take a peek at BetFair which is an UK online betting site that allows wagers on elections. It takes a little training to understand what the current prices mean, but in essence this site tells you which candidates people are putting their actual money on. Prediction markets fascinate me; using this site to predict who might win is fun and geeky.
  • I will often check out Pew’s politics site. Pew tends to poll more on issues than “horse race” matchups on who is winning. Pew is perhaps the most highly respected source within the research field.
  • Finally, I go to the media. I tend to start with major media sites that seem to be somewhat neutral (the BBC, NPR, USA TODAY). After reviewing these sites, I then look at Fox News and MSNBC’s website because it is interesting to see how their biases cause them to say very different things about the same polls. I stay away from the cable channels (CNN, Fox, MSNBC) just because I can’t stand hearing boomers argue back and forth for hours on end.

This is, admittedly, way harder than it used to be. We used to just be able to let Peter Jennings or Walter Cronkite tell us what the polls said. Now, there is so much out there that to truly get an objective handle on what is going on takes serious work. I truly think that if you can become an intelligent, unbiased consumer of polls it will make you a better market researcher. Reading polls objectively takes a skill that applies well to data analysis and insight generation, which is what market research is all about.

Why Your Child Hates Sports

It surprises many to learn that on most measures of well-being today’s youth are the healthiest generation in history. Violent crime against and by young people is historically low. Teen pregnancy and birth rates continue to decline. Most measures of drug and alcohol use among teens and young adults show significant declines from a generation ago. Tobacco use is at a low point. In short, most problems that are a result of choices young people make have shown marked improvement since information on Millennials entered the data sets.

But an important measure of well-being has tracked significantly worse during the Millennial and post-Millennial era:  childhood obesity. According to the CDC, the prevalence of obesity has roughly tripled in the past 40 years. This is a frightful statistic.

This is not new news as many books, documentaries, and scholars have presented possible reasons for the spike in youth obesity. Beyond genetics, there are two likely determinants of obesity: 1) nutrition and 2) physical activity. Discussions of obesity’s “nutritional” causes are fraught with controversy. The food industry involves a lot of interests and money, nutritional science is rarely definitive, and seemingly everyone has their own opinions on what is healthy or unhealthy to eat. The nutritional roots of obesity (while likely very significant) are far from settled.

However, the “physical activity” side of the discussion tends to not be so heated. Nearly everyone agrees that today’s youth aren’t as physically active as they should be. There are likely many causes for this as well, but I believe the way youth sports operate merit some discussion.

When I was young, sports were every bit as important to my life as they became to my Millennial children. The difference is my sports experiences as a child were mostly kid-directed. Almost daily, we gathered in the largest yard in the neighborhood and played whichever sport was in season. It took up an hour or two on most days and sometimes the entire weekend. The biggest difference to today’s youth sports environment is there wasn’t an adult in sight. There were arguments, injuries, and conflicts, all of which got resolved without adult mediation.

Contrast this to today’s youth sports environment. Today’s kids specialize in one sport year-round and from a very young age join travel and elite leagues organized by adults. There is a general dearth of unstructured play time. Correlation and causation are never the same thing but the rise in youth obesity has correlated closely with the rise in youth sports leagues organized by adults. Once adults started making the decisions about sports, our kids got fatter.

As a matter of personal perspective, I have two adult children and I can count six sports (baseball, soccer, ice hockey, track, skiing, cross country) that they played in an adult-organized fashion while growing up. We encountered situations where I had a child who was one of the least talented kids on a team, others where I had a child that was the star of the team, and many others where my child was somewhere in the middle. Between them, my kids were on teams that dominated their leagues and went undefeated, they were on some that lost almost every game, and they were on some teams that both won and lost. I coached for a while and my wife was “team mom” for most teams they were on.

Along the way I noticed that kids seemed to have the most fun when they won just a few more games than they lost. The kids didn’t seem to think it was as fun to dominate the competition and it was even less fun to be constantly on the losing end. 

I remember once when in the car after a hockey game I asked my son what he wanted to happen when he had the puck. He said, “I want to score.” I asked him “suppose you scored every single time you touched the puck. Would that be any fun?” At 10 years old, he didn’t have to think long to say that wouldn’t be very fun at all. But, that is what most hockey dads are hoping will happen.

There seems to be a natural force kids apply to sports equality when adults get out of the way. Left to their own devices, the first things kids will do when choosing up teams is to try to get the teams to be evenly matched. Then, if the game starts to get too one-sided the next thing they will do is swap some players around to balance it out. This seems to be ingrained – nobody teaches kids to do this, but left on their own this is what they tend to do. They will also change the rules of the game to make it more fun.

I’ve encountered many parents who are delusional when it comes to the athletic capabilities of their children. I don’t think I have ever met a dad (including myself) who didn’t think their child was better than he/she really was. We want our kids to succeed of course. But we have to have the right definition of success. Are they having fun? Are they improving? Learning how to work as a team and treat competition with respect? Making friendships? That is what is going to matter down the line.

Far too many parents look to the future too much and don’t let their kids enjoy the moment. They will spend thousands and sacrifice nearly every weekend to send their kid to a camp that might get them noticed by college recruiters. The reality is, their child probably won’t get an athletic scholarship, and if he/she does it probably won’t come close to offsetting the money spent getting him/her to all of the camps and travel league games. Parents also don’t realize that most kids don’t find participating in college sports to be as fun as participating in them was in high school.

When I coached Little League baseball, I used to tell the kids to play catch with their mom or dad every day. I remember a mom once asking me why I was pushing them to do this so much. I told her that playing catch with a baseball in the backyard with your kid is one of the great moments in parenthood. It forces you to talk and listen to your kid. I told her that her son would remember that time with his mom or dad far more than playing on our team.

There are debates over rewards for participation in sports. In my day, you had to win to get the trophy and sometimes you didn’t even get that. Now, kids get trophies for showing up. That is not necessarily a bad thing. As Woody Allen says, “80% of success is showing up.” So, why not reward it?

My youngest son was fortunate to run cross country for a coach that most would classify as a local legend. He has coached the team for 30+ years, has had many state championship teams and individuals, and is widely respected. My favorite memory of him was something I observed when he didn’t know I was looking and it had nothing to do with championships and developing elite athletes.  For the first race of a new season, he took the varsity teams to an out-of-state invitational. The girls team was quite good, and for his 7th (and slowest) runner he brought a freshman girl who was inexperienced and running her very first race. She didn’t do very well and came in about 120th place in the race. I saw the coach come up to her right afterword with a beaming smile on his face. The first thing he said to her was “was that FUN or what?” as he gave her a hug. She smiled, hugged him back and ended up staying on the team for all four years of high school and last weekend (8 years later) I saw her jogging in a local park. She didn’t excel at running in high school, but the coach sparked a lifelong interest in fitness in her.

To me, that signified not just what sports should all be about, but what adults’ role in sports should be all about. We have a real problem with childhood obesity. The cure is to make sports and physical activity more fun, and many times that means getting the adults out of the way.

Demand Curves Always Slope Downward

Last month marked 30 years since I received my MBA. This anniversary has made me think critically about what I learned in business school and to judge what proved helpful and what did not. I will be the first to admit I have a good, yet sometimes selective memory for these things.

I had many outstanding business professors. They were far superior to the teachers I encountered as an undergraduate. There was one professor in particular I will always remember. I took an economics course from him and later took a business law course from him. I had little interest in business law and took the course solely because he was such a great lecturer.

He devoted the final lecture of his business law course to a topic that had nothing to do with law. He stated that there was a simple tenet we should always keep in mind. If we learned nothing else from our time in the program it should be this: demand curves always slope downward.

He then predicted that during our careers we will encounter many situations where people will try to convince us otherwise. We will see things in the business and popular press that ignore this basic concept. But, unlike the others, we will never fall for it because it is the one mistake he was on a crusade to ensure that none of his students would ever get wrong.

Demand curves always slope downward.

What does this mean? It is a simple concept most kindergartners can explain: If the cost of something goes up, fewer people will want it and less of it will be sold. Simple, huh?

My professor was prescient. In the past 30 years, I have encountered dozens, perhaps hundreds of cases where somebody was convinced that a cost change won’t have an effect on volume.

I’ve seen it a lot in business planning. I worked for a consumer goods firm for a short time. One year, a product manager decided to take a price increase. The business plan she created showed that in the current year we had sold 1 million units at $3 for a revenue of $3 million. Her planning assumed a 10 percent price increase would increase revenue by $300K. Not! If the price goes to $3.30 the only guarantee I can think of is that we would sell less than 1 million units. Nevertheless, her plan got through.

I’ve seen public policy makers forget this simple concept as well. They will propose tax changes and then assume that the change won’t affect consumer behavior.

The error is most commonly made when people only consider price and not “cost” in a broader since. Cost involves price but is also comprised of other things, such as the value of your time, the inconvenience of traveling to make a purchase, etc. As Adam Smith said, the real price of something is the “toil and trouble of acquiring it.”

A good example of this happened earlier this year in the county I live in. Our county legislature decided against raising the legal smoking age from 18 to 21. A quote from my representative indicated that because surrounding counties sell tobacco to those 18 and older, raising the age to 21 in our county would not change smoking behavior because young smokers will simply drive elsewhere.

Wrong! Demand curves slope downward. Raising the age to 21 in our county most certainly will decrease tobacco use because we have raised the cost of obtaining cigarettes by making it more inconvenient. 18-21-year olds would now have to drive further to get tobacco. They might have to bug someone of legal age to buy them for them. They may need to risk buying while underage. This all increases the cost to them and they will buy less. It is okay if you are against raising the legal age but it is not okay to use flawed logic to get there.

It is fine to argue that raising the age won’t have a large effect, but arguing that it won’t have any effect at all ignores a basic economic tenet. Demand curves slope downward. Thinking otherwise would sort of be like trying to convince a physicist that gravity only exists in some cases.

To illustrate this point, look to CVS. In 2014, CVS decided to stop selling tobacco products. This increased the cost of buying cigarettes because it became a bit less convenient to find them. Although many felt that this wouldn’t do anything to overall smoking behavior (thus ignoring that demand curves slope downward), a recent study by CVS concluded that 95 million fewer packs of cigarettes were bought by smokers in an 8-month period studied. If we pro-rate that over the 5 years since CVS has stopped selling cigarettes, the implication is that as a result of CVS’s decision, about 3 billion fewer cigarettes have been smoked per year.

Without wading too deeply into a one of the hottest of hot-button political issues, I do hear things from the pro-gun lobby that clearly shows they don’t recognize that demand curves slope downward. I think it is legitimate to be against gun restrictions from a philosophical viewpoint (e.g. gun ownership is a citizen right, guaranteed in the constitution, etc.). But the pro-gun lobby often claims that restricting which weapons that can be sold, taxing them, making it more onerous to register them, etc. will have no effect on “bad guys” getting guns.

Of course it will. Raise the cost of something and people will do it less. I have no idea how to resolve the gun debate in the US, but I am 100% confident that if we make guns harder to obtain fewer guns will be obtained. By good guys and bad guys. Whether that is a good or bad thing depends on which side of the debate you are on.

This concept can influence behavior in unexpected ways. There are studies that show how frequently employees interact varies inversely with the geographic distance their desks are from one another. I noticed this first-hand. At one point, my office was moved literally about 30 feet down a hallway, further away from where the bulk of the people on my team sat. I noticed right away that I conversed with them about half as often as a result. Why? Because the cost for us to interact increased and our behavior changed. It became a bit more inconvenient to interact with them.

“Demand curves slope downward” can have a converse affect: lower the cost of something and people will buy more. The rise of online retail demonstrates this. Shopping online is so convenient and easy that consumers have moved to it quickly – because their cost of shopping has come down. However, in my experience you don’t see people making mistakes on this side of the argument. People seem to know that lowering cost increases volume. It is more that they often fail to see that increasing costs lowers volume.

So, 30 years later, I am going to send a link to this post to my former professor. He will be pleased that at least one of his students remembered his advice.

NOTE: Economic geeks will note that there are some cases where demand curves don’t slope downward. There are “Giffen goods” – items where consumers will buy more of when price goes up. In reality, it is rare to ever see a discussion of this outside of an Economics class.


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