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Oops, the polls did it again

Many people had trouble sleeping last night wondering if their candidate was going to be President. I couldn’t sleep because as the night wore on it was becoming clear that this wasn’t going to be a good night for the polls.

Four years ago on the day after the election I wrote about the “epic fail” of the 2016 polls. I couldn’t sleep last night because I realized I was going to have to write another post about another polling failure. While the final vote totals may not be in for some time, it is clear that the 2020 polls are going to be off on the national vote even more than the 2016 polls were.

Yesterday, on election day I received an email from a fellow market researcher and business owner. We are involved in a project together and he was lamenting how poor the data quality has been in his studies recently and was wondering if we were having the same problems.

In 2014 we wrote a blog post that cautioned our clients that we were detecting poor quality interviews that needed to be discarded about 10% of the time. We were having to throw away about 1 in 10 of the interviews we collected.

Six years later that percentage has moved to be between 33% and 45% and we tend to be conservative in the interviews we toss. It is fair to say that for most market research studies today, between a third and a half of the interviews being collected are, for a lack of a better term, junk.  

It has gotten so bad that new firms have sprung up that serve as a go-between from sample providers and online questionnaires in order to protect against junk interviews. They protect against bots, survey farms, duplicate interviews, etc. Just the fact that these firms and terms like “survey farms” exist should give researchers pause regarding data quality.

When I started in market research in the late 80s/early 90’s we had a spreadsheet program that was used to help us cost out projects. One parameter in this spreadsheet was “refusal rate” – the percent of respondents who would outright refuse to take part in a study. While the refusal rate varied by study, the beginning assumption in this program was 40%, meaning that on average we expected 60% of the time respondents would cooperate. 

According to Pew and AAPOR in 2018 the cooperation rate for telephone surveys was 6% and falling rapidly.

Cooperation rates in online surveys are much harder to calculate in a standardized way, but most estimates I have seen and my own experience suggest that typical cooperation rates are about 5%. That means for a 1,000-respondent study, at least 20,000 emails are sent, which is about four times the population of the town I live in.

This is all background to try to explain why the 2020 polls appear to be headed to a historic failure. Election polls are the public face of the market research industry. Relative to most research projects, they are very simple. The problems pollsters have faced in the last few cycles is emblematic of something those working in research know but rarely like to discuss: the quality of data collected for research and polls has been declining, and should be alarming to researchers.

I could go on about the causes of this. We’ve tortured our respondents for a long time. Despite claims to the contrary, we haven’t been able to generate anything close to a probability sample in years. Our methodologists have gotten cocky and feel like they can weight any sampling anomalies away. Clients are forcing us to conduct projects on timelines that make it impossible to guard against poor quality data. We focus on sampling error and ignore more consequential errors. The panels we use have become inbred and gather the same respondents across sources. Suppliers are happy to cash the check and move on to the next project.

This is the research conundrum of our times: in a world where we collect more data on people’s behavior and attitudes than ever before, the quality of the insights we glean from these data is in decline.

Post 2016 the polling industry brain trust rationalized and claimed that the polls actually did a good job, convened some conferences to discuss the polls, and made modest methodological changes. Almost all of these changes related to sampling and weighting. But, as it appears that the 2020 polling miss is going to be way beyond what can be explained by sampling (last night I remarked to my wife that “I bet the p-value of this being due to sampling is about 1 in 1,000”), I feel that pollsters have addressed the wrong problem.

None of the changes pollsters made addressed the long-term problems researchers face with data quality. When you have a response rate of 5% and up to half of those are interviews you need to throw away, errors that can arise are orders of magnitude greater than the errors that are generated by sampling and weighting mistakes.

I don’t want to sound like I have the answers.  Just a few days ago I posted that I thought that on balance there were more reasons to conclude that the polls would do a good job this time than to conclude that they would fail. When I look through my list of potential reasons the polls might fail, nothing leaps to me as an obvious cause, so perhaps the problem is multi-faceted.

What I do know is the market research industry has not done enough to address data quality issues. And every four years the polls seem to bring that into full view.

Will the polls be right this time?

The 2016 election was damaging to the market research industry. The popular perception has been that in 2016 the pollsters missed the mark and miscalled the winner. In reality, the 2016 polls were largely predictive of the national popular vote. But, 2016 was largely seen by non-researchers as disastrous. Pollsters and market researchers have a lot riding on the perceived accuracy of 2020 polls.

The 2016 polls did a good job of predicting the national vote total but in a large majority of cases final national polls were off in the direction of overpredicting the vote for Clinton and underpredicting the vote for Trump. That is pretty much a textbook definition of bias. Before the books are closed on the 2016 pollster’s performance, it is important to note that the 2012 polls were off even further and mostly in the direction of overpredicting the vote for Romney and underpredicting the vote for Obama. The “bias,” although small, has swung back and forth between parties.

Election Day 2020 is in a few days and we may not know the final results for a while. It won’t be possible to truly know how the polls did for some weeks or months.

That said, there are reasons to believe that the 2020 polls will do an excellent job of predicting voter behavior and there are reasons to believe they may miss the mark.  

There are specific reasons why it is reasonable to expect that the 2020 polls will be accurate. So, what is different in 2020? 

  • There have been fewer undecided voters at all stages of the process. Most voters have had their minds made up well in advance of election Tuesday. This makes things simpler from a pollster’s perspective. A polarized and engaged electorate is one whose behavior is predictable. Figuring out how to partition undecided voters moves polling more in a direction of “art” than “science.”
  • Perhaps because of this, polls have been remarkably stable for months. In 2016, there was movement in the polls throughout and particularly over the last two weeks of the campaign. This time, the polls look about like they did weeks and even months ago.
  • Turnout will be very high. The art in polling is in predicting who will turn out and a high turnout election is much easier to forecast than a low turnout election.
  • There has been considerable early voting. There is always less error in asking about what someone has recently done than what they intend to do in the future. Later polls could ask many respondents how they voted instead of how they intended to vote.
  • There have been more polls this time. As our sample size of polls increases so does the accuracy. Of course, there are also more bad polls out there this cycle as well.
  • There have been more and better polls in the swing states this time. The true problem pollsters had in 2016 was with state-level polls. There was less attention paid to them, and because the national pollsters and media didn’t invest much in them, the state-level polling is where it all went wrong. This time, there has been more investment in swing-state polling.
  • The media invested more in polls this time. A hidden secret in polling is that election polls rarely make money for the pollster. This keeps many excellent research organizations from getting involved in them or dedicating resources to them. The ones that do tend to do so solely for reputational reasons. An increased investment this time has helped to get more researchers involved in election polling.
  • Response rates are upslightly. 2020 is the first year where we have seen a long-term trend towards declining response rates on survey stabilize and even kick up a little. This is likely a minor factor in the success of the 2020 polls, but it is in the right direction.
  • The race isn’t as close as it was in 2016. This one might only be appreciated by statisticians. Since variability is maximized in a 50/50 distribution the further away from an even race it is the more accurate a poll will be. This is another small factor in the direction of the polls being accurate in 2020.
  • There has not been late breaking news that could influence voter behavior. In 2016, the FBI director’s decision to announce a probe into Clinton’s emails came late in the campaign. There haven’t been any similar bombshells this time.
  • Pollsters started setting quotas and weighting on education. In the past, pollsters would balance samples on characteristics known to correlate highly with voting behavior – characteristics like age, gender, political party affiliation, race/ethnicity, and past voting behavior. In 2016, pollsters learned the hard way that educational attainment had become an additional characteristic to consider when crafting samples because voter preferences vary by education level. The good polls fixed that this go round.
  • In a similar vein, there has been a tighter scrutiny of polling methodology. While the media can still be a cavalier about digging into methodology, this time they were more likely to insist that pollsters outline their methods. This is the first time I can remember seeing news stories where pollsters were asked questions about methodology.
  • The notion that there are Trump supporters who intentionally lie to pollsters has largely been disproven by studies from very credible sources, such as Yale and Pew. Much more relevant is the pollster’s ability to predict turnout from both sides.

There are a few things going on that give the polls some potential to lay an egg.

  • The election will be decided by a small number of swing states. Swing state polls are not as accurate and are often funded by local media and universities that don’t have the funding or the expertise to do them correctly. The polls are close and less stable in these states. There is some indication that swing state polls have been tightening, and Biden’s lead in many of them isn’t much different than Clinton’s lead in 2020.
  • Biden may be making the same mistake Clinton made. This is a political and not a research-related reason, but in 2016 Clinton failed to aggressively campaign in the key states late in the campaign while Trump went all in. History could be repeating itself. Field work for final polls is largely over now, so the polls will not reflect things that happen the last few days.
  • If there is a wild-card that will affect polling accuracy in 2020, it is likely to center around how people are voting. Pollsters have been predicting election day voting for decades. In this cycle votes have been coming in for weeks and the methods and rules around early voting vary widely by state. Pollsters just don’t have past experience with early voting.
  • There is really no way for pollsters to account for potential disqualifications for mail-in votes (improper signatures, late receipts, legal challenges, etc.) that may skew to one candidate or another.
  • Similarly, any systematic voter suppression would likely cause the polls to underpredict Trump. These voters are available to poll, but may not be able to cast a valid vote.
  • There has been little mention of third-party candidates in polling results. The Libertarian candidate is on the ballot in all 50 states. The Green Party candidate is on the ballot in 31 states. Other parties have candidates on the ballot in some states but not others. These candidates aren’t expected to garner a lot of votes, but in a close election even a few percentage points could matter to the results. I have seen national polls from reputable organizations where they weren’t included.
  • While there is little credible data supporting that there are “shy” Trump voters that are intentionally lying to pollsters, there still might be a social desirability bias that would undercount Trump’s support. That social desirability bias could be larger than it was in 2016, and it is still likely in the direction of under predicting Trump’s vote count.
  • Polls (and research surveys) tend to underrepresent rural areas. Folks in rural areas are less likely to be in online panels and to cooperate on surveys. Few pollsters take this into account. (I have never seen a corporate research client correcting for this, and it has been a pet peeve of mine for years.) This is a sample coverage issue that will likely undercount the Trump vote.
  • Sampling has continued to get harder. Cell phone penetration has continued to grow, online panel quality has fallen, and our best option (ABS sampling) is still far from random and so expensive it is beyond the reach of most polls.
  • “Herding” is a rarely discussed, but very real polling problem. Herding refers to pollsters who conduct a poll that doesn’t conform to what other polls are finding. These polls tend to get scrutinized and reweighted until they fit to expectations, or even worse, buried and never released. Think about it – if you are a respected polling organization that conducted a recent poll that showed Trump would win the popular vote, you’d review this poll intensely before releasing it and you might choose not to release it at all because it might put your firm’s reputation at risk to release a poll that looks different than the others. The only polls I have seen that appear to be out of range are ones from smaller organizations who are likely willing to run the risk of being viewed as predicting against the tide or who clearly have a political bias to them.

Once the dust settles, we will compose a post that analyzes how the 2020 polls did. For now, we feel there are a more credible reasons to believe the polls will be seen as predictive than to feel that we are on the edge of a polling mistake.  From a researcher’s standpoint, the biggest worry is that the polls will indeed be accurate, but won’t match the vote totals because of technicalities in vote counting and legal challenges. That would reflect unfairly on the polling and research industries.

Researchers should be mindful of “regression toward the mean”

There is a concept in statistics known as regression toward the mean that is important for researchers to consider as we look at how the COVID-19 pandemic might change future consumer behavior. This concept is as challenging to understand as it is interesting.

Regression toward the mean implies that an extreme example in a data set tends to be followed by an example that is less extreme and closer to the “average” value of the population. A common example is if two parents that are above average in height have a child, that child is demonstrably more likely to be closer to average height than the “extreme” height of their parents.

This is an important concept to keep in mind in the design of experiments and when analyzing market research data. I did a study once where we interviewed the “best” customers of a quick service restaurant, defined as those that had visited the restaurant 10 or more times in the past month. We gave each of them a coupon and interviewed them a month later to determine the effect of the coupon. We found that they actually went to the restaurant less often the month after receiving the coupon than the month before.

It would have been easy to conclude that the coupon caused customers to visit less frequently and that there was something wrong with it (which is what we initially thought). What really happened was a regression toward the mean. Surveying customers who had visited a large number of times in one month made it likely that these same customers would visit a more “average” amount in a following month whether they had a coupon or not. This was a poor research design because we couldn’t really assess the impact of the coupon which was our goal.

Personally, I’ve always had a hard time understanding and explaining regression toward the mean because the concept seems to be counter to another concept known as “independent trials”. You have a 50% chance of flipping a fair coin and having it come up heads regardless of what has happened in previous flips. You can’t guess whether the roulette wheel will come up red or black based on what has happened in previous spins. So, why would we expect a restaurant’s best customers to visit less in the future?

This happens when we begin with a skewed population. The most frequent customers are not “average” and have room to regress toward the mean in the future. Had we surveyed all customers across the full range of patronage there would be no mean to regress to and we could have done a better job of isolating the effect of the coupon.

Here is another example of regression toward the mean. Suppose the Buffalo Bills quarterback, Josh Allen, has a monster game when they play the New England Patriots. Allen, who has been averaging about 220 yards passing per game in his career goes off and burns the Patriots for 450 yards. After we are done celebrating and breaking tables in western NY, what would be our best prediction for the yards Allen will throw for the second time the Bills play the Patriots?

Well, you could say the best prediction is 450 yards as that is what he did the first time. But, regression toward the mean would imply that he’s more likely to throw close to his historic average of 220 yards the second time around. So, when he throws for 220 yards the second game it is important to not give undue credit to Bill Belichick for figuring out how to stop Allen.

Here is another sports example. I have played (poorly) in a fantasy baseball league for almost 30 years. In 2004, Derek Jeter entered the season as a career .317 hitter. After the first 100 games or so he was hitting under .200. The person in my league that owned him was frustrated so I traded for him. Jeter went on to hit well over .300 the rest of the season. This was predictable because there wasn’t any underlying reason (like injury) for his slump. His underlying average was much better than his current performance and because of the concept of regression toward the mean it was likely he would have a great second half of the season, which he did.

There are interesting HR examples of regression toward the mean. Say you have an employee that does a stellar job on an assignment – over and above what she normally does. You praise her and give her a bonus. Then, you notice that on the next assignment she doesn’t perform on the same level. It would be easy to conclude that the praise and bonus caused the poor performance when in reality her performance was just regressing back toward the mean. I know sales managers who have had this exact problem – they reward their highest performers with elaborate bonuses and trips and then notice that the following year they don’t perform as well. They then conclude that their incentives aren’t working.

The concept is hard at work in other settings. Mutual funds that outperform the market tend to fall back in line the next year. You tend to feel better the day after you go to the doctor. Companies profiled in “Good to Great” tend to have hard times later on.

Regression toward the mean is important to consider when designing sampling plans. If you are sampling an extreme portion of a population it can be a relevant consideration. Sample size is also important. When you have just a few cases of something, mathematically an extreme response can skew your mean.

The issue to be wary of is that when we fail to consider regression toward the mean, we tend to overstate the importance of correlation between two things. We think our mutual fund manager is a genius when he just got lucky, that our coupon isn’t working, or that Josh Allen is becoming the next Drew Brees. All of these could be true, but be careful in how you interpret data that result from extreme or small sample sizes.

How does this relate to COVID? Well, at the moment, I’d say we are still in an “inflated expectations” portion of a hype curve when we think of what permanent changes may take place resulting from the pandemic. There are a lot of examples. We hear that commercial real estate is dead because businesses will keep employees working from home. Higher education will move entirely online. In-person qualitative market research will never happen again. Business travel is gone forever. We will never again work in an office setting. Shaking hands is a thing of the past.

I’m not saying there won’t be a new normal that results from COVID, but if we believe in regression toward the mean and the hype curve we’d predict that the future will look more like the past than how it is currently being portrayed. The post-COVID world will certainly look more like the past than a more extreme version of the present. We will naturally regress back toward the past and not to a more extreme version of current behaviors. The “mean” being regressed to has likely changed, but not as much as the current, extreme situation implies.

“Margin of error” sort of explained (+/-5%)

It is now September of an election year. Get ready for a two-month deluge of polls and commentary on them. One thing you can count on is reporters and pundits misinterpreting the meaning behind “margin of error.” This post is meant to simplify the concept.

Margin of error refers to sampling error and is present on every poll or market research survey. It can be mathematically calculated. All polls seek to figure out what everybody thinks by asking a small sample of people. There is always some degree of error in this.

The formula for margin of error is fairly simple and depends mostly on two things: how many people are surveyed and their variability of response. The more people you interview, the lower (better) the margin of error. The more the people you interview give the same response (lower variability), the better the margin of error. If a poll interviews a lot of people and they all seem to be saying the same thing, the margin of error of the poll is low. If the poll interviews a small number of people and they disagree a lot, the margin of error is high.

Most reporters understand that a poll with a lot of respondents is better than one with fewer respondents. But most don’t understand the variability component.

There is another assumption used in the calculation for sampling error as well: the confidence level desired. Almost every pollster will use a 95% confidence level, so for this explanation we don’t have to worry too much about that.

What does it mean to be within the margin of error on a poll? It simply means that the two percentages being compared can be deemed different from one another with 95% confidence. Put another way, if the poll was repeated a zillion times, we’d expect that at least 19 out of 20 times the two numbers would be different.

If Biden is leading Trump in a poll by 8 points and the margin of error is 5 points, we can be confident he is really ahead because this lead is outside the margin of error. Not perfectly confident, but more than 95% confident.

Here is where reporters and pundits mess it up.  Say they are reporting on a poll with a 5-point margin of error and Biden is leading Trump by 4 points. Because this lead is within the margin of error, they will often call it a “statistical dead heat” or say something that implies that the race is tied.

Neither is true. The only way for a poll to have a statistical dead heat is for the exact same number of people to choose each candidate. In this example the race isn’t tied at all, we just have a less than 95% confidence that Biden is leading. In this example, we might be 90% sure that Biden is leading Trump. So, why would anyone call that a statistical dead heat? It would be way better to be reporting the level of confidence that we have that Biden is winning, or the p-value of the result. I have never seen a reporter do that, but some of the election prediction websites do.

Pollsters themselves will misinterpret the concept. They will deem their poll “accurate” as long as the election result is within the margin of error. In close elections this isn’t helpful, as what really matters is making a correct prediction of what will happen.

Most of the 2016 final polls were accurate if you define being accurate as coming within the margin of error. But, since almost all of them predicted the wrong winner, I don’t think we will see future textbooks holding 2016 out there as a zenith of polling accuracy.

Another mistake reporters (and researchers make) is not recognizing that the margin of error only refers to sampling error which is just one of many errors that can occur on a poll. The poor performance of the 2016 presidential polls really had nothing to do with sampling error at all.

I’ve always questioned why there is so much emphasis on sampling error for a couple of reasons. First, the calculation of sampling error assumes you are working with a random sample which in today’s polling world is almost never the case. Second, there are many other types of errors in survey research that are likely more relevant to a poll’s accuracy than sampling error. The focus on sampling error is driven largely because it is the easiest error to mathematically calculate. Margin of error is useful to consider, but needs to be put in context of all the other types of errors that can happen in a poll.

Online education will need to change before it rules higher education

We recently conducted a poll of college students around the world about their experiences with online education this spring that resulted from the pandemic. The short answer is students didn’t fare well and are highly critical of the ability of online education to engage them and to deliver instruction. This isn’t a subtle, nuanced finding. A large majority of college students worldwide thought the online education they received this spring was ineffective and unengaging.

I held out hope that the pandemic would be the event that finally kickstarted online education. Our poll results have me doubting it will, which is a shame as online education holds enormous potential. It is a new technology that is, for some reason, being held back. If you think about it, we have had all the technology needed to take education online for at least 10 years, yet for the most part the traditional university system has remained as it was a generation ago.

I’ve always been interested in new “media” technologies because I’ve noticed a pattern in their emergence. Almost always, they begin as a nifty new delivery system for content that was developed with the “old” media. The earliest radio shows largely consisted of people reading the newspapers aloud and playing music. Early television mostly adapted content from radio – serialized dramas, variety shows, baseball games, etc. The Internet 1.0 largely just electronically expectorated content that existed in other forms.

After a bit of a gestation period, “new” media eventually thrive as they take advantage of their technological uniqueness and content evolves along with the new distribution system. The result is something really special and not just a new way to deliver old things.

There are many examples. Radio moved to become central to family entertainment and ritual in a way the newspaper could not. Television developed the Saturday morning lineup, the situation comedy, talk shows, etc., none of which could have worked as well on radio. And, the Internet evolved and became interactive, with user-created content, product reviews, with a melding of content and commerce that isn’t possible in other media. In all cases, the “new” media gestated awhile by mimicking the old but once they found their way their value grew exponentially. The old media didn’t go away, but got repositioned to a narrower niche.

This hasn’t happened in higher education. Streaming your lecture on Zoom might be necessary during a pandemic but it is not what online education should be about. Students consistently tell us it doesn’t work for them. Parents and students don’t feel it provides the value they expect from college, which is why we are starting to see lawsuits where students are demanding tuition refunds from colleges that moved education online this spring.

We composed a post a little while ago that posited that the reason digital textbooks really haven’t made much of a difference in colleges is because textbook publishers have prevented this synergy from happening. Most digital textbooks today are simply a regurgitation of a printed textbook that you can read on a computer. Our surveys show that the number one way a digital textbook is read remains by viewing a PDF. That is hardly taking advantage of what today’s technology has to offer.

The potential for the digital textbook is much greater. In fact, it wouldn’t be a textbook at all. Instead, there could be a digital nexus of all that is going on in a course, conducted, coached, and curated by the instructor. Imagine a “book” that could take you on an interactive tour. It could link you to lectures by world-renown people. It could show practitioners applying the knowledge they gained in the course. It could contain formative assessments where you could determine how you are progressing and then adapt to focus you where you need individualized help. A tutor would be a link away. Other students could comment and help you.

Your instructor could become a coach rather than a sage. This wouldn’t be a textbook at all, but a melding of course materials and instruction and collaborative tools.

This technology exists today, yet publishers and colleges have too much of a self-interest not to innovate. Education is suffering because of it.

This spring most college instructors had one or two weeks to figure out how to move their instruction online with little help from textbook publishers or technology companies. They had no choice but to adapt their existing course to a new delivery system. So, they pointed a camera on themselves and called it online education.

It is no wonder that online education largely failed our students. Every poll I have seen, including a few Crux has conducted, has shown that students found online education to be vastly inferior to traditional instruction this spring.

But, did you know this isn’t new? College students have long been critical of online education. I’ve asked questions about online education to college students for almost 20 years. While many appreciate the convenience of an online course and that it can cost less, a very large majority of those taking online courses say they aren’t an effective way to learn. Almost all say that they would have learned better in a traditional course. It is a rare student that chooses an online course because it is an effective way to learn. When they choose an online course, it is because it fits better into their life situation and not because it is an effective way to learn.

Why? Because online course providers really haven’t taken advantage of a “new” medium. They are still adapting traditional education and placing it online rather than embracing the uniqueness that online education can provide. They are firmly ensconced in Internet 1.0 a decade or two after all other industries have moved on. Compared to a decade ago we shop completely differently. We watch entertainment completely differently. We communicate with others completely differently. Yet, our children attend college the same way their parents and grandparents did.

Course management systems do exist, but to date they haven’t fundamentally changed the nature of a college course. We ask about course management systems on surveys as well, and college students find them to be moderately helpful, but hardly game changing.

One of Crux’s largest clients is a supplemental education company that provides resources to college students who don’t feel they are getting the support they need from their college or their professors. This company has been one of the best performing companies in the US since COVID-19 hit and so many courses moved online. This client is well-managed and has a great vision and brilliant employees. But, if educators had fully figured out how to effectively educate online, I don’t think they could be as successful as they have been because students wouldn’t have such a pressing need for outside help. Because of higher education’s unwillingness or inability to adapt, I expect this client to thrive for a long time.

It is sad in a way to think that our colleges and universities, who should be on the forefront of technology and innovation, are sadly lacking in adapting course materials and instruction to the Internet. Especially when you consider that these are the same entities that largely invented the Internet.

Living near Rochester NY, it is easy to see a parallel to the Eastman Kodak company. Kodak had one of the strongest brands in the world, was tightly identified with imaging and photography, and had invented almost all of the core technologies needed for digital photography. All at a time when the number of images consumers were about to capture was about to explode literally by a factor of about 10,000, maybe 100,000. But, because of an inability to break out of an old way of thinking and an inertia to hang on too long to an “old” media, one of America’s great companies was essentially reduced to a business school case in how to grab defeat from the jaws of opportunity.

Is this a cautionary tale for colleges and universities? Sure. I suspect that elite college brands will continue to do well as they cater to a wealthy demographic that has done quite well during the pandemic. But, for the rest of us, who send students to non-elite institutions, I expect to see colleges face enormous financial pressures and to see many college brands go the road of Kodak over the next decade. Their ticket to a better path is to more effectively use technology.

Online education has the potential to cure some of what ails the US higher education system. It can adapt quickly to market demand for workers. It can provide much wider access to the best and brightest teachers. It can aggregate a mass of students who might be interested in a highly specialized field, and thus become more targeted. And, it may finally be what finally fixes the high cost of higher education.

Will online education will thrive in the US? Not until it changes to take advantage of what an interconnected world has to offer. The time is right for colleges to truly tap into the power of what online education can be. This is really the only way colleges will be able to charge the tuition levels they have become accustomed to charging and until online education becomes synonymous with quality education, many colleges will struggle.

This is taking far too long but I am hopeful that kickstarting this process will be one silver lining to come out of the upheaval to education that has been caused by the pandemic.

I have more LinkedIn contacts named “Steve” than contacts who are Black

There have been increasing calls for inclusiveness and fairness across America and the world. The issues presented by the MeToo and Black Lives Matter movements affect all sectors of society and the business world. Market research is no exception. Recent events have spurred me to reflect on my experiences and to think about whether the market research field is diverse enough and ready to make meaningful changes. Does market research have structural, systemic barriers preventing women and minorities from succeeding?

My recollections are anecdotal – just one person’s experiences when working in market research for more than 30 years. What follows isn’t based on an industry study or necessarily representative of all researchers’ experiences.

Women in Market Research

When it comes to gender equity in the market research field, my gut reaction is to think that research is a good field for women and one that I would recommend. I reviewed Crux Research’s client base and client contacts. In 15 years, we have worked with about 150 individual research clients across 70 organizations. 110 (73%) of those 150 clients are female. This dovetails with my recollection of my time at a major research supplier. Most of my direct clients there were women.

Crux’s client base is largely mid-career professionals – I’d say our typical client is a research manager or director in his/her 30’s or 40’s. I’d conclude that in my experience, women are well represented at this level.

But, when I look through our list of 70 clients and catalog who the “top” research manager is at these organizations, I find that 42 (60%) of the 70 research VPs and directors are male. And, when I catalog who these research VP’s report into, typically a CMO, I find that 60 (86%) of the 70 individuals are male. To recap, among our client base, 73% of the research managers are female, 40% of the research VPs are female, and 14% of the CMO’s are female.

This meshes with my experience working at a large supplier. While I was there, women were well-represented in our research director and VP roles but there were almost no women represented in the C-suite or among those that report to them. There seem to be a clear but firm glass ceiling in place in market research suppliers and in clients.

Minorities in Market Research

My experience paints a bleaker picture when I think of ethnic minority representation in market research. Of our 150 individual research clients, just 25 (17%) have been non-white and just 3 (2%) have been black. Moving up the corporate ladder, in only 5 (13%) of our 70 clients is the top researcher in the organization non-white and in only 4 (6%) of the 70 companies is the CMO non-white, and none of the CMOs are black. Undoubtedly, we have a long way to go.

A lack of staff diversity in research suppliers and market research corporate staffs is a problem worth resolving for a very important reason: market researchers and pollsters are the folks providing the information to the rest of the world on diversity issues. Our field can’t possibly provide an appropriate perspective to decision makers if we aren’t more diverse. Our lack of diversity affects the conversation because we provide the data the conversation is based upon.  

Non-profits seem to be a notable exception when it comes to ethnic diversity. I have had large non-profit clients that have wonderfully diverse employee bases, to the point where it is not uncommon to attend meetings and Zoom calls where I am the only white male in the session. These non-profits make an effort to recruit and train diverse staffs and their work benefits greatly from the diversity of perspectives this brings. There is a palpable openness of ideas in these organizations. Research clients and suppliers would do well to learn from their example.  

I can’t think of explicit structural barriers that limit the progression of minorities thought the market research ranks, but that just illustrates the problem: the barriers aren’t explicit, they are more subtle and implicit. Which is what makes them so intractable.

We have to make a commitment to develop more diverse employee bases. I worked directly for the CEO of a major supplier for a number of years. One thing I respected about him was he was confident enough in himself that he was not afraid to hire people who were smarter than him or didn’t think like him or came from an entirely different background. It made him unique. In my experience, most hiring managers unintentionally hire “mini-me’s” – younger variants of themselves whom they naturally like in a job interview. Well, if the hiring managers are mostly white males and they are predisposed to hire a lot of “mini-me’s” over time this perpetuates a privilege and is an example of an unintentional, but nonetheless structural bias that limits the progress of women and minorities.

If you don’t think managers tend to hire in their own image, consider a recent Economist article that states “In 2018 there were more men called Steve than there were women among the chief executives of FTSE 100 companies.” I wouldn’t be surprised if there are more market researchers in the US named Steve than there are black market researchers.

To further illustrate that we naturally seek people like ourselves, I reviewed my own LinkedIn contact list. This list is made up of former colleagues, clients, people I have met along the way, etc. It is a good representation of the professional circle I exist within. It turns out that my LinkedIn contact list is 60% female and has 25% non-whites. But, just 3% of my LinkedIn contacts are black. And, yes, I have more LinkedIn contacts named Steve than I have contacts who are black.

This is a problem because as researchers we need to do our best to cast aside our biases and provide an objective analysis of the data we collect. We cannot do that well if we do not have a diverse array of people working on our projects.

Many managers will tell you that they would like to hire a minority for a position but they just don’t get quality candidates applying. This is not taking ownership of the issue. What are you doing to generate candidates in the first place?

It is all too easy to point the finger backwards at colleges and universities and say that we aren’t getting enough qualified candidates of color. And that might be true. MBA programs continue to enroll many more men than women and many more whites than non-whites. They should be taken to task for this. As employers we also need to be making more demands on them to recruit women and minorities to their programs in the first place.

I like that many research firms have come out with supportive statements and financial contributions to relevant causes recently. This is just a first step and needs to be the catalyst to more long-lasting cultural changes in organizations.

We need to share best practices, and our industry associations need to step up and lead this process. Let’s establish relationships with HCBU’s and other institutions to train the next generation of black researchers.

The need to be diverse is also important in the studies we conduct. We need to call more attention to similarities and differences in our analyses – and sample enough minorities in the first place so that we can do this. Most researchers do this already when we have a reason to believe before we launch the study that there might be important differences by race/ethnicity. However, we need to do this more as a matter of course, and become more attuned to highlighting the nuances in our data sets that are driven by race.

Our sample suppliers need to do a better job of recruiting minorities to our studies, and to ensure that the minorities we sample are representative of a wider population. As their clients, we as suppliers need to make more demands about the quality of the minority samples we seek.

We need an advocacy group for minorities in market research. There is an excellent group, Women in Research https://www.womeninresearch.org/ advocating for women. We need an analogous organization for minorities.

Since I am in research, I naturally think that measurement is key to the solution. I’ve long thought that organizations only change what they can measure. Does your organization’s management team have a formal reporting process that informs them of the diversity of their staff, of their new hires, of the candidates they bring in for interviews? If they do not, your organization is not poised to fix the problem. If your head of HR cannot readily tell you what proportion of your staff is made up of minorities, your firm is likely not paying enough attention.

Researchers will need to realize that their organizations will become better and more profitable when they recruit and develop a more diverse client base. Even though it is the right thing to do, we need to view resolving these issues not solely as altruism. It is in our own self-interest to work on this problem. It is truly the case that if we aren’t part of the solution, we are likely part of the problem. And again, because we are the ones that inform everyone else about public opinion on these issues, we need to lead the way.

My belief is it that this issue will be resolved by Millennials once they get to an age when they are more senior in organizations. Millennials are a generation that is intolerant to unfairness of this sort and notices the subtle biases that add up. They are the most diverse generation in US history. The oldest Millennials are currently in their mid-30’s. In 10-20 years’ time they will be in powerful positions in business, non-profits, education, and government.

Optimistically, I believe Millennials will make a big difference. Pessimistically, I wonder if real change will happen before they are the ones managing suppliers and clients, as thus far the older generations have not shown that they are up to the task.

Shift the Infield, Go for Two, and Pull the Goalie Sooner!

Moneyball is one of my favorite books. It combines many interests of mine – statistics, baseball, and management. I once used it to inspire a client to think about their business differently. This client was a newly-named President of a firm and had brought us in to conduct some consumer market research. New management teams often like to bring in new research suppliers and shed their old ones, and in this case we were the beneficiaries.

In our initial meeting, I asked some basic marketing questions about how they decide to price their products or how much to spend on advertising. Each time his response was “this is how we have always done it” rather than a well-thought out rationale supporting his decision. For instance, most of his products were priced to retailers at 50% of the price to consumers because that is how it had been for decades. I asked him “what are the odds that your optimal pricing is 50% rather than something higher or lower?” What are the chances that a round number like 50% could be optimal for all products in all cases when he literally had thousands of products?

I sent him a copy of Moneyball when I returned from the trip because I knew he was a sports fan. He read it immediately. It sparked him to commission a consulting firm to delve deeply into pricing models and ultimately led to a significant change in their pricing policies. They no longer used 50% as a target and established different wholesale prices for each of their SKU’s based on demand and updated these prices regularly. A few years later, he told me that decision literally saved his firm millions of dollars and the pricing efficiency helped to distribute his products more effectively. He said this was probably the project he had led that had the biggest impact on his business since he had been there.

Businesses can use sports analogies too readily, but in this case it really worked. The rise of statisticians in sports has worked and there are lessons that businesses can learn from this.

I find it fascinating when old-timers and sports talk radio hosts lament the rise of “analytics” in sports. You can see the impact of statisticians every time you see a baseball team set up in a defensive shift, when you see a football team go for it on fourth down, or when you see a hockey team pull its goalie earlier than usual. These decisions being made more frequently and in situations where the prior norms of the game would have prevented them from happening. It is all because data jockeys have been given a seat at the sports management table to the chagrin of the purists.

But data geeks haven’t totally taken over sports and longstanding traditions continue to hold sway. For instance, in baseball it can be shown that more runs on average are scored in the first inning than in any other inning. This makes sense, as the first inning is the only time in the game when you can be sure your best hitters will be at the top of the batting order. So, why don’t major league teams start their closer and have him pitch the first inning? Instead, they reserve their most powerful pitcher for the 9th inning, when, more often or not, the game is already decided. I’ve been predicting that teams will figure this out and start their closer for about 20 years now and they haven’t done it yet. (The Tampa Rays did something close to this and had an “opener” pitcher in their rotation, but it didn’t work well because this pitcher wasn’t their most powerful arm.)

Similarly, hockey teams continue to be slow to pull their goalie when behind late in the game. Hockey coaches also continue to make a decision that baffles me every time. They are down by one goal late in the game so they pull their goalie and promptly surrender a goal. The first thing they do is put their goalie back in which makes no rational sense at all. If you are willing to take the risk of being scored upon when losing by one goal, you should be even more willing to do so when losing by two goals. There is an excellent paper on pulling the goalie (“Pulling the Goalie:  Hockey and Investment Implications.”) which shows that coaches aren’t pulling their goalie even close to quick enough.

These sports cases are interesting because it is the fans that always seem to notice the coaching strategy errors before the coaches and general managers. This illustrates the value of an outside perspective in organizations that have longstanding policies and traditions. I don’t think my client could have accomplished his pricing changes if he wasn’t brand new to the organization or if he didn’t hire a consulting firm to work out the optimal strategy. This change was not going to come from within his organization.

Businesses have been slow to adapt their thinking despite the vast amount of data at their disposal. Decisions are made all the time without consulting what the data are indicating. More relevant to our industry, in most organizations market research is still seen as a support function to marketing, as opposed to its equal. I don’t think I have ever heard of an organization where market research reports directly to senior management or where marketing reports into research, yet we often hear senior managers say that connecting to customers is the most critical part of their organization’s success.

Many saw Moneyball as a book about sports or a great movie. I saw it as one of the most important business books ever written. Its key message is to use data to break out of existing decision patterns, often to great success.

What are the best COVID-19 polls?

The COVID-19 crisis is affecting all types of organizations. Many, including some of our clients, are commissioning private polls to help predict the specific impact of the pandemic on their business. Fortunately, there are a number of well-regarded research and polling organizations conducting polls that are publicly released. Unfortunately, there are also disreputable polls out there and can be challenging to sort out the good from the bad.

We’ve been closely watching the COVID-19 polls and have found some that stand out from the rest. We felt it would be a good idea to list them here to save you some time as you look for polling information.

  • First, although it is not a poll, a useful site to look at is the Institute for Health Metrics and Evaluation at the University of Washington. This site contains the results of a model projecting numbers of deaths from COVID-19, beds needed versus hospital capacity, etc. This is one of the most credible models out there, and the one that seems to be cited the most in the media and by the federal government.
  • Johns Hopkins University maintains a coronavirus tracking center which is the definitive place to go to track cases, hospitalizations, and deaths from COVID-19. 

Below is a list of opinion polls we’ve found most interesting. There are a lot of polls out there. The ones listed below are from trusted organizations and would be a good place to start your search. There are many polls available that concentrate on things like the public’s opinion of the government’s handling of the crisis. The polls below go a bit deeper and are far more interesting in our view. There are likely other good polls out there, but these are the best ones we have found thus far.

  • The Harris Poll COVID-19 trackerThis is perhaps the most comprehensive COVID-19 polling we have discovered, and it tracks back to early March. If you have time for just one polling site this is probably the one to check out.
  • PewPew is a widely-respected organization that has conducted many polls on COVID-19 topics.
  • The COVID Impact Survey. This is an independent, non-governmental survey being conducted by NORC along with some respected foundations. 
  • Dynata. Dynata has a tracking poll going on COVID-19 that is interesting because it spans multiple countries. Dynata is also doing a “symptom map” based on their polling worldwide. This is interesting as it shows how symptoms are trending around the world, in the US by state, and even in NYC by neighborhood. However, we feel that a Google Trends search would provide better data that survey research on symptoms. 
  • IPSOS.  IPSOS is also conducting worldwide polls
  • Simpson Scarborough. This poll is specific to higher education and the implications of COVID-19 on college students. If you work in higher education or have a college-aged child, you are likely to find this one interesting.
  • University of Massachusetts Amherst. This one is different and interesting. It shows the results of an ongoing survey of infectious disease experts, containing their predictions for the impacts of the disease.  It is updated weekly.  FiveThirtyEight is summarizing this work and it is probably easiest to read their summaries than to go to the original source. I must say, though, I have been watching this poll carefully, and the experts haven’t been all that accurate in their predictions, missing on the high side consistently.

There are undoubtedly many more good polls out there. Those mentioned above are from non-partisan, trusted organizations.


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