Posts Tagged 'Crux Research'

How Did Pollsters Do in the Midterm Elections?

Our most read blog post was posted the morning after the 2016 Presidential election. It is a post we are proud of because it was composed in the haze of a shocking election result. While many were celebrating their side’s victory or in shock over their side’s losses, we mused about what the election result meant for the market research industry.

We predicted pollsters would become defensive and try to convince everyone that the polls really weren’t all that bad. In fact, the 2016 polls really weren’t. Predictions of the popular vote tended to be within a percent and a half or so of the actual result which was better than for the previous Presidential election in 2012. However, the concern we had about the 2016 polls wasn’t related to how close they were to the result. The issue we had was one of bias: 22 of the 25 final polls we found made an inaccurate prediction and almost every poll was off in the same direction. That is the very definition of bias in market research.

Suppose that you had 25 people flip a coin 100 times. On average, you’d expect 50% of the flips to be “heads.” But, if say, 48% of them were “heads” you shouldn’t be all that worried as that can happen. But, if 22 of the 25 people all had less than 50% heads you should worry that there was something wrong with the coins or they way they were flipped. That is, in essence, what happened in the 2016 election with the polls.

Anyway, this post is being composed the aftermath of the 2018 midterm elections. How did the pollsters do this time?

Let’s start with FiveThirtyEight.com. We like this site because they place probabilities around their predictions. Of course, this gives them plausible deniability when their prediction is incorrect, as probabilities are never 0% or 100%. (In 2016 they gave Donald Trump a 17% chance of winning and then defended their prediction.) But this organization looks at statistics in the right way.

Below is their final forecast and the actual result. Some results are still pending, but at this moment, this is how it shapes up.

  • Prediction: Republicans having 52 seats in the Senate. Result: It looks like Republicans will have 53 seats.
  • Prediction: Democrats holding 234 and Republicans holding 231 House seats. Result: It looks like Democrats will have 235 or 236 seats.
  • Prediction: Republicans holding 26 and Democrats holding 24 Governorships. Result: Republicans now hold 26 and Democrats hold 24 Governorships.

It looks like FiveThirtyEight.com nailed this one. We also reviewed a prediction market and state-level polls, and it seems that this time around the polls did a much better job in terms of making accurate predictions. (We must say that on election night, FiveThirtyEight’s predictions were all over the place when they were reporting in real time. But, as results settled, their pre-election forecast looked very good.)

So, why did polls seem to do so much better in 2018 than 2016? One reason is the errors cancel out when you look at large numbers of races. Sure, the polls predicted Democrats would have 234 seats, and that is roughly what they achieved. But, in how many of the 435 races did the polls make the right prediction? That is the relevant question, as it could be the case that the polls made a lot of bad predictions that compensated for each other in the total.

That is a challenging analysis to do because some races had a lot of polling, others did not, and some polls are more credible than others. A cursory look at the polls suggests that 2018 was a comeback victory for the pollsters. We did sense a bit of an over-prediction favoring the Republican Senatorial candidates, but on the House side there does not seem to be a clear bias.

So, what did the pollsters do differently? Not much really. Online sampling continues to evolve and get better, and the 2016 result has caused polling firms to concentrate more carefully on their sampling. One of the issues that may have caused the 2016 problem is that pollsters are starting to almost exclusively use the top 2 or 3 panel companies. Since 2016, there has been a consolidation among sample suppliers, and as a result, we are seeing less variance in polls as pollsters are largely all using the same sample sources. The same few companies provide virtually all the sample used by pollsters.

Another key difference was that turnout in the midterms was historically high. Polls are more accurate in high turnout races, as polls almost always survey many people who do not end up showing up on election day, particularly young people. However, there are large and growing demographic differences (age, gender, race/ethnicity) in supporters of each party, and that greatly complicates polling accuracy. Some demographic subgroups are far more likely than others to take part in a poll.

Pollsters are starting to get online polling right. A lot of the legacy firms in this space are still entrenched in the telephone polling world, have been protective of their aging methodologies, and have been slow to change. After nearly 20 years of online polling the upstarts have finally forced the bigger polling firms to question their approaches and to move to a world where telephone polling just doesn’t make a lot of sense. Also, many of the old guard, telephone polling experts are now retired or have passed on, and they have largely led the resistance to online polling.

Gerrymandering helps the pollster as well. It still remains the case that relatively few districts are competitive. Pew suggests that only 1 in 7 districts was competitive. You don’t have to be a pollster to accurately predict how about 85% of the races will turn out. Only about 65 of the 435 house races were truly at stake. If you just flipped a coin in those races, in total your prediction of house seats would have been fairly close.

Of course, pollsters may have just gotten lucky. We view that as unlikely though, as there were too many races. Unlike in 2018 though, in 2016 we haven’t seen any evidence of bias (in a statistical sense) in the direction of polling errors.

So, this is a good comeback success for the polling industry and should give us greater confidence for 2020. It is important that the research industry broadcasts this success. When pollsters have a bad day, like they did in 2016, it affects market research as well. Our clients lose confidence in our ability to provide accurate information. When the pollsters get it right, it helps the research industry as well.

The most selective colleges have the least effective marketing

Recently, Stanford University made headlines for deciding to stop issuing an annual press release documenting its number of applicants and acceptances.

There has been a bit of an arms race among colleges with competitive admissions to be able to claim just how selective they are. The smaller the proportion of applicants accepted, the better the college does in many ranking systems and the more exclusive the “brand” of the college becomes.

This seems to be a bit crazy, as publicizing how few students are accepted is basically broadcasting how inefficient your college marketing system has become. We can’t think of any organization beyond colleges that would even consider doing something analogous to this – broadcasting to the world that they have enticed non-qualified buyers to consider their product.

I learned firsthand how ingrained this behavior is among college admissions and marketing personnel. About five years ago I had the pleasure to speak in front of a group of about 200 college marketers and high school counselors. I created what I felt was a compelling and original talk which took on this issue. I have given perhaps 200 talks in my career, and this one might have been the single most poorly received presentation I have ever delivered.

The main thrust of my argument was that as a marketer, you want to be as targeted as possible so as to not waste resources. “Acquisition cost” is an important success metric for markers: how much do you spend in marketing for every customer you are able to obtain? Efficiency in obtaining customers is what effective marketing is all about.

I polled the audience to ask what they felt the ideal acceptance rate would be for their applicants. Almost all responded “under 10%” and most responded “under 5%.” I then stated that the ideal acceptance rate for applicants would be 100%. The ideal scenario would be this: every applicant to your college would be accepted, would then choose to attend your institution, would go on to graduate, become a success, and morph into an engaged alumnus.

I used an analogy of a car dealership. Incenting college marketers to increase applications is akin to compensating a car salesperson for how many test drives he/she takes customers on. The dealership derives no direct value from a test drive. Every test drive that does not result in a car purchase is a waste of resources. The test drive is a means to an end and car dealers don’t tend to track it as a success metric. Instead, they focus on what matters – how many cars are sold and how much was spent in marketing to make that happen.

Colleges reward their marketers to get students to test drive when they should be rewarding their marketers for getting them to buy. This wouldn’t matter much if a high proportion of applicants were accepted and ending up attending.  But, even at highly selective colleges it is not uncommon for less than 10% of applicants to be accepted, less than 33% of those accepted to choose to attend, and less than 50% of those that enroll to actually end up graduating. At those rates, for every 1,000 applicants, just 17 will end up graduating from the institution. That is a success rate of 1.7%.

These are metrics that in any business context would be seen as a sign of an organization in serious trouble. Can you imagine if only 10% of the people who came in your store qualified to buy your product? And then if only a third of those would actually decide to do so? And then if half of those that do buy don’t end up using your product or return it? That is pretty much what happens at selective colleges.

This issue is a failure of leadership. College marketers I have worked with can often see this problem, but feel pressured by their Deans and College Presidents to maximize their applicant base. Granted, this can help build the college’s brand, but it is a huge drain on resources that are better spent ensuring targeting applicants who are poised for success at the institution. It has happened because selectivity is considered important in building a college’s brand. Stanford has taken a useful first step, and hopefully other colleges will follow their lead.

Is segmentation just discrimination with an acceptable name?

A short time ago we posted a basic explanation of the Cambridge Analytica/Facebook scandal (which you can read here). In it, we stated that market segmentation and stereotyping are essentially the same thing. This presents an ethical quandary for marketers as almost every marketing organization makes heavy use of market segmentation.

To review, marketers place customers into segments so that they can better understand and serve them. Segmentation is at the essence of marketing. Segments can be created along any measurable dimension, but since almost all segments have a demographic component we will focus on that for this post.

It can be argued that segmentation and stereotyping are the same thing. Stereotyping is attaching perceived group characteristic to an individual. For instance, if you are older I might assume your political views lean conservative, since it is known that political views tend to be more conservative in older Americans that they are in general among younger Americans. If you are female I might assume you are more likely to be the primary shopper for your household, since females in total do more of the family shopping than males. If you are African-American, I might assume you have a higher likelihood than others to listen to rap music, since that genre indexes high among African-Americans.

These are all stereotypes. These examples can be shown to true of a larger group, but that doesn’t necessarily imply that they apply to all the individuals in the group. There are plenty of liberal older Americans, females who don’t shop at all, and African-Americans who can’t stand rap music.

Segmenting consumers (which is applying stereotypes) isn’t inherently a bad thing. It leads to customized products and better customer experiences. The potential problem isn’t with stereotyping, it is when doing so moves to a realm of being discriminatory that we have to be careful. As marketers we tread a fine line. Stereotyping oversimplifies the complexity of consumers by forming an easy to understand story. This is useful in some contexts and discriminatory in others.

Some examples are helpful. It can be shown that African-Americans have a lower life expectancy than Whites. A life insurance company could use this information to charge African-Americans higher premiums than Whites. (Indeed, many insurance companies used to do this until various court cases prevented them from doing so.) This is a segmentation practice that many would say crosses a line to become discriminatory.

In a similar vein, car insurance companies routinely charge higher risk groups (for example younger drivers and males) higher rates than others. That practice has held up as not being discriminatory from a legal standpoint, largely because the discrimination is not against a traditionally disaffected group.

At Crux, we work with college marketers to help them make better admissions offer decisions. Many colleges will document the characteristics of their admitted students who thrive and graduate in good standing. The goal is to profile these students and then look back at how they profiled as applicants. The resulting model can be used to make future admissions decisions. Prospective student segments are established that have high probabilities of success at the institution because they look like students known to be successful, and this knowledge is used to make informed admissions offer decisions.

However, this is a case where a segmentation can cross a line and become discriminatory. Suppose that the students who succeed at the institution tend to be rich, white, female, and from high performing high schools. By benchmarking future admissions offers against them, an algorithmic bias is created. Fewer minorities, males, and students from urban districts will be extended admissions offers What turns out to be a good model from a business standpoint ends up perpetuating a bias., and places certain demographics of students at a further disadvantage.

There is a burgeoning field in research known as “predictive analytics.” It allows data jockeys to use past data and artificial intelligence to make predictions on how consumers will react. It is currently mostly being used in media buying. Our view is it helps in media efficiency, but only if the future world can be counted on to behave like the past. Over-reliance on predictive analytics will result in marketers missing truly breakthrough trends. We don’t have to look further than the 2016 election to see how it can fail; many pollsters were basing their modeling on how voters had performed in the past and in the process missed a fundamental shift in voter behavior and made some very poor predictions.

That is perhaps an extreme case, but shows that segmentations can have unintended consequences. This can happen in consumer product marketing as well. Targeted advertising can become formulaic. Brands can decline distribution in certain outlets. Ultimately, the business can suffer and miss out on new trends.

Academics (most notably Kahneman and Tversky) have established that people naturally apply heuristics to decision making. These are “rules of thumb” that are often useful because they allow us to make decisions quickly. However, these academics have also demonstrated how the use of heuristics often result in sub-optimal and biased decision making.

This thinking applies to segmentation. Segmentation allows us to make marketing decisions quickly because we assume that individuals take on the characteristics of a larger group. But, it ignores the individual variability within the group, and often that is where the true marketing insight lies.

We see this all the time in the generational work we do. Yes, Millennials as a group tend to be a bit sheltered, yet confident and team-oriented. But this does not mean all of them fit the stereotype. In fact, odds are high that if you profile an individual from the Millennial generation, he/she will only exhibit a few of the characteristics commonly attributed to the generation. Taking the stereotype too literally can lead to poor decisions.

This is not to say that marketers shouldn’t segment their customers. This is a widespread practice that clearly leads to business results. But, they should do so considering the errors and biases applying segments can create, and think hard about whether this can unintentionally discriminate and, ultimately, harm the business in the long term.

Has market research become Big Brother?

Technological progress has disrupted market research. Data are available faster and cheaper than ever before. Many traditional research functions have been automated out of existence or have changed significantly because of technology. Projects take half the time to complete that they did just a decade ago. Decision making has moved from an art to a science. Yet, as with most technological disruptions, there are just as many potential pitfalls as efficiencies to be wary of as technology changes market research.

“Passive” data collection is one of these potential pitfalls. It is used by marketers in good ways: the use of passive data helps understand consumers better, target meaningful products and services, and create value for both the consumer and the marketer. However, much of what is happening with passive data collection is done without the full knowledge of the consumer and this process has the potential of being manipulative. The likelihood of backlash towards the research industry is high.

The use of passive data in marketing and research is new and many researchers may not know what is happening so let us explain. A common way to obtain survey research respondents is to tap into large, opt-in online panels that have been developed by a handful of companies. These panels are often augmented with social (river) channels whereby respondents are intercepted while taking part in various online activities. A recruitment email or text is delivered, respondents take a survey, and data are analyzed. Respondents provide information actively and with full consent.

There have been recent mergers which have resulted in fewer but larger and more robust online research panels available. This has made it feasible for some panel companies to gain the scale necessary to augment this active approach with passive data.

It is possible to append information from all sorts of sources to an online panel database. For instance, voter registration files are commonly appended. If you are in one of these research panels, clients likely know if you are registered to vote, if you actually voted, and your political party association. They will have made a prediction of how strong a liberal or conservative you likely are. They may have even run models to predict which issues you care most about. You are likely linked into a PRIZM cluster that associates you with characteristics of the neighborhood where you reside, which in turn can score your potential to be interested in all sorts of product categories. This is all in your file.

These panels also have the potential to link to other publicly-available databases such as car registration files, arrest records, real estate transactions, etc. If you are in these panels, whether you have recently bought a house, how much you paid for it, if you have been convicted of a crime, may all be in your “secret file.”

But, it doesn’t stop there. These panels are now cross-referenced to other consumer databases. There are databases that gather the breadcrumbs you leave behind in your digital life: sites you are visiting, ads you have been served, and even social media posts you have made. There is a tapestry of information available that is far more detailed than most consumers realize. From the research panel company’s perspective, it is just a matter of linking that information to their panel.

This opens up exciting research possibilities. We can now conduct a study among people who are verified to have been served by a specific client’s digital advertising. We can refine our respondent base further by those who are known to have clicked on the ad. As you can imagine, this can take ad effectiveness research to an entirely different level. It is especially interesting to clients because it can help optimize media spending which is by far the largest budget item for most marketing departments.

But, therein lies the ethical problem. Respondents, regardless of what privacy policies they may have agreed to, are unlikely to know that their passive web behavior is being linked into their survey responses. This alone should ring alarm bells for an industry suffering from low response rates and poor data quality. Respondents are bound to push back when they realize there is a secret file panel companies are holding on them.

Panel companies are straying from research into marketing. They are starting to encourage clients to use the survey results to better target individual respondents in direct marketing. This process can close a loop with a media plan. So, say on a survey you report that you prefer a certain brand of a product. That can now get back to you and you’ll start seeing ads for that product, likely without your knowledge that this is happening because you took part in a survey.

To go even further, this can affect advertising people not involved in the survey may see. If you prefer a certain brand and I profile a lot like you, as a result of your participation in a survey I may end up seeing specific ads. Even if I don’t know you or have any connection to you.

In some ways, this reeks of the Cambridge Analytica scandal (which we explain in a blog post here). We’ll be surprised if this practice doesn’t eventually create a controversy in the survey research industry. This sort of sales targeting resulting from survey participation will result in lower response rates and a further erosion of confidence in the market research field. However, it is also clear that these approaches are inevitable and will be used more and more as panel companies and clients gain experience with them.

It is the blurring of the line between marketing and market research that has many old-time researchers nervous. There is a longstanding ethical tenet in the industry that participation in research project should in no way result in the respondent being sold or marketed to. The term for this is SUGGING (Selling Under the Guise of research) and all research industry trade groups have a prohibition against SUGGING embedded in their codes of ethics. It appears that some research firms are ignoring this. But, this concept has always been central to the market research field: we have traditionally assured respondents that they can be honest on our surveys because we will in no way market to them directly because of their answers.

In the novel 1984 George Orwell describes a world where the government places its entire civilization under video surveillance. For most of the time since its publication, this has appeared as a frightening, far-fetched cautionary tale. Recent history has suggested this world may be upon us. The NSA scandal (precipitated by Edward Snowden) showed how much of our passive information is being shared with the government without our knowledge. Rather than wait for the government to surveil the population, we’ve turned the cameras on ourselves. Marketers can do things I don’t feel people realize and research respondents are unknowingly enabling this. The contrails you leave as you simply navigate your life online can be used to follow you and the line between research and marketing is fading, and this will eventually be to the detriment of our field.

Sexual harassment/abuse among college students – new survey results released

Sexual harassment and abuse on college campuses has garnered increased attention in the media and by political leaders. Surprisingly, there is little research documenting what is actually happening among college students – what the levels of abuse and harassment are, who is being victimized, and how students feel their college administrators are dealing with these issues.

In the spring of 2018 Crux Research surveyed 717 current college students to learn more about the current state of these issues. An issue like sexual harassment can be challenging to get right from a polling standpoint because it can be difficult to define. As a general term, it can be too broad to interpret as different experiences may be construed by one person as harassment and as another as not being harassment. The best way to address this is to be specific in our questioning. To be sure respondents understood our objectives, we developed a list of statements under three harassment categories shown below:

Verbal/Non-Physical harassment

  • Being called gay or lesbian in a negative way
  • Being shown sexy or sexual pictures you didn’t want to see
  • Being verbally intimidated in a sexual way
  • Having someone make unwelcome sexual comments, jokes, or gestures to or about you
  • Having someone flash or expose themselves to you

Online harassment

  • Being called gay or lesbian in a negative way online
  • Having someone spread unwelcome sexual rumors about you online
  • Having someone post unwelcome sexual comments, jokes, or pictures about or of you online
  • Being sent unwelcome sexual comments, jokes, or pictures electronically

Physical harassment

  • Being physically intimidated in a sexual way
  • Being touched in an unwelcome sexual way
  • Being forced to do something sexual you didn’t want to do

For each, we asked the college student if he/she had been a victim of the specific type of harassment since they had been a college student. We found that 54% of college students have been a victim of some form of verbal/non-physical harassment, 45% have been a victim of some sort of online harassment, and 32% have been a victim of some sort of physical harassment.

Importantly, this study finds that while victimization is usually thought of as an issue for college women, college men are also common victims of sexual harassment:

  • 55% of college females have been the victims of verbal harassment, compared to 52% of college males.
  • 42% of college females have been the victims of online harassment, compared to 47% of college males.
  • 32% of college females have been the victims of physical harassment, compared to 32% of college males.

There are some large differences in college males and females, depending on the specific form of harassment:

College females are more likely than college males to report that…

  • Someone has made unwelcome sexual comments, jokes, or gestures to or about them (41% of females; 17% of males).
  • They have been verbally intimidated in a sexual way (27% of females; 17% of males).
  • They have been sent unwelcome sexual comments, jokes, or pictures electronically (30% of females; 17% of males).

College males are more likely than college females to report that…

  • Being called gay or lesbian in a negative way (20% of males; 14% of females).
  • Being called gay or lesbian in a negative way online (20% of males; 8% of females).

Perhaps most surprising is that for the most serious abuse item presented (“being forced to do something sexual that you didn’t want to do”) there was no statistical difference between college males and college females. Overall, 13% of college students indicated this has happened to them since they have been at college – about 1 in 8 college students. Again, the most serious types of sexual harassment and abuse happening on campuses is not solely a female issue. College men are reporting being sexual abused in a physical way as well.

Although we have shown that victimization is not solely an issue for college females, it is clear from our study that the perpetrators of sexual harassment/abuse are predominantly male. Overall, victims report that 72% of the time their harasser was male, 16% of the time the harasser was female, and 12% of the time it was both.

Most commonly, victims report that their harasser was a fellow college student (53%) or a friend (26%). 12% report that their harasser was a romantic partner. It is rare that students will report that their instructors/professors (4% of cases) or another adult at college (3%) are the harassers. Sexual harassment on college campuses appears to be mostly peer-to-peer.

Unique to this study, we also asked college students if they had done anything since they had been a student that could be correctly interpreted as being sexual harassment. Seventeen percent (17%) of students said they had – including 28% of all college males. To repeat: more than one in four (28%) of college males admit that they have done something to sexually harass another student since they have been in college.

Perhaps most troubling is how infrequently instances of abuse are reported. This study indicates that just 37% of harassment gets reported. Females (reporting 24% of instances) are less likely than males (54%) to make a report. For every report made by a college female, there are three incidents that are not reported.  And, our study also found that instances where the harasser was a fellow student are the ones that are least likely to be reported.

This issue has been brought more front and center at colleges in the past few years. College culture is moving towards supporting the victim/accuser. Compared to a year ago, about half (52%) of students are more likely to believe someone that reports being sexually harassed and 15% are less likely to believe someone who reports harassment. About two-thirds (65%) of students think the greater focus on these issues will result in a long-term change in attitudes about sexual harassment at college. Three-quarters (74%) feel that unreported sexual harassment is a bigger issue than false reporting of sexual harassment.

College students are largely satisfied with how their administration has addressed sexual misconduct and harassment. Overall, just 6% felt that their administration is not taking this issue seriously. Seventy percent (70%) feel that their college provides enough protection against sexual harassment and abuse.

In sum, sexual harassment and abuse occurs at a troubling level at colleges – and both college females and males are victims. Students are rallying behind the accusers, yet far too few victims are reporting harassment incidences, especially when they happen student-to-student. It appears that students have confidence in their administrators to handle these issues and protect them.

Market research isn’t about storytelling, it is about predicting the future

We recently had a situation that made me question the credibility of market research. We had fielded a study for a long-term client and were excited to view the initial version of the tabs. As we looked at results by age groupings we found them to be surprising. But this was also exciting because we were able to weave a compelling narrative around why the age results seemed counter-intuitive.

Then our programmer called to say a mistake had been made in the tabs and the banner points by age had been mistakenly reversed.

So, we went back to the drawing board ad constructed another, equally compelling story, as to why the data were behaving as they were.

This made me question the value of research. Good researchers can review seemingly disparate data points from a study and generate a persuasive story as to why they are as they are. Our entire business is based on this skill – in the end clients pay us to use data to provide insight into their marketing issues. Everything else we do is a means to this end.

Our experience with the flipped age banner points illustrates that stories can be created around any data. In fact, I’d bet that if you gave us a randomly-generated data set we could convince you as to its relevance to your marketing issues. I actually thought about doing this – taking the data we obtain by running random data through a questionnaire when testing it before fielding, handing it to an analyst, and seeing what happens. I’m convinced we could show you a random data set’s relevance to your business.

This issue is at the core of polling’s PR problem. We’ve all heard people say that you can make statistics say anything, therefore polls can’t be trusted. There are lies, damn lies, and statistics. I’ve argued against this for a long time because the pollsters and researchers I have known have universally been well-intentioned and objective and never try to draw a pre-determined conclusion from the data.

Of course, this does not mean that all of the stories we tell with data aren’t correct or enlightening. But, they all come from a perspective. Clients value external suppliers because of this perspective – we are third-party observers who aren’t wrapped up in the internal issues client’s face and we are often in a good position to view data with an objective mind. We’ve worked with hundreds of organizations and can bring these experiences bring that to bear on your study. Our perspective is valuable.

But, it is this perspective that creates an implicit bias in all we do. You will assess a data set from a different set of life experiences and background than I will. That is just human nature. Like all biases in research, our implicit bias may or not be relevant to a project. In most cases, I’d say it likely isn’t.

So, how can researchers reconcile this issue and sleep at night knowing their careers haven’t been a sham?

First and foremost, we need to stop saying that research is all about storytelling. It isn’t. The value of market research isn’t in the storytelling it is in the predictions of the future it makes. Clients aren’t paying us to tell them stories. They are paying us to predict the future and recommend actions that will enhance their business. Compelling storytelling is a means to this but is not our end goal. Data-based storytelling provides credibility to our predictions and gives confidence that they have a high probability of being correct.

In some sense, it isn’t the storytelling that matters, it is the quality of the prediction. I remember having a college professor lecturing on this. He would say that the quality of a model is judged solely by its predictive value. Its assumptions, arguments, and underpinnings really didn’t matter.

So, how do we deal with this issue … how do we ensure that the stories we tell with data are accurate and fuel confident predictions? Below are some ideas.

  1. Make predictions that can be validated at a later date. Provide a level of confidence or uncertainty around the prediction. Explain what could happen to prevent your prediction from coming true.
  2. Empathize with other perspectives when analyzing data. One of the best “tricks” I’ve ever seen is to re-write a research report as if you were writing it for your client’s top competitor. What conclusions would you draw for them? If it is an issue-based study, consider what you would conclude from the data if your client was on the opposite side of the issue.
  3. Peg all conclusions to specific data points in the study. Straying from the data is where your implicit bias may tend to take over. Being able to tie conclusions directly to data is dependent on solid questionnaire design.
  4. Have a second analyst review your work and play devil’s advocate. Show him/her the data without your analysis and see what stories and predictions he/she can develop independent of you. Have this same person review your story and conclusions and ask him/her to try to knock holes in them. The result is a strengthened argument.
  5. Slow down. It just isn’t possible to provide stories, conclusions, and predictions from research data that consider differing perspectives when you have just a couple of days to do it. This requires more negotiation upfront as to project timelines. The ever-decreasing timeframes for projects are making it difficult to have the time needed to objectively look at data.
  6. Realize that sometimes a story just isn’t there. Your perspective and knowledge of a client’s business should result in a story leaping out at you and telling itself. If this doesn’t happen, it could be because the study wasn’t designed well or perhaps there simply isn’t a story to be told. The world can be a more random place than we like to admit, and not everything you see in a data set is explainable. Don’t force it – developing a narrative that is reaching for explanations is inaccurate and a disservice to your client.

Why aren’t there more digital textbooks?

On college campuses, technology is like air – always present, necessary, and only noticed when it is lacking. College networks reach seemingly everywhere. Today’s courses use technology for enrollment, collaboration, communication, etc. Much of the basic research that underlies technological breakthroughs in business and industry is pioneered on college campuses. We find on employee surveys that recent Millennial graduates are often underwhelmed by the technology they have access to at their employers because they became accustomed to a higher standard when they were students.

Why then has a technological revolution that colleges are such a central part of seemingly skipped over what is at the core of most college courses:  the college textbook?

Depending on which source you consult, digital textbooks currently comprise between 10% and 15% of college textbooks and this percentage has been growing glacially … at like 1-2% per year.

Contrast this with other types of books. There are currently nearly half a billion digital trade books sold each year. In the “normal” (non-textbook) book world, there are about two digital books sold for every three printed books sold. In trade publishing the conversation isn’t about whether digital books will continue to grow and dominate (as there is a consensus that they will), but more about how massive Amazon will become in the space and what the impact of a growing audiobook segment will be.

Clearly, penetration of digital books is happening much slower in college textbook world than the trade book world.

But why?

First and foremost, the role of publishers in the college textbook market is different than in the trade book world. About 80% of the college textbook market is controlled by just five publishers and there is a trend towards further market consolidation. Publishers have the lion’s share of market power; after all, they control nearly all the content. So they can also control how this content is distributed.

Publishers’ market power is even greater than one might initially imagine. There might be just one or two viable choices for textbooks to select for a course. The result has been an increase in textbook prices of +1,000% or more since the mid-seventies, and, importantly, little incentive on the part of publishers to innovate. Publishers have created digital options and online learning systems, but these aren’t terribly innovative and largely serve to protect existing (and profitable) print textbook franchises. Textbook publishing is a cash cow and publishers protect it.

A finger can also be pointed at colleges. The college bookstore was once seen as an essential service to provide for students. It is now viewed as a profit center, giving colleges little incentive to push back on publishers to keep prices low and to innovate. The college bookstore’s mission has moved from being educational to being profit-centered.

College professors are unwittingly part of the problem. We have done studies that show that students largely buy the textbooks professors tell them to buy. Publishers market textbooks one professor at a time. There are no buying groups or purchasing departments negotiating prices on behalf of students. Our studies show that professors don’t think much about the cost of a book to a student before putting it on the list for the semester. Textbook costs and innovation just aren’t something professors seem to think much about.

There are a few countervailing forces. Used textbook distributors help recycle books and keep prices down. Textbook rental firms have had a similar effect. Increased online buying options have created price competition. But, these forces are swimming upstream in the face of the power held by publishers. Our data show that although the total textbook market is growing (because more students are going to college) the average number of textbooks obtained is decreasing. But, the average price per textbook continues to increase. This leads us to conclude that students are managing increasing textbook costs by going without some books to compensate for increased prices on books they cannot do without. This clearly isn’t the right thing to do from an educational standpoint. Students should be able to afford the materials they need to learn.

The internet has a way of being a disintermediater – of removing barriers between buyers and sellers and decreasing transaction costs. This effect has taken some market power away from publishers of traditional books. The ease of buying online at Amazon, the growth of digital books, etc., has served to make trade publishers less dominant than they used to be. And, in the non-textbook world, there has been a proliferation of self-publishing. An author no longer needs a publisher to reach an audience. Publishers are still important, but they are getting repositioned.

This hasn’t happened with textbooks. Academic book authors still largely use the traditional route via publishers (although some do self-publish, but mostly for students at their own universities).

What is most troubling about the lack of innovation in college textbooks is the academic impact it can have. There is lots of grumbling among student groups and elected officials about the cost of college textbooks. Few mention how true digital innovation in college textbooks would transform education.

We’ve often talked about how when a new medium arises, it initially isn’t all that innovative from a content standpoint. As an example, when television first became established, its content was largely just adapted from the successful radio content of the day (news, variety shows, serials, etc.). Once the new, innovative delivery mechanism was established, the content itself changed to take advantage of the unique features of the new media. The Internet was similar – initially its popularity was as a new delivery mechanism for content that could be found on other media (information like news, weather, encyclopedias, etc.). Once the mechanism was established, the unique power of the Internet (communication, collaboration, etc.) became evident.

Digital textbooks are following this pattern. Currently, digital textbooks are pretty much printed textbooks forced into a digital format – not much more exciting than a PDF copy of a textbook. But, digital textbooks hold much greater potential than printed textbooks. They can share highlights across students, catalyze students to collaborate on content they don’t understand, link to additional sources of information if an area is unclear, illustrate concepts with animations and video, adapt content based on formative assessments along the way, etc. It is easy to get enthusiastic about what a digital textbook could potentially do. It could transform education and teaching. It is easy to see a future where the textbook is the primary method of instruction and the professor becoming more of a coach and less of a lecturer.

The incredible potential of digital textbooks won’t happen until textbook authors see this and start creating textbooks differently and until publishers move past their reliance on traditional printed textbooks and find a profitable path. This seems to be an industry ripe for disruption.

We’d like to say this change is coming soon and is inevitable – but this entire blog post was based on a presentation we gave eight years ago to an industry event, so we have reservations that this change is impending.

The Cambridge Analytica scandal points to marketing’s future

There has been a lot of press, almost universally bad, regarding Cambridge Analytica recently. Most of this discussion has centered on political issues (how their work may have benefitted the Trump campaign) and on data privacy issues (how this scandal has shined a light on the underpinnings of Facebook’s business model). One thing that hasn’t been discussed is the technical brilliance of this approach to combining segmentation, big data, and targeted communications to market effectively. In the midst of an incredibly negative PR story lurks the story of a controversial future of market research and marketing.

To provide a cursory and perhaps oversimplified recap of what happened, this all began with a psychographic survey which provided input into a segmentation. This is a common type of market research project. Pretty much every brand you can think of has done it. The design usually has a basis in psychology and the end goal is typically to create subgroups of consumers that provide a better customer understanding and ultimately help a client spend marketing resources more efficiently by targeting these subgroups.

Almost every marketer targets demographically – by easy to identify characteristics such as age, gender, race/ethnicity, and geography. Many also target psychographic ally – by personality characteristics and deeper psychological constructs. The general approach taken by Cambridge Analytics has been perfected over decades and is hardly new. I’d say I’ve been involved in about 100 projects that involve segmenting on a psychographic basis.

To give a concrete example, this type of approach is used by public health campaigns seeking to minimize drug and alcohol use. Studies will be done on a demographic basis that indicate things like drug use skews towards males more than females, towards particular age groups, and perhaps even certain regions of the country. But, it can also be shown that those most at risk of addiction also have certain personality types – they are risk takers, sensation seekers, extroverts, etc. Combined with demographic information, this can allow a public health marketer to target their marketing spend as well as help them craft messages that will resound with those most at risk.

Segmentation is essentially stereotyping with another name. It is associating perceived characteristics of a group with an individual. At its best, this approach can provide the consumer with relevant marketing and products customized to his/her needs. At its worst, it can ignore variation within a group and devalue the consumer as an individual. Segmentation can turn to prejudice and profiling fast and marketers can put too much faith in it.

Segmentation is imperfect. Just because you are a male, aged 15-17, and love to skateboard without a helmet and think jumping out of an airplane would be cool does not necessarily mean you are at risk to initiate drug use. But, our study might show that for every 100 people like you, 50 of them are at risk, and that is enough to merit spending prevention money towards reaching you. You might not be at risk for drug use, but we think you have a 50% chance of being so and this is much higher than the general risk in the population. This raises the efficiency of marketing spending.

What Cambridge Analytica did was analogous to this. The Facebook poll users completed provided data needed to establish segments. These segments were then used to predict your likelihood to care about an issue. Certain segments might be more associated with hot button issues in the election campaign, say gun rights, immigration, loss of American jobs, or health care. So, once you filled out the survey, combined with demographic data, it became possible to “score” you on these issues. You might not be a “gun nut” but your data can provide the researcher with the probability that you are, and if it is high enough you might get an inflammatory gun rights ad targeted to you.

Where this got controversial was, first and foremost, regardless of what Facebook’s privacy policy may say, most users had no clue that answering an innocuous quiz might enable them to be targeted in this way. Cambridge Analytica had more than the psychographic survey at their disposal – they also had demographics, user likes and preferred content, and social connections. They also had much of this information on the user’s Facebook friends as well. It is the depth of the information they gathered than has led to the crisis at Facebook.

People tend to associate most strongly with people who are like them. So, if I score you high on a “gun nut scale” chances are reasonably high that your close friends will have a high probability of being like you. So, with access to your friends, a marketer can greatly expand the targeted reach of the campaign.

It is hard to peel away from the controversies to see how this story really points to the future of marketing, and how research will point the way. Let me explain.

Most segmentations suffer from a fatal flaw: they segment with little ability to follow up by targeting. With a well-crafted survey we can almost always create segments help a marketer better understand his/her customers. But, often (and I would even say most of the time) it is next to impossible to target these segments. Back to the drug campaign example, since I know what shows various demographic groups watch, I can tell you to spend your ad dollars on males aged 16-17. But, how the heck do you then target further and find a way to reach the “risk taking” segment you really want? If you can’t target, segmentation is largely an academic exercise.

Traditionally you couldn’t target psychographic segments all that well. But, with what Google and Facebook now know about their users, you can. If we can profile enough of the Facebook teenage user base and have access to who their friends are, we can get incredibly efficient in our targeting.  Ad spend can get to those who have a much higher propensity for drug use and we can avoid wasting money on those who have low propensity.

It is a brilliant approach. But, like most things on the Internet, it can be a force for bad as well as good. If what Cambridge Analytica had done was for the benefit of an anti-drug campaign, I don’t think it would be nearly the story it has become. Once it went into a polarized political climate, it became news gold.

Even when an approach like this is applied to what most would call legitimate marketing, say for a consumer packaged good, it can get a bit creepy and feel manipulative. It is conceivable that via something one of my Facebook friends did, I can get profiled as a drinker of a specific brand of beer. Since Google also knows where my phone is, I can then be sent an ad or a coupon at the exact moment I walk by the beer case in my local grocery store. Or, my friends can be sent the same message. And I didn’t do anything to knowingly opt into being targeted like this.

There are ethical discussions that need to be had regarding whether this is good or bad, if it is a service to the consumer, or if it is too manipulative. But, this sort of targeting and meshing of research and marketing is not futuristic – all of the underpinning technology is there at the ready and it is only a matter of time until marketers really learn how to tap into it. It is a different world for sure and one that is coming fast.


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