Archive for the 'Marketing' Category

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.

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.

Congrats to Truth Initiative – Wins Gold at Ogilvy Awards!

Congratulations to our client Truth Initiative on winning Gold at the David Ogilvy Awards. The Ogilvy awards are unique in that they celebrate campaigns that effectively use market research to spark an insightful campaign. Truth Initiative won gold in the “Unexpected Targeting and Segmentation” category.

The Truth Campaign was called “Stop Profiling.” It centered on a social justice theme – that today’s youth will ban together if they perceive a segment of the population is being treated unfairly. Truth’s ad (“Market Priority”) can be seen here.

Crux Research partnered with CommSight to provide formative research, copy testing, and campaign tracking. We are excited to be a part of this award-winning effort – and this award is the third Ogilvy we have been involved in for Truth Initiative.

The types of people you find in a market research presentation

Last summer I led a market research results presentation at a client’s office. I had not met any of the individuals in the meeting prior to the presentation other than my immediate client-contact. During introductions I tried my best to understand who was who and to carefully observe the dynamics between people. “Knowing thy audience” is key to an effective presentation.

And, I have to admit – within a few minutes I found myself stereotyping the members of my audience. I have delivered scores of presentations in the past and I can usually quickly assess what the dynamic of the room is going to be like and categorize attendees. But, I can also be wrong in my assessment and it isn’t healthy to make assumptions about people without taking the time to truly get to know them. I sort of feel guilty that I find myself doing this.

This particular presentation had gathered an interesting cast of characters and I couldn’t help but think about how they each were similar to people I have presented to in the past at various clients. Anyway, the list below is meant to be a bit humorous, and I think that anyone who has been in market research presentations will see people they recognize below.

“The Characters You Find in a Market Research Presentation.”

  • The Introvert. This is a person who says little during the meeting but her mind is racing. She tends to get active late in the meeting and provides insightful comments because she doesn’t feel a need to chime in on every obvious point. Others in the organization often ignore her because she is introverted but she is often the smartest person in the room. However, she has the potential to derail the end of the meeting by starting an entirely new line of conversation as you are trying to wrap up. How to succeed with the Introvert: Try to engage her early and ask for her perspective late in the meeting as this person often has the best things to say and adds a lot to the discussion if you can draw her out.
  • Mr. (Lack of) Attention Span. This is a person who probably comes late to the meeting and forces you to start over and repeat the first 10 minutes. Once in the meeting, he is constantly checking his phone, having side conversations, and asking questions that you just answered. This is also the person that skips ahead in the deck and won’t let you build a story as you would like. How to succeed with Mr. Attention Span: Do not provide handouts beforehand or during this meeting. Keep the presentation short if possible. State ground rules up front as to when you will pause for questions.
  • The Poseur. This person has a clear view of the world in his mind and will find a way to massage every fact you present to make it fit with a pre-conceived view. He uses your facts to illustrate just how insightful he is and what he already knows. This is the marketer that personifies David Ogilvy’s quote that marketers use research “as a drunkard uses a lamp post, for support rather than for illumination.”   He uses the meeting to become the center of attention. He has to provide his view on every slide and every conclusion you have no matter what the size of the meeting. He dominates and other attendees tend to defer to him before offering their own opinions.  How to succeed with the Poseur:  At the onset, set “pause points” in the presentation — at the end of each section you will call for a discussion. Establish ground rules for the meeting. Ask everyone to write down a prediction on how a research result came out on paper before you show the actual result. Then, call on other individuals to discuss their prediction. Look to qualitative techniques for inspiration on how to handle a dominant focus group participant for inspiration.
  • The Jargon Guy. This is a person who talks a lot but doesn’t really say anything. He is a master of business jargon – it is the person who will use words like “bandwidth”, “game changer”, “visioning”, etc.  He will add “ize” onto nouns to turn them into verbs and use acronyms as much as possible. He reads popular business books on the side. You’ll feel like you are in an episode of “The Office” when you meet him. How to succeed with the Jargon Guy: Learn some of the proprietary jargon and acronyms used by your client’s firm beforehand.
  • The Cherry Picker.  Similar to the Poseur, this is the client who also has a clear “map of the world” established in her head and won’t let facts get in the way of a good opinion. She is active in the discussion but what she does is cherry pick results – and criticizes every point that doesn’t fit with her vision, and falls in love with every point that does. How to succeed with the Cherry Picker: Try to get her to buy into your methodology and lead with conclusions you think are likely to fit with how she thinks. That may get her to listen more to findings that don’t fit with her outlook later on.
  • The Naysayer.  This person doesn’t believe in market research and once he learns the study isn’t perfect will challenge everything you say. He straddles a line between “critic” and “cynic”. How to succeed with the Naysayer: This person can be a useful contributor if you can get his negativity to become constructive and establish the right tone. Fortunately, his concerns can often be anticipated beforehand, and you can often address his concerns before he gets a chance to raise them.
  • The Academic.  The academic asks incredibly detailed questions about the methodology and slows down the initial part of the presentation. This person is usually highly educated and understands the details of statistics and experimental design, sometimes better than you do. The good news is she rarely questions your findings if she agrees with the methods you have employed. How to succeed with the Academic: get to her beforehand and share the details of the methodology so she doesn’t get the meeting off to a bad start by bogging it down with methodological details. This person can be a great ally for you during the talk.
  • The Box Checker.  This is a person who is mainly concerned that the research got done because it is part of a larger marketing process that he is responsible for. He is much more of a “process” than an “outcomes” person and tends to be bureaucratic. How to succeed with the Box Checker:  Make sure he knows the project got done efficiently, on time, and within budget.
  • The Enlightened Leader.  This is the person we all want to present to. It is the highest ranking person in the room, but she casts aside all her other responsibilities for the hour you have with her. For at least one hour, you and your client feel that this study is the most important thing in her life.  She truly listens, doesn’t presume anything, and allows the research to add nuance to her view of the world. She usually insists that others in the meeting take action based on the findings.  How to succeed with the Enlightened Leader: Bring her into the conversation early, as it sets the tone for everyone.

I should note, that with very few exceptions, these personalities tend to be respectful and courteous and less challenging to present to than the above descriptions imply. Above all, preparation is key to success with all types of people. You need to deeply know your data set and have well-supported conclusions and implications, as in the end that tends to get you over any rough spots that arise. Your day-to-day contact needs to be your ally, and running through the presentation in advance with him/her often helps stave off any rough moments. Most research presentations go well, but we aim for them to not just go well, but to be effective. While it might not be appropriate to stereotype as I have done here, it is appropriate to realize each individual is coming to your presentation with his/her own perspective. Understanding that perspective can be as important as the study itself in terms of having research inform better decisions.

 “Gen Z” should make you cringe!

Adults have a number of misconceptions about youth generations. A glaring one is a tendency to think that a new generation will become a more intense version of the previous generation. That is rarely the case – new generations tend to sharply break with the old.

Let’s start by reviewing what a generation is. A generation is a cohort of people who share a common location in history. A generation progresses through life stages together and experiences key life events (childhood, adolescence, family life, retirement) at the same time. While our life stages change as we age, our generation does not. There is a commonality of experience and perspective that influences how a generation reacts to challenges presented by any given life stage.

While generational beginning and end points are hotly debated by academics, they tend to be bounded by historical events. For instance, the Boomer generation is known as the generation born after WWII ended as birth rates rapidly grew. Xers are those that were born during the subsequent demographic dip. Millennials began as an “echo” boom occurred as the large Boomer generation had their own children.

Generational change is abrupt and disruptive.  My own experience with this goes back to when the Millennial Generation (born 1982 – 2004) was coming of age in the 1990’s. At the time I was conducting studies of young people and was noticing clear breaks in the data sets. Inflection points often appeared when we graphed research measures by age. It took me years to realize these inflection points weren’t linked to a stage of development or age as they were migrating upwards over time. Eventually, I discovered these inflections were happening right at the generational break line – as soon as individuals born in the early 80’s came into the data sets, things changed.

It took me years to figure this out because this generation was most commonly referred to as Gen Y at the time. What does Gen Y mean? To me, it meant this new group would be a continuation of Gen X – only they would exhibit Gen X traits at higher intensity. I went to many youth conferences where speakers said precisely this. I often left puzzled, as what they were saying didn’t line up with what I was seeing in the data we gathered.

This new generation wasn’t behaving anything like Gen X. While Gen X was filled with latchkey kids who had developed a strong sense of individualism, independence, and self-worth, this new generation was all about teamwork, parental structure and oversight, and continuous feedback and validation. Calling them Gen Y seemed ridiculous as it implied they were merely an extension of Gen X. Thankfully, although the Gen Y moniker persisted, the term Millennial soon took hold.

Generations have unique characteristics and tendencies. These characteristics are almost never simply continuations of a previous generation’s characteristics. We can all agree that Boomers have not acted at all like their Silent Generation predecessors or that Xers haven’t been at all like Boomers. Millennials represent a further break with Xers.

There is no authority that has been commissioned to name a generation. Generations prior to Boomers weren’t really named during their time and many will claim that the Boomers were the first named generation. Prior generations were largely named by historians long after they had existed. For example, nobody called the WWII generation the “greatest generation” or the “GI generation” at the time – these terms took hold well after Boomers had been named.

Generational names evolve. Names often begin as something that underscore how adults don’t understand that generations are not just continuations of the previous generations. As an example, Gen X was most commonly called “the baby bust” generation at first, implying that they were  merely a consequence of a birth rate decline extending from the baby boom era. The term “Gen X” was popularized in a novel by Douglas Coupland. It became popular not because of the letter X but what this letter signified – a lack of a name for a largely forgotten generation, but also one that wasn’t particularly interested in being categorized or targeted.

The term Millennial was also established relatively late in the game. It was popularized in a book called Millennials Rising, and prior names either reflected a continuation of a parental generation (“the echo boom”, the “boomlet”) or of Gen X (“Generation Y.”). Millennials is a much better name and has largely taken over for “Generation Y.”

The whole purpose of naming generations from a marketing sense is that generations represent segments of consumers with unique needs. Our goal in naming them should be to show how they are distinct from each other.

Which brings me to Gen Z. This is a term we are seeing more and more, and I am tending to feel that those who use it are displaying a fundamental ignorance not only of generational change but even what a generation is. Gen Z tends to be used to describe today’s adolescents. But, because the youngest Millennial is currently 13 years old, the term Gen Z isn’t being applied to a new generation at all. It is being used to describe young, late-stage Millennials, which is sort of a segment of a segment.

The key characteristic of this microsegment (late-stage Millennials) of interest to researchers is that their parental generation has changed. Whereas the oldest half of the Millennial generation was largely parented by Boomers, the younger half has been parented by Gen X. This has some implications, but today’s teens are still Millennials and will exhibit Millennial traits.

The term “Gen Z” makes is cringe-worthy as it lays bare a fundamental misunderstanding of the generations. I even saw a study released recently on “Gen Z college students.”  Not sure I understand that, as the leading edge of the generation after Millennials is at most 12 years old currently. We are at least five years from the first member of the next generation showing up on campus.

“Gen Z” is also being used to refer to the generation that will come after Millennials (currently children aged up to 12 and yet to be born).  I have also seen this new generation referred to as “post-Millennial.”  And, what are we to name the generation that comes after this Gen Z? We’ve run out of letters, so perhaps we will have to use a spreadsheet convention and call them Generation AA.

Just like for previous generations, I’d expect to see today’s youngest generation eventually named in a way that describes who they are. I have heard some reasonable candidates:  The Homeland Generation, the iGen, The Pluralist Generation, etc. These all are descriptive. If the past is any indication, sometime in the next 10 years some name will achieve consensus (and it won’t be “Gen Z”).

For now please join me in cringing whenever you hear someone say the term “Gen Z.” J.


Visit the Crux Research Website www.cruxresearch.com

Enter your email address to follow this blog and receive notifications of new posts by email.