Archive for the 'Methodology' Category

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The myth of the random sample

Sampling is at the heart of market research. We ask a few people questions and then assume everyone else would have answered the same way.

Sampling works in all types of contexts. Your doctor doesn’t need to test all of your blood to determine your cholesterol level – a few ounces will do. Chefs taste a spoonful of their creations and then assume the rest of the pot will taste the same. And, we can predict an election by interviewing a fairly small number of people.

The mathematical procedures that are applied to samples that enable us to project to a broader population all assume that we have a random sample. Or, as I tell research analysts: everything they taught you in statistics assumes you have a random sample. T-tests, hypotheses tests, regressions, etc. all have a random sample as a requirement.

Here is the problem: We almost never have a random sample in market research studies. I say “almost” because I suppose it is possible to do, but over 30 years and 3,500 projects I don’t think I have been involved in even one project that can honestly claim a random sample. A random sample is sort of a Holy Grail of market research.

A random sample might be possible if you have a captive audience. You can random sample some the passengers on a flight or a few students in a classroom or prisoners in a detention facility. As long as you are not trying to project beyond that flight or that classroom or that jail, the math behind random sampling will apply.

Here is the bigger problem: Most researchers don’t recognize this, disclose this, or think through how to deal with it. Even worse, many purport that their samples are indeed random, when they are not.

For a bit of research history, once the market research industry really got going the telephone random digit dial (RDD) sample became standard. Telephone researchers could randomly call land line phones. When land line telephone penetration and response rates were both high, this provided excellent data. However, RDD still wasn’t providing a true random, or probability sample. Some households had more than one phone line (and few researchers corrected for this), many people lived in group situations (colleges, medical facilities) where they couldn’t be reached, some did not have a land line, and even at its peak, telephone response rates were only about 70%. Not bad. But, also, not random.

Once the Internet came of age, researchers were presented with new sampling opportunities and challenges. Telephone response rates plummeted (to 5-10%) making telephone research prohibitively expensive and of poor quality. Online, there was no national directory of email addresses or cell phone numbers and there were legal prohibitions against spamming, so researchers had to find new ways to contact people for surveys.

Initially, and this is still a dominant method today, research firms created opt-in panels of respondents. Potential research participants were asked to join a panel, filled out an extensive demographic survey, and were paid small incentives to take part in projects. These panels suffer from three response issues: 1) not everyone is online or online at the same frequency, 2) not everyone who is online wants to be in a panel, and 3) not everyone in the panel will take part in a study. The result is a convenience sample. Good researchers figured out sophisticated ways to handle the sampling challenges that result from panel-based samples, and they work well for most studies. But, in no way are they a random sample.

River sampling is a term often used to describe respondents who are “intercepted” on the Internet and asked to fill out a survey. Potential respondents are invited via online ads and offers placed on a range of websites. If interested, they are typically pre-screened and sent along to the online questionnaire.

Because so much is known about what people are doing online these days, sampling firms have some excellent science behind how they obtain respondents efficiently with river sampling. It can work well, but response rates are low and the nature of the online world is changing fast, so it is hard to get a consistent river sample over time. Nobody being honest would ever use the term “random sampling” when describing river samples.

Panel-based samples and river samples represent how the lion’s share of primary market research is being conducted today. They are fast and inexpensive and when conducted intelligently can approximate the findings of a random sample. They are far from perfect, but I like that the companies providing them don’t promote them as being random samples. They involve some biases and we deal with these biases as best we can methodologically. But, too often we forget that they violate a key assumption that the statistical tests we run require: that the sample is random. For most studies, they are truly “close enough,” but the problem is we usually fail to state the obvious – that we are using statistical tests that are technically not appropriate for the data sets we have gathered.

Which brings us to a newer, shiny object in the research sampling world: ABS samples. ABS (addressed-based samples) are purer from a methodological standpoint. While ABS samples have been around for quite some time, they are just now being used extensively in market research.

ABS samples are based on US Postal Service lists. Because USPS has a list of all US households, this list is an excellent sampling frame. (The Census Bureau also has an excellent list, but it is not available for researchers to use.) The USPS list is the starting point for ABS samples.

Research firms will take the USPS list and recruit respondents from it, either to be in a panel or to take part in an individual study. This recruitment can be done by mail, phone, or even online. They often append publicly-known information onto the list.

As you might expect, an ABS approach suffers from some of the same issues as other approaches. Cooperation rates are low and incentives (sometimes large) are necessary. Most surveys are conducted online, and not everyone in the USPS list is online or has the same level of online access. There are some groups (undocumented immigrants, homeless) that may not be in the USPS list at all. Some (RVers, college students, frequent travelers) are hard to reach. There is evidence that ABS approaches do not cover rural areas as well as urban areas. Some households use post office boxes and not residential addresses for their mail. Some use more than one address. So, although ABS lists cover about 97% of US households, the 3% that they do not cover are not randomly distributed.

The good news is, if done correctly, the biases that result from an ABS sample are more “correctable” than those from other types of samples because they are measurable.

A recent Pew study indicates that survey bias and the number of bogus respondents is a bit smaller for ABS samples than opt-in panel samples.

But ABS samples are not random samples either. I have seen articles that suggest that of all those approached to take part in a study based on an ABS sample, less than 10% end up in the survey data set.

The problem is not necessarily with ABS samples, as most researchers would concur that they are the best option we have and come the closest to a random sample. The problem is that many firms that are providing ABS samples are selling them as “random samples” and that is disingenuous at best. Just because the sampling frame used to recruit a survey panel can claim to be “random” does not imply that the respondents you end up in a research database constitute a random sample.

Does this matter? In many ways, it likely does not. There are biases and errors in all market research surveys. These biases and errors vary not just by how the study was sampled, but also by the topic of the question, its tone, the length of the survey, etc. Many times, survey errors are not the same throughout an individual survey. Biases in surveys tend to be “unknown knowns” – we know they are there, but aren’t sure what they are.

There are many potential sources of errors in survey research. I am always reminded of a quote from Humphrey Taylor, the past Chairman of the Harris Poll who said “On almost every occasion when we release a new survey, someone in the media will ask, “What is the margin of error for this survey?” There is only one honest and accurate answer to this question — which I sometimes use to the great confusion of my audience — and that is, “The possible margin of error is infinite.”  A few years ago, I wrote a post on biases and errors in research, and I was able to quickly name 15 of them before I even had to do an Internet search to learn more about them.

The reality is, the improvement in bias that is achieved by an ABS sample over a panel-based sample is small and likely inconsequential when considered next to the other sources of error that can creep into a research project. Because of this, and the fact that ABS sampling is really expensive, we tend to only recommend ABS panels in two cases: 1) if the study will result in academic publication, as academics are more accepting of data that comes from and ABS approach, and 2) if we are working in a small geography, where panel-based samples are not feasible.

Again, ABS samples are likely the best samples we have at this moment. But firms that provide them are often inappropriately portraying them as yielding random samples. For most projects, the small improvements in bias they provide is not worth the considerable increased budget and increased study time frame, which is why, for the moment, ABS samples are currently used in a small proportion of research studies. I consider ABS to be “state of the art” with the emphasis on “art” as sampling is often less of a science than people think.

Should we get rid of statistical significance?

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

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

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

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

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

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

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

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

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

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

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

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

Jeff Bezos is right about market research

In an annual shareholder letter, Amazon’s Jeff Bezos recently stated that market research isn’t helpful. That created some backlash among researchers, who reacted defensively to the comment.

For context, below is the text of Bezos’ comment:

No customer was asking for Echo. This was definitely us wandering. Market research doesn’t help. If you had gone to a customer in 2013 and said “Would you like a black, always-on cylinder in your kitchen about the size of a Pringles can that you can talk to and ask questions, that also turns on your lights and plays music?” I guarantee you they’d have looked at you strangely and said “No, thank you.”

This comment is reflective of someone who understands the role market research can play for new products as well as its limitations.

We have been saying for years that market research does a poor job of predicting the success of truly breakthrough products. What was the demand for television sets in the 1920’s and 1930’s before there was even content to broadcast or a way to broadcast it? Just a decade ago, did consumers know they wanted a smartphone they would carry around with them all day and constantly monitor? Henry Ford once said that if he had asked customers what they wanted they would have wanted faster horses and not cars.

In 2014, we wrote a post (Writing a Good Questionnaire is Just Like Brian Surgery) that touched on this issue. In short, consumer research works best when the consumer has a clear frame-of-reference from which to draw. New product studies on line extensions or easily understandable and relatable new ideas tend to be accurate. When the new product idea is harder to understand or is outside the consumer’s frame-of-reference research isn’t as predictive.

Research can sometimes provide the necessary frame-of-reference. We put a lot of effort to be sure that concept descriptions are understandable. We often go beyond words to do this and produce short videos instead of traditional concept statements. But even then, if the new product being tested is truly revolutionary the research will probably predict demand inaccurately. The good news is few new product ideas are actually breakthroughs – they are usually refinements on existing ideas.

Failure to provide a frame-of-reference or realize that one doesn’t exist leads to costly research errors. Because this error is not quantifiable (like a sample error) it gets little attention.

The mistake people are making when reacting to Bezos’ comment is they are viewing it as an indictment of market research in general. It is not. Research still works quite well for most new product forecasting studies. For new products, companies are often investing millions or tens of millions in development, production, and marketing. It usually makes sense to invest in market research to be confident these investments will pay off and to optimize the product.

It is just important to recognize that there are cases where respondents don’t have a good frame-of-reference and the research won’t accurately predict demand. Truly innovative ideas are where this is most likely to happen.

I’ve learned recently that this anti-research mentality pervades the companies in Silicon Valley. Rather than use a traditional marketing approach of identifying a need and then developing a product to fulfill the need, tech firms often concern themselves first with the technology. They develop a technology and then look for a market for it. This is a risky strategy and likely fails more than it succeeds, but the successes, like the Amazon Echo, can be massive.

I own an Amazon Echo. I bought it shortly after it was launched having little idea what it was or what it could do. Even now I am still not quite sure what it is capable of doing. It probably has a lot of potential that I can’t even conceive of. I think it is still the type of product that might not be improved much by market research, even today, when it has been on the market for years.

Will adding a citizenship question to the Census harm the Market Research Industry?

The US Supreme Court appears likely to allow the Department of Commerce to reinstate a citizenship question on the 2020 Census. This is largely viewed as a political controversy at the moment. The inclusion of a citizenship question has proven to dampen response rates among non-citizens, who tend to be people of color. The result will be gains in representation for Republicans at the expense of Democrats (political district lines are redrawn every 10 years as a result of the Census). Federal funding will likely decrease for states with large immigrant populations.

It should be noted that the Census bureau itself has come out against this change, arguing that it will result in an undercount of about 6.5 million people. Yet, the administration has pressed forward and has not committed funds needed by the Census Bureau to fully research the implications. The concern isn’t just about non-response from non-citizens. In tests done by the Census Bureau, non-citizens are also more likely to inaccurately respond to this question than citizens, meaning the resulting data will be inaccurate.

Clearly this is a hot-button political issue. However, there is not much talk of how this change may affect research. Census data are used to calibrate most research studies in the US, including academic research, social surveys, and consumer market research. Changes to the Census may have profound effects on data quality.

The Census serves as a hidden backbone for most research studies whether researchers or clients realize it or not. Census information helps us make our data representative. In a business climate that is becoming more and more data-driven the implications of an inaccurate Census are potentially dire.

We should be primarily concerned that the Census is accurate regardless of the political implications. Adding questions that temper response will not help accuracy. Errors in the Census have a tendency to become magnified in research. For example, in new product research it is common to project study data from about a thousand respondents to a universe of millions of potential consumers. Even a small error in the Census numbers can lead businesses to make erroneous investments. These errors create inefficiencies that reverberate throughout the economy. Political concerns aside, US businesses undoubtably suffer from a flawed Census. Marketing becomes less efficient.

All is not lost though. We can make a strong case that there are better, less costly ways to conduct the Census. Methodologists have long suggested that a sampling approach would be more accurate than the current attempt at enumeration. This may never happen for the decennial Census because the Census methodology is encoded in the US Constitution and it might take an amendment to change it.

So, what will happen if this change is made? I suspect that market research firms will switch to using data that come from the Census’ survey programs, such as the American Community Survey (ACS). Researchers will rely less on the actual decennial census. In fact, many research firms already use the ACS rather than the decennial census (and the ACS currently contains the citizenship question).

The Census bureau will find ways to correct for resulting error, and to be honest, this may not be too difficult from a methodological standpoint. Business will adjust because there will be economic benefits to learning how to deal with a flawed Census, but in the end, this change will take some time for the research industry to address. Figuring things like this out is what good researchers do. While it is unfortunate that this change looks likely to be made, its implications are likely more consequential politically than it will be to the research field.

Long Live the Focus Group!

Market research has changed over the past two decades. Telephone research has faded away, mail studies are rarely considered, and younger researchers have likely never conducted a central location test in a mall. However, there is an old-school type of research that has largely survived this upheaval:  the traditional, in-person focus group.

There has been extensive technological progress in qualitative research. We can now conduct groups entirely online, in real-time, with participants around the globe. We can conduct bulletin board style online groups that take place over days. Respondents can respond via text or live video, can upload assignments we give them, and can take part in their own homes or workplaces. We can intercept them when they enter a store and gather insights “in the moment.” We even use technology to help make sense of the results, as text analytics has come a long way and is starting to prove its use in market research.

These new, online qualitative approaches are very useful. They save on travel costs, can be done quickly, and are often less expensive than traditional focus groups. But we have found that they are not a substitute for traditional focus groups, at least not in the way that online surveys have substituted for telephone surveys. Instead, online qualitative techniques are new tools that can do new things, but traditional focus groups are still the preferred method for many projects.

There is just no real substitute for the traditional focus group that allows clients to see actual customers interact around their product or issue. In some ways, as our world has become more digital traditional focus groups provide a rare opportunity to see and hear from customers. They are often the closest clients get to actually seeing their customers in a live setting.

I’ve attended hundreds of focus groups. I used to think that the key to a successful focus group was the skill of the moderator followed by a cleverly designed question guide. Clients spend a lot of time on the question guide. But they spend very little time on something that is critical to every group’s success: the proper screening of participants.

Seating the right participants is every bit as important as constructing a good question guide. Yet, screening is given passing attention by researchers and clients. Typically, once we decide to conduct groups a screener is turned around within a day because we need to get moving on the recruitment. In contrast, a discussion guide is usually developed over a full week or two.

Developing an outstanding screener starts by having a clear sense of objectives. What decisions are being made as a result of the project? Who is making them? What is already known? How will the decision path differ based on what we find? I am always surprised that in probably half of our qualitative projects our clients don’t have answers to these questions.

Next, it is important to remind clients that focus groups are qualitative research and we shouldn’t be attempting to gather a “representative” sample. Focus groups happen with a limited number of participants in a handful of cities and we shouldn’t be trying to project findings to a larger audience. If that is needed, a follow-up quantitative phase is required. Instead, in groups we are trying to delve deeply into motivations, explore ideas, and develop with new hypotheses we can test later.

It is a common mistake to try to involve enough participants to make findings “valid.” This is important, as we are looking for thoughtful participants and not necessarily “typical” customers. We want folks that will expand our knowledge of a subject and of customers will help us explore deeply into topics and develop new lines of inquiry we haven’t considered.

“Representative” participants can be quiet and reserved and not necessarily useful to this phase of research. For this reason, we always use articulation screening questions which raise the odds that we will get a talkative participant who enjoys sharing his/her opinions.

An important part of the screening process is determining how to segment the groups. It is almost never a good idea to hold all of your sessions with the same audience. We tend to segment on age, potentially gender, and often by the participants’ experience level with the product or issue. Contrasting findings from these groups is often where the key qualitative insights lie.

It is also necessary to over-recruit. Most researchers overrecruit to protect against participants who fail to show up to the sessions. We do it for another reason. We like to have a couple of extra participants in the waiting area. Before the groups start, the moderator spends some time with them. This accomplishes two things. First, the groups are off and running the moment participants enter the focus group room because a rapport with the moderator has been established. Second, spending a few minutes with participants before groups begin allows the moderator to determine in advance which participants are going to be quiet or difficult, and allows us to pay them the incentive and send them home.

Clients tend to insist on group sizes that are too large. I have viewed groups with as many as 12 respondents. Even in a two-hour session, the average participant will be talking for just 10 minutes in this case and that is if there are no silences or the moderator doesn’t talk! In reality, with 12 participants you will get maybe five minutes out of each one. How is that useful?

Group dynamics are different in smaller groups. We like to target having about six participants. This group size is small enough that all must participate and engage, but large enough to get a diversity of views.  We also prefer to have groups run for 90 minutes or less.

We like to schedule some downtime in between groups. The moderator needs this to recharge (and eat!), but this also gives time for a short debrief and to adjust the discussion guide on the fly. I have observed groups where the moderator is literally doing back-to-back sessions for six hours and it isn’t productive. Similarly, it is ideal to have a rest day in between cities to regroup to provide an opportunity to develop new questions. (Although, this is rarely done in practice.)

Clients also need to learn to leave the moderator alone for at least 30 minutes before the first group begins. Moderating is stressful, even for moderators who have led thousands of groups. They need time to review the guide and converse with the participants. Too many times, clients are peppering the moderator with last second changes to the guide and in general are stressing the moderator right before the first session. These discussions need to be held before focus group day.

We’d also caution against conducting too many groups. I remember working on a proposal many years ago when our qualitative director was suggesting we conduct 24 focus groups. She was genuinely angry at me when I asked her “what are we going to learn in that 24th group that we didn’t learn in the first 23?”.

In all candor, in my experience you learn about 80% of what you will learn in the first evening of groups. It is useful to conduct another evening or two to confirm what you have heard. But it is uncommon for a new insight arises after the first few groups. It is a rare project that needs more than about two cities’ worth of groups.

It is also critical to have the right people from the clients attending the sessions. With the right people present discussions behind the mirror become insightful and can be the most important part of the project. Too often, clients send just one or two people from the research team and the internal decision makers stay home. I have attended groups where the client hasn’t shown up at all and it is just the research supplier who is there. If the session isn’t important enough to send decision makers to attend, it probably isn’t important enough to be doing in the first place.

I have mixed feelings about live streaming sessions. This can be really expensive and watching the groups at home is not the same as being behind the mirror with your colleagues. Live streaming is definitely better than not watching them at all. But I would say about half the time our clients pay for live streaming nobody actually logs in to watch them.

Focus groups are often a lead-in to a quantitative study. We typically enter into the groups with an outline of the quantitative questionnaire at the ready. We listen purposefully at the sessions to determine how we need to refine our questionnaire. This is more effective than waiting for the qualitative to be over before starting the quantitative design. We can usually have the quant questionnaire ready for review before the report for the groups is available because we take this approach.

Finally, it is critical to debrief at the end of each evening. This is often skipped. Everyone is tired, has been sitting in the dark for hours, and have to get back to a hotel and get up early for a flight. But, a quick discussion to agree on the key takeaways while they are fresh in mind is very helpful. We try to get clients to agree to these debriefings before the groups are held.

Traditional groups provide more amazing moments and unexpected insights than any other research method. I think this may be why, despite all the new options for qualitative, clients are conducting just as many focus groups as ever.

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.


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