Posts Tagged 'Market Research'

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.

The Cambridge Analytica scandal points to marketing’s future

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

Millennial College Students Are Torn Between Open Speech and Protecting the Vulnerable

We recently completed a poll of 1,000 college students on the topic of free speech on campus. Previous postings (here and here) have shown that students are reticent to support controversial speakers on campus and do not support any speakers who might have viewpoints that some students find to be uncomfortable.

In this final post on our poll results, we take a look at some contradictions in our data that demonstrate that today’s college students are torn between a desire to favor a campus that promotes free and open debate and an ethos that makes them want to protect the vulnerable from feeling uncomfortable.

There has been a long-held belief by conservatives that colleges are bastions of liberal thinking and perhaps indoctrination. Our poll results lend support to this viewpoint, as 52% of college students feel their professors tend to be more liberal in their thinking than the nation as a whole while just 23% feel their professors are more conservative:

Compared to the views of the nation as a whole, would you say that your current professors/instructors tend to be:
More conservative in their thinking 23%
About the same as the nation as a whole 25%
More liberal in their thinking 52%

Students tend to express a desire for their professors to be given a wide latitude to express their views and are largely not in support of administrators censoring how professors express their views to students.

Which statement below comes closest to your opinion?
College administrators should closely monitor what professors/instructors teach to make sure all students are comfortable 33%
College professors/instructors should be given a wide degree of freedom to express their views to students 67%

The result below shows that students report that colleges should encourage students to have an open mind to ideas that they may find uncomfortable. At first glance, college students seem to favor an atmosphere of openness on campus.

Which statement below comes closest to your opinion?
Colleges should attempt to shield students from ideas and opinions they may find unwelcome and offensive 25%
Colleges should encourage students to be exposed to ideas and opinions they may find unwelcome and offensive 75%

Millennial college students also recognize that free and open speech is central to university life. For example:

  • Two-thirds (66%) agree that the intellectual vitality of a university depends on open and free expression of ideas.
  • 63% agree that free speech, including controversial speech, is central to college teaching and learning.
  • 57% agree that student-run newspapers have a first amendment right to publish controversial stories without running afoul of college administrators.

That said, this poll also shows that Millennials also hold some views that run counter to the free speech ethos they express:

  • 57% agree that students should be encouraged to report instances of professor bias to administrators.
  • 48% feel that students should be provided warnings in advance to alert them to potentially troublesome readings.
  • 45% feel that colleges should provide intellectual safe spaces, where students can retreat from ideas and perspectives that are at odds with their own.

And, as we discussed in our previous postings, students shy away from permitting almost any type of speaker on campus that could potentially communicate anything that might cause a subgroup of students discomfort.

So, there are some contradictions in our findings that needs explaining. We feel that there is likely some nuance on Millennial opinion. The Millennial college student seems torn between realizing that exposure to ideas counter to their own is essential to their education and a strong ethos of protecting the vulnerable.

Which statement below comes closest to your opinion?
It is more important that colleges stick up for the vulnerable 50%
It is more important that colleges stand up for a spirit of inquiry 50%

This nuance is difficult for Boomer and Xers (who make up most college administrators and professors) to grasp. Older generations grew up not only at a time when free and open speech was held to a higher standard but also at a time where the college/university campus was the nexus of student opinion and influence. Today’s Millennial student has experienced more cultural diversity on campus and has established digital meeting spaces are their nexus for opinion and community. Millennials are exposed to diverse and controversial opinions constantly, to the point where their desire to protect the campus from controversy and discomfort may be a defense mechanism. It is an environment they can control.

What this all means for the university has yet to be seen. But, campus life is changing, and it will be key that the pendulum that is now swinging towards safety and comfort doesn’t swing so far as to limit student exposure to valuable viewpoints and a well-rounded worldview.

Students Are More Likely to Oppose Campus Speakers Than to Support Them

We recently posted a result from an in-depth poll we conducted among 1,000 college students last fall. In this poll we asked students about specific speakers they may or may not support coming to their campus. Among our conclusions was that students largely aren’t supportive of very many speakers – particularly individuals who might be considered to be controversial or present ideas some might find uncomfortable.

In this same poll, we asked students about types of speakers that might come to a college campus. We included speaker types we felt most observers would feel are appropriate as well as speaker types that we felt even the most passionate free speech advocates might question. Our goal was to see where “the line” might be for today’s college students. The answer is the line is very high – students largely don’t want campus speakers at all.

The table below shows the percentage of US college students who would support each type of speaker coming to their campus to speak:

A leader from the Black Lives Matter movement 50%
An advocate for the legalization of marijuana 46%
An elected official with views that are vastly different than yours 22%
A publisher of pornographic videos 21%
An activist who has a different view on abortion than you do 19%
A speaker who strongly opposes the Black Lives matter movement 19%
A politician who is against gay marriage 17%
A speaker who believes that there are racial differences in intelligence 17%
A tobacco company executive 14%
A speaker who is known to have sexually harassed a colleague in the past 11%
Muslim who advocates hatred towards the United States 10%
A speaker who believes that the Holocaust did not happen 10%
A white supremacist 10%

Some interesting conclusions can be made by looking at whom students are willing to support coming to their campus to speak:

  • Even the most highly supported type of speaker (A leader from the Black Lives Matter movement) is only supported by half (50%) of students. Support for any type of campus speaker is tepid.
  • Two types of speakers stood out as having the most support: Leaders from the Black Lives Matter movement and advocates for the legalization of marijuana.
  • It is perhaps troubling that only about 1 in 5 students (22%) support an elected official with views different from their own.
  • Racially insensitive speakers (white supremacists and Holocaust deniers) are the least supported types of speakers.

We can also look at the same list, but this time sorted by the percentage of students who oppose this type of speaker coming to their campus to speak:

A white supremacist 68%
A speaker who believes that the Holocaust did not happen 68%
A speaker who is known to have sexually harassed a colleague in the past 67%
Muslim who advocates hatred towards the United States 66%
A speaker who believes that there are racial differences in intelligence 51%
A politician who is against gay marriage 50%
A tobacco company executive 49%
A speaker who strongly opposes the Black Lives matter movement 46%
A publisher of pornographic videos 39%
An activist who has a different view on abortion than you do 27%
An elected official with views that are vastly different than yours 25%
An advocate for the legalization of marijuana 16%
A leader from the Black Lives Matter movement 16%

Here we see that:

  • In general, students are more passionate in their opposition to speaker types than in their support.
  • Speakers with racially insensitive views and those known to have sexually harassed someone are the most opposed types of speakers. Speakers who have sexually harassed are opposed just as much as white supremacists.
  • About half of students oppose politicians who are against gay marriage and tobacco company executives. This is about the same level of opposition as to a speaker who believes there are racial differences in intelligence.
  • About 1 in 4 students would oppose an elected official that has different views than the student.

Because there have been instances of speakers being shouted down and even physically confronted by college students, we posed a question that asked students what they felt were acceptable ways to protest against a campus speaker.

Which of the following actions would you take if you were strongly opposed to a speaker your college had invited to speak on campus?
Disagree with the speaker during a question-and-answer period 25%
Organize a boycott of the speech 22%
Stage a protest outside of the building where the speech is taking place 21%
Host a concurrent speech from a speaker with an opposing view 16%
Stage a sit-in at an administrative building 12%
Physically confront the speaker 8%
Disrupt the speech while it is going on 7%

For the most part, students don’t support any actions if they strongly oppose a campus speaker. While it is encouraging to see that they do not support disrupting the speech or physically confronting a speaker, it is perhaps just as disheartening to see that only 1 in 4 would be willing to disagree with the speaker during a Q&A period. So, not only do students not want most types of speakers, they aren’t willing to step up and do something if a speaker they find controversial does come to campus.

Just as we found when we looked at specific speakers, students seem to be shying away from not just controversial speakers, but also those that might make some portion of the student body uncomfortable. Based on these results, we predict that there will be fewer speakers invited to college campuses in the future and that attendance at these events will decline.

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.

Going Mobile

There has been a critical trend happening in market research data collection that is getting little attention. If you are gathering data in online surveys and polls, chances are that most of your respondents are now answering your questionnaires on mobile devices.

This trend snuck up on us. Just three years ago we were advising clients that we were noticing that about 25% of respondents were answering on mobile devices. Of the last 10 projects we have completed, that percentage is now between 75% and 80%. (Our firm conducts a lot of research with younger respondents which likely skews this higher for us than other firms, but it remains the case mobile response has become the norm.)

Survey response tools have evolved considerably. Respondents initially used either the mail or provided responses to an interviewer on the other end of a clipboard. Then, people primarily answered surveys from a tethered land-line phone. The internet revolution made it possible to move data collection to a (stationary) computer. Now, respondents are choosing to answer on a device that is always with them and when and where they choose.

There are always “mode” effects in surveys – whereby the mode itself can influence results. However, the mode effects involved in mobile data collection has not been well-studied. We will sometimes compare mobile versus non-mobile respondents on a specific project, but in our data this is not a fair comparison because there is a self-selection that occurs. Our respondents can choose to respond either on a mobile device or on a desktop/laptop. If we see differences across modes it could simply be due to the nature of the choice respondents make and have little to do with the mode itself.

To study this properly, an experimental design would be needed – where respondents are randomly assigned to a mobile or desktop mode. After searching and asking around to the major panel companies, I wasn’t able to find any such studies that have been conducted.

That is a bit crazy – our respondents are providing data in a new and interesting fashion, and our industry has done little to study how that might influence the usefulness of the information we collect.

Here is what we do know. First, questionnaires do not look the same on mobile devices as they do on laptops. Most types of questions look similar, but grid-style questions look completely different.  Typically, on a mobile device respondents will see one item at a time and on a desktop they will see the entire list. This will create a greater response-set type bias on the desktop version. I’d say that this implies that a mode effect likely does occur and that it doesn’t vary in the same way across all types of questions you are asking.

Second, the limited real estate of a mobile device makes wordy questions and responses look terrible. Depending on the survey system you are using, a lengthy question can require both horizontal and vertical scrolling, almost guaranteeing that respondents won’t attend to it.

Our own anecdotal information suggests that mobile respondents will complete a questionnaire faster, are more likely to suspend the survey part-way, and provide less rich open-ended responses.

So, how can we guard against these mode effects? Well, in the absence of research-on-research that outlines their nature, we have a few suggestions:

  • First and foremost, we need to develop a “mobile-first” mentality when designing questionnaires. Design your questionnaire for mobile and adapt it as necessary for the desktop. This is likely opposite to what you are currently doing.
  • Mobile-first means minimizing wording and avoiding large grid-type questions. If you must use grids, use fewer scale points and keep the number of items to a minimum.
  • Visuals are tough … remember that you have a 5 or 6 inch display to work with when showing images. You are limited here.
  • Don’t expect much from open-ended questions. Open-ends on mobile have to be precisely worded and not vague. We often find that clients expect too much from open-ended responses.
  • Test the questionnaire on mobile. Most researchers who are designing and testing questionnaires are looking at a desktop/laptop screen all day long, and our natural tendency is to only test on a desktop. Start your testing on mobile and then move to the desktop.
  • Shorten your questionnaires. It seems likely that respondents will have more patience for lengthy surveys when they are taking them on stationary devices as opposed to devices that are with them at all (sometimes distracting) times.
  • Finally, educate respondents not to answer these surveys when they themselves are “mobile.” With the millions of invitations and questionnaires our industry is fulfilling, we need to be sure we aren’t distracting respondents while they are driving.

In the long run, as even more respondents choose mobile this won’t be a big issue. But, if you have a tracking study in place you should wonder if the movement to mobile is affecting your data in ways you aren’t anticipating.

Will Big Data Kill Traditional Market Research?

Most of today’s research methods rely on a simple premise:  asking customers questions can yield insights that drive better decisions. This is traditionally called primary research because it involves gathering new data. It is often supplemented with secondary research, which involves looking at information that already exists, such as sales data, publicly available data, etc.  Primary and secondary research yield what I would call active data –individuals are providing data with their knowledge and consent.

We are moving to a passive data world. This involves analyzing data left behind as we live increasingly digital lives. When we breathe online we leave a trail of digital data crumbs everywhere – where we visit, what we post about, link to, the apps we use, etc. We also leave trails as to when and where we are when we do these things, what sequence we do them in, and even what those close to us do.

Our digital shadows are long. And these shadows provide an incredibly accurate version of ourselves. You may not remember what you had to eat a few days ago, but the Internet knows exactly what books you read, how fast you read them, and when you bought them. The Internet knows where you were when you looked up health information, your favorite places to travel, whether you lean liberal or conservative, and much more. Your digital shadow is alarmingly accurate.

Privacy issues aside, this creates exciting possibilities for market research.

The amount of information available is staggering.  It is estimated that the volume of digital information available is doubling about every 18 months. This means in the next year and a half we will create as much data as we have since the Internet was created. Clearly it is easy to drown in the noise of this data, and many certainly do. But, in some ways analyzing this data isn’t unlike what we have been doing for years. It is easy to drown in a data set if you don’t have clear hypotheses that you are pursuing.  Tapping into the power of Big Data is all about formulating the right questions before firing up the laptop.

So, how will Big Data change traditional, “active” research? Why would we need to ask people questions when we can track their actual behaviors more accurately?

Big Data will not obviate the need for traditional survey research. But, it will reposition it. Survey research will change and be reserved for marketing problems it is particularly well suited for.  Understanding underlying motivations of behavior will always require that we talk directly to consumers, if only to probe why their reported behavior differs from their actual behavior.

There are situations when Big Data techniques will triumph. We are noticing compelling examples of how Big Data analysis can save the world.  For instance, medical researchers are looking into diseases that are asymptomatic. Typically, an early doctor’s appointment for these diseases will consist of a patient struggling to remember symptoms and warning signs and when they might have had them.  An analysis of Google searches can look at people who can be inferred to have been diagnosed with the disease from their search behavior. Then, their previous search behavior can be analyzed to see if they were curious about symptoms and when.  In the hands of a skilled analyst, this can lead to new insights regarding the early warning signs of diseases that often are diagnosed too late.

There has been chatter that public health officials can track the early spread of the flu better each year by analyzing search trends than by using their traditional ways, which track doctor visits for the flu and prescriptions dispensed. The reason is that people Google for “flu symptoms” in advance of going to the doctor, and many who have symptoms don’t go to the doctor at all. A search trend analysis can help public health officials react faster to outbreaks.

This is all pretty cool. Marketers are all about delivering the right message to the right people at the right time, and understanding how prior online behavior predicts future decisions will be valued. Big Data is accurate in a way that surveys cannot be because memory is imperfect.

Let’s be clear. I don’t think that people lie on surveys, at least not purposefully. But there are memory errors that harm the ability of a survey to uncover the truth. For instance, I could ask on a survey what books you have read in the past month. But, sales data from the Kindle Store would probably be more accurate.

However, what proponents of “Big Data will take over the world” don’t realize is the errors that respondents make on surveys can be more valuable to marketers than the truth because their recollections are often more predictive of their future behavior than their actual past behavior. What you think you had for dinner two nights ago probably predicts what you will eat tonight better than what you actually may have eaten. Perceptions can be more important than reality and marketing is all about dealing with perceptions.

The key for skilled researchers is going to be to learn when Big Data techniques are superior and when traditional techniques will yield better insights. Big Data is a very big hammer, but isn’t suitable for every size nail.

It is an exciting time for our field. Data science and data analysis skills are going to become even more valuable in the labor market than they are today. While technical database and statistical skills will be important, in a Big Data era it will be even more important to have skills in developing the right questions to pursue in the first place and a solid understanding of the issues our clients face.

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