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Teaching Machines to Feel

The next generation of AI will actually transcend information processing, which is quantitative nature, and step firmly into the qualitative realm.

Glimpzitai

Editor’s Note: This post is part of our Big Ideas Series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Parry Bedi will be speaking at IIeX North America (June 13-15 in Atlanta). If you liked this article, you’ll LOVE IIeX NA. Click here to learn more.

By Parry Bedi, CEO, GlimpzIt

In the not so distant future machines will become sentient. Not in a “Terminator” kind of way, but more in a way where they genuinely feel empathy, happiness, even serenity. They are our trusted counselors; our thought partners that understand our ways of life and accompany us on the human journey.

Welcome to the age of Emotional Machines.

But isn’t Artificial Intelligence (AI) just about asking Siri for directions to the nearest Chinese restaurant? Or beating Lee Sedol in a game of Go? These are applications of AI, indeed, but this is just the beginning. The next generation of AI will actually transcend information processing, which is quantitative nature, and step firmly into the qualitative realm. Machines will be able to perform small talk, get you as a person and, perhaps more importantly, share your feelings, hopes and aspirations.

Heady stuff, “but what does MR have to do with this future?” you ask. Everything. Imparting emotions on machines first requires that we understand humans. This is where qualitative MR with its emphasis on emotions and behaviors comes in. The rich qualitative datasets we obtain through qualitative MR methods are ideal for training machine algorithms through supervised learning.

Here is a quick overview of how GlimpzIt is approaching this fascinating new space:

Just like humans, AI learns through repetition and practice (known as training in machine learning parlance). But finding accurately categorized data sets to train on has traditionally been an insurmountable challenge. Especially considering that we not only need volume and diversity but also contextual specificity in our data sets. As you can imagine, this is not an easy feat to accomplish using traditional methodologies such as focus groups or IDIs. However, thanks to two of the biggest secular trends of our times – namely the rise of the visual web and crowdsourcing – we not only get access to a trove of rich qualitative data (ie. visuals), but can also use crowdsourcing to effectively categorize it. This combination enables AI algorithms to determine when a they have made a mistake (loss function for those who are technically inclined) and learn from it.

So why start with visuals? The simple reason is that visuals are the new lingua franca of the world (one that transcends cultural, and linguistic barriers) as they are the most concise and expressive units of communication. In fact, by 2017, 74% of the traffic on the internet is projected to be visual. Is this any surprise, given the rise of platforms such as Pinterest, Instagram, Snapchat, etc?

Understanding the human context behind this language can still be challenging. While large strides have been made in the last 3 years in the area of computer vision to identify image content, machines have so far struggled to understand human emotional factors. For example, a computer today will classify an image of a bedroom closet as “Levi’s” or “Hanging Jeans” (object identification), but humans may additionally associate “Organized” or “Accessible” (sentiment attribution) to the image. Thankfully, by mining open social media platforms and crowdsourcing, we can quickly gather visual + text content that can be analyzed for emotions and behaviors as well. When processed using deep learning algorithms, this visual and text data together is used to build a dynamic ontology of meaningful insight categories, which are then auto cleansed and fed back into the system, thereby creating an ever-evolving virtuous cycle.

Even though we are still in the early stages of realizing the full potential of emotional AI, several companies today are using this technology to 1) Create products that compete on value not price and 2) refine their pitch and ad creative so their marketing content effectively conveys benefits (not simply features).

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Quality vs Quantity: The ‘TAO’ of Client Experiences

How many of us in market research are crafting experiences with our clients, collaborators and colleagues to be as effortless and magical as we’re telling them they need to be to their consumers?

Quality And Quantity Computer Keys Showing Choice Between Excellence Or Numbers

Editor’s Note: This post is part of our Big Ideas Series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Catherine Rickwood will be speaking at IIeX North America (June 13-15 in Atlanta). If you liked this article, you’ll LOVE IIeX NA. Click here to learn more.

Catherine Rickwood, Experience Director & US Team Lead, MESH Experience

Everyone is always busy. Consumers are too busy to watch ads, too busy to consider all the options, too busy to take it all in. They will gravitate towards brands that offer them the easy route, provide shortcuts to the information they need, use intuitive visual cues and streamline the whole consumer experience to be a flawless, magical journey; As a result, Marketers have been using these heuristic and other behavioural economics techniques to influence consumers for years (Welch, 2010).

But how many of us in the research industry apply these principles to what we’re delivering? How many of us are crafting experiences with our clients, collaborators and colleagues to be as effortless and magical as we’re telling them they need to be to their consumers?

Our colleagues are also just as busy. Our colleagues will also want shortcuts to relevant information. We should be practicing what we preach and distilling the killer points to hit people over the head like the heavy weight game changers that they are; even if it’s a stop-everything, turn-your-world-upside-down insight, if it’s buried like a datapoint needle in a Powerpoint haystack, then it’s not going to have its moment.

Liberation can come in the form of short, sharp, sharable reports that we like to call ‘Snackables’. These are just one or two pages, visually appealing and easy to interpret. We’ve found this approach really effective both internally, to truly refine our recommendations and externally, in the value we’re then able to deliver to clients. For those struggling through long documents, here’s an acronym that can be useful for creating successful ‘Snackables’, based on a well-known Chinese word ‘Tao’, poignantly sometimes translated as ‘principle’:

1) Trust: You need to build a trusting partnership; often researchers can hide behind mountains of charts and tables to prove how much work they have done and to justify themselves. If you have put the work into the analysis – a huge part of which is this thinking as well as producing – then it will show through.

2) Audience: As with all deliverables, you need to know your audience. This is especially true if you’re distilling a huge piece of analysis into a few key points; will they be thinking about financials? Statistical significance? Translate accordingly to ensure the points hit home. <

3) Objective: Always, always refer back to the objective! Is this to answer a specific question, in which case, will a one-word answer get everyone’s attention? Or is the focus to creating something that’s so engaging it gets shared internally through different departments? Design the document with the objective at the top of your mind.

Sure, you’ve got to tell a coherent story and you do need to provide the relevant background information with this, but serving up a ‘snack’ before the main meal is a great way to build appetite with a tasty teaser. This should maximize your chances of making an impact, ultimately insuring that the valuable golden nuggets within are digested and actioned. Happy snacking!

 

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Are Smartphones in your Survey Sample Yet?

If we want people to choose the altruistic activity of survey-taking over all other things they could be doing with their time, we need to lower the barriers to entry.

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By Hilary DeCamp

If you want someone to do you a favor, you make it easy for them…right?

Answering your survey is doing you a favor. Sure, there is often some financial incentive or drawing involved… but, for most consumers, that reward is too small to justify their time. People mostly do surveys to voice their opinions, influence product and service decisions, satisfy their curiosity, pass the time, or help out their favorite brands. That’s why brand-identified surveys off client lists have such high response rates (and the resulting samples are so biased toward fans of the brand).

If you want to maximize your business, you don’t force moviegoers to drive to the ticket-office… you let them buy through Fandango; you don’t force patrons to call your restaurant… you let them reserve through OpenTable; you don’t require shoppers to visit your store… you let them buy from your website… if that is their preference. People have more choices than ever these days and you need to make every effort to accommodate them.

In all those examples, consumers are seeking your service and thus should be motivated to go the extra mile, yet you still recognize the need to make it easy for them. When it comes to market research, few people are chomping at the bit to take your survey when they could instead be chatting with friends, streaming videos, playing games, or simply sleeping. If we want people to choose the altruistic activity of survey-taking over all other things they could be doing with their time, we need to lower the barriers to entry.  Surveys need to be available on the respondent’s device of choice… not require people to put down their phones, walk to their offices, boot up their PCs, and reply that way.

We’ve been through this before when we had to shift from CATI to web research. We didn’t do it ONLY because it had the ability to be faster and cheaper; the best of us did it because the non-response bias that was being created by caller ID, answering machines, and wholesale abandonment of landlines made it impossible to get quality samples by telephone without spending a fortune.

Just like web surveys were a great way to reach people who had cut the (phone) cord, surveys designed for smartphones are a great way to reach respondents. In record time, behaviors and preferences have shifted in such a way that many kinds of respondents can only be surveyed via smartphone…because that is where they spend the bulk of their online lives.

For some populations, if you’re not letting them into your survey via their phones, or investing in costly central-location recruiting, then you’re certain to be missing them. According to Pew Research Center’s 2015 statistics (yes, that’s a year old already), one-quarter (23%) of U.S. Hispanics have smartphones but NO home broadband service.  Other groups with high phone-only internet service in 2015 were:

  • African Americans (19%)
  • 18-29 year-olds (19%) and 30-49 year-olds (16%)
  • Parents (17%)
  • Lower household incomes — under $20k (21%) and $20-50k (16%)

These figures represented a 63% rise in just two years. These results were released five months ago, so you might extrapolate each of these to be several points higher by now.

You might think this just impacts your incidence – and, if that’s the case, you can still use demographic controls to ensure you get the right mix of demographics. But, in reality, demographics only serve as one form of classification, one that’s easy to measure and control. But, by ignoring smartphone-only users, you neglect a key (and growing) population with meaningfully unique behaviors and attitudes. The psychographics and lifestyles of the young ethnic person you can reach only by phone are likely quite different from the ones you can reach via PC. There is no good way to correct for that bias through weighting alone.

If you are trying to understand ecommerce or multi-channel shopping and information-gathering behavior, can you draw the right conclusions from a sample that excludes smartphone-only internet users?

Sure, there are some studies where you simply have no choice but to exclude small screens (e.g., choice models and tests of fine-print ads or packages). But in cases where you’d simply PREFER to ask selected question types that are incompatible with this medium, you should think long and hard before deciding to take the non-phone route. The resulting sample bias on non-demographic traits is likely to be severe and could lead to incorrect business decisions.

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Does Machine Learning Signal the End for MR Pros?

With the dawn of machine learning, will researchers still be needed to define the questions, discover the trends, make the forecasts or influence the resultant decisions?

man to machine

By Sinead Hasson

When someone introduced me to the concept of ‘machine learning’ a few weeks ago I caught an unsettling glimpse of the not-too-distant future. For the uninitiated, machine learning is a new field of data analysis, which Stanford University defines as ‘the science of getting computers to learn without being explicitly programmed’. We’re talking seriously smart algo-based software. So smart, in fact, that it can sift through the petabytes without being directed and identify many more trends than a human analyst can, produce more reliable forecasts and as a consequence (gulp) make better decisions too.

It’s news to no-one that a human analyst’s data interpretation is innately biased. That’s human nature; absolute impartiality is impossible for us to achieve. This, in itself, isn’t a problem – we live in a human world where the idea of ‘useful knowledge’ is heavily influenced by our social, economic, historical and cultural situation. The problem here is that we don’t always know the right questions to ask in the first place. By sheer depth and volume of analysis, however, machine learning promises to reveal what those missed lines of enquiry might be, having traced them back from abstract trends that it has already discovered.

All of which begs a question; if researchers are no longer needed to define the questions, discover the trends, make the forecasts or indeed influence the resultant decisions, they might as well all go home, right?

Wrong. Because fortunately, we don’t live in a world governed by machinery, and science fiction aside, it’s pretty unlikely that we ever will. Not all insights can be gained from logic. The ‘right’ decision isn’t always made from an operational analysis, or as a result of a statistical forecast. It’s why Mr. Spock needed Captain Kirk. In fact (humour me here) I think machine learning is Kirk delegating to Spock so he can get the broadest possible picture, upon which he can then impose his ego-centric, ever-so-human judgement.

Around 90% of all decisions made in major commercial organisations are operational in nature, so you can see why people are getting so excited – machine learning’s potential to raise corporate efficiency is huge. But understanding an audience that consists of you and me, each with our own wonderful and unique set of illogical, emotional quirks, is something quite different.

I hear that the Machine to Machine (M2M) age is coming, but you know what? Until we need to understand how machines, and not us fleshy consumers, are influenced and motivated by our clients’ brands, I’m not worried. Neither should market research professionals.

Join the debate @sineadh

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Chasing The Next Big Idea – Prediction Market Volume Forecasting

In collaboration with Top Box Associates, Consensus Point recently launched the industry’s first prediction market-based volume forecasting capability.

Business vision

By Brad Marsh, CEO, Consensus Point

Like a few other “seasoned” researchers that will be attending IIeX, I have now entered my third decade in an industry that I fell into while working in a phone room in the Summer during college.  To say I never saw myself building my destination career in market research would be an under-statement, yet here I am, and I remain very excited about the future of opportunity for the next generation of innovators and entrepreneurs.

So what kept me motivated and excited over the years?  I didn’t know it at the time, but reflecting back, it was my curiosity and chase for the next Big Idea. The new approach or new process that was going to help my end client get a promotion, my team beat its goals, or my company differentiate and win.  For better or worse, I didn’t limit my thinking to what was considered “the right way” or the “way we’ve always done it,” and in hind-sight, I can tie all the critical inflection points in my career to this innate need to push the envelope on what’s possible.  To be clear, I can also attribute a couple of “un-planned transitions” to this drive to re-invent and innovate, so you have to prepare yourself for the challengers of change in your organization.

All that said, I count myself very fortunate to have worked with some of the best firms and brightest people in the industry on many of the Big Ideas of their time.  Ideas like video ad testing, web-based discrete choice technology, online concept testing & forecasting, digital ad & brand tracking, and text based drivers modeling are a few I was involved with as they were introduced.  Yes, Millennial researchers, we had to mail VHS tapes out to panelists and wait 3-4 weeks for a response to be mailed back – that was cutting edge in 1999.

So based on my confessions above, you can imagine how excited I am to be again breaking the rules with two proven industry solutions that a year ago would not have been mentioned in the same sentence, Prediction Markets and Volumetric Forecasting.  In collaboration with Top Box Associates, Consensus Point recently launched the industry’s first prediction market-based volume forecasting capability.  Consider an “agile” yet predictive volumetric forecasting alternative, where you can go from finalizing your new ideas or concepts to a full volume forecast as accurate as industry leaders in two weeks and at a fraction of the cost.  Even cooler, it’s mobile, can work with panel samples or communities, and can be delivered globally!

Intrigued to learn more?  Well we hope you’ll stop by the Consensus Point Booth or our talk on Monday afternoon in Track 4.  Either way, we look forward to seeing you in Atlanta, and best of luck discovering the next Big Idea for your career and business at IIeX!

 

 

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Online Surveys Are Not Enough

But because of the simplicity and efficiency of online surveys, the market is quickly becoming oversaturated and respondents are getting overwhelmed. So are online surveys doomed?

Voxco-online-surveys-are-not-enough-part-1

By Tim Gorham

Online will remain the #1 survey channel for the time-being, and deservedly so. The channel offers the ability for respondents to privately complete surveys on their own time. For researchers, data can be processed immediately, and there are no additional costs for interviewers.

But because of the simplicity and efficiency of online surveys, the market is quickly becoming oversaturated and respondents are getting overwhelmed. Online survey invitations are everywhere, and the surveys they link to are often sloppy. And the resulting online survey clutter leads to survey fatigue, which leads to a range of data collection issues, none of which are good news for researchers:

  • Declining response rates. Many online surveys see single-digit response rates as low as 2%. And respondents who do complete the surveys generally have an extreme opinion: they’re either ecstatic or livid. It’s difficult to get a balanced opinion when you’re only talking to outliers.
  • Reduced respondent attention span. For respondents who still complete surveys, the increased frequency can affect their in-survey attention span. The more survey questions they see, the higher their tendency to burn through questions too quickly without adequate thought.
  • Market saturation. With so many surveys vying for a respondent’s attention, how can one organization get their survey noticed and completed?

So are online surveys doomed? Of course not – but researchers tasked with deploying them are certainly being challenged. We’ve discussed numerous times in the past how to make online surveys more engaging. Here is the TL;DR version:

  • Design surveys well. A no-brainer, but so important. Keep online surveys short and sweet on the surface. Nowadays, if your survey looks cheap or takes longer than five minutes, you’ll start losing a portion of the respondents who were willing to click on the link in the first place.
  • Listen and Adapt. Surveys are a two-way conversation. Ensure that the respondent knows you’re listening to their answers. Use logic to skip irrelevant questions or pipe in past responses. If a respondent feels like they are being heard, they’re more likely to share.
  • Incentives increase response rates which offsets your sample cost. Unincentivized survey requests are a primary target in the rising pushback against online survey invitations.
  • Personalize the invitation. Ensure that survey invitations speak to the right people, and acknowledge the respondent situation. Use a clean sample source and personalize the message to clarify why they were chosen (eg. “Thanks for your purchase of X last Tuesday…”).
  • Create a community. Cut to the heart of your customer base and nurture your own panels of your loyal consumers. It’s a lot of work to create and manage, but an incentivized, permanent panel will give you a constant finger on the pulse of your most important customer segment.

Online surveys are an essential part of any Voice of Customer or research program. But in today’s industry reality, you need to accept that getting a respondent’s attention online is extremely challenging. Get your surveys noticed by complementing well-designed online surveys with surveys conducted via alternate channels.

Break through the heads-down, clutter-ignoring patterns of an average respondent’s daily routine. Think differently about how to get your survey noticed by respondents. Taking your survey project out of your own comfort zone can take you into a new zone where respondents actually notice your surveys.

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Do Respondents Even Understand Our Surveys?

Language is an imperfect method for communication. How often does the receiver of a message truly understand it exactly as the sender intended?

survey puzzle

By Allan Fromen

I’ve always been fascinated by how people perceive the world around them. Even prior to my Psychology degrees, I’ve often thought about how we communicate with each other, and have been particularly interested in understanding how our words and actions can be misconstrued.

Like many teenagers, I used to waiter to make extra cash. My friend and fellow waiter once said to me “I hate waitering so much.” It immediately dawned on me that there were (at least) two meanings to his statement. He could have meant, I really dislike waitering, with “so much” serving as a description of the intensity of his dislike (as in “I hate waitering with every fiber of my being”). Conversely, he could have meant that he dislikes waitering often, as in waitering once in a while was ok, but doing it every night was unenjoyable (“so much” in this case means “so often”).

I later learned that this is called a linguistic ambiguity, and refers to phrases that can be understood in more than one way. Consider the following examples:

  • They are hunting dogs
  • I left her behind for you
  • The police shot the rioters with guns
  • I saw the man with binoculars

These statements are all examples of phrases that we speak and write in our everyday life, but which have more than one meaning.

It turns out that language, despite our reliance on it, is an imperfect method for communication. How often does the receiver of a message truly understand it exactly as the sender intended? Does it even matter?

Turns out, in matters a great deal. In the best-seller Superforecasting, the authors introduce us to Sherman Kent, who worked in the intelligence department that eventually became the Central Intelligence Agency (CIA). After Yugoslavia broke from the Soviet Union, Kent’s team issued the following analysis: “Although it is impossible to determine which course of action the Kremlin is likely to adopt, we believe that the extent of [Eastern European] military and propaganda preparations indicates that an attack on Yugoslavia in 1951 should be considered a serious possibility” (emphasis my own).

A State Department official later asked Kent to translate the statement into odds of an attack. Everyone on the team had agreed to use the phrase “serious possibility.” But when Kent went back and asked them to convert the agreed-to phrase into actual odds, one analyst stated 80 / 20 in favor of an attack, and another stated the exact opposite, 20 / 80. Other analysts were scattered between the extremes with no apparent consensus. As the authors write “A phrase that looked informative was so vague as to be almost useless.”

In market research, it is a best practice to have anchors for each and every response option. But do we really know how respondents interpret Very Satisfied or how it differs from Satisfied? What odds would a respondent actually attach to Very Likely on a question that seeks to measure purchase intent? When respondents provide ratings via the Likert type scales we commonly use in market research, they probably interpret these anchors in a myriad of ways with little agreement. Much like the intelligence analysts described above, our respondents bring forth their own histories and biases, resulting in multiple interpretations to the same scale.

Some research on research addressing this topic would be a great start, and a significant contribution to the market research community. In the meantime, I think we all need to understand the limits of our individual research efforts, and constantly seek to draw conclusions from multiple data sources. By integrating various sources of data, we reduce the biases inherent from any one study, which strengthens our insights and conclusions. I’ll be talking about examining multiple data points, which I refer to as triangulation, at the forthcoming IIeX conference.

Side note:

While there are many great books about how we think and are prone to cognitive biases – especially in the behavioral economics genre – I highly recommend Superforecasting, as it deals with what we market researchers try to do every day. I hope you enjoy it.

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Social listening currently complements surveys and behavioral tracking. Will it replace them?

The value of social media listening becomes exponential when integrated with tracking surveys and behavioral metrics.

Social-Media-Listening-Image-two

Editor’s Note: This post is part of our Big Ideas Series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Michalis Michael will be speaking at IIeX North America (June 13-15 in Atlanta). If you liked this article, you’ll LOVE IIeX NA. Click here to learn more.

By Michalis A. Michael, CEO, DigitalMR

The value of social media listening becomes exponential when integrated with tracking surveys and behavioral metrics. This is when actionable insights otherwise unattainable become visible. Here are 3 use cases:

  1. the net promoter score and satisfaction scores from surveys can be explained and possibly predicted by the net sentiment score* from social media listening
  2. identifying specific emotions in social text can be integrated with emotional and image drivers included in tracking surveys
  3. net sentiment score from social listening may predict sales and market share that appears in retail measurement reports by Nielsen or IRI

*Net sentiment score=is a score between -100% and +100% that indicates the ratio of negative Vs positive posts for a brand or a topic.

In order to set yourselves up for success the first time you attempt such data integration, you should not just look for trend correlations in the data. Even if the metrics in the data sets you are trying to integrate do not seem to correlate, this is also a very valuable finding that can lead to further investigation. Here are some additional possibilities:

  1. Could it be that the ages of your survey respondents are different than the ones of the people posting on the internet? Would it help if we compare survey data of up to 50 y.o. respondents with social listening data?
  2. Could there be a time lag of similar metrics between two data sets, thus one of them being predictive?
  3. What can we learn if we integrate 3 or more data sets from different sources about the same/similar metrics?
  4. Could social listening metrics predict the KPIs coming out of a tracking survey or a retail measurement report?
  5. Since social listening is unsolicited and not dependent upon someone thinking about what questions to ask, we may discover something unexpected!
  6. Everything new that we discover in a social listening report could further be probed in a survey or qualitative research, ideally on a private online community for on-demand insights.

Granted, today we may not be able to replace our brand trackers with social listening tracking 100%, but as more and more consumers get connected on social media this may soon become feasible. There are predictions that more than 5 Billion smartphones with access to broadband will be owned by 2020. This means that in most countries we will have over 90% broadband penetration. This is a game-changing statistic. Are you getting prepared for that time? Do you already complement your brand tracker insights with social listening?

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Look Who’s Talking, Prequel: What Research Participants Say About Research

Time and time again, we’ve all heard talk in our industry around the quality of respondent databases and panel members. However, we are neither implementing sufficient solutions nor giving the panel members a voice regarding what we ask them to do.


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By Kerry Hecht

A few months ago, Jessica Broome and I were discussing different suppliers, respondents and an interesting nugget that popped up in the last GRIT report.  Out of this conversation came an idea – Research on Research Participants.

Time and time again, we’ve all heard talk in our industry around the quality of respondent databases and panel members.  We hear a lot about professional respondents and often seem to look at the people in our databases with a degree of disdain. However, we are neither implementing sufficient solutions nor giving the panel members a voice regarding what we ask them to do.

Back to the last GRIT report: There was one researcher who pushed back.  She called our industry out, collectively, for complaining about the quality of panel participants, but, at the same time, putting forth surveys that very few people would want to go through ever again; ultimately creating a vicious circle.

Jessica and I felt like it was time and necessary to explore why people participate in marketing research, what their journey was to becoming involved, what their motivations (beyond money) are, what they like about it and what they think we could be doing differently or better.

We started with an online community of 5 days with about 40 people.  The participants were recruited with help from both qualitative recruiting specialists and panel companies.  These are some of the key findings from the insight community we created:

  • Most start participating in MR for money: some are between jobs, SAHMs looking to bring in some cash while allowing them flexibility in scheduling, single moms trying to make ends meet.  Others are simply early on in their lives and could use an extra dollar.
  • Word of mouth is important; many have heard about research participation from co-workers, friends or family members, and many continue to spread the word.
  • Most sought out paid research opportunities, though a few were approached by research companies.
  • They really weren’t sure what to expect when they began.
  • For many, MR quickly becomes an enjoyable experience; the money is still important, but there are some other motivations including feeling like they are using their time productively and an opportunity to connect.
  • Despite busy lives full with work, family, and hobbies, most are still what we would call “professional” respondents who participate in research on a regular basis even if they don’t depend on the income they get from incentives.
  • About a third participate in studies at least once a month. Importantly, they see nothing wrong with this and feel that the “no recent participation” policy of many MR companies is not necessary.  In fact, some feel the opposite is true.  The more projects you’ve done the better respondent you’ve learned to be.
  • Long, boring grids and repetitive questioning turns many off of surveys.  Filling in endless bubbles is exhausting and the respondents often feel misled.  They’re frequently told the survey will be 15 minutes and it ends up being 45…  This leaves them with a negative impression and can actually be disruptive to their routines.
  • Most tolerate, but do not enjoy, the recruitment process – they view screeners as surveys, often long ones, for which they are not always compensated. Going through a screener and then being disqualified from a study ranges from irritating to infuriating – and creates an environment where respondents are often motivated to lie just to be allowed into a study so that the time they’ve spent already doesn’t go to waste.
  • There is a real need for respondent education: terminology (survey vs. focus group), why people get screened out or sent home.  While it may not be practical, explaining to the participants on why a study is being conducted or what may be done with the results gives them something to feel invested in.

We decided to conduct a quantitative follow-up study with 1,500 people.  This phase, in analysis now, intends to validate (or not) any impact that someone’s creative ability or learning style may have with regard to their research participation.

Tom Anderson of OdinText Analytics has graciously volunteered his skills in helping us navigate this huge lot of data.  A snippet of those results can be found in one of the recent posts on this blog.

We’ll discuss this and more few weeks from now in Atlanta at IIeX.  Hope to have you part of the conversation!


Our Research on Research Participants project wouldn’t be possible without the support of our partners.  We’d like to thank Critical Mix, Cross Tab, Dub, ProdegeMR, Propeller Insights’ Recruit and Field, Schlesinger Associates, Tango Card, and OdinText Analytics for their contribution.

Join Kerry Hecht, Jessica Broome, and Tom Anderson at IIeX in Atlanta for a workshop exploring the qualitative and quantitative findings of the Research on Research Participants project.  In addition, Jessica and Kerry will be hosting a roundtable discussion with actual market research participants invited for this very purpose.

Click here for more information about the workshop

Click here for more information about the roundtable discussion

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Dawn of the Promiscuous Consumer: Digital Natives Are “Shopping Around”

Digital natives have an openness toward products that generations before them have not exhibited. What exactly are the drivers behind these attitudinal changes and how are they changing the shopping experience for all consumers?

Online Shopping Computer Key

Editor’s Note: This post is part of our Big Ideas Series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Rebecca Brooks will be speaking at IIeX North America (June 13-15 in Atlanta). If you liked this article, you’ll LOVE IIeX NA. Click here to learn more.

By Rebecca Brooks, Co-Founder, Alter Agents

Change is everywhere. How consumers interact with brands is transforming before our eyes! This transformation has spilled over into all steps of the brand communication process from marketing research all the way to direct consumer communications. Every day, new technologies are evolving the consumer and shopper experience.

Forget the negatives typically associated with the word promiscuity. It is the best label to define how the latest generations; Millennials and Gen Z are transforming the way brands operates – specifically in the CPG sector. These groups of “Digital Natives” have an openness – or a “promiscuous” attitude, toward products that generations before them have not exhibited. What exactly are the drivers behind these attitudinal changes and how these powerful generations are changing the shopping experience for all consumers?

Grasping the Generational Shifts

Brands need to move away from the concept of building a loyal consumer base.  Marketers need to stop thinking about how to “win” customer from competitors and think about how to bring customers back. The model needs to be reframed not with loyalty as a conclusion, but with the goal of giving the consumer a reason to return. Trying to grow the percentage of the population that will only buy your product is not practical in today’s world. Market share will grow not from loyalists. Market share will grow from getting the promiscuous shopper to keep your brand in rotation.

During my presentation at IIeX North America on June 13, we will simulate the shopper journey in today’s digital age using CPG as the lens for viewing these generational changes and how to adapt. The key take aways we will discuss include:

  • Digital Natives have an entirely new way of thinking about shopping.
  • The amount of information available to them is driving anxiety and promiscuity.
  • The competitive edge is no longer just about superior product, but about providing the shopper with an experience that provides unique value.
  • Digital Natives are fluid about price and weigh many factors with each purchase, making them unpredictable.
  • Promiscuous attitudes mean we have to abandon loyalty as the end goal.
  • Brands need to provide customers with a reason to return.

Curious to learn more about this topic prior to the conference, check out our latest downloadable eBook, titled, Dawn of the Promiscuous Shopper, where we delve into the concept of who digital natives really are, how we need to change our communication to get in front of this digitally connected consumer, and present four trends emerging from our research highlighting promiscuous behavior. Another great book I would recommend is Dancing with Digital Natives: Staying in Step with the Generation That’s Transforming the Way Business Is Done.

Look forward to seeing you in Atlanta.

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