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One Small Step for Women, One Giant Leap for Conferences

Annie Pettit asked some brand new and experienced female speakers who will be taking the stage at this year’s ESOMAR congress to reflect on their experiences.

business woman - growth and success graph

By Annie Pettit FMRIA

Perhaps you’ve seen a tweet or LinkedIn update from me bemoaning the lack of women on stage at conferences. Based on my data from over 50 conferences this year, the average marketing research conference puts forward 13 male speakers for every 7 female speakers. That’s not a great statistic given that our industry is more than 50% female.

What? Why? How? These are just 3 of the 3000 questions I have regarding this phenomenon so I asked some brand new and experienced female speakers who will be taking the stage at this year’s ESOMAR congress to reflect on a series of questions.

Why did you submit a proposal to ESOMAR? Two main reasons were key for these speakers. Many submitted out of pure giddiness to share some cool insights they’d found, and they thought that the ESOMAR audience would benefit from learning about them. Since my experience tells me that giddiness leads to entertaining talks, the conference is looking promising already!  The second reason, which topped the chart, was the opportunity to demonstrate thought-leadership and innovation for the speaker personally or for their company at ESOMAR, which they view as a prestigious event.

What holds you back from submitting to conferences? These speakers recognized that writing a proposal for ESOMAR, in particular, is a lot of work and not everyone has the necessary time. And for some conferences, all the work required to write a proposal might not be worth it when research providers may be penalized for not presenting with a client. They also realized that making your work appeal to an unknown global audience from diverse backgrounds, from language to life experience to industry, isn’t easy.

On a different note, a couple of speakers mentioned that, in their household, they were the primary caregivers and, as such, weren’t as free to submit to as many conferences as they might like.

What do you worry about? The list of worries is long, varied, and extremely familiar. These speakers worry that they are less qualified than others, their topics are less interesting than others, and their work is not relevant to others. In other words, Imposter Syndrome has a hold on them, as it holds viciously on to me. And yes, some get so nervous that they wonder if there is a paper bag nearby.

On a more practical note, a few speakers lament that they are unable to talk about proprietary issues. Others were concerned that some conferences, particularly marketing conferences as opposed to academic conferences, are more concerned about the entertainment value of presentations, not the scientific merit of the content. They feel this would make it difficult for competent, but less exciting or less experienced, speakers to be appreciated once on stage, and subsequently be invited back.

How can we increase the diversity of speakers? There are so many things each one of us can do to make diversity among conference speakers a non-issue. Several people suggested that experienced speakers should encourage and mentor new speakers to submit proposals – indeed, several speakers said that they submitted simply because their employer pushed them to submit. Experienced speakers should also share the stage with new speakers who can then, over time, become mentors of their own. And, instead of putting forth a sales person who might have more speaking experience, companies ought to put the main researcher on stage and grow the expertise within the research team. Start early, and start internally! One great thing is that these women felt their employers had always encouraged them, and conference organizers always welcomed them to speak at conferences. pettit

These speakers had many suggestions for conference organizers as well. Blind submissions, where the conference organizers are unaware of who is submitting, was a popular idea. This might help to address unconscious gender issues and it would also help to get more junior, lesser known speakers on stage. And, since there may be more men in senior roles, putting more junior speakers on stage could serve double-duty to increase the number of women on stage.

They also suggested that conference organizers could be more vocal about their desire to increase diversity on stage by showing a greater diversity of speakers in their marketing materials. Sometimes, we need to see people like us doing something in order to feel comfortable doing it ourselves. In addition, some mentioned that organizers could have workshops or webinars about how to create high quality submissions, or lower fees for speakers from smaller companies or poorer countries.

Finally, our speakers recommended that being a speaker should be glamorized, particularly since ESOMAR is peer reviewed and is therefore a stamp of quality. Roll out the red carpets and put on those ball gowns and tuxedos!

To sum up, you’ve probably noticed that these opinions are gender neutral, and that you can relate to many of them. Everyone, even the most experienced speaker on the ESOMAR stage, gets nervous about public speaking or worries that their topic won’t WOW everyone in the audience. The trick is to accept that you will be nervous, and to submit and speak in spite of the nerves. The next time a request for speakers crosses your path, submit your first ever proposal. Encourage a promising young expert to submit. Encourage an underrepresented expert to submit. Don’t think about it. DO IT.

If you’ve never spoken at a conference before, you could start small by joining 1 of more than 15 New Research Speakers Club chapters around the world, each chaired by a local mentor. The club ONLY accepts people who have never spoken at a conference before so you can practice your skills among like-minded people.

So get ready to settle in for a few great days in New Orleans and say WOW as these women share their cool insights!

Thank you to Adelina Vaca Padilla from De la Riva Group, Annelies Verhaeghe from InSites Consulting, Carolin Kaiser from GfK Verein, Catherine Rickwood from MESH,  Ritanbara Mundrey from Nestlé India, Sherri Stevens from Millward Brown, and 6 other anonymous contributors, who were kind enough to share their frank opinions.


Jeffrey Henning’s #MRX Top 10: Channeling ESOMAR, Open Ends, and Research Automation

Of the 3,975 unique links shared on the Twitter #MRX hashtag over the past two weeks, here are 10 of the most retweeted…


By Jeffrey Henning

Of the 3,975 unique links shared on the Twitter #MRX hashtag over the past two weeks, here are 10 of the most retweeted…

  1. ESOMAR TV: Broadcasting 19-21 September – Can’t make it to New Orleans for the annual ESOMAR Congress? Don’t want to wait for me to liveblog some of the sessions? Then tune into three channels of ESOMAR TV to watch the main tracks as well as interviews and breaking news.
  2. Ignoring Customer Comments: A Disturbing Trend – Tom H.C. Anderson of OdinText shares a survey of over 200 market researchers: 30% exclude options for open-ended comments because they don’t want to deal with coding and analysis of such comments; 42% confess to including open-ended comments they don’t plan to use.  
  3. Everything You Wanted to Know About Automation & MR – But Were Afraid to Ask – Two key points from this free report from NewMR & GreenBook (sponsored by ZappiStore): the fundamental good news – “Automation will result in more research being conducted, and a growth in evidence-based decision making, at a lower cost per project”; the fundamental bad news – “We predict 40-60% of existing market research jobs will disappear over the next five to ten years. Roughly 20-30% new research jobs will be created.” A must read.
  4. The Silent Rise and Blossoming of Qualitative Research – Edward Appleton of Happy Thinking People looks at qualitative researchers’ embrace of digital techniques and integration of quantitative methods.
  5. The Impact of Automating Market Research – This Raconteur article follows up on the NewMR/GreenBook research into automation with the following advice: automate or become antiquated.
  6. Three Ways to Use Digital Data to Guide Your Brand – Millward Brown has introduced a tracker that integrates attitudinal surveys, search data, and social data to provide trackers to brands that previously couldn’t afford them.
  7. ESOMAR Events & Awards – The fall ESOMAR calendar includes: the Annual Congress in NOLA September 18; Marketing Intelligence and then Global Qual and then Big Data World, all in Berlin in November; and local events including meetings in Guatemala and Brazil in September.
  8. Use Big Data to Create Value for Customers, Not Just Target Them – Writing in Harvard Business Review, Niraj Dawar says that every organization should ask itself three questions about using big data: “What types of information will help my customers reduce their costs or risks? What type of information is currently widely dispersed, but would yield new insight if aggregated? Is there diversity and variance among my customers such that they will benefit from aggregating others’ data with theirs?”
  9. The Smartest SaaS Questions on Quora – Cascade Insights curates top Quora answers to questions from Software-as-a-Service businesses, and outlines how market research can help SaaS businesses grow, penetrate the enterprise, choose distribution channels, select the KPIs to monitor, and understand why customers leave.
  10. The State of Predictive Analytics [Infographic] – Econsultancy and Red Eye have a a new report based on a survey of 400 digital marketers and ecommerce pros…


Note: This list is ordered by the relative measure of each link’s influence in the first week it debuted in the weekly Top 5. A link’s influence is a tally of the influence of each Twitter user who shared the link and tagged it #MRX, ignoring retweets from closely related accounts. Only links with a research angle are considered. We also exclude any of my own articles from the list.


MR Realities: Cutting Through the Clutter

Introducing MR Realities, a series of podcasts focusing on the realities of Market Research.


By Kevin Gray and Dave McCaughan

Reality…In a world where everyone has a point of view, where marketers and their agencies are too often distracted by the tactical and immediate, it seemed to us that we both were spending an awful lot of time explaining the basics of marketing research. To clients, to colleagues, to people in general. So we decided it might be a good idea to combine our interests and backgrounds with the knowledge of some of the experts we have met along the way to try and explain, quite literally, the reality of market research.

MR RealitiesWe started MR Realities, a series of podcasts in which we discuss a wide range of topics important to marketing researchers with special guests. And our guests are special, from cutting-edge academics to veteran practitioners who have been thought leaders in our industry for many years.  We ask them questions you would want to ask, the way you would ask them.  It’s very open and free flowing.

MR Realities is not sales pitches in disguise – the purpose is purely educational. The basic idea was inspired by lunchtime (‘brownbag’) seminars which, in our experience, transfer a lot of practical knowledge very quickly, efficiently and conveniently. The podcasts are audio only so you can listen when you don’t want to stare at a computer screen or when that would be impractical, such as when you’re commuting to work.  We cover a very broad range of topics that have included:

  • Essential marketing research skills
  • Big data
  • Artificial intelligence
  • Marketing to seniors
  • Conjoint analysis
  • How to improve your critical thinking
  • Social media
  • Cross-cultural marketing
  • Qualitative research

And the great thing is we ourselves get to learn as we go as well. Nothing could be better. We talk a lot about ‘sharing’, ‘co-creation’ and ‘storytelling’ these days, and all these elements are a crucial part of the MR Realities experience.

Below we’ve listed links to the podcasts we’ve done thus far.  No registration is required.

Data, Analytics and Decisions: Rhetoric versus Reality (Professor Koen Pauwels, Ozyegin University and  University of Groningen)

Data Science Uses, Excuses and Abuses (Eric King, The Modeling Agency)

Will you still need me?  Marketing to Seniors (Professor Florian Kohlbacher, Xi’an Jiaotong-Liverpool University)

The Coming Deluge of Analytics Malpractice (Randy Bartlett, Blue Sigma Analytics)

Semiotics: The Problem Child of Qualitative Research (Sue Bell, Susan Bell Research)

Tips for Marketing Researchers, Young and ‘Old’ (Professor John Roberts, University of New South Wales)

When Bringing Technology To MR Is No Longer About Being MR Driven (Greg Armshaw, Greg Armshaw & Associates)

Thinking Mistakes Marketers Make (Terry Grapentine, Grapentine Company)

When Everyone is a Single Child?? (Kevin Lee, China Youthology)

Is There Too Much Gloom and Doom About MR? (David McCallum, Gordon & McCallum)

AI: Reality, Science Fiction and the Future (Mei Marker, ai-datascience.com)

Conjoint Analysis: Making it Work for You Part 1 (Terry Flynn, TF Choices LTD)

Conjoint Analysis: Making it Work for You Part 2 (Terry Flynn, TF Choices LTD)

Social Media: Promises, Challenges and the Future (Professor Raoul Kübler, Ozyegin University)

We hope you’ll enjoy them and find them stimulating and educational!


Kevin Gray is president of Cannon Gray, a marketing science and analytics consultancy.  Dave McCaughan is both the Storyteller at Bibliosexual, his consultancy on bringing people, media and marketing together and Chief Strategy Officer at Ai.agency, an about to be launched marketing consultancy.

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The Death of Marketing-Mix Modeling, As We Know It

If MMM is to survive, it is essential that it change and experience a rebirth.

Editor’s Note: Last week we posted a great think piece by Joel Rubinson on the need to focus on “bottom-up” marketing. One of the keys of achieving marketing ROI  is developing effective mix models that factor in the new world of digital attribution and programmatic advertizing. However, mix modelling has failed to keep up with advances in adtech and martech so it needs a real refresh. We believe so strongly in this that we’ve partnered with Sequent Partners, Time Inc, the MMA, and many other brands, agencies, tech platforms and media networks to develop a new event: the IIeX Attribution Accelerator forum.

Mix modeling needs to become more granular, timely and actionable – like attribution. Attribution needs to be more comprehensive, addressing the entire marketing mix, and needs more scientific rigor – like marketing mix modeling. As attribution moves beyond digital, and marketing mix modeling moves beyond traditional, a more integrated approach to marketing measurement is needed.

In today’s post, industry legend Michael Wolfe offers a tour de force on the problems with traditional mix modelling and a prescription on how to make them better in the modern era.



Despite my own hands-on involvement with MMM for nearly 30 years, I tend to believe that marketing-mix modeling, as currently practiced, is broken.  Sadly, I have gone back and looked at various vendor MMM power-points from 20-25 years ago; and frankly see little change from the current versions in terms of the issues covered and the underlying methods used.  Clearly, MMM does have a problem with being relevant and adapting to a more complex and fragmented market place.  Much of the criticisms are valid.

I, for one, think that rather than closing the coffin, this is an area in need of a renaissance and rebirth.  In order to do that, however, several things need to happen:  (1) an agreement and recognition of what the MMM shortcomings are, (2) a means or solution-path to solving these shortcomings, and (3) a concerted effort by MMM buyers to require, and MMM vendors accept, the solutions needed to achieve this renaissance.  This paper will outline what some of these solutions need to look like.

Recognizing the Shortcomings of MMM

Much of the criticisms of MMM derive from issues with the prevailing methods, the breadth of its ability to generate accurate insights or its inability to measure certain aspects of the marketing ecosystem.  When combined together, these are all saying that MMM is losing its relevance to answer critical business questions and has failed to keep up with the growing complexity and fragmentation of the market-place.  The interesting thing, however, is that I believe there are current solutions to most, if not all of these short-comings.


  • MMM focuses on the short-term effects of media and generally ignores or does not measure the long-term effects.

Interestingly, some MMM buyers use a rule of thumb 2X multiplier to adjust short-term ad incremental sales.   This is a cop-out.  Based on experience, these effects range from 1X to as high as 6X.


As shown below, the long-term effects significantly expand the total measured impact of all media. This is not a lost cause because there are methods for measuring these long-term effects.  Every MMM should directly derive this for every brand.


The significant value-added from measuring long-term media effects is that it often completely changes or reverses the economics of advertising.   In failing to address this, historical MMM has led companies to misallocate resources.  Often to the detriment of long-term equity building (advertising) for short term gains (e.g. excessive price discounting).

It is also said that most advertising for CPG companies loses money.   A study by the firm MMA, for example, finds that, over hundreds of brands, advertising only returns about 54 cents for every dollar spent.  However, as shown in this case, the long-term ad effect transforms the ROI of media from negative to strongly positive!  When the results of any MMM show negative returns from an activity that is actually positive, this can result in a misallocation of marketing resources going forward and sub-optimal business results.


  • MMM models only measure the impact of ad GRPs or spend and not the ad message or creative.

Whereas media channels such as TV or Digital are the delivery vehicles, the ad creative content or message is really the life-blood of advertising and how brands connect with customers.  MMM generally  fails to help advertisers understand the relative importance of message or creative content in driving brand performance.


Yet, it is quite possible, with some ad copy-test protocols, to scale the ad spend or model GRP variable with the copy test scores and, thereby, permit the separation of advertising “creative effects”  from “weight or spend effects”.  Below is an example of this.

While the mechanics and analytics behind this are all quite feasible, the challenge in doing this is that it requires the copy testing of all ads.   Copy testing is expensive and it is also not certain that every copy test method or regime will work effectively in this context.  Nevertheless, there is great value and insight gained from understanding and monetizing the value of ad creative or message. Despite all of this, there are affordable solutions. Measuring and monetizing the value of ad creative changes the focus of marketing more towards the message, where it should be.


  • MMM does not account for attribution bias, particularly within digital media.

MMM becomes confused when customers are affected by a pathway of marketing touch points, rather than a single exposure.    Therefore, MMM will be biased with respect to giving significantly more credit to that marketing channel in which the customer touches just prior to purchase.


Despite this, there are “path modeling” methods that treat marketing touch points as separate equations.   This shows great promise for overcoming this obstacle.  It also makes a major difference in how different digital media are estimated to affect purchase, as shown from the chart below.


  • MMM tends to not quantify the “synergies” across media channels, where the impact of two or more simultaneous media activations is greater than the sum of the independent parts.

The underlying principle behind econometric models of MMM is that variables or drivers are assumed to be “independent”.   Unless special interaction terms are built in or other non-regression based methods are used, all media in a model are assumed to be unrelated to the others.  We know this is not true.


As shown below, it is often the case that when two or more media are executed simultaneously, a greater impact is found.  The total is here greater than the sum of the independent parts.  As shown on the chart below, we call this “marketing synergy”.   Currently, we have found a lot of these synergies between digital and more traditional mass media.  Some of the current criticisms of MMM are that it underestimates the total impact of digital media and this phenomenon is a major factor explaining this underestimation.  By measuring these synergies, marketers will see the marketing mix as a symphony of instruments that need to work together harmoniously to maximize revenue.   This is the basis and measure of the value for “integrated marketing”.


  • MMM modeling might be able to explain what is happening to brand sales, but because it excludes the “voice-of-the-customer”, it fails to explain “why” and provide insights based on the customer’s mindset and current brand experience.

Failing to understand the mind and voice of the customer, in context of a MMM exercise, is a serious and nearly fatal weakness.   That is because, without understanding the voice-of-the-customer, it  is really next to impossible to understand the “why’s” behind a brand’s performance in the market place.  This also fails to consider how any decisions or changes to the marketing mix will be received by the customer or consumer.


One very effective way to address this issue is to impute a highly predictive social media metric into the MMM.  Below shows the sales correlations of one such metric.  In this particular instance and across 28 brands, the correlation of the Social Engagement Index or SEI is quite large.  This gives it plenty of leg-room to be included in a predictive marketing model.


By including the voice-of-the-customer into MMM, one gains a depth of insight that enables a deeper understanding of what part of the customer experience is most affecting brand performance.  As shown, in the case of a restaurant brand, issues with their menu and perceived  food quality were driving much of this company’s sales decline.  From the customers own words, this provides an explanation of the “why” behind brand performance.

Probably the most important reason for including the VOC into a MMM is that, as it represents the customer-brand-experience, its overall effect on brand sales is usually one of the largest business drivers.  It is even sometimes greater than that of all other marketing factors combined.

Reinventing Marketing-Mix Modeling

While there has been a lot of change in the complexity and elements of the marketing ecosystem over the past 30 years, the prevalent marketing measurement tool, Marketing-Mix Modeling, has failed to keep up and adapt to these changes.  In short, for the reasons cited, MMM is broken.  We are now at a cross-road.  Either MMM can continue to slowly die or else it can truly adapt and evolve.  As shown here, the solutions for overcoming its short comings already exist.  To achieve that renaissance, MMM must:

  • Expand its measurement focus towards quantifying the longer-term effects of marketing and develop more accurate and holistic ROI estimates.
  • Focus more on measuring the effectiveness of ad messaging and creative to better align and develop marketing communications strategies.
  • Adapt its method to avoid the pitfall of “last touch attribution”
  • Measure the interactions and synergies that exist between and across the marketing-mix, in order to form a foundation for integrated marketing.
  • Put the “voice-of-the-customer” front and center within the models in order to more fully understand the customer’s perspective & motivations driving business performance.

It seems that much of these changes have not been forthcoming from the MMM vendors.  Therefore, the key to effective change rests with the advertisers and buyers of MMM to require that these changes occur.  Without changes of the nature outlined here, there is a risk of MMM becoming obsolete and commoditized to the point of having little value.

This paper has been an attempt at outlining a vision which certainly can become the foundation for a renaissance and rebirth of this important tool for marketing measurement and marketing insight.  If MMM is to survive, it is essential that it change and experience a rebirth.

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Can Behavioral Science Predict Election Outcomes? We’re About To Find Out

BrainJuicer has been many things but it’s never been a polling company. And it’s still not a polling company. But it is trying something new, with the launch of its System1 Politics microsite and specialist unit, applying the principles of behavioral science to the tricky business of predicting elections. Tom Ewing, Senior Director in BrainJuicer Labs and the leader of the System1 Politics team, explains what’s going on, and why the US election is a lot closer than you might think



Editor’s Note: Anyone who has the dubious honor of being my friend on Facebook knows I am a political junkie. Elections are like sports to me, and the Presidential election is the Super Bowl. RealClearPolitics, FiveThirtyEight, The Hill, Politico and The Drudge Report are not just daily reading for me, I check them several times a day. I post things I think are interesting on Facebook and enjoy debating with my friends about them and taking a stab at being an amateur political prognosticator. I’m not very good at it (I should probably stick to market research industry trend analysis), but we all have our hobbies and trying my hand at predicting election outcomes is mine.

As we all know, this election is unique in many ways and many folks far smarter and more experienced than I have struggled to get a handle on it via traditional polling. This is compounded in a variety of ways, and has been a trend now for several years with some major failures to accurately predict outcomes in both the US and in other nations. One of the most unlikely pundits (and another daily read for me!) to emerge this year is Scott Adams, the creator of the Dilbert comic strip. Scott is a student of behavioral science (or persuasion as he terms it) and so far, his insights, especially around the Trump phenomenon, have been pretty accurate.  Others have also started to look at political races through the lens of behavioral economics, but to date, no one has combined that with quantitative research. Well, leave it to BrainJuicer to change that.

They just launched System1 Politics, which is their new weekly tracker of the US Presidential Election through their The 3 Fs framework, and it’s intensely interesting. Like Scott Adams, the BrainJuicer crew believe that the dynamics of the race and even it’s outcome can best be measured by understanding how voters are responding to the candidates on an emotional, System 1 level. Now they are putting skin in the game and are going to publicly experiment to see if they can call the horse race. It’s a bold move.

Tom Ewing of BrainJuicer reached out to see if I wanted to post something on the blog about it. Since I didn’t have time to do an interview, Tom came up with the idea of interviewing himself. I think that is pretty fun, and I think you will too. Below is Tom Ewing interviewing Tom Ewing on the launch of System1 Politics. Enjoy!    


By Tom Ewing

What is System1 Politics? Is it a polling company?

No. We have the same goal as opinion polls – predicting what’s going to happen in elections – but we’re taking a very different route to it. In a nutshell, opinion polls use claimed behavior – how people say they will vote – to predict actual behavior – how people actually will vote. We measure the basic mental shortcuts guiding a decision to predict the outcome.

Does that work?

It certainly works for consumer decisions. Fame, Feeling and Fluency – which are the measures we use – are what we use in our branding work to assess current strength and predict future growth. What we believe is that these factors are likely to lie behind political decisions, too. As with every BrainJuicer project it’s all about finding the best and most predictive proxy for people’s fast “System 1” decision-making.

But choosing someone for the most powerful job in the world is a bit different from choosing a brand of soap!

It’s a more important decision, but that doesn’t mean the factors behind it are all that different.  And this isn’t because we live in an age of “post-fact politics” – that’s about the media’s responsibility to check facts and challenge politicians. As far as decision making goes, we live, and we always have lived, in a world of pre-fact politics.

Even with highly considered System 2 decisions, we tend to be guided initially by a set of mental shortcuts which let us know whether something is a good choice or not. And those are Fame, Feeling and Fluency. Fame is the Availability Heuristic – how readily a particular option comes to mind. Feeling is the Affect Heuristic – how happy you feel with an option. Fluency is the trickiest – it’s Processing Fluency, basically how easy an option is to recognize. And we follow the Ehrenberg-Bass school of thought, which is that Fluency is all down to what Professor Byron Sharp calls distinctive assets – things like logos, colors, images, words and phrases. The more distinctive they are, and the more embedded in your memory they are, the quicker they are to process.

With Donald Trump, for instance, we realized he was pretty much a cert for the Republican nomination when we realized that just his hair was more recognizable than any of the other candidates in the race.

Let’s talk about Trump. You’re running a wave of data each week measuring Fame, Feeling and Fluency and updating your prediction accordingly. Is he going to win?

He has a very good chance. Right now he and Hillary Clinton  are absolutely neck and neck on our measures. They each have an advantage and as it happens they cancel one another out right now.

On Fame they are completely level. Fame was a massive advantage for Trump in the Republican Primary – it was the main reason we called it for Trump in January, back when commentators were still assuming the party would unite behind Rubio or even Jeb Bush. Trump just took up a lot more mental space. But Clinton is just as well-known, and of course now it’s a two horse race they’re mentioned in the same breath anyway. We’ve stopped measuring Fame because they’ve maxed it out.

Feeling is a different matter. Hillary Clinton’s scores on Feeling have generally been bad, but Trump’s have been awful, and the gap is high enough that it counts as an advantage for her. Fame doesn’t change much, but Feeling moves around quite a lot – and back in June, just after her brush with the FBI, Clinton’s Feeling took a nasty dip. She’s recovered since then and is currently well ahead of Trump, but it shows that it can change.

As for Fluency, that changes more slowly than Feeling, and Donald Trump has led on it in every wave we’ve done. He’s been a master of simple phrases and simple images – like “crooked Hillary” and the border wall – that have been big distinctive assets for him, and done a lot to define the election on his terms.

So Clinton wins on Feeling, Trump wins on Fluency. The polls are still showing a narrow Clinton lead, but on these real fundamentals, it’s neck and neck.

What does Trump need to do?

Trump needs to either make people feel better about him, or worse about Clinton, or a bit of both. At the moment – despite being very unpopular – they’re both on a Feeling high, so it might be he hasn’t got much room to make people like him better, and that’s why he’s focusing on damaging her reputation.

What does Clinton need to do?

She is also at the top of her range on Feeling, so may not have much room to improve there. She needs more Fluency, but so far Trump has been much better at coming up with memorable imagery and phrases. There’s one class of distinctive asset where Clinton has an advantage, though – she’s more strongly associated with former presidents and with the trappings of office. If being “presidential” helps when it comes to decision day, that’s good for her. You can see that play out in her tactics too – trying to reinforce the idea that Trump just isn’t presidential.

Why doesn’t your prediction match the polls? Do you think there are “shy Trumpies” who want to support him but don’t dare say so in public?

It’s possible – though remember this is a weird election in that Clinton is also hugely disliked. There are likely to be shy voters on both sides. But this is one reason we don’t ask voting intention questions. Our whole raison d’etre is – can we get to the outcome without asking people what they’ll do? So there’s no question where you might moderate your answer to be more socially acceptable.

What I will say is that our model moved from Clinton to Neck-And-Neck two weeks ago now, when she was several points ahead in the polls and before they began to tighten. One hypothesis we have is that emotion is a little ahead of polling response, because on a gut, System 1 level you feel you want to vote for or against someone, and then you wait until something happens that gives you a System 2 justification to publicly declare that. So we’re looking to test that.

Isn’t this far too simple a model for the US electoral system, where the Electoral College is what really matters, not national opinion?

With an unlimited budget we’d definitely do 50 different state-wide studies of Fame, Feeling and Fluency. (We’d also buy an office unicorn.) So I know how complex the electoral system is. But that’s one reason we didn’t want to do polling. Polling is all about a behavioral fiction – how would you vote if the election was tomorrow? But the election isn’t tomorrow! (Unless it’s November 7th) And you’re not in the voting booth. It’s claimed future behavior, which is exactly what we try to avoid asking in every other study we do. We think that by understanding the fundamentals behind the decision you can avoid that. In the long run that would hopefully let you predict elections several weeks or even months before they actually happen.

Why get involved in political work?

System1 Politics was born at 10 PM on May 7th, 2015, when Big Ben chimed and the BBC’s UK election exit poll revealed that the pollsters had called the UK general election completely wrong. John Kearon was watching and thought, look, there has to be a better way of doing this. System1 Politics is us trying to work one out in public. Also, let’s face it, these are exciting topics!

What have you learned?

I’ve learned three things.

Firstly, I have massively enhanced respect for pollsters. You need a really thick skin to put your predictions out there, week in week out, and take the flack from people who think you’ve skewed them or made them up or are just angry because their side is losing. It’s a hell of a job.

Secondly, I have to trust the data. I am a left-leaning British guy so it’s fairly obvious that I have a strong preference in this election. But you have to put that completely aside, and you also have to put hunches aside. We used behavioral methods to assess the Brexit vote, and it came up as Leave several weeks before the referendum. But we didn’t make a big thing about it because our hunch was that we’d got it wrong. We hadn’t, and we’ve learned from that: stand by the data and don’t make a prediction unless it backs you up.

And thirdly, I’ve learned from politicians that the best questions to answer are the ones you write yourself.

Thanks, Tom.

It was a pleasure, Tom.


Editor’s Post Script: 

As you can see, the System1 Politics analysis is roughly in line with the average of the latest traditional polls. It will be interesting to see how things trend from here on in!




Understanding Brand Attraction

We talk brand attraction and breakout brand strategy in the second video of a 9-part video series entitled The Future of Marketing and How to Win. In this segment, we answer the question, "What do progressive marketers need to understand about culture and brand attraction?"

Editor’s Note: Fresh Squeezed Ideas created a video series, The Future of Marketing and How to Win, to not only share ideas on where the future of marketing is headed, but to also provoke some new ways of thinking about brand strategy and marketing.”

By Fresh Squeezed Ideas

We talk brand attraction and breakout brand strategy in the second video of a 9-part video series entitled The Future of Marketing and How to Win. In this segment, we answer the question, “What do progressive marketers need to understand about culture and brand attraction?”

Brand attraction is subconscious. If Starbucks and Dunkin Donuts were side by side on a street corner, some people would go to Dunkin Donuts and some people would go to Starbucks because for some reason, one of those just fits with them. It’s internal and unconscious. They just gravitate to the thing that feels like them. This is not to be confused with the product proposition, which is all about rationally and what the functional product does. The running shoes need to run. The raincoat needs to repel rain. But, the brand is what creates preference for one thing over another when all other things are equal. And that’s the job of marketing, to create economic value in something above and beyond what the function of the product is.

Once we understand the motivators, we can then build a brand that is both differentiating, and highly relevant, and resonant with the customer. If we look to the leading brands as models of success, we can look to those that have woven themselves into our culture; Coca-Cola, Apple, McDonalds, great examples. So, it follows therefore, that you need to understand culture in order to figure out what you hook your brand to. So people are fundamentally changing their behaviour as they make different decisions as our culture evolves in this new economic era. I’ll give you an example. At retail, what’s winning are the discount retailers, and interestingly, also the premium retailers. The middle is actually dying. That’s a very different behaviour that’s driven by this economic instability, and people are making different choices.

Now, if you ask somebody, “Why are you shopping at discount, and then going across the street to a premium retailer?” they can’t rationally tell you. But, what’s driving it is the new cultural beliefs around economics. So, the brands that are attracting customers, are those that are culturally relevant.


Everything You Wanted To Know About Automation & MR – But Were Afraid To Ask

Download our free new report into the impact of Automation (including AI) on market research and insight.

Automation in MR


One of the key topics that is buzzing around the world of market research and insights is automation. Everybody is aware that automation is happening, that it will make things faster and cheaper, but people are unsure what the impact will be on them personally and on quality.

To try and answer that question Ray Poynter and I, with the generous sponsorship of ZappiStore, have prepared a report on Automation & AI in the market research industry today and what we expect to see in the future.

We developed this report by drawing on industry and published resources, some new research, and the crowdsourced thinking of industry gurus such as: Stan Sthanunathan (Executive Vice President – Consumer & Market Insights at Unilever), Sue York (Author), Pravin Shekar (Krea), Joan Lewis (former Global Officer & SVP Consumer & Market Knowledge at P&G), John Puleston (LightSpeed GMI), Fiona Blades (MESH Experience), and Stan Knoops (Global Head of Insight at IFF).

The report contains facts, information, and predictions – in fact something for everybody. The report does include some concerns, for example about the impact on employment and the challenges that suppliers and buyers will have in maintaining and enhancing quality, but overall the report is positive about Automation and its close associate Artificial Intelligence.

To give you a sense of how we see Automation, here are the thoughts we share at the end of the report:

Automation is happening. It will continue to happen, and it will change the way you work. Market research automation and AI have the opportunity to expand the role of evidence-based decision making – faster/cheaper research facilitates new ways of working, moving away from the ‘big project’ approach to an agile, learning approach. 

In the past, automation has mostly impacted manual or junior staff. In the future it will affect senior and creative staff too. There will be winners and losers – but you want to be one of the winners. 

The best place to be is in creating, implanting, or using automation. The worst is in doing the same thing you were doing five years ago.

If you want a one-stop-shop approach to being briefed on Automation then this report, sponsored by ZappiStore, is just what you have been looking for.

Download our free new report into the impact of Automation (including AI) on market research and insight.

Not convinced it’s worth a click? Here is a bit more from the Executive Summary:

  • Automation in market research will continue to evolve and accelerate.
  • Automation makes things faster, cheaper, and sometimes better.
  • Clients, not suppliers, determine quality – but suppliers need to ensure buyers can readily assess the quality of services.
  • Automation will result in more research being conducted, and a growth in evidence-based decision making, at a lower cost per project.
  • Automation will continue to generate winners and losers. Be an automation winner – lead by example and adopt innovation.
  • Clients often value speed above cost savings, as shown in interviews with Valspar and T-Mobile.
  • Most people agree that the benefits of automation include doing more with less, doing it faster, doing it consistently, and sometimes achieving superior quality.
  • People’s fears surrounding automation are less focused, though it is generally accepted that more automation is inevitable.
  • We predict 40-60% of existing market research jobs will disappear over the next five to ten years. Roughly 20-30% new research jobs will be created.
  • Artificial Intelligence will have a major impact on market research over the next five to ten years, impacting areas like qualitative research, research with images/video, and creativity.
  • Increased automation will result in more business decisions benefiting from research.

The report draws on a wide range of resources and original research and benefits from the contributions from a many leading names from the world of market research and insight, including:

  • Adriana Rocha (eCGlobal Solutions, USA)
  • Brian Ley (Valspar, USA)
  • Christian Super (ORC International, USA)
  • Dangjaithawin (Orm) Anantachai (Intage, Thailand)
  • Darren Mark Noyce (SKOPOS, UK)
  • Don DiForio (T-Mobile, USA)
  • Ellen Woods (QED Strategies, USA)
  • Fiona Blades (MESH Experience, USA)
  • Greg Dunbar (Cint, UK)
  • Jeffrey Henning (Researchscape, USA)
  • Jeffrey Resnick (Stakeholder Advisory Services, USA)
  • Joan Lewis (strategic advisor, USA)
  • Jon Puleston (Lightspeed GMI, UK)
  • Helene Protopapas (Nielsen UK)
  • Kelsy Saulsbury, (Schwan’s Shared Services, USA)
  • Lisa Horwich (Pallas Research Associates, USA)
  • Pravin Shekar (krea, India)
  • Rajesh Shirali (Tamanna Insight Partners, India)
  • Sankar Nagarajan (TEXTIENT Analytics, India)
  • Saul Dobney (dobney.com market research, UK)
  • Stan Knoops (IFF, Netherlands)
  • Stan Sthanunathan (Unilever, UK)
  • Sue York (Author, Australia)

Convinced now? Download our free new report into the impact of Automation (including AI) on market research and insight.

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Why You Should NOT Follow Procter’s Latest Marketing Advice

Marketing is transforming from top down to bottom up and you need to embrace it, not run away.



Editor’s Note: Marketing ROI, mix modelling, attribution, programmatic, cross platform measurement and similar topics are hugely important debates taking place right now. In fact, it’s so important that we’ve partnered with Sequent Partners, Time Inc, the MMA, and many other brands, agencies, tech platforms and media networks to develop a new event: the IIeX Attribution Accelerator forum.

As marketers, we all want to understand what touchpoints drive consumer purchase behavior – and allocate our marketing efforts and resources accordingly. Marketing measurement techniques like marketing mix modeling (MMM) and digital attribution enable us to do just that, but both approaches need to evolve to show us the full picture.

Mix modeling needs to become more granular, timely and actionable – like attribution. Attribution needs to be more comprehensive, addressing the entire marketing mix, and needs more scientific rigor – like marketing mix modeling. As attribution moves beyond digital, and marketing mix modeling moves beyond traditional, a more integrated approach to marketing measurement is needed.

In today’s post, Joel Rubinson (who will be presenting some ground breaking research on this topic at the AAF event) tackles this issue head on, using P&G’s recent shift back to traditional marketing as an example of how brands lack a clear and confident vision of how an integrated digital model can deliver for them. It’s good stuff.


By Joel Rubinson

Recently, Procter & Gamble sent shock waves throughout the marketing community when it announced it was abandoning precision targeting via Facebook.  “It didn’t work”, “We targeted too much and went too narrow”, said Marc Pritchard, their CMO. All generalized from a Febreze ad targeted to pet owners in large families that didn’t seem to cover its CPM costs.


Of course, when this was reported in the Wall Street Journal, and picked up by the marketing press on earth and neighboring planets, it was big news. It was interpreted as generalizable advice that all CMOs must listen to…do not target too tightly!! Do not move too far away from the old way of marketing. Some even blogged they hoped this will bring down ad-tech.

My advice? Don’t let P&G set your media strategy. Marketing is transforming from top down to bottom up and you need to embrace it, not run away.

Data-driven precision targeting is a train steaming down the tracks. Programmatic is reported to be growing at 20-50% per year. Mobile too…right place right time.  Programmatic is purely about delivering the right message to the right user at the right time, decided on in real time…something Google refers to as “micro-moments”, or Clayton Christensen might refer to as “jobs to be done”.

Programmatic is transforming marketing from “top down” (buy the whole audience of a show you feel matches your brand) to “bottom up” (choosing to buy an impression to a given user based on their profile and situational factors.) Yes, bottom up marketing is transformational; for example, hypothetically, Hillary Clinton can now runs ads to undecided voters on Foxnews.com, avoiding hardcore Republicans…before programmatic, who would have thought that? This is how Presidential marketing now works, the most sophisticated target marketing on the planet.

Yes, there are growing pains…some issues of brand fit with the media property, transparency on fees, whether CPMs are priced right, and the occasional counter-intuitive result like Procter got with Febreze, etc.

Push past the growing pains and double down on bottom up marketing. Most of my consulting is really about helping marketers or research companies develop the insights and analytics side of what this means. (Yes, insights and analytics is MORE important than ever in a data-driven marketing world.)

Doubling down on bottom up marketing (and research) opens up wonderful new possibilities:

  1. Target the 15% or consumers who are likely to account for 80% of the trial of your new product, taking money from the 70% who have virtually no interest in your brand whatsoever.
  2. Target persuadables towards your brand and not waste ad dollars against those firmly committed to other brands
  3. Activate your consumer segmentation by making it the basis for constructing audiences you target programmatically that deliver higher ad response (and if that doesn’t happen it means your segmentation approach is in need of a digital mindset refresh).
  4. Turn your DMP into your brand tracker. If you have modeled propensity scores on the DMP, you actually have a brand equity metric at scale that can be reported out continuously and in real time. Add in social media and you have a surrogate for attribute ratings (You should be able to save millions of dollars if you have numerous brands in numerous markets).
  5. Response profile your brand.  Instead of just attribute/positioning profiling, let’s profile out the distinguishing digital profile variables that differentiate those who respond to your advertising. Then turn that knowledge into audience creation rules so that you continuously improve the response to your marketing investments.
  6. Treat your campaign as a living lab with real time decision making. As Procter found out with Febreze, marketing doesn’t always follow script. Oh those wacky, lovable consumers! Every ad campaign can be optimized by moving money around in flight based on what is working, whether it conforms to preconceived notions or not…and is an opportunity to learn something…don’t squander that.

And one more point…when Pritchard said, “we targeted too narrow”, in particular resist that advice. The future of marketing IS narrow…it’s bottom up marketing, one impression at a time, perfectly chosen. It doesn’t mean that you are restricting reach…it just means you are building reach from the bottom up. In fact, I predict that even the big shows and publishers will all unpack their audiences and develop targeting plans for you that are bottom up.  It is already happening. And the reach you need is not necessarily the reach that Procter needs, so again, build your own experience and media strategy.

It’s becoming a bottom up marketing world…understand it, embrace it, master it, measure it.

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How Reconnect Research is Resurrecting Telephone Research In The Age Of AI

Reconnect Research has built a truly random sample phone survey router, with access to billions of consumers annually.



My first job in market research was in 2001 as Director of Call Center Operations for a healthcare-focused MR firm. We did NCQA CAHPS studies using a rigid CATI sampling method, and we sold excess capacity as a field & tab provider for other research companies. We also were bidding on many government studies such as the CDC BRFSS, so I was receiving a crash course in sample theory and applying very hardcore probability sampling models to the studies we were conducting.

I had come from a customer service call center operations management background, so I knew how useful IVR (Interactive Voice Response) could be and was exploring if there was a role for that technology in a research paradigm, and through that effort was recruited by my vendor, DialTek, to become their VP of Operations. DialTek did outbound (they still power the polling of Rasmussen Reports) and inbound (lots of satisfaction surveys when calling into a customer service line) IVR surveys, and during my time there we experimented with using it as a recruitment tool for qual, to build online panels, as a partial solution to streamline phone surveys, etc…  In other words, I know the technology very well and understand the best uses cases for it.

I left DialTek in 2005 to hang out my own shingle and decided that although it was useful tech for niche applications or project types, anything phone related was going to be supplanted by online and that should be my business focus. Despite the sample challenges of online research, phone (whether CATI or IVR) was going the way of the dinosaur and being the smart little mammal I am, I was going to be on the winning side of history. Most of the research I conducted between 2005 and 2010 (when I stopped conducting primary research for clients) was online, with only a smattering of CATI or IVR studies here and there for specific populations where it still made sense.

Why the history lesson?

Recently I was introduced to Scott Richards of Reconnect Research, and what they are doing has made me rethink my dismissal of phone research, especially IVR based approaches. In fact, they might have found the magic bullet to make it not just relevant again, but perhaps even a game changer.

Los Angeles-based Reconnect Research, a subsidiary of Dial800, is connecting people already on the phone with political polls and surveys. The new Inbound Calling Survey platform gives researchers fast, honest answers, while providing an additional revenue stream for carriers, and also as important, offering a consumer-friendly solution to collecting responses.

The technology behind the platform is complex, but the process is simple:

U.S. and Canadian telephone carriers route in excess of 150 billion calls per month, 5 to 10 billion of which never connect to their intended destination. These are called MIDI calls. MIDI is an acronym for Misdialed, Incomplete, Disconnected, and Inbound.   Until Reconnect Research, MIDI calls have been a big expense for carriers, but now they can be a source of monetization.

Researchers on the other hand are having a very difficult time getting consumers to respond to phone surveys as most people screen their calls. In fact, response rates to phone surveys are at a historic low. This is why about 70% of research today is done online. However, the same problems now impact online as well; not only do we have a representative sample problem, but quality, response and participation rates, and access to some populations are just as challenging as ever, maybe even more so.

Utilizing their national carrier network, now when a carrier receives a MIDI call, rather than playing an intercept message and hanging up on the caller they insert a simple message inviting the person to participate in a IVR survey. To provide the right survey, people are asked a few demographic questions like age, gender & ethnicity. As an incentive to take a survey, people can earn 25 Dining Dollars to save money at restaurants nationwide.


reconnectresearch-funnel-2 (1)


In effect, Reconnect Research has built a truly random sample phone survey router, with access to billions of consumers annually.

Case Study – A look at the Data:

RTI International, who primarily does health research for the U.S. government, conducted a case study examining the efficacy of Inbound Calling Survey (ICS).   They compared ICS to the Center for Disease Control BRFSS National health survey. BRFSS is the largest and longest running health survey in America. The study was led by Dr. Karol Krotki, Dr. Georgiy Bobashev and Burton Levine.

The study revealed that ICS was about 45% closer to the U.S. Census Bureau demographic profile for Americans than the CDC. Additionally, the answers provided by ICS respondents virtually mirrored those of the BRFSS study, which shows that people are answering honestly.
RTI Statistician Burton Levine presented this case study at the recent 2016 AAPOR conference:

“I’ve never had results where the respondents matched the population this nicely…”


Comparisons to RIWI (using broken URLs as a router for microsurveys), Google Consumer Surveys (surveys to gain access to web content) and other web-based “intercept” type approaches come to mind, but the difference is that they are limited to just the online population (including mobile users). However, Reconnect Research has developed a novel use of technology that allows for a truly randomized and representative sample of virtually the entire U.S. adult population (going global is in the works). If a voice call is made, regardless of origination device type or number, they have the potential to intercept them.

Is it a perfect fit for every study type? No. Over the past decade we’ve learned how to use the visual power of the web to create new research approaches that couldn’t be conducted in a telephone setting. Over the past 5 years, we’ve also learned to optimize mobile devices for deeply engaging, iterative approaches that don’t lend themselves to a voice-only setting or a single interaction.

However, many use cases do come to mind that this could be ideal for, chief among them political polling, governmental surveys like those conducted by the CDC, brand trackers, media exposure studies, A&U studies or anything else that doesn’t rely on a visual component, is (ideally) under 1o minutes in length and most importantly, requires a true random sample of a population.  That is still a big chunk of the research market. I also suspect there are plays here related to panel building, recruiting, market sizing, and a host of other applications that smart researchers will start experimenting with.

The speed and cost savings are immense too; comparable at least, and often far better, than using online panels.

Over the past year or two discussions around automation, agile research, AI and the “cheaper, faster, better” paradigm have ruled much of the discussion about the future of MR. Generally the assumption has been that the context for those trends was online, but that was a mistake. Reconnect Research embodies all of those trends and brings telephone research back to the table as not just a viable method, but in many cases the best one.     

As an old school telephone and IVR researcher who thought the best days of those approaches were a decade in the past, I am thrilled to find out that I am wrong and that Scott and his team are innovating to make them an important part of the researcher toolbox again.               


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Feelings. Nothing More Than Feelings.

Stephen Needel share his thoughts on a recent Quirk's article about a new voice analysis system.


By Dr. Stephen Needel

Feelings, a 1974 song by Morris Albert, might be the worst song ever to hit the charts (at least for males in the early 70s). A recent Quirk’s article (http://www.quirks.com/articles/2016/20160808.aspx) about feelings may not be the worst article they’ve ever published, but it ranks up near the top. The author is flogging a new voice analysis system that claims to detect the passion in response to a new product concept and enables better performance forecasts. Full disclosure – what I know about audio engineering and sentiment analysis would fit in a cocktail glass and leave plenty of room for my martini. The good news – you don’t need to know anything about either of these topics to appreciate the lack of empirical evidence in this article.

The article starts with, “Fundamentally, consumers adopt new products and services that improve their lives.” This is by no means fundamental; neither my new Smucker’s Chocolate Coconut Ice Cream Topping nor my new Gia Russa Bolognese Pasta Sauce is likely to improve my life, although they both taste good. Most things in our pantries are not life-altering, and food is what we all buy most of on a transactional basis.

The author believes the “enormous question on the table…is how to identify product concepts that have high probabilities of building deep emotional connections with consumers.” No, the enormous question on the table, from a new product forecasting point of view, is how to better predict new product trial; that may or may not involve emotions. “Consumers do not talk about products using multi-point scales”, the author claims, “They are unnatural modes of expression.” Consumers don’t talk much about products at all unless we ask them – they have better things to do with their lives (except those who live their lives on Facebook). A well-constructed set of scalar items is no less sensitive or less informative than the open-ended questions they would have us use. Neither modality is more or less natural to consumers.

In a comparative test, “the language-based sentiment metric [open end] yielded a coefficient of variation that was five times higher than the scalar method.” As if this is a good thing. What they obtained was a highly variable metric relative to scales, and that’s not very good for a prediction exercise.  By the way, we do not expect much variation from a 5-point scale either, unless the product is particularly polarizing. And we won’t mention the issues of a COV with a non-ratio scale or the wisdom of asking 35 purchase intent questions at one time.

In lauding their voice analysis method rather than the typing-an-open-end-answer, success for the former is claimed because respondents produced 83 words per stimulus compared to the typed 14 words. I can write a scathing review of a restaurant in lots of words or I can just say “It Sucked!!!!” I’m pretty sure the latter conveys exactly how I feel about the restaurant and why you shouldn’t go there. Word counts are a ridiculous measure of anything.

There’s a lot of fake math in this article surrounding the audio sampling rate – just ignore it, it’s wrong.

Using voice analysis, the author reconfigures the maximum trial potential of a product as the subset of respondents who (a) express positive sentiment towards the concept, (b) show positive activation (an expressed desire to do something), and (c) do so in a passionate way (as defined by the voice analysis). Spoiler alert – there is no validation for this assertion. I’m not saying we, as an industry, are great at predicting new product success. I do, however, expect someone who’s claiming to have a better way to do this to show us some data to back up the claim.

I’m sure this company wants to know what I’m feeling about their new voice analysis approach. I’m not a likely adopter, because while I experienced positive activation in a passionate way, my sentiment is anything but positive.