The Landscape Of Emotion

Measuring emotion is increasingly straightforward – interpreting the results still requires some intellectual subtlety.

Emotion, and the research techniques that measure it, remain hot topics in market research. Many of you will have read of Brainjuicer’s Valentine’s day card to Millward Brown, celebrating the latter’s purported “embracing” of emotion as a key marketing driver. A lot of fun for those of us that are observers of course, but leaving aside the question of whether this unduly caricatures Millward-Brown’s approach to emotional analysis, I detect in the discussion, another caricature: the reduction of ‘emotion’ to something simplistic and monolithic. If only we can measure this emotion stuff, we will ‘have the answer’. Maybe, if we can find the right emotional measurement machine we researchers can all retire?

As some of you know, David and I are working (with nViso SA of Switzerland) with exactly that: an “emotional measurement machine” that directly measures people’s emotional response to stimuli via a method called 3D Facial Imaging. Here’s a chart based on 3D Facial Imaging data – I’ll explain it’s significance later in this post, for the moment just note we can directly measure specific types of emotive response with a standard computer and webcam.

Hills & Valleys in The Landscape of Emotion (See Below for Explanation)

This is, I would argue, much more accurate and granular than any questionnaire based method. Yet, despite being thrilled by the results we are obtaining, I would not argue that we have reached some sort of  “deus ex machinamoment, where researchers and subtle interpretation become redundant.

To describe emotional response is, on one level, quite simple: if you want a rough, overall understanding of people’s reaction then those old measures of “Overall Liking”, “Persuasion”, “Impact” etc. are actually not as bad as is sometimes implied. Mostly (putting aside stimuli where social desirability etc., impacts reaction), if you ask people if they like something they can tell you.  Usually there is a relationship between measures like Purchase Intent and the direct measures obtained via facial imaging. The issue is, as it has always been, not so much that conventional measures are “wrong”, but that they are “gross” and often misleading. To describe emotional response usefully is to picture a complex series of interrelated reactions. Indeed, as we delve into direct measurement, we seem to be finding an increasing, not decreasing, need for thoughtful analysis and interpretation. Among the areas we are investigating:

  •  Differences in reaction between demographic and usage groups are not only often greater than we might expect, but differ markedly in build/shape and “style” of response (e.g. what specific emotions are evoked by males and females in reaction to particular images). I’m sorry, but direct measurement will not necessarily mean reduced sample sizes!
  • Build and pattern of response (e.g. how long it takes to get an emotional reaction or when Surprise occurs in relation to Happiness etc.) is probably more important than any single “overall” measure of response or a basic measure of “attention”.
  • Material with similar levels of overall emotional response, reported impact etc. can vary markedly in terms of direction (Valence) or specific emotions evoked (Sad, Happy, Anger etc.)
  • While directly measured reaction to a stimuli does tend to relate to overall self-reported response, the reality is that the differences in direct emotional response challenge the way we usually interpret scaled responses. In some cases, people with “definite buy” intentions can be seen to have massively higher levels of response to a brand’s ad than those who claim they will “probably buy”, in other cases the differences between these groups is driven by reaction to a few key seconds of message. Arbitrarily combining people into “top-two” (let alone top-three!) box categories looks increasingly suspect to me and likely to disguise all sorts of important differences.

I’ll illustrate with a couple of example charts. The first (at the top of this post) is from a pilot study we did in China on a milk powder product. It simply shows, on a second-by-second basis, “Happiness” reaction to a TV ad, broke down between those who self-reported liking the ad and those who gave a neutral/dislike reaction. (Researchers familiar with the China market will know that ‘neutral’ reactions tend to actually reflect a negative reaction, so it is not uncommon to combine this with ‘dislike’). It can be seen that the overall ‘shape’ of reaction is not that far apart between the two groups – but also that the “Likes” are a bit higher than the “Dislikes” in the middle and end of the ad (key message moments), and that paradoxically, they are lot lower for one brief moment at second 19. (I won’t expand on the latter point here, but in a paper we are preparing for ESOMAR’s APAC conference in April we will argue that this fleeting moment of ‘negativity’ is actually key to the success of the ad).

The second (bar) chart is derived from a study of six bank ads we carried out in conjunction with Saatchi & Saatchi. In this study we asked people some questions on their attitude to the brands, designed to stand as surrogate measures of Kevin Robert’s well-known ‘Lovemark’ dimensions.

A Bank Turns off Their Loyalists

In this particular ad (which to be honest, was not very strong in my view), we found that those with a higher Lovemark score (red bar) for the bank actually had less positive emotional reactions to the commercial than those whose self-reported attachment to the bank was weaker. Moreover they differed markedly on specific emotions, higher on Surprise, much lower on Happiness. It is as if their attachment to the brand firstly attracted their attention to the ad, then the content particularly disappointed them – they seem to have been let down.

I am not (yet) claiming definitive conclusions based on the work we are doing – facial imaging is new and we are still accumulating examples. However I am totally convinced that building a picture of emotional response is more akin to landscape painting than to designing a simple roadmap. Shape, style, specificity and intensity of response matter. So to does the personal context in which people interpret emotional material – men, women, category-users, brand lovers and rejectors all bring their own emotional predispositions that colour the picture and create layers of response that varies the marketing impact. It was always misleading, I believe, to pretend that reaction to ads or concepts could be summed up in a single measure – as we delve deeper into the landscape of emotional response we are discovering just how dangerously imprecise all such ‘universal’ indicators really are.


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