FEATURE21 June 2011

A picture tells a thousand words

Tapping into the imagery that we all use to understand the world around us offers researchers a way to get at consumers’ emotional responses, says BDRC Continental’s Richard Smith

Res_4005451_Richard_Smith

We’ve been using ZMET (the Zaltman Metaphor Elicitation Technique) at BDRC Continental for the past eight years. The technique works by getting respondents to collect images that reflect their thoughts and feelings in response to a stimulus, which might be a question, a phrase, an advert or an experience (such as a visit to a restaurant or a stay at a hotel). Respondents arrive at interviews armed with pictures they have found that relate to the stimulus, and these are used to inform the conversation.

ZMET, developed by Gerald Zaltman of Harvard Business School, is based on the belief that metaphor is central to how we think and feel, and that accessing the imagery that people come up with in response to stimuli provides a window on to the things we didn’t know we knew. One of its advantages is that it gets at the emotional dimension as well as the rational. Of course, in reality, these two dimensions are intertwined, but all too often we are used to separating them.

One might think that the variety of images out there would be virtually unlimited in the digital age (and we ask people to find non-literal images, which widens the pool), but it is surprising how often we see the same images reappearing. That might sound like a good argument for putting together a comprehensive image bank and getting respondents to pick from these – but in fact we have found many times that while the image may be the same, the interpretation by the respondent is quite different.

Researchers can be guilty of becoming too reliant on data and analysing responses on the basis of rational responses

For example, in a recent financial services study, many participants used images of trees. Trees are a potent metaphor for savings and investment products because they grow, often from small seeds. But or some respondents the tree was a symbol not of growth but of the ‘naturalness’ of investing. The other advantage of letting respondents find their own images is that they feel more connected to them and can unearth more layers of meaning. As respondents talk about each image, they find more levels on which the images have a deeper emotional resonance.

In a study about a casual pizza restaurant, one respondent brought in a picture of a bodybuilder. The primary reason for him choosing this image was to say that he felt this food was something he felt that he could eat occasionally whilst still keeping in shape. At a deeper level, he admitted that it was important to him to be ‘normal’, unlike the other guys at the gym who always ate health food. In his own words, ‘I’m a normal guy, I go to the gym, I have a girlfriend, I eat pizza.’ Both of these meanings relate to the idea of balance. The rationale for choosing the image was around the idea of nutritional balance – it’s OK sometimes to have a treat and still be healthy. However, more fundamentally, eating the food in question represented having a balanced life; not being obsessed by fitness to the extent that you don’t have a ‘life’.

ZMET provides the space respondents need, before and during interviews. It’s not about firing projective ideas at respondents and hoping something will stick. It’s about gently drawing from the respondent what is already within them.

Although the initial focus is on the images respondents bring with them as their pre-task, often some of the more interesting images are the ones that lie just beneath their consciousness. These are the images that they may articulate with their hands, or simply appear to be seeing in the distance as they speak. We find it useful to get respondents to talk about these images ‘as if’ they are real. As they bring them into consciousness these also reveal deeper emotions.

In one interview a respondent started talking about how music helped him to relax – he had brought along an image of someone sitting in a deckchair to illustrate this. As he spoke, he held his hands out in an embrace and seemed to be holding something. When we asked him to tell us what he was seeing, he said that he felt as if he were surrounded by a membrane that kept the rest of the world out and his own thoughts and ideas inside. This referenced the concept of a container and the notion of protection. Music represented a protective barrier between him and the stuff he had to deal with during the working day.

These are just a few examples of how images help to stimulate discussion of emotions at a deeper level, but what’s the value in this for clients?

Researchers can be guilty of becoming too reliant on data and analysing responses on the basis of rational responses. When we play findings from a ZMET study back to our clients, as well as connecting with the insights and implications, they form an immediate connection with the data, because what they are experiencing is the thoughts and feelings of real people just like them.

Richard Smith is director of qualitative research at BDRC Continental

2 Comments

13 years ago

Just like a Rorschach image. A picture tells a thousand words but one picture also tells a thousand different stories. Everyone brings different life experiences to their interpretation of brands and you simply can't assume that your interpretation will be the same.

Like Report

13 years ago

Humm, interesting article. I was directed to it by a colleague of mine. I've been working in the brand and design strategy space for 20 years and the techniques you described here are not new to us, in fact, we have been using them since the early 50's when the Applied Creativity group in Buffalo, NY first came up with them as a methodology for branding. These techniques were actually borrowed from Bauhaus industrial designers who first introduced them in the mid '30's when doing research for their projects. Curious to know how are these techniques any 'new' or different from the ones we have using? Other than analytical, i.e. quantitative researchers having just learned them and starting to see the value, I see no difference. But I may completely wrong.

Like Report