For a number of years now Repères has been conducting research on non-verbal communication. The idea behind this research is to acknowledge the importance of the body in our research processes. It is often said that non-verbal communication accounts for more than 80% of human communication and yet practically all market research relies solely on the analysis of verbal communication via respondents’ statements. Conversely the observation techniques used most often tend to focus on behaviour to the point of disregarding respondents’ statements.
Our approach is to bring to research a holistic vision of the respondent integrating verbal and non-verbal communication.
Our first experiments, conducted in collaboration with Franck Saunier, were presented at the Esomar conference in 2008 (Cf. article, under ESOMAR copyright) and at the UDA ADETEM conference in January 2009 (Cf. interview – in French - with Franck Saunier on François Laurent's blog).
The aim at this stage was to demonstrate how a qualitative approach could be enhanced by a detailed video analysis using the techniques of slow motion and of the identification of asynchrony between gestures and words: This provides us with access to communication that occurs without the subject being aware, and which when revealed offers the possibility of detecting in a statement what relates to an accepted notion and what on the contrary relates to a strong emotional implication on the part of the subject; in other words non-verbal communication is used as a tool to identify the relevant verbal message .
Since then we have pursued our research by applying these findings to a quantitative approach for the screening of concepts: Would the integration of a non-verbal reaction to a concept enable us to better identify those concepts that are more appealing?
The initial findings we obtained based on a pilot experiment were extremely promising: We built a first benchmark for non-verbal reactions to a concept (based on a combination of several objective behavioural criteria which were relatively easily codified). This non-verbal benchmark makes it possible to identify a level of emotional implication regarding a concept and when it is combined with the classic statement of interest it provides very significant added value to the analysis of the results.
Thus, from our pilot test, 8 ideas of new products were tested:
An analysis based purely on statements would have led us to conclude as follows:
1 effective concept, 5 undifferentiated “average” concepts, and 2 concepts rejected
When we combine verbal and non-verbal communication, 3 routes are identified as interesting:
. The concept stated as being the most effective also generates strong emotional implication
. 1 of the undifferentiated 5 routes also stands out because of the strong emotional implication it inspires, whereas the 4 other concepts generate little interest expressed solely by statements
. 1 of the 2 concepts generally rejected deserves selection: It proves to have a strong segmenting impact and generates strong emotional implication among all people who claim to be interested, even if they are a minority.
At the same time one of the other deliverables of the protocol is the possibility of updating the non-verbal grammar used by the respondents when they describe the inferred benefits of these new products.
We are therefore able to identify the routes that are the most promising and to recommend to the advertiser the appropriate non-verbal grammar to use to effectively communicate about the benefits of the products.
These results prefigure a strong potential for this type of approach and have led us to apply for OSEO support, which has been given to us, and which will enable us to pursue our research in two directions:
. To test the relevance of the approach within the framework of sniff-tests and taste-tests, with the creation of benchmarks for emotional implication based on non-verbal communication,
. To look for technological partners to validate the possibility of automating the codification of non-verbal reactions in order to improve productivity during analysis.
To be continued…
1) OSEO is a public organisation for the support of innovation.