Category: customer

  • Campaign Pre-Test

    Campaign Pre-Test

    One of the types of qualitative research we’ve carried out the most over these 25 years in the market is the pre-testing of advertising campaigns — whether for films, concepts, or even graphic pieces.

    Our analyses and results seem to be on the right track, judging by the volume of work and the steady return of agencies and advertisers for other projects over time.

    Starting Point: Focus Groups

    Like most players in the market, we typically rely on focus groups, usually segmented by age group, socioeconomic class, and — most of the time — geographic region.

    For smaller-scale evaluations, we run an average of around 9 groups. For larger national campaigns, demand often exceeds 20 focus groups.

    The use of verbatim quotes with edited audio — capturing participants’ actual statements and positions — was an innovation we developed that added a great deal of information and understanding to the comments and recommendations we present. Beyond boosting persuasive power, these audio clips consistently made testimonials feel real and credible, adding feeling and human nuance to the situations and considerably enriching our presentations.

    So much so that, on countless occasions, the interest of agencies and advertisers ended up concentrated almost entirely on the audio clips themselves, given the strength of the messages and information coming directly from the target audience.

    The Limits of Focus Groups

    Even so, in several of these pre-tests it wasn’t always possible to get a clear, unambiguous answer to questions of a subjective or emotional nature about the campaign concepts.

    The overriding impression was that a more adequate, reliable metric was still missing to support these kinds of questions, since focus groups alone weren’t always enough to resolve every doubt — for advertisers and agencies alike.

    Among the cases where responses weren’t always satisfactory: the impact of the campaign on those watching the commercial, or the extent to which certain positions were actually perceived as “security,” “trust,” or even what message really got through.

    On an exploratory basis, we tried complementing focus group assessments with individual footage of participants, evaluating their reactions through changes in facial expression — but without much success, since the variations were barely noticeable.

    We also tried other biometric measurement techniques, such as GSR (galvanic skin response), used abroad to predict box office performance for films and plays — but again with results that fell short of expectations and were hard to interpret for non-specialists.

    The Path to Implicit Testing

    At international conferences, both ESOMAR’s and NMSBA’s, we’ve consistently looked for new options that could add supporting elements to these questions in a way that’s transparent and clear for agencies and advertisers alike.

    This drive at Checon Pesquisa to innovate and diversify our approach — in pursuit of a better understanding of how people react to advertising campaigns — led us to implicit testing.

    Implicit association tests were originally developed as a tool to explore the unconscious roots of human thought and emotion, since people often struggle to express their own emotions, or don’t even know for certain what’s going on in their own heads.

    These challenges become even more pronounced with lower levels of education, or when people are shy — and social desirability bias (the tendency to give the “politically correct” answer) also shows up fairly often, risking the accuracy of the evaluation.

    The case for using implicit association tests in pre-test evaluations rests on their ability to measure responses, ideas and beliefs at a subconscious level, without relying on rationalization that isn’t always “genuine.” What’s more, the results are easy to interpret, with no psychology background required, while still being flexible enough to dig into the specific aspects of each individual campaign.

    The Checon Methodology

    Under our methodology, implicit tests take place after the focus groups, which serve as a reference point for developing the testing protocol — pinpointing exactly which aspects still haven’t been properly clarified.

    We recommend running implicit association tests with the same participant profile, split into a control group (people who haven’t seen the campaign) and a second group who see the material (film, spot, or graphic pieces) right before testing. This makes it possible to set a very clear comparison standard for assessing the impact of aspects that focus groups didn’t fully clarify.

    Each implicit association test generally runs 3 to 5 minutes and can assess up to 5 distinct attributes — usually more than enough for the demands we’ve encountered.

    We recommend a minimum of 30 tests: 15 typically with the control group, and another 15 with participants who watched the same material presented and tested in the focus groups. This number can be adjusted for specific situations that call for more granular results (by social class or age group, for example), or when comparisons against competitors are also needed.

    Advantages of the Method

    Among the advantages worth highlighting: needing far fewer focus groups, avoiding the repetition that typically sets in from around the 10th group onward, and focusing effort on measuring very specific aspects.

    Another strong point is having a tool that can evaluate beyond the conscious and rational — with metrics that, while not intended to replace quantitative research, complement focus group results.

    The result is savings in both processing and analysis time, as well as in overall investment, since implicit association test results are available immediately.

  • Perspectives for the use of facial expressions

    Perspectives for the use of facial expressions

    Facial Expressions

    The human face (facial expressions) consists of a vast panorama in constant movement of what is happening in our most intimate state of mind, with a considerable diversity of nuances and great complexity.

    Humanity has always been developing, in a natural way, the ability to understand the facial expressions of others, initially as a survival strategy (friend / enemy), and currently aiming at relationships of all kinds.

    Based only on sensitivity and intuition, we all spend a considerable amount of time interpreting, analyzing and reacting to the perceptible signs of facial expressions of bosses, spouses, teachers, partners, co-workers, strangers, relatives. , etc.

    With the technology and the capacity of data processing increasing, the face of each one of us and the information that is transmitted spontaneously for some time has ceased to be just the way we present, relate or behave in public.

    Far from merely signaling our momentary state of mind or our reaction to situations, people or facts, this whole set of data has been increasingly treated as something strategic and object of intense studies.

    The use of facial expression analysis continues to grow, whether with Apple using it to unlock the i-phone, in churches in the United States trying to attract believers, in England identifying those responsible for shoplifting, with the police in Wales arresting suspects at football games in China identifying undisciplined drivers, allowing tourists access to certain attractions, among others.

    In the medical field, some applications are very promising, such as the early diagnosis of genetic diseases such as Hajdu-Cheney syndrome, in hopeful attempts to treat autism, in determining whether a person is depressed or whether the pain is real or psychological.

    All these new features have sparked heated discussions regarding the invasion (or not) of privacy, since, for example, the identification of sexual preferences reached 81% of correctness by the algorithm, while people (without the resource) 61% of the time they made that identification correct.

    There would therefore be a potential risk of discrimination on the part of companies, for example, in the recruitment of employees, so much so that legislators in some countries in Europe are already moving towards considering biometric data as information belonging to the people themselves and not something. in the public domain.

    But, going back to the types of applications that would interest us, the fact is that facial expressions, most of the time, communicate more and better than words, either because they are spontaneous, sincere, natural, because they reflect the real feeling or emotion , and above all because they are not rational.

    It is thus an important resource to be used in a complementary way to the other existing techniques in neuroscience and market research, in order to understand what people do not know, cannot or may not want to verbalize.

    Paul Ekman was the pioneer in the analysis of facial expressions with his research even in the 60s. Among his greatest contributions, there is evidence of the existence of at least 6 human emotions, which can be identified regardless of gender, age or even of culture.

    This study resulted in the decoding of these emotions in different combinations of 46 types of facial movements that allowed different applications from animations to lie detector.

    With the advancement of technology and the greater capacity for processing information, the uses of this knowledge have expanded considerably. Basically the algorithm consists of measuring the movements of a series of points virtually created on the participant’s face in relation to other fixed points.

    Thus, the position of the mouth, lips, cheeks, eyebrows, eyelids, wrinkles etc. they are constantly compared with other points (like the tip of the nose, the chin), with the pattern of the facial expression of each one and also with a reference table, allowing the identification of emotions such as joy, confusion, frustration, surprise, fear, anger, disgust, etc.

    In studies that Checon has already carried out, it is possible to assess how much a particular commercial is pleasing (or not), to what extent this involvement is positive (or not), in which excerpts from the commercial are necessary adjustments or reinforcements, all without even asking the participants , always in a clear, objective way, which can be understood even by those who are not even in the area of ​​neuro or research.

    The same types of conclusions are possible in the evaluation of people’s reactions to a political discourse, in order to understand if there is understanding or engagement, which are the passages that need more clarity, if there is approval, acceptance, always in a non-invasive way, without even not even interact directly with the participants.

    In works on website usability, the difficulties in performing certain tasks are evident with negative facial expressions, or with expressions of anger, doubt, dissatisfaction or frustration, even if not admitted in the participants’ ‘rational’ testimony.

    Another very interesting use of facial expressions is the analysis of consumers’ reactions to store windows, making it possible to identify the impact caused, the strengths and weaknesses of each option, even the attractiveness as an ‘invitation’ to people for the inside the establishment.

    Not to mention the food industry analyzing facial expressions in tasting tests or preferably between products in blind tests, or in banks evaluating reactions to ATMs, or automakers evaluating the behavior of drivers driving or identifying signs of drowsiness behind the wheel.

    Micro expressions offer a valuable contribution in the evaluation of decision making, being able to predict whether a certain purchase will be made (something that a flash of disgust can deny), or if a certain price is appropriate (something that an expression of joy can confirm and one of anger may disprove).

    Since, in most cases, these micro expressions are not even perceived by the participants themselves, because they are spontaneous reactions, they are more reliable predictors than the (often) politically correct answers (not infrequently) that compromise many of the predictions of sales.

    The choice of a candidate and the intention to vote also fit perfectly into this assessment of decision-making in a seemingly more reliable way than the mere rational statement by voters.

    There is impressive data as to the correctness of the voting intentions of American voters in the 2010 presidential election, based only on the facial expressions collected in the Obama & Romney debate that reached 73% confirmation, without asking any questions.

    More information:       The Economist – What machines can tell from your face

    The New Yorker – We know how you feel

    Paul Eckman – Facial Action Coding System