now browsing by category
Getting insights from data – getting to the “why?”
When you ask consumers about your products, make sure you are using the correct research method.
You may have read about the now famous story of Herman Miller’s Aeron office chair. He developed the chair through the cycle of development, market research, more development, more market research, and so on. Finally, deciding on the design we see now. His research focussed on asking consumers two questions (1) please rate the chair on comfort and (2) please rate the chair on aesthetics. His plan was to use the design which received the highest ratings on both. The trouble was that any design he created got very low ratings on both, even though in his mind he thought he had designed the perfect office chair. Notwithstanding this poor consumer feedback, he went to market…and it became the top selling office chair!
The moral of the story? When you ask someone to rate something new, if it is not simple and obvious or they really can’t verbalise how they feel, they will say they don’t like it. Often consumers will choose the least sophisticated option when they are forced to say why they like it.
The psychologist Tim Wilson has carried out a lot of research showing that when people say they actually like something they often make up a story – an explanation that has no resemblance to reality (in a typical experiment it is the manipulation that determined the liking rather than the story the participant made up).
Consumer Insights – Beyond Liking
To yield more effective consumer insights, we need to go beyond what is immediately visible and dig deeper. We need to examine why the consumer is doing what they are doing in their own world. Insights that are fresh, true, targeted and actionable are those we need to develop.
Implicit research methods go beyond liking. They seek to ask why a consumer prefers this brand, product, or packaging rather than that brand, product or packaging. Implicit conusmer testing is able to characterise the feelings the consumer has towards the products, going much deeper than simple liking and disliking. The method is very consumer focussed and bypasses those biases that can influence verbal responses. Implicit tests are very difficult to fake, hence they provide a pure read out of consumers’ feelings.
New product development should be cyclical: design the concept, test the market, design the prototype, test the market, develop several design options and test the market. Before implicit technology, this was a slow process, but now with the aid of our IMPRESS platform this product development cycle becomes a reality. We can turn around results in 48 hours, so your development team can get on with the business of optimising the product.
Get your implicit research done in a split second with the IMPRESS Platform
Split Second Research announces its new IMPRESS Platform for the creation and instant analysis of implicit reaction time tests.
Ask us to create your test for you OR do it yourself – and get the results of your market research in 48 hours.
The IMPRESS platform is used for creating implicit reaction time tests in market research and for other research areas too, such as voting preferences, and social attitudes like racial bias, gender bias, and so on. Online, objective and cost-effective, implicit tests capture immediate, and intuitive responses to brands, packaging, product claims, advertising evaluation, brand tracking, brand positioning, new product development, and a vast array of other marketing related outputs.
IMPRESS is a platform for creating an implicit reaction time test quickly and effortlessly.
It is easy to create a test, either from scratch or by duplicating an existing project.
You can also create traditional survey-type questions. This is useful if you want to add your own screener or demographics questions. Choose from a range of question types and capture information about your respondents and their buying habits before they take the test.
Analysis can be carried out instantly.
Split Second Research offers a free training session to a technician or the main admin user at your institution or company.
The system comes with an online user manual and we offer email support with a maximum 48 hour response time.
Get your implicit research done in a split second
Dr Implicit gave a sprightly performance at the Shopper Brain Conference in Amsterdam recently. The focus was on how in-store promotions can often adversely affect a brand, especially in terms of how the brand is perceived. This can have a long-term effect on a brand’s health, especially its brand equity. The research found that for most products, a price promotion can adversely affect the brand’s perception of quality. In other words, it may ‘cheapen’ the brand, which is not good for category leaders and those for whom quality is marketed as a brand value. For other kinds of products, attributes reflecting social influence, such as popular, trendy, modern, were affected negatively, and indeed in some cases ’embarrassed’ was triggered by the promotion. Taken together, these suggest that offers can make a brand lose out on appearing to be the most popular brand; consumers may even feel a sense of embarrassment when buying such products when they are on offer.
For some other types of products brands went unscathed. Indeed, some offers can make consumers feel proud to be loyal to the brand, welcoming the offer as a reward and an opportunity for others to appreciate the brand as they do. So, the research uncovered mixed findings, and some types of offers, such as strike-though pricing (i.e., £
1.50 now £1.00) worked better than others, such as offers based on quantity like BOGOF (buy one get one free).
This research is ongoing and there are many more research questions that need to be addressed, such as, the effects of other types of offers (Special Offer, and Win a prize), a broader range of product verticals, necessities versus luxury items, the colours and fonts associated with different types of offers, seasonal offers, and much more. Keep checking this website for news about this research.
Dr Implicit reveals all !!
Update: January 2018
Further analysis has been carried out. If you want to receive a report of this research please get in touch. The brands we assessed were Activia Yoghurt, Philadelphia Soft Cheese, Tropicana Fruit Juice, Heinz Baked Beans, Fairy Washing Powder, and Tresseme Shampoo.
Split Second Research Announces its New IMPULSE Test
Split Second Research’s IMPULSE test is an implicit test for analysing audio-visual content, such as TV adverts, radio adverts, movie trailers, programme excerpts, online videos, promotional videos, training videos, political speeches, and so on. It provides a moment-to-moment analysis of emotional reactions to the content. IMPULSE can read up to six different emotions during the same test.
A typical output is shown in the embedded link below. You can play, pause, rewind, restart at any time so that you can examine the emotional reactions to the video on a moment-by-moment basis. These are completely implicit reactions to the content. In the example, below six emotional responses are shown to a video of a gorilla spinning around in a pool at Dallas Zoo (video courtesy of Dallas Zoo & BBC News).
Click on the image to view the analysis (this will open in a new window).
American Chamber of Commerce in Singapore
Dr. Eamon Fulcher presented at the American Chamber of Commerce in Singapore in May. The focus of his talk was on future developments in neuromarketing and new implicit reaction time techniques.
10 Reasons for using an implicit survey
How it works
This post also answers the related questions and issues that we are often asked:
10 reasons for using an implicit reaction time survey.
10 reasons for using an implicit association test (IAT) in consumer research.
10 reasons not to solely rely on a traditional survey.
- Traditional surveys don’t always have a very good way of measuring what is in consumers’ hearts.
- Implicit surveys get right at consumers’ hearts. It’s exactly what the method is all about.
- People often tell you what they think you want them to hear in a traditional survey.
- In an implicit reaction time survey, people don’t explicitly tell you how they feel, they reveal it to you in their reaction times.
- People often fail to make real discriminations in a traditional survey, hence all values converge around the average score.
- In an implicit survey people cannot influence their scores (they can’t play the game to influence the results). Hence they make highly discriminating responses, and we often get very distinctive profiles of a brand/pack/advert and so on.
- A traditional survey can often tell you which product or brand is most liked but not why.
- An implicit survey not only reveals the best product, pack, brand, advert, and so on, it can tell you why because it will use 20 to 40 attributes and these are typically highly discriminating. The attributes provide you with a detailed profile of each brand/item.
- In a traditional survey, people often can’t verbalise how they feel but they are required to answer anyway.
- In an implicit survey, people don’t make evaluative judgements, they just try to press the correct keys and so allowing inferences to be made about how they feel – it captures their inner feelings.
Take the Dads4Daughters Test
Split Second Research sponsors the Dads4Daughters campaign in collaboration with Blinc Partnership and St Paul’s Girls’ School.
Take the Dads4Daughters Test – How gender biased are you? Click the logo below:
The test has been featured in:
If your company took part in the test and you would like to have your company’s results, please contact us via firstname.lastname@example.org or +44 (0) 7878455944
The Dads4Daughters Test is based on a commercial test designed to measure attitudes towards brands, TV adverts, and other marketing materials. These attitudes are measured implicitly, that is, they are inferred from reaction times to words images presented on the screen. The test bypasses the need to ask explicitly about someone’s views or attitudes. This is important because often what people say they will do or what they tell you about how they feel is often at odds with how they behave! Furthermore, in difficult issues such as sexism or racism, people may be reluctant to tell you how they truly feel and in some cases they may even hold certain attitudes that they are unaware of until they are provoked.
The commercial test is itself based on the evaluative priming paradigm in academic research (e.g., Fazio, et al., 1986)1. The first phase of the test is to detect target emotion words as belonging to either one category (e.g., Happy) or another (e.g., Sad). In the second phase, the task is the same but the target emotion words are preceded very briefly by ‘primes’. These primes are either congruent with the target word (the prime is Joy when the target is Happy, or the prime is Gloomy when the target is Sad) or incongruent (the prime is Gloomy when the target is Happy, or the prime is Joy when the target is Sad). The task can be performed quicker and with fewer errors when the prime and the target are congruent than when the prime and the target are incongruent.
In the Dads4Daughters version of the test, the targets were female and male words, such as She and He. Primes were 24 words related to professions, roles, personal qualities, or career fields (e.g., engineering, manager, leader, and so on). Trials are divided into female trials (where the target is a female word that invites a specific response, e.g., press D on the keyboard or swipe left) and male trials (where the target is a male word that invites a different response, e.g., press K on the keyboard or swipe right). The logic is: if the test-taker subconsciously associates a career field (e.g., engineering) as being male, then they will be quicker to detect the male target on ‘male’ trials than the female target on ‘female’ trials when the prime is engineering.
About 10,000 people have taken the test and from all walks of life (from bus drivers to CEOs).
Note that the test does not require an explicit evaluative judgement, there is always a correct answer on each trial. Also, people often think they can out-game the test – but in fact there is no way to ‘play’ the system because the task is always the same – it is a test that is difficult to fake. The way the test measures an attitude is not to do with accuracy or generally how fast the response is, but through a comparison of reaction times. So, the association between say, engineering, and the concept Female or Male is detected (or implied) by differences in reaction times to detect the female and male targets when they are both preceded by the same prime (engineering).
For each test-taker, the result is a measure of adherence to traditional views of gender roles – that some roles are associated with men and others with women. The more a test-taker associates skilled professions, or more senior roles, or more desirable personal qualities with men and not with women, then the stronger is their measure of gender bias.
If you would like your employees to take the test and you would like to know how they compare with other companies, please contact us via email@example.com
1Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. doi:10.1037/0022-35220.127.116.11
Which is better the IAT or Affective Priming?
Comparison of two Implicit Association Tests
Affective Priming versus Implicit Association test
One criticism of the IAT is that it may merely tap ‘extrapersonal associations’ – it may be a measure of culturally shared assumptions rather than personal attitudes. For example, this would argue that an IAT that detects my strong association between nurse and female is just reflecting my knowledge that society has historically given the role mainly to women, rather than this being my own personal automated attitude (i.e., that nurses ought to be female).
Another criticism of the IAT is the reliance on the switching of blocks. In the first phase, (and in a hypothetical gender bias test), the word female is paired with gender stereotype attributes, e.g., nurse, and the word male with doctor. After the respondent has learned to do this quickly, the categories are then reversed, so that the word female is now paired with words incongruent with the gender stereotype, which make the test suddenly more difficult. This yields significant reaction time differences in the second block – it is a harder task than the first block (not because of an inherent gender bias attitude, but because the respondent had already learned the correct responses, but in the second block had to unlearn them and then re-learn the new responses). This is worrying because it means that the effect is too prone to changes in procedural issues.
A further problem is that in the IAT, only two dichotomous concepts can be paired (e.g., men vs women, gender stereotypical vs not gender stereotypical), which can be very limiting when one wishes to explore their relationship in more detail. Consequently the IAT produces a single global gender bias score. However, in affective priming one may have more than just a global score and can divide attributes into dimensions and hence provide a more detailed picture of such a relationship. So for example, a gender bias test using affective priming will be based on a large number of ‘attributes’ and these can be categorised (e.g., roles, personal qualities, professions, and so on) and this kind of test produces a score for each dimension. Another statistical advantage of the affective priming approach is that one can conduct a factor analysis on the data to reveal how attributes are grouped (grouped in the minds of the respondents who took the test). Hence it can yield groups of attributes that together are likely to represent an important feature of the concept begin measured (e.g., nurse, carer, ethical, reliable, hardworking, gentle, and female) – of course this example is too obvious and not so informative, but some patterns can emerge from this approach that weren’t predicted. This is much harder to do with the IAT.
Finally, the reasons why affective priming works is because it is based on assumptions that are highly compatible with what is known about how the brain processes information. Neural network models of the brain are based on mental associations – the stronger the association between two concepts (e.g., female and nurse) the quicker one concept will mentally trigger the other. So that’s four reasons why affective priming is the preferred approach, particularly if you are looking to understand the complex processes in the minds of consumers.
What can implicit reaction time tests tell us about consumer attitudes and intentions that traditional, explicit, methods can’t?
Implicit reaction time tests, whether based on the implicit association test (the IAT) or on affective priming are on the rise in the world of market research.
Implicit reaction time tests hold the promise to unlock deep seated consumer attitudes. Using the analogy of an archaeological dig (as a colleague of mine likes to use), implicit tests, like Split Second’s Impress Test, can help uncover the hidden treasures buried in the consumer’s mind. This is something that market researchers and brand managers have been looking to use for some time, given the weaknesses of traditional methods.
The way implicit reaction time tests can tap into deep seated feelings has been likened to an archaeological dig.
Yet, market researchers and brand managers can’t work on a promise alone. There is too much to lose – not just the research budget, but the financial consequences of bad research. So an important question is what can implicit reaction time tests tell us about consumer attitudes and intentions that traditional, explicit, methods cannot?
One way to test whether implicit reaction time tests, such as Split Second’s Impress Test, can measure anything useful about consumer attitudes and intentions might be to look for the predictive ability of implicit and explicit tests – are there circumstances in which either or both of these measures are strongly related to the purchasing behaviours of consumers.
There are numerous examples in the peer-reviewed literature demonstrating that in many circumstances implicit attitudes are better predictors of subsequent behaviour than explicit responses provided at the same time. For example, Steinman and Karpinski (2009) found that implicit but not explicit attitudes towards the brand GAP predicted GAP patronage and buying intentions. Brunel, Tietje and Greenwald (2004) showed that implicit methods can detect attitudes about brands that explicit measures cannot (e.g., how different races advocated different patterns of brand preferences implicitly but not explicitly).
Other research includes Priluck and Till (2009) who found that explicit and implicit measures were both good at detecting attitudinal differences between brands when the difference was large or obvious, but only implicit methods could detect differences when they are less obvious. Other research shows that implicit methods in a consumer context are difficult to fake. For example, Chan and Sengupta (2010) found that while the claims of an advertisement were dismissed, implicit responses revealed that the ad had induced favourable attitudes to the brand.
An interesting study published in 2010 by a team of researchers in Italy headed by from Michelangelo Vianello, shows how important it is to assess true feelings as opposed to those that people like to state in order to present themselves in a favourable light. College students were given two different measures of conscientiousness, one was a traditional explicit personality self-report questionnaire and the other was an implicit reaction time test whose attributes focussed on conscientiousness. Half of the students were further told to imagine that they were being tested for their ideal job (one with a good income, low effort, and so on) and the half were not told this. Those with the job-story scored higher on conscientiousness but only on the self-report test. This shows that they could give biased answers and present themselves in a very favourable light. Yet, both groups scored about the same on the implicit measure – this is remarkable because it shows that the implicit measure was not so easy to fake.
Academic studies like this provide very strong evidence of the usefulness of implicit reaction time tests.