Measuring Without Needing to Ask: Using Implicit Measures to Predict Choice*

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1 Despite mounting evidence of people s inability to accurately report what influences their choices, marketing research today still largely relies on self-reports to guide marketing decisions. This article discusses how implicit measures can be used to understand consumer judgment and choice. Measuring Without Needing to Ask: Using Implicit Measures to Predict Choice* It s not the consumer s job to know what they want. Steve Jobs, on why Apple didn t do market research for the ipad2 Most of the time, people have no idea why they're doing what they're doing. They have no idea, so they're going to try to make up something that makes sense. Claude Rapaille, author of the Culture Code The Limits of Explicit and Power of Implicit Research across a number of academic disciplines continues to illustrate the limited value that measures that rely on conscious thought and deliberation have on predicting what people will actually feel and do. They are called explicit measures because they ask research participants explicit questions about their intentions or behavior e.g., please rate your intentions to purchase the following products The research shows that, in many instances, implicit measures measures that tap feelings and preferences outside of conscious thought can have a powerful impact on judgment and choice. Consider some findings from the literature. The most common way of gathering data to make product and marketing decisions is currently in disfavor. Among many business executives, consultants and researchers, relying on what consumers say they will do, or asking them how they feel (whether in surveys or focus groups), is increasingly becoming viewed as a poor basis for marketing decisions. The predictions have already begun as to when the survey question will drift into extinction. It probably would not have done much good to argue with Mr. Jobs that if only he had done his market research Apple would have developed a better product. Claude Rapaille counts half of the Fortune 100 companies among his clients and advises them on critical marketing decisions using a form of modified early childhood regression. Nice work if you can get it, I guess. For researchers who do not want to tell clients that collecting information from consumers is useless (as Jobs might have) or who do not want to spend three hours with adults laying on pillows connecting with their early childhood, implicit measures represent a promising alternative. Getting Divorced. Research by John Gottman and his associates has demonstrated that non-verbal measures collected during brief, videotaped discussions between married couples serve as highly accurate predictors of divorce three years later. Some of the key predictive measures include contempt (as exhibited through eyerolling and sneering) and stonewalling (as exhibited through silence and monosyllabic mutterings ). What is important to note is that these non-verbal measures are much more accurate predictors than measures of marital satisfaction collected through surveys. Suing a Surgeon. Work by Nalini Ambady, one of the leading researchers in the area of implicit cognition, has shown that a surgeon s tone of voice while interacting with a patient (in particular the surgeon s tendency to adopt a tone of voice that signals dominance or lack of concern) can predict the likelihood that a patient will sue that surgeon for malpractice. What is particularly remarkable about this finding is that the basis of the measure is a mere 40 second audio recording of a surgeon-patient interaction. *Portions of this article were presented by Sentis at MRIA s Net Gain 6.0 conference in Toronto on Jan. 25, 2012

2 Implicit Measures 2 Choosing a Career and Place to Live. Research by Brett Pelham and his colleagues has demonstrated that people s first names predict their career choices and places that they move to. For example, people named Dennis or Denise are more likely to choose dentistry as a career. People named Louis are more likely to move to St. Louis. People named Virginia are more likely to move to Virginia Beach, and so on. What appears to be at work here is implicit egotism our non-conscious tendency to prefer things associated with ourselves. Choosing a Candidate. In any election there are a group of undecided voters. Bertram Gawronski and his associates have shown that implicit measures can predict how this undecided group will eventually vote. They had a group of undecided voters complete the Implicit Associations Test (IAT) prior to Election Day. The IAT which will be described in more detail later in this article measures automatic mental associations between concepts. In this case, the researchers measured the automatic associations between the candidates and positive and negative qualities. These automatic associations predicted how they actually voted. What do these four studied have in common? First, they all predicted important outcomes divorce, lawsuits, career choices, voting. Second, they measured the influence of mental processes at work outside of conscious awareness. Judgments and choices were made based on implicit associations. Two Thinking Systems: One Fast, One Slow In contrasting implicit versus explicit mental processes, it is helpful to differentiate the systems that govern our mental lives. There is now widespread agreement that there are, in fact, two systems of mental activity that affect judgment and choice. System 1 is the fast, automatic system. Judgments and choices are made outside conscious awareness. System 1 has been called adaptive, intuitive, implicit. System 2 is the slow, deliberate system. Judgments and choices require effort and are made consciously. System 2 has variously labeled analytical, reflective, explicit. Most of the judgments and choices we make on a day to day basis are the product of System 1 activity. Implicit measures are good measures of System 1 activity. A Problem with Most Marketing Research Today If most of the choices we make are the result of the mental activity of System 1, and if implicit measures are indeed good measures of the influence of System 1, then it would seem reasonable to conclude that a great deal of marketing research studies would use implicit measures. Reasonableness, to date, has not prevailed. The vast majority of marketing research studies designed to understand consumer choice rely on System 2 thinking, asking research participants to explicitly state the reasons why they prefer particular products or intend to make certain choices. The problem is that people are notoriously bad at accurately reporting on the real reasons for their preferences and choices. As early as 1977, psychologists began to discover that people really don t have the ability access, and to understand how, their System 1 thinking automatic, non-conscious influences their choices. In their classic paper Telling More than We Can Know, Richard Nisbett and Timothy Wilson provided evidence of this human frailty and also illustrated another important fact people think they can explain the reasons for their choices accurately. When asked why they have made a particular choice, people will give you an answer, even though that answer cannot possibly be accurate. For example, when you ask consumers to taste test two beverages both which, unbeknownst to the consumer, are identical they will choose one they like best. And they will give you a reason for their choice e.g., it s tangier, more bubbly. (No it is not). Similarly, if you ask research participants to make a choice between five differently labeled strawberry jams, each of which actually contains the same jam, participants will choose one and explain why. However, it can be demonstrated that their choice was actually due to the number or position of each jam on the table, for example. The vast majority of marketing research studies still ask participants to explicitly state why they make choices. The problem is that people are notoriously bad at accurately reporting the real reasons for their choices. Old

3 Implicit Measures 3 Why Implicit Measures Work To understand why implicit measures work it is useful to think of the mind as an associative network in which concepts differ in their associative strength. For example, the concepts of beauty, health and youth are all closely linked, and are further away from the concepts old and weak. Among closely linked concepts, activation of one concept facilitates automatic activation of the others. Implicit measures tap this automatic association between concepts. The Mind as an Associative Network The Implicit Associations Test Weak Youth The Implicit Associations Test (IAT) works because it bypasses conscious deliberation and measures the automatic association between concepts. Developed by Anthony Greenwald and his colleagues in 1998, it is probably the most widely used implicit measure among esearchers. It also can be easily administered online and participants do not need any special software on their computer. The IAT is a binary categorization task. Participants must, as quickly as possible, categorize words or images into one of two categories. Response latency (or speed of categorization) is the key dependent measure because categorizing will be faster under conditions in which concepts are closely associated and slower under conditions in which they are not. Beauty Youth Health An example will help illustrate. Suppose that a company wants to choose a spokesperson to promote the company s services. The company has a set of attributes that they want the spokesperson to represent: reliable, clear, strong, fresh. The company is deciding between using a younger or older male spokesperson. Therefore, the IAT research question is What is the automatic, implicit association between younger/older men and the target qualities? Old Weak IAT Stimuli In this example the stimuli used in the IAT are as follows: (the full illustrated example is available at /blog) Concept A: Younger or Older Younger: Images of faces of younger men Older: Images of faces of younger men Concept B: Good or Bad Good: Reliable, Clear, Strong, Fresh (Target qualities) Bad: Unreliable, Confused, Weak, Tired (Opposites)

4 Implicit Measures 4 IAT Trial Blocks The typical IAT is administered in five blocks, or trials. Each block includes a number of categorization tasks. Participants are informed that they will be presented with a set of words or images in the middle of their screen. Their task is to correctly classify these words or images as quickly as possible while making as few mistakes as possible. Block 3: Test Block: Measure implicit association between Younger and Good; Older and Bad Below are examples of what the tasks look like in each block. Block 1: Practice categorizing for Concept A Block 4: Practice categorizing good / bad because they know appear in reverse order Block 2: Practice categorizing for Concept B

5 Implicit Measures 5 Block 5: Test Block: Measure implicit association between Younger and Bad; Older and Good Areas of Application The IAT was initially developed as a way to measure attitudes and feelings people have toward a variety of ethnic and racial groups that standard survey questions would not uncover. These standard survey questions tended to elicit socially desirable responses because survey participants are generally motivated to be perceived as tolerant, open-minded and accepting of diverse groups. What is more, people not only want to appear tolerant to others, they want to see themselves as tolerant. However, early work with the IAT showed that while research participants would express attitudes of tolerance and egalitarianism in response to survey questions, these same research participants could have strong implicit negative reactions to the very ethnic and racial groups whose virtues they trumpeted explicitly. Interpreting the IAT Results As noted earlier, the response latency is measured for each categorization task. The critical measures are collected in the test blocks block 3 and block 5. Within each of these blocks the average response latency is calculated. The average latencies for block 3 and block 5 are then compared. If the comparison indicates that the response latencies are significantly shorter for block 3 than block 5, this is taken as evidence that the participant has a stronger implicit association between younger men and the target qualities. If the comparison indicates that the response latencies are significantly shorter for block 5 than block 3, this is taken as evidence that the participant has a stronger implicit association between older men and the target qualities. The IAT can be applied to measure consumers reactions to brands, product packaging and logos. It can be used to gauge voters reactions to political candidates, or citizens reactions to public policy issues. The IAT is best applied when studying judgments and choices that are heavily influenced by automatic, System 1 thinking. These include, for example, situations in which judgments need to be made quickly, product involvement is low, many alternative choices exist, and when feelings about something have been formed relatively early on in life. In sum, the IAT can tell you some very important things about people that they cannot possibly tell you themselves. Contact: Adam DiPaula, PhD ad@sentisresearch.com