Stereotypes: The Hard-Wired Way Our Brains Make Decisions
Home 5 Blogs 5 Stereotypes: The Hard-Wired Way Our Brains Make Decisions
Stereotypes: The Hard-Wired Way Our Brains Make Decisions
Home 5 Blogs 5 Stereotypes: The Hard-Wired Way Our Brains Make Decisions
This process is what decision scientists call Representativeness

I’ve got a hypothetical question for you. On a scale of one to ten, how likely is it I am a male model? 

Now before you destroy my self-esteem with your answer, I have a follow-up question. How did you come to that determination? 

The answer to that question is the subject of this post and a recent podcast, and it involves the Representative Heuristic. This psychological principle could have a significant influence on how customers behave in your Customer Experience

So, how did you arrive at your number? Most of you probably compared me to male models you have seen in the past and determined whether I fit the profile for looks and physique. Then, based on that comparison, you assigned me a number. (It is not necessary to share your number with me, by the way…in fact, I would prefer if you didn’t.)

This process is what decision scientists call Representativeness. Representativeness means that you have an idea of what typifies a group of people or an entity that share a characteristic. In other words, it is a stereotype. These stereotypes serve to help us categorize broad instances into a more manageable form. 

The Representative Heuristic uses these stereotypes to create patterns that help us make a quicker decision in certain situations. A heuristic is a decision shortcut. I also compare it to a “rule of thumb.” Heuristics are not 100 percent accurate, but they work most of the time, and help make complex cognitive decisions easier and faster. 


Your Heuristics vs. Our Heuristics

In decision sciences, there are individual level heuristics and universal heuristics. An example of an individual heuristic might be how you choose where you eat lunch, e.g., a place that I can walk to from my office. Another example is how you buy toothpaste, e.g., “find the one in the red package.” These individual heuristics are specific to you and help you simplify your choices.

Universal heuristics are broader than individual ones. These heuristics are ones we all use because of the way our brains are wired. In other words, everyone uses them all the time. 

Making decisions in these ways is simpler for us, given the kind of the strengths and weaknesses of the human mind. 

One of these universal heuristics is the Representativeness Heuristic, which is how we decide the likelihood that something is going to happen or that somebody belongs to some class of people.  One way I think of this mental shortcut is if it looks like a duck and quacks like a duck and it walks like a duck, it’s probably a duck.

However, decision scientists aren’t interested in when the animal is a duck. They study when our mental shortcut goes wrong. So, if the animal isn’t a duck but a goose, let’s say. Decision-scientists want to know, where did it go wrong and what does that tell us about decision-making.  

Expectations play into it, in how you form the category or group against which you’re going to compare. For example, you’ve got expectations about how a male model looks. Therefore, when you’re pulling mental samples to form that category, it was defined by your expectations. 

Somebody from a different culture or with a different set of experiences might have a completely different set of expectations.  So, they would form a different category. Then, that would be a different Representativeness. 


The Linda Problem

Economists Amos Tversky and Nobel-prize winner Daniel Kahneman published an example of this heuristic in the 80’s. Over the years, it has become known as “The Linda Problem.” In the study, they described “Linda” to the participants: 

Linda is 31-years-old, single, outspoken, and very bright. She majored in philosophy as a student. She was deeply concerned with issues of discrimination and social justice and also participated in anti-nuclear demonstrations. 

Then, the researchers had a series of ten or twelve descriptions of Linda, including organizations that she belongs to and jobs that she does.  They asked people to rate how probable are each of these descriptions. 

However, they are only interested in two of the descriptions:

  1.  Linda is a bank teller. 
  2. Linda is a bank teller that is active in the feminist movement. 

So, I ask you readers, which of those two seems more probable based on the description of Linda?

I said the second one. However, it’s wrong. 

It can’t be more probable for her to be a feminist bank teller than it is for her to be a bank teller because of math.  The set of feminist bank tellers is smaller than the set of all bank tellers. 

The Representative Heuristic is at work here because even though it is mathematically impossible that she belongs to the second group, it still feels right. Why? It feels right because Linda is so representative of what most of us picture as a “feminist.” She doesn’t seem like she should be a bank teller. The only way we can make that work is if she’s a feminist bank teller. 

Representativeness applies to organizations also. Consider my opinion of internet companies, which, for those of you who don’t know, is low. My opinion affects my expectations. If I were setting up service with an Internet company, if they were inattentive at the call center or unreliable with their service or incompetent at installation, I would think, “Typical.” 

However, let’s say I tried satellite service instead. Here is where Representativeness comes into play. Technically, setting up satellite service would be a new experience. However, there would be parts of the interaction that would be the same, e.g., calling in to set it up, waiting for installation, trying to use the service as advertised, and so on. My brain would think, “Hey! This experience is like the cable provider,” and then I would bring in all those associations because they were representative of that category of a service provider. 

Let’s take a broader view of this concept. Consider your experience with a sales-driven organization. You know when you are in one because the customer-facing people are aggressive and usually hard to shake. 

As a rule of thumb (see what I did there?), I don’t give my information to sales-driven organizations easily. I tend to hold back information rather than lay it all out until I determine that I want to move to the next step. My behavior is governed by what I think a sales-driven organization is going to be like based on my expectations. 

People use these probability judgments to make all kinds of decisions, and they go right most of the time. However, let me give you a specific example of how these judgments go wrong, and it has to do with neglecting base rates. When you are discussing probabilities, base rates are the likelihood that something will be accurate based on featural evidence.  

In the Linda problem, the group of bank tellers is larger than the number of feminist bank tellers. For demonstration, let’s say 99 percent of bank tellers are not feminists, and 1 percent are. That means the base rate of feminist bank tellers is 1 percent. 

We neglect base rates in the extremes and that’s where Representativeness goes wrong for us. For example, we overlooked the base rate of feminist bank tellers. We forgot that the number of bank tellers was larger than the number of feminist bank tellers. 

Another example could be a high school basketball player that you see who is fantastic, and the player represents your idea of professional players. You think, “that kid is going pro, for sure.” However, per the NCAA, the base rate for high school players playing professional basketball is 1.2 percent for men and .9 percent for women. So, even though the kid is excellent and might represent what you expect for a professional player, they probably won’t go pro. Again, because of math.

If you put this concept into a business context, it can mean that as a customer, you make the same wrong assumptions about an organization.  For example, if you engage with a company that is representative of some class of companies that you don’t like, you might overestimate the likelihood of a service failure. The opposite could be true also. The danger is that you tar everyone with the same brush and make the wrong assumption. 


How Can You Use This Concept?

There’s a reason that the scientists study the Representativeness Heuristic: it is a reliable driver of behavior. The Representative Heuristic is hard-wired into our brains, making it difficult to overcome. 

However, by maintaining rational-thinking about base rates, you can overcome it. We need to have an evidence-based probability for success or failure. Otherwise, we don’t have a sound foundation for making an estimate. We could be way off because we aren’t looking at the situation clearly enough. 

Your customers also make estimates that are way off. We have to understand where they are coming from and exercise patience with them in their decision-making. For example, if you are suffering from an overestimated sense of probable failure based on the category of business you represent in customer’s minds, it will take a long time to change customers’ minds. 

Your goal, then, should be to consistently play against type and prove to customers that you are not what they thought you were. Your Customer Experience should surprise them and make them feel differently than they expected. 

To address this implication in your Customer Experience, you should:

  • Determine what you experience makes you representative of in their mind. 
  • Decide what category your experience communicates and how that will affect customer behavior
  • Design your behavior in the Customer Experience to address these expectations to an improved outcome.

In our global Customer Experience consultancy, we take the Representative Heuristic into account when we do the Emotional Signature®. The Emotional Signature is a research exercise we do that determines the level of emotional engagement that your present experience with your customers. Within the process, we determine the segments and customer types you have presently and what their expectations might be as a result.  

You need to understand what is in your customers’ minds when they make decisions. Considering these hard-wired psychological drivers of behavior is an excellent place to start. 

If you provide an experience that positively influences customer behavior, then, by all means, keep it up. However, if you are doing things in your interactions that categorize you in an unfavorable light (I’m looking at you, internet service providers), you should recognize it. Then, you should change that moving forward so that your customers’ rule of thumb is to choose your organization first.


To hear more about How and Why We Stereotype People and Things in more detail, listen to the complete podcast here. 


emotional signatureWhat customers say they want and what they really want are often different things. It is vital to know what drives value for your organization. Our Emotional Signature research can tell you where you are compared to other organizations and what to focus on to drive value for your customers. To learn more, please click here


Podcast for Ignoring Customers’ Risk Aversion is Risky Business - BLOG Featured Image - Colin Shaw - Intuitive CustomerHear the rest of the conversation on How and Why We Stereotype People and Things on The Intuitive Customer Podcast. These informative podcasts are designed to expand on the psychological ideas behind understanding customer behavior. To listen in, please click here.



If you enjoyed this post, you might be interested in the following blogs and podcasts:


How Do Customers Decide If Their Experience is Good or Bad? [Podcast]

How We Make Decisions—Prospect Theory

Why Customers Make Strange Decisions


Colin Shaw is the founder and CEO of Beyond Philosophy, one of the world’s leading Customer experience consultancy & training organizations. Colin is an international author of six bestselling books and an engaging keynote speaker.

Follow Colin Shaw on Twitter @ColinShaw_CX



“Estimated probability of competing in professional athletics.” Web. 29 August 2019. <>.