3 Behavioral Biases that Stunt the Potential of Marketers

Kunsan Kim

What you see is not always what you get.

As marketers, we spend an enormous amount of time, energy and resources building up libraries of research data, with the expectation that more information will lead to better insights and a clearer sense of direction for our brands. But if we’re not careful, our pursuit of knowledge can also lead us down the wrong path or send us spinning in circles unnecessarily. As we mine for insights, we need to be keenly aware that lurking under the radar are behavioral biases that can undermine our efforts. Three particularly dangerous biases to watch out for are Frequency Illusion, the Actor-Observer Bias, and Quantification Bias.   

Frequency Illusion

Frequency Illusion is the cognitive bias that makes you feel like the whole world is tuned into you and how you see the world. After learning some bit of new information, we start noticing it everywhere around us. Typically, we then fall into a trap of believing it is the “norm” or a widespread trend. For example, a marketer may go on a rep ride and hear a story from a customer about their greatest frustration with the marketer’s brand. The next thing you know, seemingly every subsequent customer he or she meets shares this same frustration. In the mind of the marketer, at least, the issue comes up over and over again. Soon, priorities are shifted, strategic imperatives are thrown to the curb, and solving this particular issue becomes the marketing team’s new sole focus. 

Implication

It’s important to realize that your perceived frequency of an issue and the actual frequency of that issue, in fact, may not be consistent. So, before you decide to pivot away from established strategies on the basis of these seemingly frequent encounters of a new issue, make sure to go back to your trusted set of metrics and verify what you are seeing.   

The Actor-Observer Bias

The Actor-Observer bias, interestingly enough, is a psychological phenomenon that flips the script on frequency illusion. Rather than believe everyone in the world is experiencing what we are, this bias says we, alone, are the exception to the rule. Actor-Observer Bias attributes other people’s negative behaviors to their character, while simultaneously attributing one’s own behavior to the situation. It’s a double standard our minds accept as perfectly reasonable. For instance, a health care provider may be confronted with hard facts about low colon cancer screening rates in their country. While we, as marketers may expect this physician to respond to the facts with a commitment to take action, often, we’re disappointed to see that no change in behavior occurs. The actor-observer bias tells us that while the physician in this example may wholeheartedly agree with our message and our data, it simply doesn’t translate as an issue pertinent to them personally. “Screening rates are low in my country because most physicians just are not proficient at sharing the information the way I am. I have great screening rates in my office. Rarely, I’ll have a few patients who are bad apples, but those are lost causes anyway.”

Implication

When we look at how we message to our audiences, it serves to be mindful that our data may not be as strong as we think, if our audiences are taking in the information as a third-party observer rather than as the actor needing to play a role. We should never assume that our audiences see themselves the way we do.

Quantification Bias

Finally, a third pitfall we may encounter as we try to get smart about our respective markets is quantification bias. This is the unconscious belief that intrinsically values the measurable over the immeasurable. We hear marketers question the value of a discovery by asking, “What was the sample size for that research?” or try to minimize the impact of an insight by stating, “Well, it’s only a small sample size we’re talking about.” It’s as if to say a qualitative insight from a smaller sample has no real significance. As with frequency illusion, we absolutely believe that quantitative data can be instrumental in providing context as we look for great insights; but we are also cognizant of the fact that it too has limitations. Quantitative studies, including surveys and big data, are fundamentally designed to optimize existing business models; or what people think and do now. But oftentimes in marketing our challenge is to spur change and create completely new models. We generally seek to lead markets, not simply respond to them. So, from a research standpoint, this requires more insight into emergent human dynamics that haven't happened yet. When we actually see trends, the opportunity may have already passed.

Implication

We all have a desire for certainty, and quantitative data helps us feel as though we can be more certain. But if you are a marketer trying to change standards, utilizing and understanding the value of insights that come from a mix of quantitative, qualitative, and even individual discussions is how you’ll get the best results. An insight doesn’t care where it comes from.

Conclusion

We’re marketers and we’re human – which means our biases can cloud how we see the world. Unless we are cognizant of these biases and unless we stay on the lookout, we can easily deceive ourselves. So much effort is put into market research and strategy development. Don’t let these biases sabotage your work.

Kunsan Kim is SVP Brand Strategy at precisioneffect.