Every active company focuses on perfecting their market segment customer experience (CX). This means removing all the obstacles in the customer journey and clearing all touchpoints of obstructions. That’s easier said than done because everything depends on what you know about your prospects and customers’ behavior. Modern-day marketers have to dig deep to appreciate the drivers of customer loyalty and the triggers of customer churn. Your company’s ROI performance critically rides on obtaining and analyzing accurate market data. In turn, the latter integrates with types of bias in research, market segmentation, interviews, focus groups, NPS surveys, and feedback across the board.

Market research bias is the arch-enemy of reliable feedback. The massive waste of time and money is the unwanted collateral of restructuring market programs founded on the wrong reasons, and it’s a real issue. The only way to resolve it is to erase market research bias whenever it raises its ugly head.

What is market research bias?

During a market research project, bias refers to any distortion emerging that skews the results in different directions from the reality. It generally arises from missteps in the process, boiling down to inadequate feedback techniques. Bias puts the survey maker, quiz maker, and Likert scale surveys under the microscope. When anyone is saddled with the responsibility to create survey accuracy, the implications are indeed severe. When a particular statistic reflects false data based on a sample survey, bias is undoubtedly in the equation. We will cover the most common types of bias in research and the areas market researchers should be treading softly on.

What are the types of bias in research?

You might as well ask how long a piece a string is. Bias in market research can come at you from all sides in ways you can’t imagine. Here are the ones that plague quiz makers and other market researchers the most:

Sampling bias

In market research, the biggest culprit is sampling bias — a consistent error recurring throughout a survey. The sample itself is sometimes unnoticeably out of kilter for various reasons. Think of it this way:

• You have a distance of around half-a-mile to measure, and all you have is an old yardstick that’s supposed to be three feet long but actually is three feet and one inch.
• You use the yardstick carefully, counting the number of times it fits – end-over-end – until you cover the distance. You then multiply it by the number of rotations you took to cover the distance.
• Each one has a one-inch inaccuracy (bias) in the thousands of rotations, and the accumulated bias is more than substantial.

Surveys work in the same way because repeating the error several times will significantly skew results, thus invalidating the feedback.

It frequently occurs when a survey sample is up for selection. For accuracy assurance, it should be close to the universe it represents, which implies random selection as a start. If a sample is non-representative, the results emerging are open to suspicion, and rightly so.

Nonresponse bias

Nonresponse bias can creep into findings in a flash. When respondents in a sample fall away, the sample size (although representative going in) reduces to a number that doesn’t align numerically with the market segment you are analyzing. For example, if one structures the sample around 3000 respondents, but only three-hundred react, you’re not getting full sample feedback in the bag. Nonresponse bias likely threw the results into a grey arena, at best. In a nutshell, it’s the unexpected absence of respondents at play here that creates erroneous data.

Response bias

Response bias is quite common and a “biggie” that researchers should take into account. Sample respondents don’t enter the survey or feedback session in a vacuum. No, they come with preconceptions, opinions, beliefs, and ingrained biases. More pointedly, they often involve themselves knowing they are part of an investigative process that imposes unavoidable influences on responses. Respondents fall into the trap of answering what they think you want to hear. They have a latent urge to please the interviewer or an inner voice that tells them, “Play this one safe, dearie!” Interviewers should always remember, they are dealing with human beings, not machines, and response bias is alive and well.

Question order bias and question formatting bias

Quiz makers, survey makers, interviewers, and those into structuring focus groups should understand their tasks’ fine details. It often comes down to asking questions ordered the right way to get at the truth. There’s a school of thought that questions should seem bland and innocuous to the respondent, not asking them an opinion at all. Instead, when you put answers to more than one seemingly unrelated questions together, you get an insightful overview. Question order bias is a severe hurdle that you cannot afford to ignore.

For example, a cosmetic company that sells anti-aging face cream believes that women “white-lie” about their age if or when asked. An amateurishly contrived survey asks a sample of women in the market segment, “Do you understate your age when asked?” Such a question can get respondents backs up. They may say, “Of course not, I never lie!” when in reality, they lie about it all the time.

A professional survey may tackle the same hypothesis as follows:

• It asks a whole string of questions, like inquiring about what brands you use, preferred colors, textures, etc.
• In the middle of this, there’s a question — “How old are you?” Later on, another question pops up — “When were you born?”
• Fact — most people lie about their age if it’s a sensitive subject, but not about their birth date. In any event, if lying about one’s age is a factor, the mental gymnastics to tie it in with a birth date in a quick survey is frequently too much to handle.
• Put the answers together. If the age exceeds the date most of the time, the survey has proved the point.

In the above example, we see that the ordering of questions and how you ask them plays a huge role in creating bias or suppressing it. Also, there’s a force known as “first-shown” bias — meaning that respondents tend to select the first item versus others in a list of four or five options. It all goes to show that there are so many considerations when designing a survey to ensure you’re getting accurate, reliable results.

How can I erase all types of bias in research?

I’m a firm believer in SMEs and large enterprises doing things in-house. However, when it comes to addressing bias in your market research, get outside input. Companies like Sogolytics understand all the parameters of probing respondents with questionnaires, interviews, and the like. They have templates cleansed of common biases ready for use. If, indeed, it’s a customized research project you have in mind, get Sogolytics in your corner. Their team guarantees that you can overview markets and have confidence that the feedback is spot-on.