Gathering feedback is a key requirement for customer success.
77% of consumers have a positive outlook toward brands that ask for and implement their feedback. While there are many methods for getting feedback, surveys are among the most popular and easiest ways to elicit actionable feedback.
However, this method of gathering data has its pitfalls, as survey bias is a deep-rooted issue. So, it can be hard to trust the reliability or authenticity of data collected using surveys.
Let’s examine why this is so important to a business.
The importance of accurate feedback data
Customer feedback helps us implement necessary changes and roll out targeted products and services people want to use. It’s a core component of customer success.
However, we draw incorrect conclusions from inaccurate data if the feedback is based on biased survey questions. This directly impacts the quality and efficacy of any updates or changes we make to the business.
An example from famous biased surveys is Coca-Cola’s ‘New Coke’ launch in 1985. Blind taste tests for a sweeter formula seemed to go well. Based on the survey, Coca-Cola replaced its original formula with the new sweeter one.
However, the sweeter formula flopped miserably. Loyal consumers were upset at the over-sweet taste, and Coca-Cola quickly returned to its classic flavor, putting aside losses.
Why did this happen?
The parameters for the blind taste survey were already biased. After just a sip or two of the ‘new’ Coke, people most likely expressed no contention with it when asked for their opinion. In real-life scenarios, downing an entire bottle of an over-sweet drink is far from pleasant. Also, the brand did not consider people’s strong loyalty and association with the original flavor.
Coca-Cola quickly recovered from the mishap, but this is a lesson many businesses could learn without suffering the consequences. Avoiding survey bias is crucial to the bottom line.
So, let’s begin by understanding what survey bias is and how it manifests.
What is survey bias?
Survey bias is a type of systematic error in statistics occurring due to the survey questions, respondents, and surveyor operating under certain implicit/explicit assumptions.
The survey is based on wrong assumptions and under influenced conditions. So, the data is often inaccurate and doesn’t reflect the actual opinions or realities of the people surveyed.
Bias can creep into surveys in various forms, leading to distorted findings that are not representative of real characteristics or opinions.
Take leading questions, for example. Biased surveys often contain leading questions. Asking users, “Would you recommend our new, exciting, and beneficial energy drink to friends?” only results in forced or incomplete answers.
Words like ‘exciting’ and ‘beneficial’ psychologically and emotionally impact respondents. They introduce preset assumptions and opinions and skew user perception.
To understand survey bias psychology, let’s dive into survey bias types.
Types of survey bias
To ensure you are collecting accurate feedback data, you need to watch out for five main types of survey bias. Let’s go over these as well as some survey bias examples.
Selection Bias
For surveys to be authentic and accurate, the population selected and the sampling method should be unbiased and randomized. Selection bias is a systematic error. It occurs when you choose respondents for your survey who don’t reflect the actual concerned population.
For example, say an NGO is conducting a health survey based on clinic participants suffering from a specific health concern. This pool of participants is incomplete, which means data from all those who weren’t present in the selected environment and population will be missed.
Many people with that health problem might not even go to the clinic, so there is a gap between the target population and the sample selected for the survey.
Also, many among the present population might decline to participate in the survey, leading to non-response bias.
Response Bias
Response bias occurs when survey participants don’t answer questions honestly. Many extrinsic and intrinsic factors could cause this bias, and it can be hard to determine if someone has answered a question truthfully.
Some of the reasons why response bias occurs are:
- People may not care about answering the survey correctly.
- Some people may feel pressured into ‘doing well’ and only answer positively, even if they think differently.
- Respondents might be confused by double-barrelled questions.
- Some participants may feel they don’t know enough or aren’t ‘qualified’ to contribute due to lack of experience. So, they ‘fake’ what they think should be the ‘right’ answers.
The Coke story above is an example of response bias.
Confirmation Bias
Our minds can be very good at seeing preferred patterns and drawing conclusions heavily reliant on our own conscious and unconscious beliefs. Confirmation bias is the tendency toward information supporting or reinforcing our preexisting notions.
The direct result is that we tend to dismiss or leave out information that opposes our solid internal beliefs.
Confirmation bias can go both ways: surveyors might not know their biases are creeping into the surveys they design, and participants might not be aware of their biases when answering. This phenomenon might not be conscious or ill-intentioned, but it severely impacts the gathered data.
We can uncover insights that significantly improve customer engagement by actively seeking diverse perspectives and designing more inclusive surveys. This approach not only enhances the reliability of our survey data but also paves the way for strategies that can elevate the overall customer journey.For example, research shows how doctors’ preliminary diagnostic hunches can interfere with how they assess the information and correctly proclaim a final diagnosis and plan of action.
Social Desirability Bias
The social pressure people feel to go with the flow can be quite detrimental to survey data. Social desirability bias kicks in when respondents give answers they believe will make them look good in front of others. It’s a subset of response bias.
People need to be socially acceptable, which can influence their survey responses. Decisions made based on such data can backfire. The survey might indicate a certain attitude or behavioral preference. But in reality, this may not be the case at all.
There are many good examples here, but McDonald’s McPlant burgers stand out. These plant-based meat burgers had a limited rollout, but even that failed.
There were many reasons, like unsatisfactory taste and price. However, what was surprising was the initial positive reaction to the concept.
It’s likely pre-launch studies and surveys were rife with confirmation bias: people wanted to appear open-minded, pro-health, and anti-animal cruelty. So, McDonald’s might have expected some continued interest in these burgers. Sadly, this was not the case.
Sampling Bias
Sampling bias occurs when surveyors consciously or unconsciously use data collection methods more likely to reach certain members over others. These members are systematically more likely to be respondents.
However, data from those not accessed by the survey might differ from what’s collected from the biased sample.
A classic example of sampling bias is the 1936 US presidential elections. While this is an old example, it changed how research and surveys are conducted.
The Literary Digest, a popular weekly magazine, conducted one of the biggest polls of its era to predict the winner of the 1936 US presidential elections. According to the results, Republican Alf Landon was supposed to win.
However, to ‘everyone’s surprise,’ Democrat Franklin Roosevelt won the elections, throwing The Literary Digest into a bad light for misinformation. The company eventually went out of business.
The magazine’s polling method and sample were biased. They polled subscribers, registered vehicle owners, and others in their phone book. This sample comprised wealthy people already pro-Republican, so it wasn’t surprising that the results came back in favor of the Republican candidate.
Importance of avoiding survey bias
If there’s one thing the examples above prove, it’s that biased survey questions and methods lead to inaccurate data.
This is already far from ideal from a best-practices-and-ethicality point of view. However, the main takeaway is survey bias’s negative impact on customers and businesses.
For example, United Parcel Service suffered costly strikes just months after an annual survey on morale reported positive news. There were grievances the survey had failed to reveal.
Survey bias leads to flawed decision-making processes. Since the data acquired is either inaccurate or incomplete, any business or service changes made based on the data can be unsatisfactory or unwelcome.
In turn, this satisfactory response or change on the part of the brand affects customer service and how consumers perceive the brand.However, when you design and deploy surveys effectively, they can lead to better performance and results. Eliminating survey bias can help you better service your customers and improve your ROI.
How to avoid survey bias
The first step to avoiding survey bias is eliminating biases and preconceived notions. These can negatively impact the survey questions, the sample population, and the data collection method.
It’s important to understand multiple biases can act simultaneously. So, objectivity and randomization are often ways to mitigate these biases.
Here are three ways in which you can reduce or eliminate survey bias.
Designing unbiased surveys
To design an unbiased survey, you need to define the objectives first.
- Why are you conducting the survey?
- What information do you seek?
- How will you use it to change or update your business or service?
Once you know this, you can choose an unbiased sampling method. Leverage random sampling and ensure the sample represents the whole population of interest, not just whoever is convenient or easier to survey.
Frame questions that are easy to understand, straightforward, and devoid of technical jargon. Consider utilizing some of the best AI writing assistants to help you brainstorm and refine your questions. These tools can be particularly helpful in ensuring your questions are easy to understand, free of technical jargon, and avoid double-barreled or leading formats. This will increase your chances of getting accurate, honest responses from participants. Avoid double-barreled and leading questions since they always result in inaccurate data.
An unconscious bias questionnaire is often the start of the problem. So, here are some quick tips to help you with framing unbiased survey questions:
- Avoid emotionally charged language. For example. “How satisfied are you with our amazing remote service experience?” The word ‘amazing’ here introduces bias.
- Make the question clear and simple. It’s better to ask, “What did you use our product for?” than to ask, “What problems did our product solve in terms of superiority and cost-efficiency?”
- Remove questions with two-part answers. Don’t ask your consumers about multiple things at once.
Diversifying survey methods
Diversifying your survey methods is a tried-and-tested strategy for minimizing survey bias. Relying on just one survey or feedback form can skew the results over time.
Diversifying survey methods can help you reduce bias, reach a wider audience segment, and collect richer, more meaningful data.
Online surveys are among the most popular survey methods used today. However, there are various subcategories under this survey method. Considering expanding your methods to:
- In-app or website popup ChatBot surveys
- Social media polls and interactive posts to collect trending data
- Open-ended questions for unexpected insights
- Embeddable polls on websites
Besides these, survey methods such as focus group discussions (FGDs), face-to-face interviews, and telephonic surveys should be considered. FGDs, for instance, are a great source of diverse, qualitative data.
Analyzing data
To further avoid survey bias, there are two instances where you need to scrutinize the data.
First, a pilot survey will be conducted, and the results will be studied. Then, try to identify and correct potential biases before conducting the survey.
Second, once you obtain the survey results, quantify them with an open mind. Don’t try to wrestle the feedback into a form that suits your objective. Instead, categorize the positive, negative, and neutral points and connect them to specific aspects of your service or product.
Also, remember the total number of surveys sent and how many were answered/opened. This will factor into your final summary of the data and any actions you take.
You can use feedback analytics and other customer satisfaction survey options Nicereply offers to help you along the process.
Conclusion
Conducting surveys and gathering customer feedback are proven ways to identify and implement needed changes in your business. Customers who feel heard and seen tend to advocate their support for their favorite brands and products.
However, survey bias can result in inaccurate data and poor decision-making. This ultimately results in dissatisfied customers and financial losses. So, you must eliminate bias in your survey questions and methods. You can become a leader in customer happiness benchmarks by better understanding your customers with actionable feedback.