5 Common Market Research Mistakes and How to Avoid Them

Market research is an exceptional tool for understanding customers, developing strategies, and improving sales efforts. When done correctly, researchers can collect insights used for impactful decision making, helping businesses reach their objectives while stimulating growth. Despite its potential, one misstep may compromise the quality and effectiveness of data. Here are 5 of the most common market research mistakes and how to avoid them for exceptional results every time

1. Poor Sampling

Impactful data starts with sample quality. There are two kinds of sampling errors many researchers make when conducting a study; sampling and non-sampling. Sampling errors occur due to issues in sample size and representation. Researchers often compromise their data through insufficient sample sizes and population specification errors. This leads to the misrepresentation of specific attributes such as age, gender, income, and geographic location. Non-sampling errors derive from issues with selection, sampling frame, and non-response errors, affecting the accuracy and credibility of a research initiative. Errors result from selecting samples from the wrong sub-population, allowing respondents to self-select their participation, and having a disproportionate amount of participation from respondents with differing characteristics. For example, having more existing customers respond than potential customers. 

Many sampling errors result from a lack of knowledge of best practices, leading to errors in design and methodology execution. By using an online sample size calculator or consulting with a research solutions expert, such as those at Eyes4Research, researchers can determine the sample size needed for an accurate representation of their target audience. Researchers should also analyze questions and objectives before finding participants to prevent errors in population specification. Implementing proper survey distribution and follow-up methods can also prevent selection and non-response errors from hurting data quality. 

2. Not Knowing What You’re Looking For

Maintaining a clear objective throughout a research initiative remains crucial in collecting meaningful and impactful data. With this, a lack of focus and direction may result in using the wrong methodologies, asking the wrong questions, or recruiting the wrong participants. Unclear research goals may also deter stakeholders from signing off on an initiative due to a lack of understanding. To prevent this, researchers should write out objectives using action verbs and operational terms. By clearly defining the purpose of the initiative, researchers can develop effective methods of deployment to align efforts with overall business goals. 

3. Poorly Designed Question/Scales

Question design and measurement scales remain an essential part of collecting accurate and easy-to-interpret insights. Without taking into account best practices, researchers may find themselves sifting through irrelevant and unusable data. Common questionnaire design errors include making questions excessively long and complicated, asking too many questions, inaccurate response options, providing too large or too small of a scale, assuming prior knowledge, and poorly routed survey designs. In addition to the survey layout design, the wording of questions impacts the quality of the data collected. Asking double-barrel, leading, or loaded questions influences participant’s answers, producing inaccurate results ill-suited for decision making. 

To avoid errors in question/scale design, ask yourself these questions:

• Does this question sway the respondent to one side of the argument? 
• Can the respondent answer the question honestly in a way that accurately reflects their opinion?
• Does this question require a preliminary question before answering?
• Is only one variable being measured by asking this question?
• How much flexibility do respondents have to provide feedback?

4. Confirmation Bias

The inclination of people to interpret data as it pertains to their personal beliefs or values may hinder the accuracy of a research initiative. Confirmation bias remains hard to identify, as these actions are carried out on a subconscious level. With a biased selection and interpretation of evidence, researchers may discount data that contradicts their beliefs, negatively affecting the credibility of the study. Respondents may also fall victim to confirmation bias when answering questions, resulting in a biased recall, interpretation, and favoring of information. 

To reduce confirmation bias, researchers and respondents must first develop an awareness of the preconceived notion. Once identified, one must decide whether or not to take action to reduce the bias. With this, the researcher or subject must analyze the inner workings of their bias. This analysis may include how, where, why, and when bias becomes prevalent. This information allows one to choose the best approach to reduce their bias. Debiasing techniques for both respondents and researchers include focusing on finding the right answer rather than proving a belief. Approaching questions logically rather than emotionally also promotes a subjective approach to develop and answer questions. Also, asking respondents to explain their reasoning makes it easy for researchers to identify hidden bias in studies. Finally, avoiding forming a hypothesis prematurely further reduces personal bias for both researchers and respondents. Though bias remains an unavoidable factor when conducting research, many debiasing approaches can help maintain the integrity of data for accurate results. 

5. Hard to Interpret Results

Without easily digestible results, making decisions based on data becomes virtually impossible. Two common mistakes researchers make when presenting results are overly vague reports or reports packed full of unimportant data. These presentations of data make it hard to identify the relationship between research results and business objectives. When organizing results, creating a narrative may help researchers determine what information is most applicable to the overall research objective. Delivering insights through data storytelling not only keeps audiences engaged but helps researchers organize data points in a way that promotes actionable insights.