Common Market Research Mistakes and How To Avoid Them

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INTRODUCTION

Almost everyone believes they can be market researchers, whether they have formal training or not. Indeed, for a long time I believed that they could – but with a little practice! I believe that the main characteristics of a scientist are curiosity, interest in people and a reflexive mind.

In addition, the necessary skills are taught through a combination of formal courses and industry experience. However, as more and more people choose to do their own research, education can often be overlooked. With that in mind, I have listed the ten biggest mistakes that we scientists should avoid.

Market research is the cornerstone of informed business decisions. It gives you valuable customer insights to adjust strategies, optimize products and navigate the competitive landscape. But even the most well-intentioned research projects can fall prey to common mistakes. Here, we expose these pitfalls and provide you with strategies to avoid them so that your market research can produce actionable results.

Common Mistakes In Marketing Research

1.Poor sampling:

 If you ask the wrong people to answer your questions, your entire analysis may be flawed. While a bad question stands out, it’s not as obvious if you take a bad question. Therefore, this is definitely the most important mistake to avoid. Always remember to specify your sample at the beginning. Write who you want to talk to and why. Define the rules, which in turn define who is in the audience. Then decide if your audience needs to be split into subgroups that may require further analysis or cross-comparison.

Specify the quality you want in your sample. This is increasingly important as the growth of access panels has meant that it is often not questioned whether they have professional responders.

This applies to both qualitative and quantitative research and the recruitment of experts.2. Ambiguous Questions:

If your question is imprecise, suggests bias, or is unclear in meaning, your analysis is open to interpretation and, at best, your results may be challenged within the organization. The worst-case scenario is that this information is used to make wrong decisions. ‘How worried are you about the next thing?’ This may seem reasonable at first glance, but “concern” is a broad term that can range from anxious feelings to simple awareness. For example, the sentence “I’m worried about the traffic” could mean to one person “I’m worried because it makes my journey longer”, but to another “I’m interested in learning more about the traffic”.

Response blocks are often ambiguous. It is very easy to write the answer choices from your perspective, but leave out some critical ones that the respondents might think. This creates ambiguity in your analysis and distorts the results. Take your time and make sure that the unclear wording of the answer choices does not lead to duplication of some answer choices and does not confuse the respondents. For example, “I chose it because the price was reasonable” or “the total cost seemed reasonable to me” seem like good choices for people and things that people interact with. In terms of analysis, however, there is debate as to which prices are truly reasonable.

3. Cost reduction incentives: Incentives are best thought of as compensation for someone giving up their time. People are busy and research asks them to give up their time to help us in our work. Yes, they are motivated by an interest in the subject or the fact that they help us improve, but they should not be used as an excuse not to value a person’s time. We also don’t try to encourage or bias surveys – so we don’t want them to be harshly low or too high; it’s about fairness and reciprocity. They give us time and we compensate them for that time.

Tweet This “Poorly compensating your participants is one of the biggest market research mistakes you can make. “Conferences talk a lot about poor response rates and low commitment to research, but we often make it too difficult. One of the main reasons why a survey gets a low response rate is that it unfairly values a person’s time. Pay fairly and get better quality surveys and higher response rates.

 4.Make sure your invitations and reminders clearly state what you want someone to do and why they should participate: Write in a friendly and communicative manner – don’t be too formal or professional. In a qualitative exercise, tailor the communication to the individual as much as possible and try to avoid the temptation to program automatic reminders, as these can lead to participant alienation or worse. Good communication is the foundation and platform for successful research.

5.Heavy-handed moderation: Great qualitative research requires the right balance of questioning – be open and not too invasive or risk stifling the discussion. Too many quick and short questions can shut people down before they open up. Practice active listening as much as possible, confirm and confirm that you understood their point before asking a follow-up question. Make sure the questions are worded in a language the participants can understand, and follow areas of interest as much as possible. The best moderators can get the answers they need on a topic, but they can do it by following a different structure than originally intended; simply because they listened, let the conversation unfold, and sometimes brought it back to the topic when it got too far. Moderation isn’t just about asking questions that stakeholders want to know. Qualitative research is not designed to be a survey with inline logic and literal answers. It is intended as a discussion to understand and get behind the topics and answers.

6.We can do a lot more on the web to make it interesting and more informative: But the biggest mistake we make online is relying on question boards that are treated more like polls. They are programmed with routing logic, seek individual verbatim responses and do not generate group communication or lack sufficient personal moderation. If we apply the same approach to a face-to-face focus group, the researchers would be horrified. It’s time to stop this mistake and approach online research with the same respect and imagination that we apply to our traditional research skills.

Difficult goals. Too often research is conducted without a clear understanding of why and what it is hoped to achieve: Everyone needs to be on the same page, and ill-conceived goals always lead to misunderstandings and failures. These decisions, confusion and complexity are then used to evaluate the success of the outcome.

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Objectives are not questions that have been renamed as objectives. Frame short paragraphs that describe the goals of the project and the desired knowledge and understanding. If writing sample questions helps, remember to write them separately or as sub-points for each objective.

These should not be vague or vague goals. They must also define the purpose and reason for the work, so before writing them, be sure to describe the background and reasons for the project, what the company will do with the results, and how those decisions will be made.

8. Survey Overflow. Surveys are overused. It’s easy to see why: Almost everyone in the company knows what a survey is, and with the perception that it is quick and easy to complete, almost every department uses them. But they are also often too long, too complicated and too planned.

Tweet this “Surveys are the most common market research tool because they are the easiest to understand. not the best. “Before writing a survey, ask yourself which method or structure will be of most interest to the participant. Often, non-survey-based methods are more valuable, but are overlooked because surveys are easier to administer. If a survey is the best method for your situation, try to limit its length so as not to tire the participant. Participants don’t mind taking surveys – as long as they’re easy and quick. Don’t over plan it either. The current trend of playing research can be beneficial, but only under the right circumstances.

9 Avoid vague reports: Make sure all findings are supported by clear evidence. Try to be as brief as possible and avoid using words like “some”, “might” and “could”. Be specific about who your finding affects, what it means, and what can be done. Performance requires precision. Also, don’t exaggerate your findings. This can result in the most inaccurate report.

For example, when describing customers, don’t generalize about a customer segment or stereotypical behavior. Do not associate this behavior with every one of the same age or geography. And most importantly, don’t apply your findings to the entire company or make sweeping recommendations that go beyond your role.

10. Schedule Pocalypse: Finally, don’t pack so much information into your report that it’s hard to follow or understand. When people talk about storytelling or creating a story, that’s what they often mean. It’s the difference between choosing 50 diagrams and 50 facts that follow each other and are meant to build on each other. Use evidence, but don’t kill the project by mapping everything. If requested by the client, create a structure that uses a summary and an appendix.

What market research mistakes have you noticed in your career? Share them with us in the comments below along with your best tips for avoiding them.

HOW TO GET RELIABLE INFORMATION

According to our recent survey, less than half of managers rate the reliability of their company’s data as “very good”. Business leaders need reliable data to make sound decisions, so where are those managers whose organizational data isn’t enough? Well, they either use unreliable data or, even worse, make bold decisions, as 36% of business leaders in our survey admitted.

Improving organizational data is of course easier said than done. And for companies just starting to explore how to improve the health of their data, the flood of information and best practices can often feel overwhelming. Data quality, data integrity, data reliability, data reliability – these and other terms can be quite difficult to keep track of without knowing what to apply or where to start.

For many organizations, data integrity can be the starting point for building more effective data quality and integrity functions. But first, they need to understand what data reliability actually is, how to measure it, and how to improve the company’s overall level of data reliability.

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Definition of data reliability

Data reliability means that data is complete and accurate and is an important foundation for building trust in data throughout the organization. Ensuring data integrity is one of the main goals of data integrity projects, which are also used to maintain data security, data quality and regulatory compliance.

With reliable data, business leaders can take the guesswork out of making informed decisions. It is the fuel that provides reliable analysis and insights. And it’s one of the most important things to get right when it comes to improving the overall health of an organization’s data.

It may be tempting to jump headfirst into implementing processes and practices that will hopefully improve data reliability, but there are many problems associated with poor data reliability, and each cause must be addressed differently. The first step is to actually determine what information is reliable and what is not, and this can be determined through a process called data reliability assessment.

Data reliability assessment Data reliability assessment, also known as reliability assessment, is an important process that can uncover problems with your data that you didn’t even know existed. Evaluation typically measures three different aspects of data reliability:

Validity – Is the data formatted and stored correctly?

Completeness – Does the dataset contain values ​​for all fields required by your system? Uniqueness – Are there duplicates and false data in the data? When evaluating data reliability, other factors affecting data quality can also be considered, such as examining how many times a data set has been trusted, where it comes from, and how the data has been modified. Achieving this deeper understanding is especially important for sensitive data where complete accuracy is essential. For example, to support a financial audit, it is important to be able to demonstrate the reliability of data.

There are solutions like Talend Trust Assessor that can determine a quantitative trust score for any data set, identify trust issues in the data set and highlight areas to focus on. If the assessment reveals bad data, there are a number of steps that can be taken to correct it, depending on what problems are found. For example, incorrect data is likely to be passed in the data preparation process. Until you can assess the reliability of your data, you will never be able to make informed decisions with absolute confidence. This is what makes assessing data reliability so valuable. An assessment can either a). shows exactly where you can correct information you know to be unreliable, b). reveals hidden problems in information you believed to be reliable or c). quantitatively confirm that the data you are evaluating is reliable and ready to use.

Difference between reliability and validity of data

 One common misconception about data is that reliability and validity are one and the same. While both are important for reliable data for an organization, they actually relate to different aspects of data health.

Valid data means data properly formatted and stored. Reliable information, however depicts information that can act as a dependable basis for analytical and critical decision making. Essentially, Reliable information makes different part of reliable information, meanwhile, validity alone does not assure reliability. However, if the data is incomplete – if information such as email addresses, names or other relevant information is missing from the data – then the data will not be detected reliable. Similarly, depending on how the list is created, data redundancy problems can arise when there are duplicates of the same record in the dataset – with varying degrees of completeness. For example, validity data may still be incomplete, so relying on validity as the sole measure of reliability may still cause problems when the data are used for analysis or action. For example, you may have a database of customers that you want to send marketing emails to, and you can create a contact list filled with that information that is 100% valid. However, if the information is incomplete—if the information is missing addresses, names, or other relevant information, such as email information—the information will not be reliable for its intended purpose. Similarly, depending on how the list is created, data redundancy problems can arise when the data set contains duplicates of the same record – with varying degrees of completeness. Hitherto, it is essential to track all means of information reliability so as to gather the most perfect information in order to fully comprehend your expertise. Building up a unbreakable Database at this point in time that you have the elements of how to properly apply these tactical plans to your market activities. First, you need to assess the reliability of your data based on its accuracy, completeness and uniqueness to understand exactly what you want to improve. The easiest way to do this is with a solution like Talend Trust Assessor, which allows you to measure the trust of any data set. Once you know what you need to fix, make a plan to decide which fixes you want to make first. Actions like duplication can be simple “quick wins” that jump-start your improvement initiatives and put you on the road to success. Next, you may want to look at what corrections are being made.

HOW TO DO EFFECTIVE RESEARCH

Step 1. Develop a Topic

Choose a Topic

Develop Research Questions

Identify Keywords

Find Background

 Information

Refine the Topic

Step 2.

Search for Information

Search for Strategies

Books

eBooks

Articles Videos and Images

Databases Websites

Gray Literature

Step 3.

Data Evaluation and Analysis

Source Evaluation

Primary vs. secondary Journal types

Step 4.

Write, organize and communicate information

Take notes

Draw paper

Insert source material

Step 5. Cite sources

Avoid plagiarism

Get started

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Tel: (+234) 802 320 0801, (+234) 807 576 5799)

E-Mail: info@qeeva.com

Office Address: 5, Ishola Bello Close, Off Iyalla Street, Alausa, Ikeja, Lagos, Nigeria.

ACTION PLAN

Hopefully, you can now avoid the most common mistakes when conducting market research. And if you can overcome the odds, the good news is that you’ll benefit from high-quality, actionable information about the people who matter most to your organization. Knowing your customers is essential to growing your business in a highly competitive market, so simple research among them is key. We’ve said it before, every successful business strategy takes time. rely on market research to achieve your goals. But even the most well-intentioned information seekers can fall prey to all-too-common mistakes that can undermine the effectiveness and accuracy of their research. Indeed, we all make mistakes. But in this post, we thought it would be helpful to discuss three of the most common market research mistakes we see when working with insight seekers. We hope not only to help you avoid these mistakes, but also to comfort you if you have already made them. Again, we all make mistakes. That’s fine. But if we are aware of these pitfalls, we can all gain a better understanding, create more clarifying research initiatives and achieve better business results. But enough of the introduction – your time is valuable – let’s dive right in.

Mistake 1: Research without clear goals

Let’s start here. We see enough of it. But that’s because the prospect of data is so exciting. Sometimes we are blinded by it. Let’s say it right from the start: data and insights are not always the same. Today, researchers have a wild expectation of using technology to gather all the information they can. But gathering information and turning it into actionable, digestible intelligence are two entirely different things. Here are some things to consider:

Having a problem?

Starting a research project without a clear definition of the research problem is like making a sandwich without bread. I mean, it can be done. It might even be delicious but is it really a sandwich? If you don’t clearly define the problem you’re trying to solve, you can end up with research that doesn’t really provide meaningful information. Do you have questions that are key to narrowing the scope of your research so you can focus on the specific information you need. Without clear research questions, your research risks becoming too broad or vague, leading to nonsensical or imprecise results. Defining clear research questions helps ensure that the data collected is relevant to the research – and can save valuable time and resources. Ultimately, this race without clear goals can turn research into irrelevant, ill-targeted and (ultimately) useless research that doesn’t help stakeholders make the informed business decisions they need to make. We are trying to say that we understand that it is difficult. Sometimes it’s a mistake because of the hidden potential you can find. But by focusing on the problem and the questions you’re trying to answer, you can keep your research focused, relevant, and focused on delivering the valuable insights you need to make business decisions.

 Mistake 2:

 Poor Sampling Techniques and Data Quality Okay, now let’s get into a little more nuanced territory and talk about sampling techniques. Poorly executed sampling techniques can be a major stumbling block in market research – mainly because they can bias the results to a very specific segment of the target audience. Lowering Loyalty Levels An example of poor sampling techniques is when we only survey customers who often. to buy a particular product. It sounds like you’re getting great insights from these power users, isn’t it?

Not so much, believe it or not. In fact, this can create a myopic view of the entire customer base and exclude important prospects that could provide insight.

Do we think too little?

Another mistake we often see is when researchers design a study where the sample size is simply too small. This can make it difficult to draw firm conclusions from the data you collect, which can lead to a serious lack of confidence in the analyzes you intend to create. Not much work here, right? Making decisions based on such a small sample may not be a good idea.

We appreciate the feedback we receive It is close to our hearts. We believe (and this has been proven time and time again) that insufficiently compensating survey participants can lead to respondent disengagement. You don’t want people taking the time to tell you what they think if they rush through the survey or give you inaccurate answers.

This reduces the quality of the data, leading to misleading insights. To err is human. The end of the topic also mentions that poor data quality can also be caused by technical problems such as data entry errors, incorrect coding or malfunctions. These errors can be difficult to detect and can lead to significant errors in the final result. Automation can really help here. This can save a lot of time by speeding up data processing and greatly reduces the risk of human error.

Sampling is an important part of your research. Poor sampling technique leads to poor data quality, and poor data quality leads your research initiative far into despair and hopelessness. But fear not! There is hope! With proper attention and care, sampling can be one of your most powerful tools for gaining meaningful insights. By using technology, motivating respondents and working with experts, we are confident that you will get valuable information quickly.

Mistake 3: Inadequate Research Design

When we talk about inadequate research design, we are mostly talking about the failure to create a research design that accurately and effectively answers the research questions. We see this a lot, and it can lead to incomplete or meaningless data collection, which (you guessed it) can lead to wrong decisions. This is somewhat related to mistake number one. Except, it’s a side effect of this bug. Oh, you made a mistake that caused you to make this mistake now let’s look at all the mistakes it can cause. It’s sadistic, but also remarkable. This is sadistically significant!

Barking up the wrong tree Inadequate research can take many forms. Asking the wrong questions, using the wrong research method can be common. If you want to understand why product sales are down, but you decide to ask consumers what color they like your new packaging, you’re not really going to get the answers you need.We know this is a silly example, but it also shows in your approach. Perhaps open-ended questions should be asked instead of narrower choices? Your Bias Another example of this is not taking into account potential biases or confounding factors that you, as the researcher, may introduce into the study. Does your survey include key questions? Did you overlook the broader context or culture of some respondents? This can lead your respondents to certain answers.

Don’t Do It Alone

We wouldn’t wish this mistake on anyone, but don’t be the only person checking your research before launch. Expert investigators are not on the hook here! It is quite possible that, in your infinite wisdom, you have relied too much on your past experiences and assumptions without fully considering the unique needs and goals of the current project.

It is very possible that you forget important details or critical things while planning. I mean, you’re only human! Our advice for this mistake is to try to involve multiple stakeholders in the research design process. These may include subject matter experts, data analysts and other relevant team members. But it can also mean bringing a third party along for a tummy tuck!  Be sure to consider your audience carefully and deliberately when choosing your sample—you want to make sure it doesn’t skew your results. Finally, remember that proper research design is key to obtaining accurate, relevant and valuable insights – getting a second opinion on how to design your approach can be invaluable.

CONCLUSION

Market research is the blood of informed business decisions.

It gives you valuable customer insights to adjust strategies, optimize products and navigate the competitive environment. But even the most well-intentioned research projects can fall prey to common mistakes. As the mistakes are highlighted, please note them down and know how to avoid them for positive marketing results.

Get started

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Tel: (+234) 802 320 0801, (+234) 807 576 5799)

E-Mail: info@qeeva.com

Office Address: 5, Ishola Bello Close, Off Iyalla Street, Alausa, Ikeja, Lagos, Nigeria.

FAQ

Why is it important to avoid mistakes in market research?

Market research is an investment in understanding your target audience, competitors and market trends. Errors in this process can lead to:

• Wrong decisions: Relying on incorrect or incomplete information can harm your marketing efforts and product development.
• Waste of resources: Inefficient research methods can lead to waste of time, money and effort.
• Missed opportunities: Ignoring valuable insights due to faulty research can result in missed growth opportunities.
• Missing product launches: Products that are not understood by the target group due to lack of market knowledge.

What are the most common mistakes made in market research?

• Not knowing what you are looking for: Clear research goals are important. Before you start collecting data, decide what you want to learn.
• Rely on a single source of information: Enhance your research using a variety of methods, including surveys, surveys, and full-video focus groups.
• Choosing the Wrong Research Group: Make sure your research accurately reflects your target audience.
• Research design: Incorrect or biased questions can lead to inaccurate or unusable data.
• Ignoring qualitative research: Pure statistics provide statistics, but qualitative research provides important information about the ‘why’ behind the statistics.
• Misunderstanding your biases: Researchers may not know the answers to questions or comments.
• Not being able to analyze the competition: Try to analyze your product, marketing strategy and target group.
• Not turning insights into action: don’t leave valuable research on the shelf! Develop practical strategies based on your findings.

How can I avoid these common mistakes?

• Define clear research objectives: what specific questions need to be answered?
• Plan your research: Include your research design, sample size, and data analysis methods.
• Choose the right research method: Choose the method that best suits your goals and target group.
• Prepare effective inquiries and questions: Use clear, neutral language and avoid leading questions.
• Use a mix of quantitative and qualitative research: Gain in-depth knowledge of your market.
• Remember your own biases: acknowledge and reduce biases in research and interpretation.
• Research your competitors: understand their strengths, weaknesses and target markets.
• Strategic planning: Use insights to inform marketing activities, product development, and business decisions.

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