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  • Writer's pictureClare Otridge

Top three mistakes when you do your own research and how to avoid them



market research

Using a market research agency has a number of benefits, you get experience, objectivity, specialist software, audiences, enhanced reporting ability and sometimes it can be more cost-effective as they can operate at scale.


However...


Sometimes dipping your toe into conducting your own market research where the requirement is less complex or you have the capacity internally to undertake it seems appealing... plus it gets you closer to your consumer and is a valuable exercise for an internal team.


We chatted among ourselves and figured out the top three mistakes that we see when someone without a proper understanding of survey structure, analysis, or research objective setting;


Poorly Defined Research Objectives: One of the most critical mistakes is not clearly defining research objectives. Without well-defined goals, you may end up collecting irrelevant data or analysing data in a way that doesn't answer your business questions effectively. It's essential to know what you want to achieve through your research and set specific objectives before you begin.


Bias in Survey Design and Data Collection: Conducting market research without knowledge of survey structure can lead to biased survey questions and data collection methods. Biased questions can influence respondents' answers, leading to unreliable results. Without understanding best practices in survey design, you may not create surveys that yield unbiased, accurate, and actionable data. It's not just the way you ask questions, it's ensuring you use the right language for your audience, giving them the right options to choose from and selecting the question types and survey length that will keep them engaged.


Inadequate Data Analysis: Analysing research data requires not only collecting data but also processing and interpreting it correctly. This is perhaps the most important part as, without knowledge of data analysis techniques and statistical methods, you might misinterpret the data, draw incorrect conclusions, or overlook important insights. This can result in poor decision-making based on flawed analysis, which we know costs millions.



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