3 Consumer research mistakes most brands are guilty of

3 Consumer research mistakes most brands are guilty of

Faith Ocampo
December 15, 2016

office employee scattering paper

To meet consumers’ expectations and provide them an outstanding customer experience, you first need to know exactly what they want.

Of course, the only way to do this is to get to know your customers. By collecting firsthand information about them and making sense of the gathered data, you’ll understand the needs, wants, and priorities of your target demographic. This way, you can devise effective ways to improve customer service and boost sales.

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But how can you make sure that your findings are accurate?
Consumer research can often be a tricky process, and it’s not just because it requires rigorous conceptualization and planning. Data workers—which include data collectors, statisticians, and report writers—must be objective all the time. Otherwise, they might end up sabotaging your own customer survey, leading to faulty findings that don’t reflect consumers’ true sentiments.
To prevent this from happening, here are four of the most common research mistakes you must guard against.
 

1.     Failing to set your research objectives

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Many business teams dive straight into the data collection process without clarifying beforehand what they’re aiming to discover. On the upside, they’ll end up with gigantic amounts of consumer data. But on the downside, most of these will be disjointed bits of information that will end up unused.

To provide a clear direction for your research project, you need to state your objectives clearly and specifically. What do you want to find out? What questions must you ask to gather the data you need? What’s the best way to capture and analyze these bits of information?

By setting your goals, you can ensure that all your research tasks and activities have a consistent focus. This way, you can come up with accurate consumer insights that directly answer your research objectives.

 

2.     Data collection lapses

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Researchers must be careful as they gather data from their respondents. Here are some typical mistakes you need to watch out for.

•     Surveying the wrong demographic. Depending on your research goals, you may need to identify a particular market segment that can best answer your questions. Through a process called sampling, data experts and statisticians can identify how respondents will be selected and which among them can best represent the consumer group you’re targeting. Often, if you just randomly pick your survey participants, you’ll end up with data that don’t accurately reflect the opinions of your target population.

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•     Not having enough data. As a rule of thumb, the more people you survey, the more likely it is that your research results accurately represent the sentiments of your target demographic. Statisticians can help you determine the number of consumers you need to survey to minimize data errors. Another way to gather all the information you need is to create a comprehensive customer survey that covers all the necessary sub-topics.

•     Ending up with irrelevant data. Your research objectives should be your basis for formulating your survey questions. Be sure to stay on topic and avoid including unnecessary items in your questionnaire.

 

3.     Ignoring the proper data analysis procedures

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If you’re dealing with mostly quantitative data, you need to work closely with experts who can analyze the information you’ve gathered in the most accurate way possible. This way, you’ll be able to dig deeper into your research results.

Most research groups who don’t work with statisticians stop at descriptive statistics, such as percentages and averages, to describe their findings. Although these can also be extremely useful, there are many other meaningful ways to look at your research data.

For one thing, statisticians can help you find and identify correlations between events and make accurate inferences based on the data you have. Such data analysis processes typically fall under analytics, a multidimensional field that integrates statistics, math, and predictive modeling to make sense of large amounts of information. Executed well, these processes will enable you to build strong customer service or marketing strategies that reflect people’s expectations.

 
 

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