5 Best Practices for Market Research Borrowed from Psychology

Principles of psychology are intertwined throughout market research. Many decisions are based on an individual’s anticipated thought process or behavior. Psychology fundamentals are used to interpret market research results.

Psychology also influences the underlying processes of market research. I recently read the Quirk’s article, “Psychology’s reminder to MR: Part 2: 5 best practices” and it provided me with some great points on how important it is to keep market research in check with scientific standards. The article is the second of a two-part series to speak on what is unacceptable in market research and best practices to follow.

Below is what I found most insightful from each of the 5 practices.

5 Best Practices for Market Research Borrowed from Psychology

There is no shame in looking to other research industries for best practices for market research. In fact, there's plenty that can be learned from disciplines like psychology.


Best Practice #1: Have a clear hypothesis of what results are expected.

What I gathered from the first point is that you should always set clear objectives for the research. It's risky to go into a study hoping to find relevant answers at the end.

Creating a scope of what will be tested also makes the results more valid. An appropriate number of tests for the study reduces the risk of achieving a false positive result.

For example, asking the same question 10 different ways in a survey and getting the results you hoped for in one them is not a reliable practice.

It is good to have an idea of what the data will show to make questions more relevant and informed. Some secondary research is useful here to create optimal lists of answers and terminology in questions.


Best Practice #2: Design and document a plan based on your hypotheses.

I've learned tying your analysis plan back the objectives from the beginning makes life easier in market research. This helps lay the foundation for findings and avoid tangents in the reporting.

Be sure to also understand the sample size required to make the results statistically significant. You should consider how many potential segments within the data you need to analyze to address objectives. A sample size of 30 respondents, for example, will be difficult to split into anything beyond a total view and still be significant.

The article wisely suggests to specify data cleaning procedures, models used (if relevant), statistical significance thresholds in the analysis plan. These details take a great deal of the guesswork out of the analysis.


Best Practice #3: Follow your analysis plan.

Determining the pieces of the analysis plan beforehand will prevent data from being manipulated to fit a desired story. Experimenting with data cleaning or significance levels may discredit the end results.

Researchers should be cognizant of tunnel-vision when it comes to the results they expect. Sometimes it takes stepping away from the analysis and returning with a fresh mindset to see the real story. Be sure not to rule out possibilities too early if a couple data points support one theory.


Best Practice #4: Take steps to ensure your results are reproducible.

Documenting how the research was conducted can be helpful. This might include updating a workplan to show progress, such as specific milestones.

In reporting, summarizing background information, objectives, and data collection can provide context to the reader. This typically shared as part of a background and methodology.

In order to share your results, the report has to be easily understood by the audience. Sharing this information also promotes transparency, leaving less for the reader to question about the research.

We at Drive Research always share the margin of error to quantify the accuracy of the results. The margin of error shows the range to which the data would fall within if the results were replicated with a randomized sample.


Best Practice #5: Show all analyses not only the ones showing desirable results.

Cherry-picking data that makes for easy, believable results is not telling the whole story. Conclusions should always be drawn collectively on all data from the research.

Perhaps one of the main questions asked offers data that doesn't fit the rest of the narrative. Rather than choosing not to report on the question, it is important to look at the data another way perhaps to find out why that number is different.

Withholding information about the results comprises the credibility of the research to any stakeholders. It is always better to find the truth, even if it doesn't give you the answer you want.


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