Research Terminology in Experiential Marketing Analytics

Written by PortMA

Research Terminology in Experiential Marketing Analytics

Research Terminology in Experiential Marketing AnalyticsExperiential marketing research is a powerful tool. But, it is often misunderstood. It can be sought and embraced, but it can also be approached with skepticism and trepidation. It even can return misinformation. Unless you’re a researcher (or a research junkie), your eyes may likely glaze over when terms like sample size, margin of error, or confidence interval are mentioned.
As a consumer of experiential marketing research, it’s vital to have a basic understanding about these things. Even a fundamental awareness helps protect you from accepting a sub par research product as gospel. The “Garbage In, Garbage Out” idiom applies directly to research.
Research, or measurement, does not need to be complicated. Yes, there are doctoral programs devoted to advanced quantitative techniques and statistical theory. But in business those are rarely, if ever, needed. There are basic principles that can help anyone navigate the murky measurement waters that often have deltas muddier than the Mississippi’s. This blog post presents a few of them.

Quantitative v. Qualitative Research in Experiential Marketing

There are two primary approaches to research: Quantitative and Qualitative. Qualitative research is used most often in the process of discovery. It typically utilizes focus groups and in-depth interviews. While an important and legitimate approach, qualitative research is not the focus of this article.
Quantitative research refers primarily to things that can be measured objectively and analyzed using mathematical and statistical techniques. Survey research is a good example. Composing a proper survey is a science unto itself. Let’s just begin with three terms essential to effective quantitative research: Sample Size, Segmentation, and Margin of Error.

Sample Size – Number of Respondents in the Experiential Marketing Research

Sample size is an important aspect of any survey research. In experiential marketing practice, the more completed surveys available, the more confident we can be with drawing accurate conclusions and applying them to a broader population. For any given program, we aim to get between 350-400 completed surveys per market. This affords greater flexibility with segmenting the responses and analyze them accordingly.

Segmentation – Dividing Experiential Marketing Respondents into Key Groups

Segmentation is where we are able to provide the most value in experiential marketing analysis. It’s not enough to say that 75% of consumers surveyed are willing to purchase Product X. That may appear to be a good number. But, what if we took a closer look and segmented the data by gender? One might find that the majority of those willing to purchase Product X are women. That would be great news if Product X is geared towards female consumers. But, what if Product X is geared towards men?  The research would seem to indicate that the experiential marketing campaign is reaching the wrong target audience.
Before jumping to that conclusion, we would work with our agency client to confirm if and how the Field Staff are engaging the target consumer. We would determine if we need to make changes to better engage the target consumer. This single differentiation can provide powerful and actionable insight.

Margin of Error – Accuracy of Experiential Marketing Results

The power of that insight is greatly reduced if we have a small number of responses. As the sample size decreases, the margin of error increases. Margin of error is a statistical term that provides a sense of how much error is likely to occur in a sampling, as opposed to surveying every qualified, target consumer. Fewer responses generate a greater likelihood that results will be more prone to error (a.k.a. statistical flutter)  and, therefore, less able to be accurately generalized across the broader consumer base and .
Margin of error is often seen as +/-xx% (It’s reported all the time with political polls). If your exit survey results indicate that 75% of consumers are going to purchase Product X,  and if there is a margin of error of plus or minus two percentage points, you can reliably assume that between 73% and 77% of your consumer base has a strong intent to purchase Product X. We discuss this further in our discussion of statistical significance.
The key takeaway is that the greater the sample size, the more reliably the results and the ability to apply them to a broader consumer base. The more data, the more we can dig into it to find actionable insights that help drive the success of an experiential marketing campaign. The next time someone is concerned about sample size (number of respondents), segmentation (dividing respondents into key groups, like gender), or margin of error (how well survey responses can be applied to a broader group of consumers), don’t worry. You’ve got this.
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