While many of the same principles of traditional market research apply in the burgeoning discipline of consumer neuroscience, there are also some distinct difficulties to be met in this area.

In the past, businesses avoided neuromarketing because they thought it was too hazardous or overstated. Scepticism, however, is fading in the face of growing scientific data. Many recent studies demonstrate that brain-scan technologies (e.g. fMRI, EEG, fNRIS) can disclose the reasons behind customers’ choices, capture their emotional reactions to commercials and goods, and (in certain situations) forecast their behavior with higher accuracy than conventional focus groups and surveys.

Marketers and businesses need to set reasonable corporate guidelines for doing neuromarketing research if the area is to advance. Without this, it will be impossible to identify whether or not neuromarketing strategies should be implemented in any given real-world marketing environment.

Our recent publication of a teaching note based on an industry survey revealed encouragingly that this is, in fact, a top focus for participants in this market. Many businesses have informed us that they are using ways to validate neurometrics that are far superior to those previously used. The following are four guidelines to help you plan successful neuromarketing campaigns and contribute to this shift. For that matter, some of our tips could be applied to just about any sort of market study.

Step 1: Know what you want to know

An effective neuromarketing research will focus on resolving just a few pressing issues. If you need to compare more than that, your findings will be skewed, and you’ll have to use several comparison adjustments.

Your research topics must be amenable to neuromarketing analysis in order to yield useful results. Is it the product images or the pricing that attract my online consumers the most? Instead, “How can I get people to feel more connected to my brand?”

Take note that each of the aforementioned queries has both a dependent variable (the expected result, such as more sales or greater emotional investment) and an independent variable (the means by which this result is expected to be altered) (in the above example, visual elements on a website). In addition, the questions presuppose a causal connection between the variables, such as the idea that shifting customers’ focus from pricing to images will boost revenue. The goal of the experiment is to determine if the hypothesized connection actually exists. For this reason, it’s important to analyze the data thoroughly.

Step 2: Know what you want to do

To get the most out of your data, you should map out an in-depth analysis strategy before you begin gathering raw data. Some of these things are:

• The key questions you’re trying to answer
• How the underlying variables are measured and influenced
• What statistical analyses are planned
• How many participants are included
• Exclusion/inclusion criteria for participants
• Checks to ensure the study was designed properly (e.g. metrics that should remain unaffected)

It is standard practice in several fields to formally register and publish initial analytical plans (e.g. clinical trials in the pharmaceutical industry). Pre-registration can reduce the likelihood of questionable conclusions being drawn from the findings after they have been made public. There are a number of websites that can help with this, such as the Open Science Framework and Aspredicted.org.

Step 3: Know what you have done

Neuromarketing research is highly vulnerable to random chance and technological errors, such as a loose EEG sensor or excessive head movement distorting electromagnetic data. It is vital to visualize distributions before executing any data analysis in order to discover any mutant data before they impact outcomes.

To check if the planned processes are functioning as designed, you should also deliberately break the study’s rules. If you want to know if people are more moved by cute dog pictures or joyful patrons, you could sprinkle in a few sad faces for comparison. There would be a problem with data collection or preprocessing if the move was not accompanied by a change in the data.

Read more: How to make Consumers Happy after your algorithm Rejected them

Step 4: Know whether you could do it again

Do not make assumptions about the reproducibility and accuracy of your results. A strategy for verifying the reliability of your data collection is essential. One major internet corporation, for instance, divides its neurometric data in half. The two halves should be statistically comparable if the findings are actually representative. A random bisect may be performed on your data hundreds of times with the help of statistical tools. No matter how you break down the data, you should get the same result.

Cross-validation, in which the same question is tested using several methods, is another option. The Shopper Neuroscience division of Ferrero, a candy producer, conducts implicit association tests and in-store A/B testing to supplement the company’s neuromarketing research.

One more thing…

The Neuromarketing Science & Business Association (NMSBA) has a code of ethics for neuromarketing suppliers that businesses should review before diving in headfirst. Among the many topics the code addresses is data privacy, participant permission, and protocol openness, and all NMSBA members are formally expected to adhere to it. Over 80 businesses are included in the web directory of the group.

Concerned businesses should also take note of the five red flags of dishonest neuromarketing identified by neuroscientist Joe Devlin. Devlin recommends being skeptical of firms that make oversimplified claims about the workings of the human brain, promote “secret sauce” analytical approaches, or provide a single answer to every problem.