Most current articles relating to market research would highlight so much on behavioral insights with the thought that the use of traditional surveys and focus groups seem outdated. However, our view is that traditional research methodologies do have its own merit and when used correctly, provides a context to modern research methods like #neuromarketing.
The truth is, marrying traditional research and #neuromarketing delivers rich and actionable customer or business insights. This happens when there is merging of survey data with biometric data that is generated from neuromarketing. #machinelearning algorithms that drive neuromarketing can process millions of records and in this case algorithms can model small yet divergent data with ease. The net result is that models are robust and results are highly reliable.
Along with the robustness of the results, understanding the context surrounding the research findings is important. One of the often quoted limitation of #neuroscience research is finding answers to ‘why’. Measurement of brain waves doesn’t answer all questions pertaining to ‘why’ and is not a sufficient condition.
This is where traditional surveys and human analysis come in handy. Insight professionals can connect the dots of data that isn’t associated with emotions with those requiring explicit answers or those questions which requires human comments.
At the other end of the spectrum, results traditional surveys alone cannot be held as conclusive evidence due to the bias factor and usually entails depth analysis. When the usual questions of awareness, likes and dislikes come up, #neuroscience plays a crucial role.
Answers to #neuroscience questions are devoid of bias and is a great way to examine how a stimulus elicits emotions when being exposed. It can even tell exactly what kind of emotional response your subject has disclosed. This precision factor is an added value as it aids in the understanding of the impact consumer decision.
The point is there is a misconception that, with the advent of cutting-edge research methods, traditional survey methods should be disregarded. This need not be the case. Instead of substituting one research method over the other, both should be utilized hand-in-hand.
Researchers need to leverage the benefits of both methodologies and underlying #machinelearning models to complement each other’s setback. At Stratzie, we have noted the significance of both roles as a crucial necessity to having a holistic result, especially in today’s data rich world.