The need for neuromarketing is increasing
“Neuromarketing” is one of the concepts that everyone who is even a little bit interested in marketing has heard about in recent years. Because the sector is very promising both with the economic dimension it has reached and the potential it has.
Challenges of the neuromarketing industry in the pandemic-affected world
The Covid-19 pandemic conditions brought along the difficulties of collecting data for neuromarketing research, while the disruption in the global supply chain brings some difficulties in production and distribution, companies producing neuromarketing research devices in this period. In this context, when we look at the neuromarketing sector, we can define the sector as a moderately competitive sector with relatively few players.
Neuromarketing and the future: The use of artificial intelligence in the industry will increase
In the field of neuromarketing, which has a history of 20 years, the use of artificial intelligence methods has become widespread in the last 5 years. The use of artificial intelligence methods in neuromarketing is expected to become increasingly widespread in the next 10-15 years. While experimental paradigms shaped around expert opinion in traditional approaches are at the forefront, with the introduction of artificial intelligence methods, artificial intelligence approaches that can make data-oriented analysis and estimation begin to take the place of the expert. For example, the statistical learning theory called the Support Vector Machine and the artificial intelligence learning method, which acts with the principle of minimizing the structural risk, is an effective method for solving classification and regression problems. This method makes the model even more successful in terms of performance with the improvements to be made on the experimental model.
In this context, we can say that the need for data science experts will increase. The main challenge that is likely to arise is the need for a larger amount of data to develop successful models. Companies may need to invest separately and allocate a budget to collect higher amounts of cleaner data. However, if this investment ensures the establishment of more successful models and gives more precise results, it will be possible to return financially.