Projections indicate that the neuromarketing market is expected to reach valued at 8.5% CAGR, resulting in a market size of USD 2.99 Billion by the conclusion of 2032.

The report covers Global Neuromarketing Technology Market Size & Analysis and is Segmented by End-user Industry (BFSI, Retail/Consumer Brands, Market Research, Scientific Institutions) and Geography.

How Tariffs Are Impacting the Economy

Tariffs imposed on imported goods and materials are having a significant impact on the economy, particularly in sectors like NeuroMarketing. Rising tariffs on electronics, which are crucial for neuro-marketing tools such as EEG headsets and biometric sensors, are increasing production costs for companies.

This, in turn, raises the overall cost of neuro-marketing solutions, making them less affordable for smaller businesses and startups. Moreover, tariffs are disrupting global supply chains, leading to delays in product availability and higher shipping costs.

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The uncertainty around tariffs also affects investment decisions in the NeuroMarketing sector, as businesses hesitate to commit to long-term projects under the threat of sudden cost increases. These economic pressures are forcing companies to reconsider their sourcing strategies, exploring alternative regions or local manufacturing options to mitigate the impact of tariffs.

The Neuromarketing market has emerged as a transformative field at the intersection of neuroscience and marketing, leveraging insights from brain science to understand consumer behavior on a deeper level. By utilizing techniques such as eye-tracking, EEG, and fMRI, businesses can gain unprecedented insights into how

This research work comes in response to the question of which aspects are more relevant for consumers when purchasing educational toys as opposed to other toys that are focused solely on leisure. This empirical research focuses on an educational toy distributed in Spain by the Educa brand (Conector family, reference “I learn English”), which is the product best-selling product of its brand in this area, and analyses how consumers make decisions concerning this product in relation to other products designed by competitors. The research looks into customer reactions while looking at these products, measuring brain activity generated by different aspects of product design and its influence on choice. The aim of the present study was to propose a model that optimizes the design of educational toy packaging. Through the use of neuromarketing techniques –attention through eye tracking, and emotion using galvanic skin response– as well as qualitative research techniques, the objective of this research is to determine the motivations in the processes of buying educational toys. The packaging design elements analyzed are brand, product family, toy name, recommended age, game image, number of questions/topics, and additional texts. The results suggest that the most important elements are the graphic details of the packaging, obtaining a perception of a higher educational level as more questions are addressed by the game. The simultaneous combination of qualitative techniques monitored with galvanic skin response (neuro-qualitative study) allows additional conclusions to be aligned with the end user of the product, including a prominent social component when the product is purchased as a gift for a third party.

The neuromarketeting solutions market size was over USD 2.12 billion in 2024 and is poised to exceed USD 6.98 billion by 2037, growing at over 9.6% CAGR during the forecast period i.e., between 2025-2037. North America industry is set to account for largest revenue share of 33% by 2037, owing to increasing number of US-based marketers and heavy investment in digital marketing by consumer industry.

Interest in improving advertisement impact on potential consumers has increased recently. One well-known strategy is to use emotion-based advertisement. In this approach, an emotional link with consumers is created, aiming to enhance the memorization process. In recent years, Neuromarketing techniques have allowed us to obtain more objective information on this process. However, the role of the autonomic nervous system (ANS) in the memorization process using emotional advertisement still needs further research. In this work, we propose the use of two physiological signals, namely, an electrocardiogram (heart rate variability, HRV) and electrodermal activity (EDA), to obtain indices assessing the ANS. We measured these signals in 43 subjects during the observation of six different spots, each conveying a different emotion (rational, disgust, anger, surprise, and sadness). After observing the spots, subjects were asked to answer a questionnaire to measure the spontaneous and induced recall. We propose the use of a statistical data-driven model based on Partial Least Squares-Path Modeling (PSL-PM), which allows us to incorporate contextual knowledge by defining a relational graph of unobservable variables (latent variables, LV), which are, in turn, estimated by measured variables (indices of the ANS). We defined four LVs, namely, sympathetic, vagal, ANS, and recall. Sympathetic and vagal are connected to the ANS, the latter being a measure of recall, estimated from a questionnaire. The model is then fitted to the data. Results showed that vagal activity (described by HRV indices) is the most critical factor to describe ANS activity; they are inversely related except for the spot, which is mainly rational. The model captured a moderate-to-high variability of ANS behavior, ranging from 38% up to 64% of the explained variance of the ANS. However, it can explain at most 11% of the recall score of the subjects. The proposed approach allows for the easy inclusion of more physiological measurements and provides an easy-to-interpret model of the ANS response to emotional advertisement.

Recent research has shown that event-related brain potentials (ERPs) recorded while participants view lists of different consumer goods can be modulated by their preferences toward these products. However, it remains largely unknown whether ERP activity specific to a single consumer item can be informative about whether or not this item will be preferred in a shopping context. In this study, we examined whether single-item ERPs could reliably predict consumer preferences toward specific consumer goods. We recorded scalp EEG from 40 participants while they were viewing pictures of consumer goods and we subsequently asked them to indicate their preferences for each of these items. Replicating previous results, we found that ERP activity averaged over the six most preferred products was significantly differentiated from ERP activity averaged across the six least preferred products for three ERP components: The N200, the late positive potential (LPP) and positive slow waves (PSW). We also found that using single-item ERPs to infer behavioral preferences about specific consumer goods led to an overall predictive accuracy of 71%, although this figure varied according to which ERPs were targeted. Later positivities such as the LPP and PSW yielded relatively higher predictive accuracy rates than the frontal N200. Our results suggest that ERPs related to single consumer items can be relatively accurate predictors of behavioral preferences depending on which type of ERP effects are chosen by the researcher, and ultimately on the level of prediction errors that users choose to tolerate.

A neuromarketing study conducted in 2019 revealed that branded podcasts were more effective in generating engagement than the radio was. The engagement rate of the listeners reached 0.78 percent during the brand advertising in the podcast, while the advertising broadcasted by the radio produce a lower engagement rate at 0.53 percent.