دانلود رایگان مقاله انگلیسی تجزیه و تحلیل سیگنال های EEG و کاربرد آن در بازاریابی عصبی - اشپرینگر 2017

Abstract

Marketing and promotions of various consumer products through advertisement campaign is a well known practice to increase the sales and awareness amongst the consumers. This essentially leads to increase in profit to a manufacturing unit. Re-production of products usually depends on the various facts including consumption in the market, reviewer’s comments, ratings, etc. However, knowing consumer preference for decision making and behavior prediction for effective utilization of a product using unconscious processes is called “Neuromarketing”. This field is emerging fast due to its inherent potential. Therefore, research work in this direction is highly demanded, yet not reached a satisfactory level. In this paper, we propose a predictive modeling framework to understand consumer choice towards E-commerce products in terms of “likes” and “dislikes” by analyzing EEG signals. The EEG signals of volunteers with varying age and gender were recorded while they browsed through various consumer products. The experiments were performed on the dataset comprised of various consumer products. The accuracy of choice prediction was recorded using a user-independent testing approach with the help of Hidden Markov Model (HMM) classifier. We have observed that the prediction results are promising and the framework can be used for better business model.

5 Conclusion

In this paper, we have applied neuroscience to predict the choice preference of a user for a product using EEG signals. The brain activity of 40 participants comprised of 25 male and 15 female have been recorded while viewing products. Next, the signals have been smoothed and classified using HMM classifier. The result shows the effectiveness of the proposed framework and provides a complementary solution to the traditional measures of predicting the product success in the market. The framework could be used in developing market strategies, research and predicting market success by extending the existing models. In our study we did not analyze fake answer towards product preference. Thus, approaches to deal with fake responses could be studied in future work. Moreover, a neutral choice for the products could also be employed to provide more preferences to the users. The tracking of user’s eye movement while watching products could be viewed as another parameter in predicting preferred choices. More robust features and classifier combination could be explored to improve the prediction results.