Agile Product Development by Prediction of Consumers’ Behaviour; using Neurobehavioral and Social Media Sentiment Analysis Approaches
Amir Jahanian Najafabadi, Anastasiia Skryzhadlovska, Omid Fatahi Valilai
Abstract
Nowadays, the product development processes have faced challenges for more effective, agile characteristics. Moreover, traditional methods for data retrieval from customers for their behavioral prediction have lost their efficiency. Social media platforms have provided enormous potentials for discovery of customer behavioral models. In the current study we investigated the association between brain activities and consumers’ behaviour. Additionally, we investigated whether pre-trained Python models for sentiment analyses predict consumer behaviour and assessed it's association with brain activities during decision-making tasks. Neural and behavioral responses of participants used for analysis of their decision making processes when choosing their product of interest. This has been accomplished while the participants’ brain activities were recorded at pre-test (baseline) and during each decision-making tasks. Results of this study revealed significant association between certain resting-state EEG power spectral densities and consumer decisions. Moreover, sentiment analysis models showed good accuracy in predicting product like/disliking. These findings demonstrates the potential of combination of neuroscience and sentiment analysis methods in consumers’ behaviour research especially for agile product development processes.