Abstract:
Whilst digital marketing is growing exponentially, the conversion rate stands significantly low compared to traditional marketing practices, as a result of engagement challenges in digital media. Customer Engagement (CE) is an important factor for business success and in the digital marketing sphere, content is the only way of curating engagement. Content generates stimuli and patterns in the human brain which can map using EEG and PET, which referrers to as Neuro-marketing. Support vector machine and k-nearest are machine learning algorithms used for encoding recorded images. Existing literature has discussed, employing consumer cocreation theory in conjunction with Neuro-marketing for CE research. Though it is a highly successful methodology, the cost of implementing is high and complicated. This empirical research aims to conceptualize a cost-effective model for content AB testing with deep insights.
