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The potential of deep learning in marketing : insights from predicting conversion with deep learning

Ruizendaal, Rutger (2017) The potential of deep learning in marketing : insights from predicting conversion with deep learning.

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Abstract:Jordan and Mitchell (2015) and Najafabadi et al. (2015) have discussed the potential of deep learning in marketing. At the same time, the hype surrounding deep learning has been exponentially growing and is at an all-time high. However, there are few empirical studies researching applications of deep learning in marketing. This study tries to gain an understanding of the value of deep learning in predicting conversion. In order to fully understand the strengths and weaknesses of deep learning models they are also compared with traditional machine learning models. Specifically, this study attempts to capture the value of deep learning models for predicting conversion. The dataset for this research has been collected at StudyPortals, the global study choice platform. The dataset consists of click-stream data containing over 56 million events. The dataset has been balanced to contain behaviour from over 36.000 converting users and over 36.000 non-converting users. The value of deep learning is mainly found in its ability to capture complex patterns in the data which then allows it to make better predictions than traditional machine learning models. The findings of this study are not limited to predicting conversion, but can be generalized towards other marketing cases like churn prediction.
Item Type:Essay (Master)
Clients:
StudyPortals, Eindhoven, Netherlands
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:05 communication studies, 54 computer science
Programme:Communication Studies MSc (60713)
Link to this item:http://purl.utwente.nl/essays/73655
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