Unveiling Hero Creatives: Predicting and Understanding Key Features in Video-Audio Content through Machine Learning
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School of Science |
Master's thesis
Authors
Date
2024-10-14
Department
Major/Subject
Data Science
Mcode
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
64
Series
Abstract
Creative videos are an important component in the digital marketing domain, which enables marketing managers to convey complex visual and auditory information via diverse formats to acquire targeting customers. Normally, those videos who perform extremely well and acquire a large amount of valuable customers are called hero creatives. This study is initiated to help the collaborative partner company to better identify hero creatives instead of testing all candidate creative videos in advertising networks using real money. Besides, a derivative second objective is to understand key features from previous hero creatives. For this purpose, firstly, data cleaning and feature engineering on the original creative videos and audios inside are conducted to extract useful information. Secondly, a set of machine learning models is tested and evaluated, including linear models such as Logistic Regression, ensemble models such as XGBoost etc. and Convolutional Neural Networks (CNN)-based models. The experiment results show that only Logistic Regression and one specific augmented CNN-based model turn out to surpass the performance of the baseline method, which is a simple 3-line rule based on previous domain knowledge. Both methods balanced and improved recall both on positive and negative samples, leading to macro recalls of 63% and 69% respectively whereas 58% for the baseline method. Last but not the least, important features are identified and discussed for both methods. For example, audio features do not stand out when feature importance is checked, which may suggest that background music or the creative videos do not play an important role in determining a hero creative.Description
Supervisor
Savioja, LauriThesis advisor
Carrasco, JorgeKeywords
video classification, binary classification, machine learning, audio content analysis, digital marketing, convolutional neural network