Journal
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
Volume 14, Issue 4, Pages 927-945Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11634-020-00429-0
Keywords
Brand confusion; Brand positioning; Convolutional Neural Network (CNN); Grad-CAM; VGG16
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Funding
- JSPS KAKENHI [JP20K01963, JP16K00052]
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In the consumer goods industry, unique brand positionings are assumed to be the road to success. They document product distinctiveness and so justify high prices. However, as products are getting more and more interchangeable, brand positionings must rely-at least partially-on supporting advertisements. Here, especially ads with visual content (e.g. photos, video clips) are able to connect brands with desirable emotions and values. Recently, besides TV, cinema, newspaper, also search engines, social networks, photo-, video-sharing platforms are used to spread such ads. In this paper, we demonstrate, how deep learning based on such ads can be used to predict uniqueness of brand positionings. A sample application to the German Pils beer market is used for demonstration.
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